chore: import upstream snapshot with attribution
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wehub-resource-sync
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"<h1>Latent Diffusion Models</h1>\n<p>Latent diffusion models use an auto-encoder to map between image space and latent space. The diffusion model works on the latent space, which makes it a lot easier to train. It is based on paper <a href=\"https://arxiv.org/abs/2112.10752\">High-Resolution Image Synthesis with Latent Diffusion Models</a>.</p>\n<p>They use a pre-trained auto-encoder and train the diffusion U-Net on the latent space of the pre-trained auto-encoder.</p>\n<p>For a simpler diffusion implementation refer to our <a href=\"../ddpm/index.html\">DDPM implementation</a>. We use same notations for <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> schedules, etc.</p>\n": "<h1>\u6f5c\u5728\u62e1\u6563\u30e2\u30c7\u30eb</h1>\n<p>\u6f5c\u5728\u62e1\u6563\u30e2\u30c7\u30eb\u3067\u306f\u3001\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u30fc\u3092\u4f7f\u7528\u3057\u3066\u753b\u50cf\u7a7a\u9593\u3068\u6f5c\u5728\u7a7a\u9593\u3092\u30de\u30c3\u30d4\u30f3\u30b0\u3057\u307e\u3059\u3002\u62e1\u6563\u30e2\u30c7\u30eb\u306f\u6f5c\u5728\u7a7a\u9593\u3067\u6a5f\u80fd\u3059\u308b\u305f\u3081\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u304c\u306f\u308b\u304b\u306b\u7c21\u5358\u306b\u306a\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u3001<a href=\"https://arxiv.org/abs/2112.10752\">\u6f5c\u5728\u62e1\u6563\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u8ad6\u6587\u306e\u9ad8\u89e3\u50cf\u5ea6\u753b\u50cf\u5408\u6210\u306b\u57fa\u3065\u3044\u3066\u3044\u307e\u3059</a></p>\u3002\n<p>\u4e8b\u524d\u306b\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u305f\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u30fc\u3092\u4f7f\u7528\u3057\u3001\u4e8b\u524d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307f\u306e\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u30fc\u306e\u6f5c\u5728\u7a7a\u9593\u3067\u62e1\u6563 U-Net \u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3059\u3002</p>\n<p><a href=\"../ddpm/index.html\">\u3088\u308a\u5358\u7d14\u306a\u62e1\u6563\u5b9f\u88c5\u306b\u3064\u3044\u3066\u306f\u3001DDPM \u5b9f\u88c5\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002</a><span translate=no>_^_1_^_</span>\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb\u306a\u3069\u306b\u3082\u540c\u3058\u8868\u8a18\u3092\u4f7f\u3044\u307e\u3059</p>\u3002<span translate=no>_^_0_^_</span>\n",
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"<h2>Latent diffusion model</h2>\n<p>This contains following components:</p>\n<ul><li><a href=\"model/autoencoder.html\">AutoEncoder</a> </li>\n<li><a href=\"model/unet.html\">U-Net</a> with <a href=\"model/unet_attention.html\">attention</a> </li>\n<li><a href=\"model/clip_embedder.html\">CLIP embeddings generator</a></li></ul>\n": "<h2>\u6f5c\u4f0f\u62e1\u6563\u30e2\u30c7\u30eb</h2>\n<p>\u3053\u308c\u306b\u306f\u4ee5\u4e0b\u306e\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8\u304c\u542b\u307e\u308c\u307e\u3059\u3002</p>\n<ul><li><a href=\"model/autoencoder.html\">\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0</a></li>\n<li><a href=\"model/unet.html\"><a href=\"model/unet_attention.html\">\u6ce8\u610f\u3092\u5411\u3051\u305fU-Net</a></a></li>\n<li><a href=\"model/clip_embedder.html\">CLIP \u57cb\u3081\u8fbc\u307f\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u30fc</a></li></ul>\n",
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"<h3>Get <a href=\"model/clip_embedder.html\">CLIP embeddings</a> for a list of text prompts</h3>\n": "<h3>\u30c6\u30ad\u30b9\u30c8\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u30ea\u30b9\u30c8\u306e <a href=\"model/clip_embedder.html\">CLIP \u57cb\u3081\u8fbc\u307f\u3092\u53d6\u5f97\u3059\u308b</a></h3>\n",
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"<h3>Get image from the latent representation</h3>\n<p>We scale down by the scaling factor and then decode.</p>\n": "<h3>\u6f5c\u5728\u8868\u73fe\u304b\u3089\u753b\u50cf\u3092\u53d6\u5f97</h3>\n<p>\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u4fc2\u6570\u3067\u30b9\u30b1\u30fc\u30eb\u30c0\u30a6\u30f3\u3057\u3066\u304b\u3089\u30c7\u30b3\u30fc\u30c9\u3057\u307e\u3059\u3002</p>\n",
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"<h3>Get model device</h3>\n": "<h3>\u30e2\u30c7\u30eb\u30c7\u30d0\u30a4\u30b9\u3092\u53d6\u5f97</h3>\n",
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"<h3>Get scaled latent space representation of the image</h3>\n<p>The encoder output is a distribution. We sample from that and multiply by the scaling factor.</p>\n": "<h3>\u753b\u50cf\u306e\u62e1\u5927\u7e2e\u5c0f\u3055\u308c\u305f\u6f5c\u5728\u7a7a\u9593\u8868\u73fe\u3092\u53d6\u5f97</h3>\n<p>\u30a8\u30f3\u30b3\u30fc\u30c0\u51fa\u529b\u306f\u30c7\u30a3\u30b9\u30c8\u30ea\u30d3\u30e5\u30fc\u30b7\u30e7\u30f3\u3067\u3059\u3002\u305d\u3053\u304b\u3089\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u3001\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u4fc2\u6570\u3092\u639b\u3051\u307e\u3059</p>\u3002\n",
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"<h3>Predict noise</h3>\n<p>Predict noise given the latent representation <span translate=no>_^_0_^_</span>, time step <span translate=no>_^_1_^_</span>, and the conditioning context <span translate=no>_^_2_^_</span>.</p>\n<p><span translate=no>_^_3_^_</span></p>\n": "<h3>\u30ce\u30a4\u30ba\u3092\u4e88\u6e2c</h3>\n<p>\u6f5c\u5728\u8868\u73fe\u3001\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>\u3001<span translate=no>_^_2_^_</span>\u6761\u4ef6\u4ed8\u3051\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u8003\u616e\u3057\u3066\u30ce\u30a4\u30ba\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002</p>\n<p><span translate=no>_^_3_^_</span></p>\n",
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"<p> <em>This is an empty wrapper class around the <a href=\"model/unet.html\">U-Net</a>. We keep this to have the same model structure as <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we do not have to map the checkpoint weights explicitly</em>.</p>\n": "<p><em>\u3053\u308c\u306f <a href=\"model/unet.html\">U-Net</a> \u5468\u8fba\u306e\u7a7a\u306e\u30e9\u30c3\u30d1\u30fc\u30af\u30e9\u30b9\u3067\u3059\u3002\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u306e\u91cd\u307f\u3092\u660e\u793a\u7684\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3059\u308b\u5fc5\u8981\u304c\u306a\u3044\u3088\u3046\u306b\u3001\u3053\u308c\u3092 <a href=\"https://github.com/CompVis/stable-diffusion\">compVis/Stable-Diffusion</a> \u3068\u540c\u3058\u30e2\u30c7\u30eb\u69cb\u9020\u306b\u3057\u3066\u304a\u304d\u307e\u3059</em></p>\u3002\n",
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"<p><a href=\"model/clip_embedder.html\">CLIP embeddings generator</a> </p>\n": "<p><a href=\"model/clip_embedder.html\">CLIP \u57cb\u3081\u8fbc\u307f\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u30fc</a></p>\n",
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"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
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"<p><span translate=no>_^_0_^_</span> schedule </p>\n": "<p><span translate=no>_^_0_^_</span>\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb</p>\n",
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"<p>Auto-encoder and scaling factor </p>\n": "<p>\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u3068\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u30d5\u30a1\u30af\u30bf\u30fc</p>\n",
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"<p>Number of steps <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b9\u30c6\u30c3\u30d7\u6570 <span translate=no>_^_0_^_</span></p>\n",
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"<p>Wrap the <a href=\"model/unet.html\">U-Net</a> to keep the same model structure as <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a>. </p>\n": "<p><a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/Stable-Diffusion\u3068\u540c\u3058\u30e2\u30c7\u30eb\u69cb\u9020\u3092\u4fdd\u3064\u305f\u3081\u306b</a> <a href=\"model/unet.html\">U-Net</a> \u3092\u30e9\u30c3\u30d7\u3057\u3066\u304f\u3060\u3055\u3044\u3002</p>\n",
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"<ul><li><span translate=no>_^_0_^_</span> is the <a href=\"model/unet.html\">U-Net</a> that predicts noise <span translate=no>_^_1_^_</span>, in latent space </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"model/autoencoder.html\">AutoEncoder</a> </li>\n<li><span translate=no>_^_3_^_</span> is the <a href=\"model/clip_embedder.html\">CLIP embeddings generator</a> </li>\n<li><span translate=no>_^_4_^_</span> is the scaling factor for the latent space. The encodings of the autoencoder are scaled by this before feeding into the U-Net. </li>\n<li><span translate=no>_^_5_^_</span> is the number of diffusion steps <span translate=no>_^_6_^_</span>. </li>\n<li><span translate=no>_^_7_^_</span> is the start of the <span translate=no>_^_8_^_</span> schedule. </li>\n<li><span translate=no>_^_9_^_</span> is the end of the <span translate=no>_^_10_^_</span> schedule.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span><a href=\"model/unet.html\"><span translate=no>_^_1_^_</span>\u6f5c\u4f0f\u7a7a\u9593\u306e\u30ce\u30a4\u30ba\u3092\u4e88\u6e2c\u3059\u308bU-Net\u3067\u3059</a></li>\n<li><span translate=no>_^_2_^_</span><a href=\"model/autoencoder.html\">\u306f\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u3067\u3059</a></li>\n<li><span translate=no>_^_3_^_</span><a href=\"model/clip_embedder.html\">CLIP \u57cb\u3081\u8fbc\u307f\u30b8\u30a7\u30cd\u30ec\u30fc\u30bf\u3067\u3059</a></li>\n<li><span translate=no>_^_4_^_</span>\u306f\u6f5c\u5728\u7a7a\u9593\u306e\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u4fc2\u6570\u3067\u3059\u3002\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u306e\u30a8\u30f3\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u306f\u3001U-Net\u306b\u30d5\u30a3\u30fc\u30c9\u3059\u308b\u524d\u306b\u3053\u308c\u306b\u3088\u3063\u3066\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3055\u308c\u307e\u3059</li>\u3002\n<li><span translate=no>_^_5_^_</span>\u306f\u62e1\u6563\u30b9\u30c6\u30c3\u30d7\u306e\u6570\u3067\u3059\u3002<span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span><span translate=no>_^_8_^_</span>\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb\u306e\u59cb\u307e\u308a\u3067\u3059\u3002</li>\n<li><span translate=no>_^_9_^_</span><span translate=no>_^_10_^_</span>\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb\u306f\u7d42\u4e86\u3067\u3059\u3002</li></ul>\n",
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"Annotated PyTorch implementation/tutorial of latent diffusion models from paper High-Resolution Image Synthesis with Latent Diffusion Models": "\u8ad6\u6587\u304b\u3089\u306e\u6f5c\u5728\u62e1\u6563\u30e2\u30c7\u30eb\u306e\u6ce8\u91c8\u4ed8\u304dPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u6f5c\u4f0f\u62e1\u6563\u30e2\u30c7\u30eb\u306b\u3088\u308b\u9ad8\u89e3\u50cf\u5ea6\u753b\u50cf\u5408\u6210",
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"Latent Diffusion Models": "\u6f5c\u5728\u62e1\u6563\u30e2\u30c7\u30eb"
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}
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{
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"<h1>Latent Diffusion Models</h1>\n<p>Latent diffusion models use an auto-encoder to map between image space and latent space. The diffusion model works on the latent space, which makes it a lot easier to train. It is based on paper <a href=\"https://arxiv.org/abs/2112.10752\">High-Resolution Image Synthesis with Latent Diffusion Models</a>.</p>\n<p>They use a pre-trained auto-encoder and train the diffusion U-Net on the latent space of the pre-trained auto-encoder.</p>\n<p>For a simpler diffusion implementation refer to our <a href=\"../ddpm/index.html\">DDPM implementation</a>. We use same notations for <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> schedules, etc.</p>\n": "<h1>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab \u0d86\u0d9a\u0dd8\u0dad\u0dd2</h1>\n<p>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d86\u0d9a\u0dd8\u0dad\u0dd2 \u0dbb\u0dd6\u0db4 \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0dc3\u0dc4 \u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0d85\u0dad\u0dbb \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca \u0d9c\u0dad \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba\u0d9a\u0dca \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0dba\u0dd2. \u0dc0\u0dd2\u0dc3\u0dbb\u0dab \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0dc3\u0dd0\u0dc4\u0dd0\u0dbd\u0dca\u0dbd\u0dd4 \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0db8\u0dad \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf \u0d9a\u0dbb\u0dba\u0dd2, \u0d91\u0dba \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0db4\u0dc4\u0dc3\u0dd4 \u0d9a\u0dbb\u0dba\u0dd2. \u0d91\u0dba \u0db4\u0daf\u0db1\u0db8\u0dca \u0dc0\u0dd3 \u0d87\u0dad\u0dca\u0dad\u0dda <a href=\"https://arxiv.org/abs/2112.10752\">\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab \u0d86\u0d9a\u0dd8\u0dad\u0dd2 \u0dc3\u0db8\u0d9f \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0d85\u0db0\u0dd2-\u0dc0\u0dd2\u0db7\u0dda\u0daf\u0db1 \u0dbb\u0dd6\u0db4 \u0dc3\u0d82\u0dc1\u0dca\u0dbd\u0dda\u0dc2\u0dab\u0dba</a> \u0db8\u0dad \u0dba.</p>\n<p>\u0d94\u0dc0\u0dd4\u0db1\u0dca \u0db4\u0dd9\u0dbb \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba\u0d9a\u0dca \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1 \u0d85\u0dad\u0dbb \u0db4\u0dd9\u0dbb \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba\u0dda \u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0db8\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0dba\u0dd2.</p>\n<p>\u0dc3\u0dbb\u0dbd \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db4\u0d9c\u0dda <a href=\"../ddpm/index.html\">DDPM \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8</a> \u0dc0\u0dd9\u0dad \u0dba\u0ddc\u0db8\u0dd4 \u0dc0\u0db1\u0dca\u0db1. <span translate=no>_^_1_^_</span>\u0d9a\u0dcf\u0dbd\u0dc3\u0da7\u0dc4\u0db1\u0dca<span translate=no>_^_0_^_</span> \u0d86\u0daf\u0dd2\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db4\u0dd2 \u0d91\u0d9a\u0db8 \u0d85\u0d82\u0d9a\u0db1 \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd4.</p>\n",
|
||||
"<h2>Latent diffusion model</h2>\n<p>This contains following components:</p>\n<ul><li><a href=\"model/autoencoder.html\">AutoEncoder</a> </li>\n<li><a href=\"model/unet.html\">U-Net</a> with <a href=\"model/unet_attention.html\">attention</a> </li>\n<li><a href=\"model/clip_embedder.html\">CLIP embeddings generator</a></li></ul>\n": "<h2>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba</h2>\n<p>\u0db4\u0dc4\u0dad \u0dc3\u0db3\u0dc4\u0db1\u0dca \u0dc3\u0d82\u0dbb\u0da0\u0d9a \u0d85\u0da9\u0d82\u0d9c\u0dd4 \u0dc0\u0dda:</p>\n<ul><li><a href=\"model/autoencoder.html\">\u0dc3\u0dca\u0dc0\u0dba\u0d82 \u0d86\u0d9a\u0dda\u0dad\u0d9a\u0dba</a></li>\n<li><a href=\"model/unet.html\">U-Net</a> <a href=\"model/unet_attention.html\">\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba\u0dd9\u0db1\u0dca</a></li>\n<li><a href=\"model/clip_embedder.html\">CLIP \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0dba\u0db1\u0dca\u0dad\u0dca\u0dbb\u0dba</a></li></ul>\n",
|
||||
"<h3>Get <a href=\"model/clip_embedder.html\">CLIP embeddings</a> for a list of text prompts</h3>\n": "<h3>\u0db4\u0dd9\u0dc5 \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca \u0dbd\u0dd0\u0dba\u0dd2\u0dc3\u0dca\u0dad\u0dd4\u0dc0\u0d9a\u0dca <a href=\"model/clip_embedder.html\">\u0dc3\u0db3\u0dc4\u0dcf CLIP \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca</a> \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</h3>\n",
|
||||
"<h3>Get image from the latent representation</h3>\n<p>We scale down by the scaling factor and then decode.</p>\n": "<h3>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0db1\u0dd2\u0dbb\u0dd6\u0db4\u0dab\u0dba\u0dd9\u0db1\u0dca \u0dbb\u0dd6\u0db4\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</h3>\n<p>\u0d85\u0db4\u0dd2 \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab \u0dc3\u0dcf\u0db0\u0d9a\u0dba \u0d85\u0db1\u0dd4\u0dc0 \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0dbb \u0dc0\u0dd2\u0d9a\u0dda\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db8\u0dd4.</p>\n",
|
||||
"<h3>Get model device</h3>\n": "<h3>\u0d86\u0daf\u0dbb\u0dca\u0dc1 \u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</h3>\n",
|
||||
"<h3>Get scaled latent space representation of the image</h3>\n<p>The encoder output is a distribution. We sample from that and multiply by the scaling factor.</p>\n": "<h3>\u0dbb\u0dd6\u0db4\u0dba\u0dda \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0db1\u0dd2\u0dbb\u0dd6\u0db4\u0dab\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</h3>\n<p>\u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dca \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba \u0db6\u0dd9\u0daf\u0dcf \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0d9a\u0dd2. \u0d85\u0db4\u0dd2 \u0d91\u0dba\u0dd2\u0db1\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d9a\u0dbb \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dc3\u0dcf\u0db0\u0d9a\u0dba \u0db8\u0d9c\u0dd2\u0db1\u0dca \u0d9c\u0dd4\u0dab \u0d9a\u0dbb\u0db8\u0dd4.</p>\n",
|
||||
"<h3>Predict noise</h3>\n<p>Predict noise given the latent representation <span translate=no>_^_0_^_</span>, time step <span translate=no>_^_1_^_</span>, and the conditioning context <span translate=no>_^_2_^_</span>.</p>\n<p><span translate=no>_^_3_^_</span></p>\n": "<h3>\u0dc1\u0db6\u0dca\u0daf\u0dba \u0db4\u0dd4\u0dbb\u0ddd\u0d9a\u0dae\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</h3>\n<p>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0db1\u0dd2\u0dbb\u0dd6\u0db4\u0dab\u0dba<span translate=no>_^_0_^_</span>, \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb<span translate=no>_^_1_^_</span> \u0dc3\u0dc4 \u0d9a\u0db1\u0dca\u0da9\u0dd2\u0dc2\u0db1\u0dda\u0dc2\u0db1\u0dca \u0dc3\u0db1\u0dca\u0daf\u0dbb\u0dca\u0db7\u0dba \u0d85\u0db1\u0dd4\u0dc0 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db4\u0dd4\u0dbb\u0ddd\u0d9a\u0dae\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_2_^_</span>.</p>\n<p><span translate=no>_^_3_^_</span></p>\n",
|
||||
"<p> <em>This is an empty wrapper class around the <a href=\"model/unet.html\">U-Net</a>. We keep this to have the same model structure as <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we do not have to map the checkpoint weights explicitly</em>.</p>\n": "<p><em>\u0db8\u0dd9\u0dba <a href=\"model/unet.html\">\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca</a> \u0dc0\u0da7\u0dcf \u0dc4\u0dd2\u0dc3\u0dca \u0daf\u0dc0\u0da7\u0db1 \u0db4\u0db1\u0dca\u0dad\u0dd2\u0dba\u0d9a\u0dd2. \u0da0\u0dd9\u0d9a\u0dca\u0db4\u0ddc\u0dba\u0dd2\u0db1\u0dca\u0da7\u0dca \u0db6\u0dbb \u0db4\u0dd0\u0dc4\u0dd0\u0daf\u0dd2\u0dbd\u0dd2\u0dc0 \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca \u0d9c\u0dad \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d85\u0dc0\u0dc1\u0dca\u0dba \u0db1\u0ddc\u0dc0\u0db1 \u0db4\u0dbb\u0dd2\u0daf\u0dd2 <a href=\"https://github.com/CompVis/stable-diffusion\">\u0d9a\u0ddc\u0db8\u0dca\u0dc0\u0dd2\u0dc3\u0dca/\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba</a> \u0dc4\u0dcf \u0dc3\u0db8\u0dcf\u0db1 \u0d86\u0d9a\u0dd8\u0dad\u0dd2 \u0dc0\u0dca\u0dba\u0dd4\u0dc4\u0dba\u0d9a\u0dca \u0dad\u0dd2\u0db6\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db4\u0dd2 \u0db8\u0dd9\u0dba \u0dad\u0db6\u0dcf</em> \u0d9c\u0db1\u0dd2\u0db8\u0dd4.</p>\n",
|
||||
"<p><a href=\"model/clip_embedder.html\">CLIP embeddings generator</a> </p>\n": "<p><a href=\"model/clip_embedder.html\">CLIP \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0dba\u0db1\u0dca\u0dad\u0dca\u0dbb\u0dba</a></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> schedule </p>\n": "<p><span translate=no>_^_0_^_</span>\u0d9a\u0dcf\u0dbd\u0dc3\u0da7\u0dc4\u0db1</p>\n",
|
||||
"<p>Auto-encoder and scaling factor </p>\n": "<p>\u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba \u0dc3\u0dc4 \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab \u0dc3\u0dcf\u0db0\u0d9a\u0dba</p>\n",
|
||||
"<p>Number of steps <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Wrap the <a href=\"model/unet.html\">U-Net</a> to keep the same model structure as <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a>. </p>\n": "<p><a href=\"https://github.com/CompVis/stable-diffusion\">Compvis/\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab \u0dbd\u0dd9\u0dc3 \u0d91\u0db8 \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0dc0\u0dca\u0dba\u0dd4\u0dc4\u0dba \u0dad\u0db6\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8\u0da7 <a href=\"model/unet.html\">U-\u0dc1\u0dd4\u0daf\u0dca\u0db0</a></a> \u0d86\u0dc0\u0dbb\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dc0\u0dcf.</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the <a href=\"model/unet.html\">U-Net</a> that predicts noise <span translate=no>_^_1_^_</span>, in latent space </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"model/autoencoder.html\">AutoEncoder</a> </li>\n<li><span translate=no>_^_3_^_</span> is the <a href=\"model/clip_embedder.html\">CLIP embeddings generator</a> </li>\n<li><span translate=no>_^_4_^_</span> is the scaling factor for the latent space. The encodings of the autoencoder are scaled by this before feeding into the U-Net. </li>\n<li><span translate=no>_^_5_^_</span> is the number of diffusion steps <span translate=no>_^_6_^_</span>. </li>\n<li><span translate=no>_^_7_^_</span> is the start of the <span translate=no>_^_8_^_</span> schedule. </li>\n<li><span translate=no>_^_9_^_</span> is the end of the <span translate=no>_^_10_^_</span> schedule.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0dc1\u0db6\u0dca\u0daf\u0dba<span translate=no>_^_1_^_</span> \u0db4\u0dd4\u0dbb\u0ddd\u0d9a\u0dae\u0db1\u0dba \u0d9a\u0dbb\u0db1 <a href=\"model/unet.html\">\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca</a> \u0dba</li>\n<li><span translate=no>_^_2_^_</span>\u0db8\u0dd9\u0db8 <a href=\"model/autoencoder.html\">\u0dc3\u0dca\u0dc0\u0dba\u0d82 \u0d86\u0d9a\u0dda\u0dad\u0d9a\u0dba</a> \u0dc0\u0dda</li>\n<li><span translate=no>_^_3_^_</span><a href=\"model/clip_embedder.html\">CLIP \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0d8b\u0dad\u0dca\u0db4\u0dcf\u0daf\u0d9a \u0dba\u0db1\u0dca\u0dad\u0dca\u0dbb\u0dba</a></li>\n<li><span translate=no>_^_4_^_</span>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab \u0dc3\u0dcf\u0db0\u0d9a\u0dba \u0dc0\u0dda. \u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0dc0\u0dd9\u0dad \u0db4\u0ddd\u0dc2\u0dab\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0db4\u0dd9\u0dbb \u0d94\u0da7\u0ddd\u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba\u0dda \u0d9a\u0dda\u0dad\u0dd3\u0d9a\u0dbb\u0dab \u0db8\u0dda \u0db8\u0d9c\u0dd2\u0db1\u0dca \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0dd0\u0db6\u0dda.</li>\n<li><span translate=no>_^_5_^_</span>\u0dba\u0db1\u0dd4 \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0dc0\u0dda<span translate=no>_^_6_^_</span>.</li>\n<li><span translate=no>_^_7_^_</span><span translate=no>_^_8_^_</span>\u0d9a\u0dcf\u0dbd\u0dc3\u0da7\u0dc4\u0db1\u0dda \u0d86\u0dbb\u0db8\u0dca\u0db7\u0dba \u0dc0\u0dda.</li>\n<li><span translate=no>_^_9_^_</span><span translate=no>_^_10_^_</span>\u0d9a\u0dcf\u0dbd\u0dc3\u0da7\u0dc4\u0db1\u0dda \u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dba\u0dba\u0dd2.</li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of latent diffusion models from paper High-Resolution Image Synthesis with Latent Diffusion Models": "PyTorch \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8/\u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1\u0dba \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0dc0\u0dbd\u0dd2\u0db1\u0dca \u0d9c\u0dd4\u0db4\u0dca\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab \u0d86\u0d9a\u0dd8\u0dad\u0dd2 \u0dc3\u0db8\u0d9f \u0d85\u0db0\u0dd2-\u0dc0\u0dd2\u0db7\u0dda\u0daf\u0db1 \u0dbb\u0dd6\u0db4 \u0dc3\u0d82\u0dc1\u0dca\u0dbd\u0dda\u0dc2\u0dab\u0dba",
|
||||
"Latent Diffusion Models": "\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab \u0d86\u0d9a\u0dd8\u0dad\u0dd2"
|
||||
}
|
||||
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"<h1>Latent Diffusion Models</h1>\n<p>Latent diffusion models use an auto-encoder to map between image space and latent space. The diffusion model works on the latent space, which makes it a lot easier to train. It is based on paper <a href=\"https://arxiv.org/abs/2112.10752\">High-Resolution Image Synthesis with Latent Diffusion Models</a>.</p>\n<p>They use a pre-trained auto-encoder and train the diffusion U-Net on the latent space of the pre-trained auto-encoder.</p>\n<p>For a simpler diffusion implementation refer to our <a href=\"../ddpm/index.html\">DDPM implementation</a>. We use same notations for <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> schedules, etc.</p>\n": "<h1>\u6f5c\u5728\u6269\u6563\u6a21\u578b</h1>\n<p>\u6f5c\u5728\u6269\u6563\u6a21\u578b\u4f7f\u7528\u81ea\u52a8\u7f16\u7801\u5668\u5728\u56fe\u50cf\u7a7a\u95f4\u548c\u6f5c\u5728\u7a7a\u95f4\u4e4b\u95f4\u8fdb\u884c\u6620\u5c04\u3002\u6269\u6563\u6a21\u578b\u9002\u7528\u4e8e\u6f5c\u5728\u7a7a\u95f4\uff0c\u8fd9\u4f7f\u5f97\u8bad\u7ec3\u53d8\u5f97\u5bb9\u6613\u5f97\u591a\u3002\u5b83\u57fa\u4e8e<a href=\"https://arxiv.org/abs/2112.10752\">\u5e26\u6709\u6f5c\u5728\u6269\u6563\u6a21\u578b\u7684\u7eb8\u8d28\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u5408\u6210</a>\u3002</p>\n<p>\u5b83\u4eec\u4f7f\u7528\u9884\u8bad\u7ec3\u7684\u81ea\u52a8\u7f16\u7801\u5668\uff0c\u5728\u9884\u8bad\u7ec3\u7684\u81ea\u52a8\u7f16\u7801\u5668\u7684\u6f5c\u5728\u7a7a\u95f4\u4e0a\u8bad\u7ec3\u6269\u6563 U-Net\u3002</p>\n<p>\u6709\u5173\u66f4\u7b80\u5355\u7684\u6269\u6563\u5b9e\u73b0\uff0c\u8bf7\u53c2\u9605\u6211\u4eec\u7684 <a href=\"../ddpm/index.html\">DDPM \u5b9e\u73b0</a>\u3002\u6211\u4eec\u5bf9<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>\u65f6\u95f4\u8868\u7b49\u4f7f\u7528\u76f8\u540c\u7684\u7b26\u53f7\u3002</p>\n",
|
||||
"<h2>Latent diffusion model</h2>\n<p>This contains following components:</p>\n<ul><li><a href=\"model/autoencoder.html\">AutoEncoder</a> </li>\n<li><a href=\"model/unet.html\">U-Net</a> with <a href=\"model/unet_attention.html\">attention</a> </li>\n<li><a href=\"model/clip_embedder.html\">CLIP embeddings generator</a></li></ul>\n": "<h2>\u6f5c\u5728\u6269\u6563\u6a21\u578b</h2>\n<p>\u5b83\u5305\u542b\u4ee5\u4e0b\u7ec4\u4ef6\uff1a</p>\n<ul><li><a href=\"model/autoencoder.html\">\u81ea\u52a8\u7f16\u7801\u5668</a></li>\n<li><a href=\"model/unet_attention.html\">\u5907\u53d7\u5173\u6ce8</a>\u7684 <a href=\"model/unet.html\">U-Net</a></li>\n<li><a href=\"model/clip_embedder.html\">CLIP \u5d4c\u5165\u5f0f\u751f\u6210\u5668</a></li></ul>\n",
|
||||
"<h3>Get <a href=\"model/clip_embedder.html\">CLIP embeddings</a> for a list of text prompts</h3>\n": "<h3>\u83b7\u53d6 <a href=\"model/clip_embedder.html\">CLIP \u5d4c\u5165</a>\u4ee5\u83b7\u53d6\u6587\u672c\u63d0\u793a\u5217\u8868</h3>\n",
|
||||
"<h3>Get image from the latent representation</h3>\n<p>We scale down by the scaling factor and then decode.</p>\n": "<h3>\u4ece\u6f5c\u5728\u8868\u793a\u4e2d\u83b7\u53d6\u56fe\u50cf</h3>\n<p>\u6211\u4eec\u6309\u7f29\u653e\u7cfb\u6570\u5411\u4e0b\u7f29\u653e\uff0c\u7136\u540e\u89e3\u7801\u3002</p>\n",
|
||||
"<h3>Get model device</h3>\n": "<h3>\u83b7\u53d6\u8bbe\u5907\u6a21\u578b</h3>\n",
|
||||
"<h3>Get scaled latent space representation of the image</h3>\n<p>The encoder output is a distribution. We sample from that and multiply by the scaling factor.</p>\n": "<h3>\u83b7\u53d6\u56fe\u50cf\u7684\u7f29\u653e\u6f5c\u5728\u7a7a\u95f4\u8868\u793a</h3>\n<p>\u7f16\u7801\u5668\u8f93\u51fa\u662f\u5206\u5e03\u5f0f\u3002\u6211\u4eec\u4ece\u4e2d\u53d6\u6837\u5e76\u4e58\u4ee5\u7f29\u653e\u7cfb\u6570\u3002</p>\n",
|
||||
"<h3>Predict noise</h3>\n<p>Predict noise given the latent representation <span translate=no>_^_0_^_</span>, time step <span translate=no>_^_1_^_</span>, and the conditioning context <span translate=no>_^_2_^_</span>.</p>\n<p><span translate=no>_^_3_^_</span></p>\n": "<h3>\u9884\u6d4b\u566a\u97f3</h3>\n<p>\u6839\u636e\u6f5c\u5728\u8868\u793a<span translate=no>_^_0_^_</span>\u3001\u65f6\u95f4\u6b65<span translate=no>_^_1_^_</span>\u957f\u548c\u6761\u4ef6\u73af\u5883\u9884\u6d4b\u566a\u58f0<span translate=no>_^_2_^_</span>\u3002</p>\n<p><span translate=no>_^_3_^_</span></p>\n",
|
||||
"<p> <em>This is an empty wrapper class around the <a href=\"model/unet.html\">U-Net</a>. We keep this to have the same model structure as <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we do not have to map the checkpoint weights explicitly</em>.</p>\n": "<p><em>\u8fd9\u662f\u56f4\u7ed5 <a href=\"model/unet.html\">U-Net</a> \u7684\u7a7a\u5305\u88c5\u7c7b\u3002\u6211\u4eec\u4fdd\u6301\u5b83\u4e0e <a href=\"https://github.com/CompVis/stable-diffusion\">compVIS/Stable-</a> Difusion \u76f8\u540c\u7684\u6a21\u578b\u7ed3\u6784\uff0c\u8fd9\u6837\u6211\u4eec\u5c31\u4e0d\u5fc5\u660e\u786e\u5730\u6620\u5c04\u68c0\u67e5\u70b9\u6743\u91cd</em>\u3002</p>\n",
|
||||
"<p><a href=\"model/clip_embedder.html\">CLIP embeddings generator</a> </p>\n": "<p><a href=\"model/clip_embedder.html\">CLIP \u5d4c\u5165\u5f0f\u751f\u6210\u5668</a></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> schedule </p>\n": "<p><span translate=no>_^_0_^_</span>\u65f6\u95f4\u8868</p>\n",
|
||||
"<p>Auto-encoder and scaling factor </p>\n": "<p>\u81ea\u52a8\u7f16\u7801\u5668\u548c\u7f29\u653e\u7cfb\u6570</p>\n",
|
||||
"<p>Number of steps <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6b65\u6570<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Wrap the <a href=\"model/unet.html\">U-Net</a> to keep the same model structure as <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a>. </p>\n": "<p>\u5c01\u88c5 <a href=\"model/unet.html\">U-Net</a> \u4ee5\u4fdd\u6301\u4e0e <a href=\"https://github.com/CompVis/stable-diffusion\">compVIS/Stable-</a> Difusion \u76f8\u540c\u7684\u6a21\u578b\u7ed3\u6784\u3002</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the <a href=\"model/unet.html\">U-Net</a> that predicts noise <span translate=no>_^_1_^_</span>, in latent space </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"model/autoencoder.html\">AutoEncoder</a> </li>\n<li><span translate=no>_^_3_^_</span> is the <a href=\"model/clip_embedder.html\">CLIP embeddings generator</a> </li>\n<li><span translate=no>_^_4_^_</span> is the scaling factor for the latent space. The encodings of the autoencoder are scaled by this before feeding into the U-Net. </li>\n<li><span translate=no>_^_5_^_</span> is the number of diffusion steps <span translate=no>_^_6_^_</span>. </li>\n<li><span translate=no>_^_7_^_</span> is the start of the <span translate=no>_^_8_^_</span> schedule. </li>\n<li><span translate=no>_^_9_^_</span> is the end of the <span translate=no>_^_10_^_</span> schedule.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u9884\u6d4b\u6f5c\u5728\u7a7a\u95f4\u4e2d\u566a\u58f0<span translate=no>_^_1_^_</span>\u7684 <a href=\"model/unet.html\">U-Ne</a> t</li>\n<li><span translate=no>_^_2_^_</span>\u662f<a href=\"model/autoencoder.html\">\u81ea\u52a8\u7f16\u7801\u5668</a></li>\n<li><span translate=no>_^_3_^_</span>\u662f <a href=\"model/clip_embedder.html\">CLIP \u5d4c\u5165\u751f\u6210\u5668</a></li>\n<li><span translate=no>_^_4_^_</span>\u662f\u6f5c\u5728\u7a7a\u95f4\u7684\u7f29\u653e\u7cfb\u6570\u3002\u5728\u9988\u5165 U-Net \u4e4b\u524d\uff0c\u81ea\u52a8\u7f16\u7801\u5668\u7684\u7f16\u7801\u4f1a\u6309\u6b64\u8fdb\u884c\u7f29\u653e\u3002</li>\n<li><span translate=no>_^_5_^_</span>\u662f\u6269\u6563\u6b65\u9aa4\u7684\u6570\u91cf<span translate=no>_^_6_^_</span>\u3002</li>\n<li><span translate=no>_^_7_^_</span>\u662f<span translate=no>_^_8_^_</span>\u65f6\u95f4\u8868\u7684\u5f00\u59cb\u3002</li>\n<li><span translate=no>_^_9_^_</span>\u662f<span translate=no>_^_10_^_</span>\u65f6\u95f4\u8868\u7684\u7ed3\u675f\u3002</li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of latent diffusion models from paper High-Resolution Image Synthesis with Latent Diffusion Models": "\u5e26\u6ce8\u91ca\u7684 PyTorch \u5b9e\u73b0/\u6559\u7a0b\u6765\u81ea\u8bba\u6587\u7684\u6f5c\u5728\u6269\u6563\u6a21\u578b\u4f7f\u7528\u6f5c\u5728\u6269\u6563\u6a21\u578b\u8fdb\u884c\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u5408\u6210",
|
||||
"Latent Diffusion Models": "\u6f5c\u5728\u6269\u6563\u6a21\u578b"
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"<h1><a href=\"../index.html\">Stable Diffusion</a> Models</h1>\n<ul><li><a href=\"autoencoder.html\">AutoEncoder</a> </li>\n<li><a href=\"unet.html\">U-Net</a> with <a href=\"unet_attention.html\">attention</a> </li>\n<li><a href=\"clip_embedder.html\">CLIP embedder</a>.</li></ul>\n": "<h1><a href=\"../index.html\">\u5b89\u5b9a\u62e1\u6563\u30e2\u30c7\u30eb</a></h1>\n<ul><li><a href=\"autoencoder.html\">\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0</a></li>\n<li><a href=\"unet.html\"><a href=\"unet_attention.html\">\u6ce8\u610f\u3092\u5411\u3051\u305fU-Net</a></a></li>\n<li><a href=\"clip_embedder.html\">\u30af\u30ea\u30c3\u30d7\u30a8\u30f3\u30d9\u30c0\u30fc\u3002</a></li></ul>\n",
|
||||
"Models and components for stable diffusion.": "\u5b89\u5b9a\u62e1\u6563\u306e\u305f\u3081\u306e\u30e2\u30c7\u30eb\u3068\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8",
|
||||
"Modules used in stable diffusion": "\u5b89\u5b9a\u62e1\u6563\u306b\u4f7f\u7528\u3055\u308c\u308b\u30e2\u30b8\u30e5\u30fc\u30eb"
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"<h1><a href=\"../index.html\">Stable Diffusion</a> Models</h1>\n<ul><li><a href=\"autoencoder.html\">AutoEncoder</a> </li>\n<li><a href=\"unet.html\">U-Net</a> with <a href=\"unet_attention.html\">attention</a> </li>\n<li><a href=\"clip_embedder.html\">CLIP embedder</a>.</li></ul>\n": "<h1><a href=\"../index.html\">\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab</a> \u0d86\u0d9a\u0dd8\u0dad\u0dd2</h1>\n<ul><li><a href=\"autoencoder.html\">\u0dc3\u0dca\u0dc0\u0dba\u0d82 \u0d86\u0d9a\u0dda\u0dad\u0d9a\u0dba</a></li>\n<li><a href=\"unet.html\">U-Net</a> <a href=\"unet_attention.html\">\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba\u0dd9\u0db1\u0dca</a></li>\n<li><a href=\"clip_embedder.html\">\u0d9a\u0dca\u0dbd\u0dd2\u0db4\u0dca \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0db1\u0dca\u0db1\u0dcf</a>.</li></ul>\n",
|
||||
"Models and components for stable diffusion.": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0d86\u0d9a\u0dd8\u0dad\u0dd2 \u0dc3\u0dc4 \u0dc3\u0d82\u0dbb\u0da0\u0d9a.",
|
||||
"Modules used in stable diffusion": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1 \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd"
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"<h1><a href=\"../index.html\">Stable Diffusion</a> Models</h1>\n<ul><li><a href=\"autoencoder.html\">AutoEncoder</a> </li>\n<li><a href=\"unet.html\">U-Net</a> with <a href=\"unet_attention.html\">attention</a> </li>\n<li><a href=\"clip_embedder.html\">CLIP embedder</a>.</li></ul>\n": "<h1><a href=\"../index.html\">\u7a33\u5b9a\u7684\u6269\u6563</a>\u6a21\u578b</h1>\n<ul><li><a href=\"autoencoder.html\">\u81ea\u52a8\u7f16\u7801\u5668</a></li>\n<li><a href=\"unet_attention.html\">\u5907\u53d7\u5173\u6ce8</a>\u7684 <a href=\"unet.html\">U-Net</a></li>\n<li><a href=\"clip_embedder.html\">CLIP \u5d4c\u5165\u5668</a>\u3002</li></ul>\n",
|
||||
"Models and components for stable diffusion.": "\u7528\u4e8e\u7a33\u5b9a\u6269\u6563\u7684\u6a21\u578b\u548c\u7ec4\u4ef6\u3002",
|
||||
"Modules used in stable diffusion": "\u7528\u4e8e\u7a33\u5b9a\u6269\u6563\u7684\u6a21\u5757"
|
||||
}
|
||||
@@ -0,0 +1,84 @@
|
||||
{
|
||||
"<h1>Autoencoder for <a href=\"../index.html\">Stable Diffusion</a></h1>\n<p>This implements the auto-encoder model used to map between image space and latent space.</p>\n<p>We have kept to the model definition and naming unchanged from <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we can load the checkpoints directly.</p>\n": "<h1><a href=\"../index.html\">\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u3092\u5b9f\u73fe\u3059\u308b\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u30fc</a></h1>\n<p>\u3053\u308c\u306f\u3001\u753b\u50cf\u7a7a\u9593\u3068\u6f5c\u5728\u7a7a\u9593\u306e\u30de\u30c3\u30d4\u30f3\u30b0\u306b\u4f7f\u7528\u3055\u308c\u308b\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u30e2\u30c7\u30eb\u3092\u5b9f\u88c5\u3057\u3066\u3044\u307e\u3059\u3002</p>\n<p>\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u3092\u76f4\u63a5\u8aad\u307f\u8fbc\u3081\u308b\u3088\u3046\u306b\u3001<a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/Stable-Diffusion\u304b\u3089\u30e2\u30c7\u30eb\u5b9a\u7fa9\u3068\u547d\u540d\u3092\u5909\u66f4\u3057\u3066\u3044\u307e\u305b\u3093</a>\u3002</p>\n",
|
||||
"<h2>Attention block</h2>\n": "<h2>\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30d6\u30ed\u30c3\u30af</h2>\n",
|
||||
"<h2>Autoencoder</h2>\n<p>This consists of the encoder and decoder modules.</p>\n": "<h2>\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0</h2>\n<p>\u3053\u308c\u306f\u30a8\u30f3\u30b3\u30fc\u30c0\u30e2\u30b8\u30e5\u30fc\u30eb\u3068\u30c7\u30b3\u30fc\u30c0\u30e2\u30b8\u30e5\u30fc\u30eb\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002</p>\n",
|
||||
"<h2>Decoder module</h2>\n": "<h2>\u30c7\u30b3\u30fc\u30c0\u30e2\u30b8\u30e5\u30fc\u30eb</h2>\n",
|
||||
"<h2>Down-sampling layer</h2>\n": "<h2>\u30c0\u30a6\u30f3\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30ec\u30a4\u30e4\u30fc</h2>\n",
|
||||
"<h2>Encoder module</h2>\n": "<h2>\u30a8\u30f3\u30b3\u30fc\u30c0\u30e2\u30b8\u30e5\u30fc\u30eb</h2>\n",
|
||||
"<h2>Gaussian Distribution</h2>\n": "<h2>\u30ac\u30a6\u30b9\u5206\u5e03</h2>\n",
|
||||
"<h2>ResNet Block</h2>\n": "<h2>\u30ea\u30cd\u30c3\u30c8\u30d6\u30ed\u30c3\u30af</h2>\n",
|
||||
"<h2>Up-sampling layer</h2>\n": "<h2>\u30a2\u30c3\u30d7\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30ec\u30a4\u30e4\u30fc</h2>\n",
|
||||
"<h3>Decode images from latent representation</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the latent representation with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<h3>\u6f5c\u5728\u8868\u73fe\u304b\u3089\u753b\u50cf\u3092\u30c7\u30b3\u30fc\u30c9</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u5f62\u3092\u4f7f\u3063\u305f\u6f5c\u5728\u8868\u73fe\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<h3>Encode images to latent representation</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the image tensor with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<h3>\u753b\u50cf\u3092\u6f5c\u5728\u8868\u73fe\u306b\u30a8\u30f3\u30b3\u30fc\u30c9</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u306e\u3042\u308b\u30a4\u30e1\u30fc\u30b8\u30c6\u30f3\u30bd\u30eb\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<h3>Group normalization</h3>\n<p>This is a helper function, with fixed number of groups and <span translate=no>_^_0_^_</span>.</p>\n": "<h3>\u30b0\u30eb\u30fc\u30d7\u6b63\u898f\u5316</h3>\n<p>\u3053\u308c\u306f\u30d8\u30eb\u30d1\u30fc\u95a2\u6570\u3067\u3001\u30b0\u30eb\u30fc\u30d7\u306e\u6570\u306f\u56fa\u5b9a\u3055\u308c\u3066\u3044\u307e\u3059\u3002<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<h3>Swish activation</h3>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h3>\u30b9\u30a6\u30a3\u30c3\u30b7\u30e5\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30b7\u30e7\u30f3</h3>\n<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution mapping </p>\n": "<p><span translate=no>_^_0_^_</span>\u30b3\u30f3\u30dc\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u30de\u30c3\u30d4\u30f3\u30b0</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution with stride length of <span translate=no>_^_1_^_</span> to down-sample by a factor of <span translate=no>_^_2_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u30b9\u30c8\u30e9\u30a4\u30c9\u306e\u9577\u3055\u304c\u306e\u7573\u307f\u8fbc\u307f\u304b\u3089\u3001<span translate=no>_^_1_^_</span>\u306e\u4fc2\u6570\u3067\u30c0\u30a6\u30f3\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u307e\u3059 <span translate=no>_^_2_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> mapping layer for residual connection </p>\n": "<p><span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>\u6b8b\u7559\u63a5\u7d9a\u7528\u306e\u30de\u30c3\u30d4\u30f3\u30b0\u30ec\u30a4\u30e4\u3078</p>\n",
|
||||
"<p>Add ResNet Blocks </p>\n": "<p>ResNet \u30d6\u30ed\u30c3\u30af\u3092\u8ffd\u52a0</p>\n",
|
||||
"<p>Add padding </p>\n": "<p>\u30d1\u30c7\u30a3\u30f3\u30b0\u3092\u8ffd\u52a0</p>\n",
|
||||
"<p>Add residual connection </p>\n": "<p>\u6b8b\u4f59\u63a5\u7d9a\u3092\u8ffd\u52a0</p>\n",
|
||||
"<p>Apply convolution </p>\n": "<p>\u30b3\u30f3\u30dc\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u3092\u9069\u7528</p>\n",
|
||||
"<p>Attention scaling factor </p>\n": "<p>\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u30d5\u30a1\u30af\u30bf\u30fc</p>\n",
|
||||
"<p>Calculate standard deviation </p>\n": "<p>\u6a19\u6e96\u504f\u5dee\u306e\u8a08\u7b97</p>\n",
|
||||
"<p>Clamp the log of variances </p>\n": "<p>\u5dee\u7570\u306e\u5bfe\u6570\u3092\u30af\u30e9\u30f3\u30d7</p>\n",
|
||||
"<p>Compute <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b3\u30f3\u30d4\u30e5\u30fc\u30c8 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Convolution to map from embedding space to quantized embedding space moments (mean and log variance) </p>\n": "<p>\u57cb\u3081\u8fbc\u307f\u7a7a\u9593\u304b\u3089\u91cf\u5b50\u5316\u3055\u308c\u305f\u57cb\u3081\u8fbc\u307f\u7a7a\u9593\u306e\u30e2\u30fc\u30e1\u30f3\u30c8\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3059\u308b\u305f\u3081\u306e\u7573\u307f\u8fbc\u307f (\u5e73\u5747\u3068\u5bfe\u6570\u5206\u6563)</p>\n",
|
||||
"<p>Convolution to map from quantized embedding space back to embedding space </p>\n": "<p>\u91cf\u5b50\u5316\u3055\u308c\u305f\u57cb\u3081\u8fbc\u307f\u7a7a\u9593\u304b\u3089\u57cb\u3081\u8fbc\u307f\u7a7a\u9593\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3059\u308b\u7573\u307f\u8fbc\u307f</p>\n",
|
||||
"<p>Create top-level blocks </p>\n": "<p>\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u30d6\u30ed\u30c3\u30af\u3092\u4f5c\u6210</p>\n",
|
||||
"<p>Decode the image of shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5f62\u72b6\u306e\u753b\u50cf\u3092\u30c7\u30b3\u30fc\u30c9 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Down-sampling </p>\n": "<p>\u30c0\u30a6\u30f3\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0</p>\n",
|
||||
"<p>Down-sampling at the end of each top level block except the last </p>\n": "<p>\u6700\u5f8c\u3092\u9664\u304f\u5404\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u30d6\u30ed\u30c3\u30af\u306e\u6700\u5f8c\u3067\u306e\u30c0\u30a6\u30f3\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0</p>\n",
|
||||
"<p>Each top level block consists of multiple ResNet Blocks and down-sampling </p>\n": "<p>\u5404\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u30d6\u30ed\u30c3\u30af\u306f\u8907\u6570\u306eResNet\u30d6\u30ed\u30c3\u30af\u3068\u30c0\u30a6\u30f3\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059</p>\n",
|
||||
"<p>Each top level block consists of multiple ResNet Blocks and up-sampling </p>\n": "<p>\u5404\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u30d6\u30ed\u30c3\u30af\u306f\u8907\u6570\u306eResNet\u30d6\u30ed\u30c3\u30af\u3068\u30a2\u30c3\u30d7\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059</p>\n",
|
||||
"<p>Final <span translate=no>_^_0_^_</span> convolution layer </p>\n": "<p><span translate=no>_^_0_^_</span>\u6700\u7d42\u7573\u307f\u8fbc\u307f\u5c64</p>\n",
|
||||
"<p>Final ResNet blocks with attention </p>\n": "<p>\u6700\u5f8c\u306e ResNet \u30d6\u30ed\u30c3\u30af\u306b\u306f\u6ce8\u610f\u304c\u5fc5\u8981\u3067\u3059\u3002</p>\n",
|
||||
"<p>First normalization and convolution layer </p>\n": "<p>\u6700\u521d\u306e\u6b63\u898f\u5316\u3068\u7573\u307f\u8fbc\u307f\u5c64</p>\n",
|
||||
"<p>Get embeddings with shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u56f3\u5f62\u4ed8\u304d\u306e\u57cb\u3081\u8fbc\u307f\u3092\u3059\u308b <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Get query, key and vector embeddings </p>\n": "<p>\u30af\u30a8\u30ea\u3001\u30ad\u30fc\u3001\u30d9\u30af\u30bf\u30fc\u306e\u57cb\u3081\u8fbc\u307f\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Get the moments in the quantized embedding space </p>\n": "<p>\u91cf\u5b50\u5316\u3055\u308c\u305f\u57cb\u3081\u8fbc\u307f\u7a7a\u9593\u306e\u30e2\u30fc\u30e1\u30f3\u30c8\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Group normalization </p>\n": "<p>\u30b0\u30eb\u30fc\u30d7\u6b63\u898f\u5316</p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution layer that maps the embedding space to <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u57cb\u3081\u8fbc\u307f\u30b9\u30da\u30fc\u30b9\u3092\u30de\u30c3\u30d4\u30f3\u30b0\u3059\u308b\u6700\u521d\u306e\u7573\u307f\u8fbc\u307f\u30ec\u30a4\u30e4\u30fc <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution layer that maps the image to <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u753b\u50cf\u3092\u30de\u30c3\u30d7\u3059\u308b\u6700\u521d\u306e\u7573\u307f\u8fbc\u307f\u30ec\u30a4\u30e4\u30fc <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>List of top-level blocks </p>\n": "<p>\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u30d6\u30ed\u30c3\u30af\u306e\u30ea\u30b9\u30c8</p>\n",
|
||||
"<p>Map and add residual </p>\n": "<p>\u6b8b\u5dee\u3092\u30de\u30c3\u30d4\u30f3\u30b0\u3057\u3066\u8ffd\u52a0</p>\n",
|
||||
"<p>Map to <span translate=no>_^_0_^_</span> with the initial convolution </p>\n": "<p><span translate=no>_^_0_^_</span>\u6700\u521d\u306e\u7573\u307f\u8fbc\u307f\u3067\u306b\u30de\u30c3\u30d4\u30f3\u30b0</p>\n",
|
||||
"<p>Map to embedding space from the quantized representation </p>\n": "<p>\u91cf\u5b50\u5316\u3055\u308c\u305f\u8868\u73fe\u304b\u3089\u57cb\u3081\u8fbc\u307f\u7a7a\u9593\u306b\u30de\u30c3\u30d4\u30f3\u30b0</p>\n",
|
||||
"<p>Map to embedding space with a <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u7573\u307f\u8fbc\u307f\u306b\u3088\u308b\u57cb\u3081\u8fbc\u307f\u7a7a\u9593\u306b\u30de\u30c3\u30d4\u30f3\u30b0 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Map to image space with a <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u7573\u307f\u8fbc\u307f\u306b\u3088\u308b\u753b\u50cf\u7a7a\u9593\u306b\u30de\u30c3\u30d4\u30f3\u30b0 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Normalize <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30ce\u30fc\u30de\u30e9\u30a4\u30ba <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Normalize and map to embedding space </p>\n": "<p>\u6b63\u898f\u5316\u3057\u3066\u57cb\u3081\u8fbc\u307f\u30b9\u30da\u30fc\u30b9\u306b\u30de\u30c3\u30d4\u30f3\u30b0</p>\n",
|
||||
"<p>Normalize and map to image space </p>\n": "<p>\u6b63\u898f\u5316\u3057\u3066\u753b\u50cf\u7a7a\u9593\u306b\u30de\u30c3\u30d4\u30f3\u30b0</p>\n",
|
||||
"<p>Number of blocks of different resolutions. The resolution is halved at the end each top level block </p>\n": "<p>\u89e3\u50cf\u5ea6\u306e\u7570\u306a\u308b\u30d6\u30ed\u30c3\u30af\u6570\u3002\u89e3\u50cf\u5ea6\u306f\u3001\u5404\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u30d6\u30ed\u30c3\u30af\u306e\u6700\u5f8c\u3067\u534a\u5206\u306b\u306a\u308a\u307e\u3059</p>\u3002\n",
|
||||
"<p>Number of channels in each top level block </p>\n": "<p>\u5404\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u30d6\u30ed\u30c3\u30af\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</p>\n",
|
||||
"<p>Number of channels in each top level block, in the reverse order </p>\n": "<p>\u5404\u6700\u4e0a\u4f4d\u30d6\u30ed\u30c3\u30af\u5185\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570 (\u9006\u9806)</p>\n",
|
||||
"<p>Number of channels in the top-level block </p>\n": "<p>\u6700\u4e0a\u4f4d\u30d6\u30ed\u30c3\u30af\u5185\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</p>\n",
|
||||
"<p>Prepend to be consistent with the checkpoint </p>\n": "<p>\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u3068\u4e00\u81f4\u3059\u308b\u3088\u3046\u306b\u30d7\u30ea\u30da\u30f3\u30c9\u3092\u4ed8\u3051\u308b</p>\n",
|
||||
"<p>Query, key and value mappings </p>\n": "<p>\u30af\u30a8\u30ea\u3001\u30ad\u30fc\u3001\u5024\u306e\u30de\u30c3\u30d4\u30f3\u30b0</p>\n",
|
||||
"<p>ResNet Blocks </p>\n": "<p>ResNet \u30d6\u30ed\u30c3\u30af</p>\n",
|
||||
"<p>ResNet blocks with attention </p>\n": "<p>ResNet \u30d6\u30ed\u30c3\u30af\u306b\u306f\u6ce8\u610f\u304c\u5fc5\u8981</p>\n",
|
||||
"<p>Reshape back to <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5f62\u72b6\u3092\u5909\u3048\u3066\u5143\u306b\u623b\u3059 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Reshape to query, key and vector embeedings from <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u5f62\u72b6\u3092\u5909\u3048\u3066\u30af\u30a8\u30ea\u3001\u30ad\u30fc\u3001\u30d9\u30af\u30bf\u30fc\u306e\u57cb\u3081\u8fbc\u307f\u3092\u304b\u3089\u3078 <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Return the distribution </p>\n": "<p>\u30c7\u30a3\u30b9\u30c8\u30ea\u30d3\u30e5\u30fc\u30b7\u30e7\u30f3\u3092\u8fd4\u3059</p>\n",
|
||||
"<p>Sample from the distribution </p>\n": "<p>\u30c7\u30a3\u30b9\u30c8\u30ea\u30d3\u30e5\u30fc\u30b7\u30e7\u30f3\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb</p>\n",
|
||||
"<p>Second normalization and convolution layer </p>\n": "<p>2 \u756a\u76ee\u306e\u6b63\u898f\u5316\u3068\u7573\u307f\u8fbc\u307f\u5c64</p>\n",
|
||||
"<p>Split mean and log of variance </p>\n": "<p>\u5206\u5272\u5e73\u5747\u3068\u5206\u6563\u5bfe\u6570</p>\n",
|
||||
"<p>Top-level block </p>\n": "<p>\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u30d6\u30ed\u30c3\u30af</p>\n",
|
||||
"<p>Top-level blocks </p>\n": "<p>\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u30d6\u30ed\u30c3\u30af</p>\n",
|
||||
"<p>Up-sample by a factor of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6b21\u306e\u500d\u307e\u3067\u306e\u30a2\u30c3\u30d7\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Up-sampling </p>\n": "<p>\u30a2\u30c3\u30d7\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0</p>\n",
|
||||
"<p>Up-sampling at the end of each top level block except the first </p>\n": "<p>\u6700\u521d\u306e\u30d6\u30ed\u30c3\u30af\u3092\u9664\u304f\u5404\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u30d6\u30ed\u30c3\u30af\u306e\u6700\u5f8c\u3067\u306e\u30a2\u30c3\u30d7\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> are the means and log of variances of the embedding of shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u56f3\u5f62\u306e\u57cb\u3081\u8fbc\u307f\u306e\u5e73\u5747\u3068\u5206\u6563\u306e\u5bfe\u6570\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the embedding tensor with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u4ed8\u304d\u306e\u57cb\u3081\u8fbc\u307f\u30c6\u30f3\u30bd\u30eb\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the encoder </li>\n<li><span translate=no>_^_1_^_</span> is the decoder </li>\n<li><span translate=no>_^_2_^_</span> is the number of dimensions in the quantized embedding space </li>\n<li><span translate=no>_^_3_^_</span> is the number of channels in the embedding space</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u30a8\u30f3\u30b3\u30fc\u30c0\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u30c7\u30b3\u30fc\u30c0\u3067\u3059</li>\n<li><span translate=no>_^_2_^_</span>\u306f\u91cf\u5b50\u5316\u3055\u308c\u305f\u57cb\u3081\u8fbc\u307f\u7a7a\u9593\u306e\u6b21\u5143\u6570\u3067\u3059</li>\n<li><span translate=no>_^_3_^_</span>\u306f\u57cb\u3081\u8fbc\u307f\u30b9\u30da\u30fc\u30b9\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u3067\u3059</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the image tensor with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u306e\u3042\u308b\u30a4\u30e1\u30fc\u30b8\u30c6\u30f3\u30bd\u30eb\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u4ed8\u304d\u306e\u5165\u529b\u30d5\u30a3\u30fc\u30c1\u30e3\u30de\u30c3\u30d7\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the final convolution layer </li>\n<li><span translate=no>_^_1_^_</span> are the multiplicative factors for the number of channels in the previous blocks, in reverse order </li>\n<li><span translate=no>_^_2_^_</span> is the number of resnet layers at each resolution </li>\n<li><span translate=no>_^_3_^_</span> is the number of channels in the image </li>\n<li><span translate=no>_^_4_^_</span> is the number of channels in the embedding space</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u6700\u5f8c\u306e\u7573\u307f\u8fbc\u307f\u5c64\u306e\u30c1\u30e3\u30cd\u30eb\u6570\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u524d\u306e\u30d6\u30ed\u30c3\u30af\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u306e\u4e57\u7b97\u4fc2\u6570 (\u9006\u9806)</li>\n<li><span translate=no>_^_2_^_</span>\u306f\u5404\u89e3\u50cf\u5ea6\u3067\u306e\u518d\u30cd\u30c3\u30c8\u5c64\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_3_^_</span>\u306f\u753b\u50cf\u5185\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</li>\n<li><span translate=no>_^_4_^_</span>\u306f\u57cb\u3081\u8fbc\u307f\u30b9\u30da\u30fc\u30b9\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u3067\u3059</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the first convolution layer </li>\n<li><span translate=no>_^_1_^_</span> are the multiplicative factors for the number of channels in the subsequent blocks </li>\n<li><span translate=no>_^_2_^_</span> is the number of resnet layers at each resolution </li>\n<li><span translate=no>_^_3_^_</span> is the number of channels in the image </li>\n<li><span translate=no>_^_4_^_</span> is the number of channels in the embedding space</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u6700\u521d\u306e\u7573\u307f\u8fbc\u307f\u5c64\u306e\u30c1\u30e3\u30cd\u30eb\u6570\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u306f\u5f8c\u7d9a\u306e\u30d6\u30ed\u30c3\u30af\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u306e\u4e57\u6570\u3067\u3059</li>\n<li><span translate=no>_^_2_^_</span>\u306f\u5404\u89e3\u50cf\u5ea6\u3067\u306e\u518d\u30cd\u30c3\u30c8\u5c64\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_3_^_</span>\u306f\u753b\u50cf\u5185\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</li>\n<li><span translate=no>_^_4_^_</span>\u306f\u57cb\u3081\u8fbc\u307f\u30b9\u30da\u30fc\u30b9\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u3067\u3059</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the input </li>\n<li><span translate=no>_^_1_^_</span> is the number of channels in the output</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u5165\u529b\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</li>\n<li><span translate=no>_^_1_^_</span>\u306f\u51fa\u529b\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u30c1\u30e3\u30cd\u30eb\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the tensor of shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u306e\u30c6\u30f3\u30bd\u30eb\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of the autoencoder for stable diffusion.": "\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u306e\u305f\u3081\u306e\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u30fc\u306e\u6ce8\u91c8\u4ed8\u304dPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3002",
|
||||
"Autoencoder for Stable Diffusion": "\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u3092\u5b9f\u73fe\u3059\u308b\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u30fc"
|
||||
}
|
||||
@@ -0,0 +1,84 @@
|
||||
{
|
||||
"<h1>Autoencoder for <a href=\"../index.html\">Stable Diffusion</a></h1>\n<p>This implements the auto-encoder model used to map between image space and latent space.</p>\n<p>We have kept to the model definition and naming unchanged from <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we can load the checkpoints directly.</p>\n": "<h1><a href=\"../index.html\">\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba</a> \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d86\u0d9a\u0dda\u0dad\u0d9a\u0dba</h1>\n<p>\u0dbb\u0dd6\u0db4 \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0dc3\u0dc4 \u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0d85\u0dad\u0dbb \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca \u0d9c\u0dad \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1 \u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dca \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0db8\u0dd9\u0dba \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dbb\u0dba\u0dd2.</p>\n<p>\u0d85\u0db4\u0dd2 \u0d86\u0daf\u0dbb\u0dca\u0dc1 \u0d85\u0dbb\u0dca\u0dae \u0daf\u0dd0\u0d9a\u0dca\u0dc0\u0dd3\u0db8 \u0dad\u0db6\u0dcf \u0d87\u0dad\u0dd2 \u0d85\u0dad\u0dbb <a href=\"https://github.com/CompVis/stable-diffusion\">\u0d9a\u0ddc\u0db8\u0dca\u0dc0\u0dd2\u0dc3\u0dca/\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab</a> \u0dc3\u0dd2\u0da7 \u0db1\u0ddc\u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0dc0 \u0db1\u0db8\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0d85\u0db4\u0da7 \u0db8\u0dd4\u0dbb\u0db4\u0ddc\u0dbd\u0dc0\u0dbd\u0dca \u0d9a\u0dd9\u0dbd\u0dd2\u0db1\u0dca\u0db8 \u0db4\u0dd0\u0da7\u0dc0\u0dd2\u0dba \u0dc4\u0dd0\u0d9a\u0dd2 \u0dc0\u0db1 \u0db4\u0dbb\u0dd2\u0daf\u0dd2.</p>\n",
|
||||
"<h2>Attention block</h2>\n": "<h2>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc0\u0dcf\u0dbb\u0dab</h2>\n",
|
||||
"<h2>Autoencoder</h2>\n<p>This consists of the encoder and decoder modules.</p>\n": "<h2>\u0d94\u0da7\u0ddd\u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba</h2>\n<p>\u0db8\u0dd9\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba \u0dc3\u0dc4 \u0dc0\u0dd2\u0d9a\u0dda\u0dad\u0d9a \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd \u0dc0\u0dbd\u0dd2\u0db1\u0dca \u0dc3\u0db8\u0db1\u0dca\u0dc0\u0dd2\u0dad \u0dc0\u0dda.</p>\n",
|
||||
"<h2>Decoder module</h2>\n": "<h2>\u0dc0\u0dd2\u0d9a\u0dda\u0dad\u0d9a \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dba</h2>\n",
|
||||
"<h2>Down-sampling layer</h2>\n": "<h2>\u0db4\u0dc4\u0dc5-\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0dc3\u0dca\u0dae\u0dbb\u0dba</h2>\n",
|
||||
"<h2>Encoder module</h2>\n": "<h2>\u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dca \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dba</h2>\n",
|
||||
"<h2>Gaussian Distribution</h2>\n": "<h2>\u0d9c\u0dc0\u0dd4\u0dc3\u0dd2\u0dba\u0dcf\u0db1\u0dd4 \u0db6\u0dd9\u0daf\u0dcf\u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0dca</h2>\n",
|
||||
"<h2>ResNet Block</h2>\n": "<h2>\u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca</h2>\n",
|
||||
"<h2>Up-sampling layer</h2>\n": "<h2>\u0daf\u0d9a\u0dca\u0dc0\u0dcf-\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8\u0dca \u0dc3\u0dca\u0dae\u0dbb\u0dba</h2>\n",
|
||||
"<h3>Decode images from latent representation</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the latent representation with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<h3>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0db1\u0dd2\u0dbb\u0dd6\u0db4\u0dab\u0dba\u0dd9\u0db1\u0dca \u0dbb\u0dd6\u0db4 \u0dc0\u0dd2\u0d9a\u0dda\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0d9c\u0dd4\u0db4\u0dca\u0dad \u0db1\u0dd2\u0dbb\u0dd6\u0db4\u0dab\u0dba\u0dba\u0dd2<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<h3>Encode images to latent representation</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the image tensor with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<h3>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0db1\u0dd2\u0dbb\u0dd6\u0db4\u0dab\u0dba\u0da7 \u0dbb\u0dd6\u0db4 \u0d9a\u0dda\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0dbb\u0dd6\u0db4 \u0da7\u0dd9\u0db1\u0dca\u0dc3\u0dbb\u0dba\u0dba\u0dd2<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<h3>Group normalization</h3>\n<p>This is a helper function, with fixed number of groups and <span translate=no>_^_0_^_</span>.</p>\n": "<h3>\u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba</h3>\n<p>\u0db8\u0dd9\u0dba \u0d8b\u0db4\u0d9a\u0dcf\u0dbb\u0d9a \u0dc1\u0dca\u0dbb\u0dd2\u0dad\u0dba\u0d9a\u0dca \u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0d9c\u0dab\u0db1 \u0dc3\u0dc4<span translate=no>_^_0_^_</span>.</p>\n",
|
||||
"<h3>Swish activation</h3>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h3>\u0dc3\u0dca\u0dc0\u0dd2\u0dc2\u0dca \u0dc3\u0d9a\u0dca\u0dbb\u0dd2\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8</h3>\n<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution mapping </p>\n": "<p><span translate=no>_^_0_^_</span>\u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd3\u0db8\u0dda \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca\u0d9a\u0dbb\u0dab\u0dba</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution with stride length of <span translate=no>_^_1_^_</span> to down-sample by a factor of <span translate=no>_^_2_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u0d9a \u0dc3\u0dcf\u0db0\u0d9a\u0dba\u0d9a\u0dca \u0dc0\u0dd2\u0dc3\u0dd2\u0db1\u0dca \u0db4\u0dc4\u0dc5-\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2<span translate=no>_^_1_^_</span> \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 stride \u0daf\u0dd2\u0d9c \u0dc3\u0db8\u0d9c convolution<span translate=no>_^_2_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> mapping layer for residual connection </p>\n": "<p><span translate=no>_^_0_^_</span>\u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0\u0dad\u0dcf\u0dc0\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dca\u0dad\u0dbb\u0dba<span translate=no>_^_1_^_</span> \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca\u0d9c\u0dad \u0d9a\u0dd2\u0dbb\u0dd3\u0db8</p>\n",
|
||||
"<p>Add ResNet Blocks </p>\n": "<p>\u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0d91\u0d9a\u0dad\u0dd4 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Add padding </p>\n": "<p>\u0db4\u0dd1\u0da9\u0dd2\u0db1\u0dca \u0d91\u0d9a\u0dad\u0dd4 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Add residual connection </p>\n": "<p>\u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0\u0dad\u0dcf\u0dc0\u0dba \u0d91\u0d9a\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Apply convolution </p>\n": "<p>\u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd2\u0db8 \u0dba\u0ddc\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Attention scaling factor </p>\n": "<p>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab \u0dc3\u0dcf\u0db0\u0d9a\u0dba</p>\n",
|
||||
"<p>Calculate standard deviation </p>\n": "<p>\u0dc3\u0db8\u0dca\u0db8\u0dad \u0d85\u0db4\u0d9c\u0db8\u0db1\u0dba \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Clamp the log of variances </p>\n": "<p>\u0dc0\u0dd2\u0da0\u0dbd\u0dca\u0dba\u0dba\u0db1\u0dca\u0d9c\u0dda \u0dbd\u0ddc\u0d9c\u0dca \u0daf\u0dd0\u0db8\u0dd3\u0db8</p>\n",
|
||||
"<p>Compute <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Convolution to map from embedding space to quantized embedding space moments (mean and log variance) </p>\n": "<p>\u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0dc3\u0dd2\u0da7 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0d85\u0dc0\u0d9a\u0dcf\u0dc1 \u0d85\u0dc0\u0dc3\u0dca\u0dae\u0dcf \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca \u0d9c\u0dad \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 (\u0db8\u0db0\u0dca\u0dba\u0db1\u0dca\u0dba \u0dc3\u0dc4 \u0dbd\u0ddc\u0d9c\u0dca \u0dc0\u0dd2\u0da0\u0dbd\u0dad\u0dcf\u0dc0)</p>\n",
|
||||
"<p>Convolution to map from quantized embedding space back to embedding space </p>\n": "<p>\u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0dc3\u0dd2\u0da7 \u0db1\u0dd0\u0dc0\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0da7 \u0dc3\u0d82\u0d9a\u0ddd\u0da0\u0db1\u0dba</p>\n",
|
||||
"<p>Create top-level blocks </p>\n": "<p>\u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0d9a\u0dd4\u0da7\u0dca\u0da7\u0dd2 \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Decode the image of shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0dbb\u0dd6\u0db4\u0dba \u0dc0\u0dd2\u0d9a\u0dda\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Down-sampling </p>\n": "<p>\u0db4\u0dc4\u0dc5-\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8\u0dca</p>\n",
|
||||
"<p>Down-sampling at the end of each top level block except the last </p>\n": "<p>\u0d85\u0db1\u0dca\u0dad\u0dd2\u0db8 \u0dc4\u0dd0\u0dbb \u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0d9a\u0ddc\u0da7\u0dc3 \u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dba\u0dda \u0db4\u0dc4\u0dc5-\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8</p>\n",
|
||||
"<p>Each top level block consists of multiple ResNet Blocks and down-sampling </p>\n": "<p>\u0dc3\u0dd1\u0db8 \u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0d91\u0d9a\u0d9a\u0dca\u0db8 \u0db6\u0dc4\u0dd4 \u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0dc3\u0dc4 \u0db4\u0dc4\u0dc5-\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dbd\u0dd2\u0db1\u0dca \u0dc3\u0db8\u0db1\u0dca\u0dc0\u0dd2\u0dad \u0dc0\u0dda</p>\n",
|
||||
"<p>Each top level block consists of multiple ResNet Blocks and up-sampling </p>\n": "<p>\u0dc3\u0dd1\u0db8 \u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0d91\u0d9a\u0d9a\u0dca\u0db8 \u0db6\u0dc4\u0dd4 \u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0dc3\u0dc4 \u0d89\u0dc4\u0dc5 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dbd\u0dd2\u0db1\u0dca \u0dc3\u0db8\u0db1\u0dca\u0dc0\u0dd2\u0dad \u0dc0\u0dda</p>\n",
|
||||
"<p>Final <span translate=no>_^_0_^_</span> convolution layer </p>\n": "<p>\u0d85\u0dc0\u0dc3\u0dcf\u0db1<span translate=no>_^_0_^_</span> \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd4\u0dab\u0dd4 \u0dc3\u0dca\u0dae\u0dbb\u0dba</p>\n",
|
||||
"<p>Final ResNet blocks with attention </p>\n": "<p>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4 \u0d9a\u0dbb\u0db1 \u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca</p>\n",
|
||||
"<p>First normalization and convolution layer </p>\n": "<p>\u0db4\u0dc5\u0db8\u0dd4 \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba \u0dc3\u0dc4 \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd4\u0dab\u0dd4 \u0dc3\u0dca\u0dae\u0dbb\u0dba</p>\n",
|
||||
"<p>Get embeddings with shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc4\u0dd0\u0da9\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Get query, key and vector embeddings </p>\n": "<p>\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8, \u0dba\u0dad\u0dd4\u0dbb \u0dc3\u0dc4 \u0daf\u0ddb\u0dc1\u0dd2\u0d9a \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get the moments in the quantized embedding space </p>\n": "<p>\u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0db8\u0ddc\u0dc4\u0ddc\u0dad \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Group normalization </p>\n": "<p>\u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba</p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution layer that maps the embedding space to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca \u0d9c\u0dad \u0d9a\u0dbb\u0db1 \u0db8\u0dd6\u0dbd\u0dd2\u0d9a<span translate=no>_^_0_^_</span> \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dca\u0dc0\u0dd3\u0db8\u0dda \u0dc3\u0dca\u0dae\u0dbb\u0dba<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution layer that maps the image to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0dbb\u0dd6\u0db4\u0dba \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca<span translate=no>_^_0_^_</span> \u0d9c\u0dad \u0d9a\u0dbb\u0db1 \u0db8\u0dd6\u0dbd\u0dd2\u0d9a \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd4\u0dab\u0dd4 \u0dc3\u0dca\u0dae\u0dbb\u0dba<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>List of top-level blocks </p>\n": "<p>\u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0d9a\u0dd4\u0da7\u0dca\u0da7\u0dd2 \u0dbd\u0dd0\u0dba\u0dd2\u0dc3\u0dca\u0dad\u0dd4\u0dc0</p>\n",
|
||||
"<p>Map and add residual </p>\n": "<p>\u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8 \u0dc3\u0dc4 \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d91\u0d9a\u0dad\u0dd4 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Map to <span translate=no>_^_0_^_</span> with the initial convolution </p>\n": "<p>\u0d86\u0dbb\u0db8\u0dca\u0db7\u0d9a \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dca\u0db8<span translate=no>_^_0_^_</span> \u0dc3\u0db8\u0d9f \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8</p>\n",
|
||||
"<p>Map to embedding space from the quantized representation </p>\n": "<p>\u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0db1\u0dd2\u0dbb\u0dd6\u0db4\u0dab\u0dba\u0dd9\u0db1\u0dca \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8</p>\n",
|
||||
"<p>Map to embedding space with a <span translate=no>_^_0_^_</span> convolution </p>\n": "<p><span translate=no>_^_0_^_</span>\u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd3\u0db8\u0d9a\u0dd2\u0db1\u0dca \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8</p>\n",
|
||||
"<p>Map to image space with a <span translate=no>_^_0_^_</span> convolution </p>\n": "<p><span translate=no>_^_0_^_</span>\u0dc3\u0d82\u0d9a\u0ddd\u0da0\u0db1\u0dba \u0dc3\u0db8\u0d9f \u0dbb\u0dd6\u0db4 \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0da7 \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8</p>\n",
|
||||
"<p>Normalize <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u200d\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Normalize and map to embedding space </p>\n": "<p>\u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba \u0d9a\u0dbb \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca \u0d9c\u0dad \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Normalize and map to image space </p>\n": "<p>\u0dbb\u0dd6\u0db4 \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0da7 \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba \u0d9a\u0dbb \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca \u0d9c\u0dad \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Number of blocks of different resolutions. The resolution is halved at the end each top level block </p>\n": "<p>\u0dc0\u0dd2\u0dc0\u0dd2\u0db0 \u0dc0\u0dd2\u0db7\u0dda\u0daf\u0db1 \u0dc0\u0dbd \u0d9a\u0dd4\u0da7\u0dca\u0da7\u0dd2 \u0d9c\u0dab\u0db1. \u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0d9a\u0ddc\u0da7\u0dc3 \u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dba\u0dda \u0dc0\u0dd2\u0db7\u0dda\u0daf\u0db1\u0dba \u0d85\u0da9\u0d9a\u0dd2\u0db1\u0dca \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda</p>\n",
|
||||
"<p>Number of channels in each top level block </p>\n": "<p>\u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0d91\u0d9a\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</p>\n",
|
||||
"<p>Number of channels in each top level block, in the reverse order </p>\n": "<p>\u0db4\u0dca\u0dbb\u0dad\u0dd2\u0dbd\u0ddd\u0db8 \u0d85\u0db1\u0dd4\u0db4\u0dd2\u0dc5\u0dd2\u0dc0\u0dd9\u0dbd\u0dd9\u0dc4\u0dd2 \u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0d91\u0d9a\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</p>\n",
|
||||
"<p>Number of channels in the top-level block </p>\n": "<p>\u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0d91\u0d9a\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</p>\n",
|
||||
"<p>Prepend to be consistent with the checkpoint </p>\n": "<p>\u0db8\u0dd4\u0dbb\u0db4\u0ddc\u0dbd\u0da7 \u0d85\u0db1\u0dd4\u0d9a\u0dd6\u0dbd \u0dc0\u0dd3\u0db8\u0da7 \u0dc3\u0dd6\u0daf\u0dcf\u0db1\u0db8\u0dca \u0dc0\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Query, key and value mappings </p>\n": "<p>\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca, \u0dba\u0dad\u0dd4\u0dbb \u0dc3\u0dc4 \u0d85\u0d9c\u0dba \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca</p>\n",
|
||||
"<p>ResNet Blocks </p>\n": "<p>\u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca</p>\n",
|
||||
"<p>ResNet blocks with attention </p>\n": "<p>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca</p>\n",
|
||||
"<p>Reshape back to <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db1\u0dd0\u0dc0\u0dad \u0dc3\u0d9a\u0dc3\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Reshape to query, key and vector embeedings from <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8, \u0db4\u0dca\u0dbb\u0db0\u0dcf\u0db1 \u0dc3\u0dc4 \u0daf\u0ddb\u0dc1\u0dd2\u0d9a \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca<span translate=no>_^_0_^_</span> \u0dc0\u0dd9\u0dad \u0db1\u0dd0\u0dc0\u0dad \u0dc3\u0d9a\u0dc3\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Return the distribution </p>\n": "<p>\u0db6\u0dd9\u0daf\u0dcf \u0dc4\u0dd0\u0dbb\u0dd3\u0db8 \u0d86\u0db4\u0dc3\u0dd4 \u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Sample from the distribution </p>\n": "<p>\u0db6\u0dd9\u0daf\u0dcf\u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0dd9\u0db1\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba</p>\n",
|
||||
"<p>Second normalization and convolution layer </p>\n": "<p>\u0daf\u0dd9\u0dc0\u0db1 \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba \u0dc3\u0dc4 \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd4\u0dab\u0dd4 \u0dc3\u0dca\u0dae\u0dbb\u0dba</p>\n",
|
||||
"<p>Split mean and log of variance </p>\n": "<p>\u0db7\u0dda\u0daf\u0dba \u0db8\u0db0\u0dca\u0dba\u0db1\u0dca\u0dba\u0dba \u0dc3\u0dc4 \u0dc0\u0dd2\u0da0\u0dbd\u0dad\u0dcf\u0dc0 \u0dbd\u0d9d\u0dd4-\u0dc3\u0da7\u0dc4\u0db1</p>\n",
|
||||
"<p>Top-level block </p>\n": "<p>\u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca</p>\n",
|
||||
"<p>Top-level blocks </p>\n": "<p>\u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0d9a\u0dd4\u0da7\u0dca\u0da7\u0dd2</p>\n",
|
||||
"<p>Up-sample by a factor of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc3\u0dcf\u0db0\u0d9a\u0dba\u0d9a\u0dca \u0d85\u0db1\u0dd4\u0dc0 \u0d89\u0dc4\u0dc5 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Up-sampling </p>\n": "<p>\u0d89\u0dc4\u0dc5\u0da7 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8</p>\n",
|
||||
"<p>Up-sampling at the end of each top level block except the first </p>\n": "<p>\u0db4\u0dc5\u0db8\u0dd4\u0dc0\u0dd0\u0db1\u0dca\u0db1 \u0dc4\u0dd0\u0dbb \u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0d89\u0dc4\u0dc5 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0d9a\u0ddc\u0da7\u0dc3 \u0d85\u0dc0\u0dc3\u0dcf\u0db1\u0dba\u0dda \u0d89\u0dc4\u0dc5\u0da7 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> are the means and log of variances of the embedding of shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0db4\u0dd2\u0dc5\u0dd2\u0db6\u0db3 \u0dc0\u0dd2\u0da0\u0dbd\u0db1\u0dba\u0db1\u0dca\u0d9c\u0dda \u0db8\u0dcf\u0db0\u0dca\u0dba\u0dba\u0db1\u0dca \u0dc3\u0dc4 \u0dbd\u0ddc\u0d9c\u0dca \u0dc0\u0dda<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the embedding tensor with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 tensor \u0dc0\u0dda<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the encoder </li>\n<li><span translate=no>_^_1_^_</span> is the decoder </li>\n<li><span translate=no>_^_2_^_</span> is the number of dimensions in the quantized embedding space </li>\n<li><span translate=no>_^_3_^_</span> is the number of channels in the embedding space</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0d86\u0d9a\u0dda\u0dad\u0d9a\u0dba \u0dc0\u0dda</li>\n<li><span translate=no>_^_1_^_</span>\u0dc0\u0dd2\u0d9a\u0dda\u0dad\u0d9a\u0dba \u0dc0\u0dda</li>\n<li><span translate=no>_^_2_^_</span>\u0dba\u0db1\u0dd4 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0db8\u0dcf\u0db1\u0dba\u0db1\u0dca \u0d9c\u0dab\u0db1</li>\n</ul><li><span translate=no>_^_3_^_</span>\u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the image tensor with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0dbb\u0dd6\u0db4 \u0da7\u0dd9\u0db1\u0dca\u0dc3\u0dbb\u0dba\u0dba\u0dd2<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0d86\u0daf\u0dcf\u0db1 \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dba\u0dd2<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the final convolution layer </li>\n<li><span translate=no>_^_1_^_</span> are the multiplicative factors for the number of channels in the previous blocks, in reverse order </li>\n<li><span translate=no>_^_2_^_</span> is the number of resnet layers at each resolution </li>\n<li><span translate=no>_^_3_^_</span> is the number of channels in the image </li>\n<li><span translate=no>_^_4_^_</span> is the number of channels in the embedding space</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0dc3\u0d82\u0dc0\u0dc4\u0db1 \u0dc3\u0dca\u0dae\u0dbb\u0dba\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n<li><span translate=no>_^_1_^_</span>\u0db4\u0dd9\u0dbb \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0dc0\u0dbd \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc3\u0db3\u0dc4\u0dcf \u0db6\u0dc4\u0dd4\u0d9a\u0dcf\u0dbb\u0dca\u0dba \u0dc3\u0dcf\u0db0\u0d9a, \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0dbd\u0ddd\u0db8 \u0d85\u0db1\u0dd4\u0db4\u0dd2\u0dc5\u0dd2\u0dc0\u0dd9\u0dbd</li>\n<li><span translate=no>_^_2_^_</span>\u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0dc0\u0dd2\u0db7\u0dda\u0daf\u0db1\u0dba\u0dda \u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0dc3\u0dca\u0dae\u0dbb \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n<li><span translate=no>_^_3_^_</span>\u0dba\u0db1\u0dd4 \u0dbb\u0dd6\u0db4\u0dba\u0dda \u0d87\u0dad\u0dd2 \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</li>\n</ul><li><span translate=no>_^_4_^_</span>\u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the first convolution layer </li>\n<li><span translate=no>_^_1_^_</span> are the multiplicative factors for the number of channels in the subsequent blocks </li>\n<li><span translate=no>_^_2_^_</span> is the number of resnet layers at each resolution </li>\n<li><span translate=no>_^_3_^_</span> is the number of channels in the image </li>\n<li><span translate=no>_^_4_^_</span> is the number of channels in the embedding space</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0db4\u0dc5\u0db8\u0dd4 \u0dc3\u0d82\u0dc0\u0dc4\u0db1 \u0dc3\u0dca\u0dae\u0dbb\u0dba\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n<li><span translate=no>_^_1_^_</span>\u0db4\u0dc3\u0dd4\u0d9a\u0dcf\u0dbd\u0dd3\u0db1 \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0dc0\u0dbd \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0 \u0dc3\u0db3\u0dc4\u0dcf \u0db6\u0dc4\u0dd4\u0d9a\u0dcf\u0dbb\u0dca\u0dba \u0dc3\u0dcf\u0db0\u0d9a \u0dc0\u0dda</li>\n<li><span translate=no>_^_2_^_</span>\u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0dc0\u0dd2\u0db7\u0dda\u0daf\u0db1\u0dba\u0dda \u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0dc3\u0dca\u0dae\u0dbb \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n<li><span translate=no>_^_3_^_</span>\u0dba\u0db1\u0dd4 \u0dbb\u0dd6\u0db4\u0dba\u0dda \u0d87\u0dad\u0dd2 \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</li>\n</ul><li><span translate=no>_^_4_^_</span>\u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the input </li>\n<li><span translate=no>_^_1_^_</span> is the number of channels in the output</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dba\u0db1\u0dd4 \u0d86\u0daf\u0dcf\u0db1\u0dba\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</li>\n<li><span translate=no>_^_1_^_</span>\u0db1\u0dd2\u0db8\u0dd0\u0dc0\u0dd4\u0db8\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dba\u0db1\u0dd4 \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the tensor of shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d86\u0dad\u0dad\u0dd2\u0d9a\u0dba \u0dc0\u0dda<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of the autoencoder for stable diffusion.": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba\u0dda PyTorch \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8/\u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1\u0dba.",
|
||||
"Autoencoder for Stable Diffusion": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d86\u0d9a\u0dda\u0dad\u0d9a\u0dba"
|
||||
}
|
||||
@@ -0,0 +1,84 @@
|
||||
{
|
||||
"<h1>Autoencoder for <a href=\"../index.html\">Stable Diffusion</a></h1>\n<p>This implements the auto-encoder model used to map between image space and latent space.</p>\n<p>We have kept to the model definition and naming unchanged from <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we can load the checkpoints directly.</p>\n": "<h1>\u7528\u4e8e<a href=\"../index.html\">\u7a33\u5b9a\u6269\u6563</a>\u7684\u81ea\u52a8\u7f16\u7801\u5668</h1>\n<p>\u8fd9\u5b9e\u73b0\u4e86\u7528\u4e8e\u5728\u56fe\u50cf\u7a7a\u95f4\u548c\u6f5c\u5728\u7a7a\u95f4\u4e4b\u95f4\u8fdb\u884c\u6620\u5c04\u7684\u81ea\u52a8\u7f16\u7801\u5668\u6a21\u578b\u3002</p>\n<p>\u6211\u4eec\u4fdd\u6301\u4e86 <a href=\"https://github.com/CompVis/stable-diffusion\">compvis/Stable-Difusi</a> on \u7684\u6a21\u578b\u5b9a\u4e49\u548c\u547d\u540d\u4e0d\u53d8\uff0c\u8fd9\u6837\u6211\u4eec\u5c31\u53ef\u4ee5\u76f4\u63a5\u52a0\u8f7d\u68c0\u67e5\u70b9\u3002</p>\n",
|
||||
"<h2>Attention block</h2>\n": "<h2>\u6ce8\u610f\u65b9\u5757</h2>\n",
|
||||
"<h2>Autoencoder</h2>\n<p>This consists of the encoder and decoder modules.</p>\n": "<h2>\u81ea\u52a8\u7f16\u7801\u5668</h2>\n<p>\u5b83\u7531\u7f16\u7801\u5668\u548c\u89e3\u7801\u5668\u6a21\u5757\u7ec4\u6210\u3002</p>\n",
|
||||
"<h2>Decoder module</h2>\n": "<h2>\u89e3\u7801\u5668\u6a21\u5757</h2>\n",
|
||||
"<h2>Down-sampling layer</h2>\n": "<h2>\u5411\u4e0b\u91c7\u6837\u5c42</h2>\n",
|
||||
"<h2>Encoder module</h2>\n": "<h2>\u7f16\u7801\u5668\u6a21\u5757</h2>\n",
|
||||
"<h2>Gaussian Distribution</h2>\n": "<h2>\u9ad8\u65af\u5206\u5e03</h2>\n",
|
||||
"<h2>ResNet Block</h2>\n": "<h2>ResNet \u533a\u5757</h2>\n",
|
||||
"<h2>Up-sampling layer</h2>\n": "<h2>\u5411\u4e0a\u91c7\u6837\u5c42</h2>\n",
|
||||
"<h3>Decode images from latent representation</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the latent representation with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<h3>\u4ece\u6f5c\u5728\u8868\u73b0\u4e2d\u89e3\u7801\u56fe\u50cf</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u5e26\u6709\u5f62\u72b6\u7684\u6f5c\u5728\u8868\u793a\u5f62\u5f0f<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<h3>Encode images to latent representation</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the image tensor with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<h3>\u5c06\u56fe\u50cf\u7f16\u7801\u4e3a\u6f5c\u5728\u8868\u793a</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u5e26\u6709\u5f62\u72b6\u7684\u56fe\u50cf\u5f20\u91cf<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<h3>Group normalization</h3>\n<p>This is a helper function, with fixed number of groups and <span translate=no>_^_0_^_</span>.</p>\n": "<h3>\u7fa4\u7ec4\u6807\u51c6\u5316</h3>\n<p>\u8fd9\u662f\u4e00\u4e2a\u8f85\u52a9\u51fd\u6570\uff0c\u5177\u6709\u56fa\u5b9a\u6570\u91cf\u7684\u7ec4\u548c<span translate=no>_^_0_^_</span>\u3002</p>\n",
|
||||
"<h3>Swish activation</h3>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h3>Swish \u6fc0\u6d3b</h3>\n<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution mapping </p>\n": "<p><span translate=no>_^_0_^_</span>\u5377\u79ef\u6620\u5c04</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution with stride length of <span translate=no>_^_1_^_</span> to down-sample by a factor of <span translate=no>_^_2_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u5377\u79ef\uff0c\u6b65\u957f\u4e3a<span translate=no>_^_1_^_</span>\u5411\u4e0b\u91c7\u6837\u7684\u7cfb\u6570\u4e3a<span translate=no>_^_2_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> mapping layer for residual connection </p>\n": "<p><span translate=no>_^_0_^_</span>\u5230\u5269\u4f59\u8fde\u63a5\u7684<span translate=no>_^_1_^_</span>\u6620\u5c04\u5c42</p>\n",
|
||||
"<p>Add ResNet Blocks </p>\n": "<p>\u6dfb\u52a0 ResNet \u533a\u5757</p>\n",
|
||||
"<p>Add padding </p>\n": "<p>\u6dfb\u52a0\u5185\u8fb9\u8ddd</p>\n",
|
||||
"<p>Add residual connection </p>\n": "<p>\u6dfb\u52a0\u5269\u4f59\u8fde\u63a5</p>\n",
|
||||
"<p>Apply convolution </p>\n": "<p>\u5e94\u7528\u5377\u79ef</p>\n",
|
||||
"<p>Attention scaling factor </p>\n": "<p>\u6ce8\u610f\u529b\u7f29\u653e\u7cfb\u6570</p>\n",
|
||||
"<p>Calculate standard deviation </p>\n": "<p>\u8ba1\u7b97\u6807\u51c6\u5dee</p>\n",
|
||||
"<p>Clamp the log of variances </p>\n": "<p>\u9650\u5236\u65b9\u5dee\u65e5\u5fd7</p>\n",
|
||||
"<p>Compute <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8ba1\u7b97<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Convolution to map from embedding space to quantized embedding space moments (mean and log variance) </p>\n": "<p>\u4ece\u5d4c\u5165\u7a7a\u95f4\u5230\u91cf\u5316\u5d4c\u5165\u7a7a\u95f4\u77e9\u7684\u5377\u79ef\u5230\u6620\u5c04\uff08\u5747\u503c\u548c\u5bf9\u6570\u65b9\u5dee\uff09</p>\n",
|
||||
"<p>Convolution to map from quantized embedding space back to embedding space </p>\n": "<p>\u5377\u79ef\u5c06\u4ece\u91cf\u5316\u5d4c\u5165\u7a7a\u95f4\u6620\u5c04\u56de\u5d4c\u5165\u7a7a\u95f4</p>\n",
|
||||
"<p>Create top-level blocks </p>\n": "<p>\u521b\u5efa\u9876\u7ea7\u533a\u5757</p>\n",
|
||||
"<p>Decode the image of shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u89e3\u7801\u5f62\u72b6\u7684\u56fe\u50cf<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Down-sampling </p>\n": "<p>\u5411\u4e0b\u91c7\u6837</p>\n",
|
||||
"<p>Down-sampling at the end of each top level block except the last </p>\n": "<p>\u5728\u6bcf\u4e2a\u9876\u7ea7\u533a\u5757\u7684\u672b\u5c3e\u5904\u5411\u4e0b\u91c7\u6837\uff08\u6700\u540e\u4e00\u4e2a\u533a\u5757\u9664\u5916\uff09</p>\n",
|
||||
"<p>Each top level block consists of multiple ResNet Blocks and down-sampling </p>\n": "<p>\u6bcf\u4e2a\u9876\u7ea7\u533a\u5757\u7531\u591a\u4e2a ResNet \u6a21\u5757\u548c\u5411\u4e0b\u91c7\u6837\u7ec4\u6210</p>\n",
|
||||
"<p>Each top level block consists of multiple ResNet Blocks and up-sampling </p>\n": "<p>\u6bcf\u4e2a\u9876\u7ea7\u533a\u5757\u7531\u591a\u4e2a ResNet \u6a21\u5757\u548c\u5411\u4e0a\u91c7\u6837\u7ec4\u6210</p>\n",
|
||||
"<p>Final <span translate=no>_^_0_^_</span> convolution layer </p>\n": "<p>\u6700\u7ec8<span translate=no>_^_0_^_</span>\u5377\u79ef\u5c42</p>\n",
|
||||
"<p>Final ResNet blocks with attention </p>\n": "<p>\u6700\u540e\u4e00\u4e2a\u503c\u5f97\u6ce8\u610f\u7684 ResNet \u5c01\u9501</p>\n",
|
||||
"<p>First normalization and convolution layer </p>\n": "<p>\u7b2c\u4e00\u4e2a\u5f52\u4e00\u5316\u548c\u5377\u79ef\u5c42</p>\n",
|
||||
"<p>Get embeddings with shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u83b7\u53d6\u5e26\u6709\u5f62\u72b6\u7684\u5d4c\u5165\u7269<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Get query, key and vector embeddings </p>\n": "<p>\u83b7\u53d6\u67e5\u8be2\u3001\u952e\u548c\u5411\u91cf\u5d4c\u5165</p>\n",
|
||||
"<p>Get the moments in the quantized embedding space </p>\n": "<p>\u83b7\u53d6\u91cf\u5316\u5d4c\u5165\u7a7a\u95f4\u4e2d\u7684\u77ac\u95f4</p>\n",
|
||||
"<p>Group normalization </p>\n": "<p>\u7fa4\u7ec4\u6807\u51c6\u5316</p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution layer that maps the embedding space to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u5c06\u5d4c\u5165\u7a7a\u95f4\u6620\u5c04\u5230\u7684\u521d\u59cb<span translate=no>_^_0_^_</span>\u5377\u79ef\u5c42<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution layer that maps the image to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u5c06\u56fe\u50cf\u6620\u5c04\u5230\u7684\u521d\u59cb<span translate=no>_^_0_^_</span>\u5377\u79ef\u5c42<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>List of top-level blocks </p>\n": "<p>\u9876\u7ea7\u533a\u5757\u5217\u8868</p>\n",
|
||||
"<p>Map and add residual </p>\n": "<p>\u6620\u5c04\u5e76\u6dfb\u52a0\u6b8b\u5dee</p>\n",
|
||||
"<p>Map to <span translate=no>_^_0_^_</span> with the initial convolution </p>\n": "<p><span translate=no>_^_0_^_</span>\u4f7f\u7528\u521d\u59cb\u5377\u79ef\u6620\u5c04\u5230</p>\n",
|
||||
"<p>Map to embedding space from the quantized representation </p>\n": "<p>\u4ece\u91cf\u5316\u8868\u793a\u6620\u5c04\u5230\u5d4c\u5165\u7a7a\u95f4</p>\n",
|
||||
"<p>Map to embedding space with a <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u7528<span translate=no>_^_0_^_</span>\u5377\u79ef\u6620\u5c04\u5230\u5d4c\u5165\u7a7a\u95f4</p>\n",
|
||||
"<p>Map to image space with a <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u4f7f\u7528<span translate=no>_^_0_^_</span>\u5377\u79ef\u6620\u5c04\u5230\u56fe\u50cf\u7a7a\u95f4</p>\n",
|
||||
"<p>Normalize <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6807\u51c6\u5316<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Normalize and map to embedding space </p>\n": "<p>\u5f52\u4e00\u5316\u5e76\u6620\u5c04\u5230\u5d4c\u5165\u7a7a\u95f4</p>\n",
|
||||
"<p>Normalize and map to image space </p>\n": "<p>\u5f52\u4e00\u5316\u5e76\u6620\u5c04\u5230\u56fe\u50cf\u7a7a\u95f4</p>\n",
|
||||
"<p>Number of blocks of different resolutions. The resolution is halved at the end each top level block </p>\n": "<p>\u4e0d\u540c\u5206\u8fa8\u7387\u7684\u533a\u5757\u6570\u3002\u6bcf\u4e2a\u9876\u5c42\u65b9\u5757\u7684\u7ed3\u5c3e\u5904\u5206\u8fa8\u7387\u51cf\u534a</p>\n",
|
||||
"<p>Number of channels in each top level block </p>\n": "<p>\u6bcf\u4e2a\u9876\u7ea7\u533a\u5757\u4e2d\u7684\u9891\u9053\u6570</p>\n",
|
||||
"<p>Number of channels in each top level block, in the reverse order </p>\n": "<p>\u6bcf\u4e2a\u9876\u7ea7\u5757\u4e2d\u7684\u901a\u9053\u6570\uff0c\u6309\u76f8\u53cd\u987a\u5e8f\u6392\u5217</p>\n",
|
||||
"<p>Number of channels in the top-level block </p>\n": "<p>\u9876\u7ea7\u533a\u5757\u4e2d\u7684\u9891\u9053\u6570</p>\n",
|
||||
"<p>Prepend to be consistent with the checkpoint </p>\n": "<p>\u9884\u5148\u8bbe\u7f6e\u4ee5\u4e0e\u68c0\u67e5\u70b9\u4fdd\u6301\u4e00\u81f4</p>\n",
|
||||
"<p>Query, key and value mappings </p>\n": "<p>\u67e5\u8be2\u3001\u952e\u548c\u503c\u6620\u5c04</p>\n",
|
||||
"<p>ResNet Blocks </p>\n": "<p>ResNet \u533a\u5757</p>\n",
|
||||
"<p>ResNet blocks with attention </p>\n": "<p>ResNet \u8981\u6ce8\u610f\u5c01\u9501</p>\n",
|
||||
"<p>Reshape back to <span translate=no>_^_0_^_</span> </p>\n": "<p>\u91cd\u5851\u56de\u539f\u72b6<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Reshape to query, key and vector embeedings from <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u91cd\u5851\u4e3a\u67e5\u8be2\uff0c\u952e\u5d4c\u5165\u548c\u5411\u91cf\u5d4c\u5165\u4ece<span translate=no>_^_0_^_</span>\u4e3a<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Return the distribution </p>\n": "<p>\u8fd4\u56de\u5206\u5e03</p>\n",
|
||||
"<p>Sample from the distribution </p>\n": "<p>\u6765\u81ea\u5206\u5e03\u7684\u6837\u672c</p>\n",
|
||||
"<p>Second normalization and convolution layer </p>\n": "<p>\u7b2c\u4e8c\u4e2a\u5f52\u4e00\u5316\u548c\u5377\u79ef\u5c42</p>\n",
|
||||
"<p>Split mean and log of variance </p>\n": "<p>\u5206\u5272\u5747\u503c\u548c\u65b9\u5dee\u5bf9\u6570</p>\n",
|
||||
"<p>Top-level block </p>\n": "<p>\u9876\u7ea7\u533a\u5757</p>\n",
|
||||
"<p>Top-level blocks </p>\n": "<p>\u9876\u7ea7\u533a\u5757</p>\n",
|
||||
"<p>Up-sample by a factor of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6309\u7cfb\u6570\u5411\u4e0a\u91c7\u6837<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Up-sampling </p>\n": "<p>\u5411\u4e0a\u91c7\u6837</p>\n",
|
||||
"<p>Up-sampling at the end of each top level block except the first </p>\n": "<p>\u5728\u6bcf\u4e2a\u9876\u7ea7\u533a\u5757\u7684\u7ed3\u5c3e\u5904\u5411\u4e0a\u91c7\u6837\uff08\u7b2c\u4e00\u4e2a\u9664\u5916\uff09</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> are the means and log of variances of the embedding of shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5f62\u72b6\u5d4c\u5165\u7684\u65b9\u5dee\u7684\u5747\u503c\u548c\u5bf9\u6570<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the embedding tensor with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5e26\u6709\u5f62\u72b6\u7684\u5d4c\u5165\u5f20\u91cf<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the encoder </li>\n<li><span translate=no>_^_1_^_</span> is the decoder </li>\n<li><span translate=no>_^_2_^_</span> is the number of dimensions in the quantized embedding space </li>\n<li><span translate=no>_^_3_^_</span> is the number of channels in the embedding space</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u7f16\u7801\u5668</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u89e3\u7801\u5668</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u91cf\u5316\u5d4c\u5165\u7a7a\u95f4\u4e2d\u7684\u7ef4\u6570</li>\n<li><span translate=no>_^_3_^_</span>\u662f\u5d4c\u5165\u7a7a\u95f4\u4e2d\u7684\u901a\u9053\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the image tensor with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5e26\u6709\u5f62\u72b6\u7684\u56fe\u50cf\u5f20\u91cf<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5e26\u6709\u5f62\u72b6\u7684\u8f93\u5165\u8981\u7d20\u56fe<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the final convolution layer </li>\n<li><span translate=no>_^_1_^_</span> are the multiplicative factors for the number of channels in the previous blocks, in reverse order </li>\n<li><span translate=no>_^_2_^_</span> is the number of resnet layers at each resolution </li>\n<li><span translate=no>_^_3_^_</span> is the number of channels in the image </li>\n<li><span translate=no>_^_4_^_</span> is the number of channels in the embedding space</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u6700\u7ec8\u5377\u79ef\u5c42\u4e2d\u7684\u901a\u9053\u6570</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u524d\u9762\u533a\u5757\u4e2d\u4fe1\u9053\u6570\u7684\u4e58\u6cd5\u56e0\u5b50\uff0c\u987a\u5e8f\u76f8\u53cd</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u6bcf\u79cd\u5206\u8fa8\u7387\u4e0b\u7684 resnet \u5c42\u6570</li>\n<li><span translate=no>_^_3_^_</span>\u662f\u56fe\u50cf\u4e2d\u7684\u901a\u9053\u6570</li>\n<li><span translate=no>_^_4_^_</span>\u662f\u5d4c\u5165\u7a7a\u95f4\u4e2d\u7684\u901a\u9053\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the first convolution layer </li>\n<li><span translate=no>_^_1_^_</span> are the multiplicative factors for the number of channels in the subsequent blocks </li>\n<li><span translate=no>_^_2_^_</span> is the number of resnet layers at each resolution </li>\n<li><span translate=no>_^_3_^_</span> is the number of channels in the image </li>\n<li><span translate=no>_^_4_^_</span> is the number of channels in the embedding space</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u7b2c\u4e00\u4e2a\u5377\u79ef\u5c42\u4e2d\u7684\u901a\u9053\u6570</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u540e\u7eed\u533a\u7ec4\u4e2d\u4fe1\u9053\u6570\u91cf\u7684\u4e58\u6cd5\u56e0\u5b50</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u6bcf\u79cd\u5206\u8fa8\u7387\u4e0b\u7684 resnet \u5c42\u6570</li>\n<li><span translate=no>_^_3_^_</span>\u662f\u56fe\u50cf\u4e2d\u7684\u901a\u9053\u6570</li>\n<li><span translate=no>_^_4_^_</span>\u662f\u5d4c\u5165\u7a7a\u95f4\u4e2d\u7684\u901a\u9053\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the input </li>\n<li><span translate=no>_^_1_^_</span> is the number of channels in the output</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u8f93\u5165\u4e2d\u7684\u901a\u9053\u6570</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u8f93\u51fa\u4e2d\u7684\u901a\u9053\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u9891\u9053\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the tensor of shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5f62\u72b6\u7684\u5f20\u91cf<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of the autoencoder for stable diffusion.": "\u5e26\u6709\u6ce8\u91ca\u7684\u81ea\u52a8\u7f16\u7801\u5668\u7684 PyTorch \u5b9e\u73b0/\u6559\u7a0b\uff0c\u7528\u4e8e\u7a33\u5b9a\u6269\u6563\u3002",
|
||||
"Autoencoder for Stable Diffusion": "\u7528\u4e8e\u7a33\u5b9a\u6269\u6563\u7684\u81ea\u52a8\u7f16\u7801\u5668"
|
||||
}
|
||||
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"<h1>CLIP Text Embedder</h1>\n<p>This is used to get prompt embeddings for <a href=\"../index.html\">stable diffusion</a>. It uses HuggingFace Transformers CLIP model.</p>\n": "<h1>CLIP \u30c6\u30ad\u30b9\u30c8\u30a8\u30f3\u30d9\u30c0\u30fc</h1>\n<p><a href=\"../index.html\">\u3053\u308c\u3092\u4f7f\u3046\u3068\u3001\u9ad8\u901f\u306b\u57cb\u3081\u8fbc\u3080\u3053\u3068\u304c\u3067\u304d\u3001\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u304c\u5f97\u3089\u308c\u307e\u3059\u3002</a>\u30cf\u30ae\u30f3\u30b0\u30d5\u30a7\u30a4\u30b9\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fcCLIP\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059</p>\u3002\n",
|
||||
"<h2>CLIP Text Embedder</h2>\n": "<h2>CLIP \u30c6\u30ad\u30b9\u30c8\u30a8\u30f3\u30d9\u30c0\u30fc</h2>\n",
|
||||
"<p>Get CLIP embeddings </p>\n": "<p>CLIP \u57cb\u3081\u8fbc\u307f\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Get token ids </p>\n": "<p>\u30c8\u30fc\u30af\u30f3 ID \u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Load the CLIP transformer </p>\n": "<p>CLIP \u30c8\u30e9\u30f3\u30b9\u3092\u30ed\u30fc\u30c9\u3057\u307e\u3059</p>\n",
|
||||
"<p>Load the tokenizer </p>\n": "<p>\u30c8\u30fc\u30af\u30ca\u30a4\u30b6\u30fc\u3092\u30ed\u30fc\u30c9</p>\n",
|
||||
"<p>Tokenize the prompts </p>\n": "<p>\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u30c8\u30fc\u30af\u30f3\u5316</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> are the list of prompts to embed</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u57cb\u3081\u8fbc\u3080\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u30ea\u30b9\u30c8\u3067\u3059</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model version </li>\n<li><span translate=no>_^_1_^_</span> is the device </li>\n<li><span translate=no>_^_2_^_</span> is the max length of the tokenized prompt</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u30e2\u30c7\u30eb\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u30c7\u30d0\u30a4\u30b9\u3067\u3059</li>\n<li><span translate=no>_^_2_^_</span>\u30c8\u30fc\u30af\u30f3\u5316\u3055\u308c\u305f\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u6700\u5927\u9577\u3067\u3059</li></ul>\n",
|
||||
"CLIP Text Embedder": "CLIP \u30c6\u30ad\u30b9\u30c8\u30a8\u30f3\u30d9\u30c0\u30fc",
|
||||
"CLIP embedder to get prompt embeddings for stable diffusion": "CLIP\u30a8\u30f3\u30d9\u30c0\u30fc\u306b\u3088\u308a\u3001\u8fc5\u901f\u306a\u57cb\u3081\u8fbc\u307f\u304c\u53ef\u80fd\u3067\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u304c\u53ef\u80fd"
|
||||
}
|
||||
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"<h1>CLIP Text Embedder</h1>\n<p>This is used to get prompt embeddings for <a href=\"../index.html\">stable diffusion</a>. It uses HuggingFace Transformers CLIP model.</p>\n": "<h1>\u0d9a\u0dca\u0dbd\u0dd2\u0db4\u0dca \u0db4\u0dd9\u0dc5 \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0db1\u0dca\u0db1\u0dcf</h1>\n<p><a href=\"../index.html\">\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba</a> \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0da9\u0dd2\u0db1\u0db8\u0dca \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8\u0da7 \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0dba\u0dd2. \u0d91\u0dba HuggingFace \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca Clip \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0dba\u0dd2.</p>\n",
|
||||
"<h2>CLIP Text Embedder</h2>\n": "<h2>\u0d9a\u0dca\u0dbd\u0dd2\u0db4\u0dca \u0db4\u0dd9\u0dc5 \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0db1\u0dca\u0db1\u0dcf</h2>\n",
|
||||
"<p>Get CLIP embeddings </p>\n": "<p>\u0d9a\u0dca\u0dbd\u0dd2\u0db4\u0dca \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get token ids </p>\n": "<p>\u0da7\u0ddd\u0d9a\u0db1\u0dca \u0dc4\u0dd0\u0db3\u0dd4\u0db1\u0dd4\u0db8\u0dca\u0db4\u0dad\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Load the CLIP transformer </p>\n": "<p>\u0d9a\u0dca\u0dbd\u0dd2\u0db4\u0dca \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dba \u0db4\u0da7\u0dc0\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Load the tokenizer </p>\n": "<p>\u0da7\u0ddd\u0d9a\u0db1\u0dba\u0dd2\u0dc3\u0dbb\u0dca \u0db4\u0da7\u0dc0\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Tokenize the prompts </p>\n": "<p>\u0dc0\u0dd2\u0db8\u0dc3\u0dd3\u0db8\u0dca \u0da7\u0ddd\u0d9a\u0db1\u0dca\u0da7\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> are the list of prompts to embed</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0da7 \u0dc0\u0dd2\u0db8\u0dc3\u0dd3\u0db8\u0dca \u0dbd\u0dd0\u0dba\u0dd2\u0dc3\u0dca\u0dad\u0dd4\u0dc0</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model version </li>\n<li><span translate=no>_^_1_^_</span> is the device </li>\n<li><span translate=no>_^_2_^_</span> is the max length of the tokenized prompt</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dba\u0db1\u0dd4 \u0d86\u0daf\u0dbb\u0dca\u0dc1 \u0d85\u0db1\u0dd4\u0dc0\u0dcf\u0daf\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_1_^_</span>\u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba \u0dc0\u0dda</li>\n<li><span translate=no>_^_2_^_</span>\u0da7\u0ddd\u0d9a\u0db1\u0dd3\u0d9a\u0dd8\u0dad \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dda \u0d8b\u0db4\u0dbb\u0dd2\u0db8 \u0daf\u0dd2\u0d9c \u0dc0\u0dda</li></ul>\n",
|
||||
"CLIP Text Embedder": "\u0d9a\u0dca\u0dbd\u0dd2\u0db4\u0dca \u0db4\u0dd9\u0dc5 \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0db1\u0dca\u0db1\u0dcf",
|
||||
"CLIP embedder to get prompt embeddings for stable diffusion": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0da9\u0dd2\u0db1\u0db8\u0dca \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0dca\u0dbd\u0dd2\u0db4\u0dca \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0db1\u0dca\u0db1\u0dcf"
|
||||
}
|
||||
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"<h1>CLIP Text Embedder</h1>\n<p>This is used to get prompt embeddings for <a href=\"../index.html\">stable diffusion</a>. It uses HuggingFace Transformers CLIP model.</p>\n": "<h1>CLIP \u6587\u672c\u5d4c\u5165\u5668</h1>\n<p>\u8fd9\u7528\u4e8e\u83b7\u53d6\u63d0\u793a\u5d4c\u5165\u4ee5\u5b9e\u73b0<a href=\"../index.html\">\u7a33\u5b9a\u7684\u6269\u6563</a>\u3002\u5b83\u4f7f\u7528 HuggingFace \u53d8\u5f62\u91d1\u521a CLIP \u6a21\u578b\u3002</p>\n",
|
||||
"<h2>CLIP Text Embedder</h2>\n": "<h2>CLIP \u6587\u672c\u5d4c\u5165\u5668</h2>\n",
|
||||
"<p>Get CLIP embeddings </p>\n": "<p>\u83b7\u53d6 CLIP \u5d4c\u5165\u5185\u5bb9</p>\n",
|
||||
"<p>Get token ids </p>\n": "<p>\u83b7\u53d6\u4ee3\u5e01 ID</p>\n",
|
||||
"<p>Load the CLIP transformer </p>\n": "<p>\u52a0\u8f7d CLIP \u53d8\u538b\u5668</p>\n",
|
||||
"<p>Load the tokenizer </p>\n": "<p>\u52a0\u8f7d\u4ee3\u5e01\u751f\u6210\u5668</p>\n",
|
||||
"<p>Tokenize the prompts </p>\n": "<p>\u5bf9\u63d0\u793a\u8fdb\u884c\u6807\u8bb0\u5316</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> are the list of prompts to embed</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u8981\u5d4c\u5165\u7684\u63d0\u793a\u5217\u8868</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model version </li>\n<li><span translate=no>_^_1_^_</span> is the device </li>\n<li><span translate=no>_^_2_^_</span> is the max length of the tokenized prompt</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u6a21\u578b\u7248\u672c</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u8bbe\u5907\u5417</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u6807\u8bb0\u5316\u63d0\u793a\u7684\u6700\u5927\u957f\u5ea6</li></ul>\n",
|
||||
"CLIP Text Embedder": "CLIP \u6587\u672c\u5d4c\u5165\u5668",
|
||||
"CLIP embedder to get prompt embeddings for stable diffusion": "CLIP \u5d4c\u5165\u5668\u53ef\u83b7\u5f97\u63d0\u793a\u6027\u5d4c\u5165\u4ee5\u5b9e\u73b0\u7a33\u5b9a\u7684\u6269\u6563"
|
||||
}
|
||||
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"<h1>U-Net for <a href=\"../index.html\">Stable Diffusion</a></h1>\n<p>This implements the U-Net that gives <span translate=no>_^_0_^_</span></p>\n<p>We have kept to the model definition and naming unchanged from <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we can load the checkpoints directly.</p>\n": "<h1><a href=\"../index.html\">\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u3092\u5b9f\u73fe\u3059\u308bU\u30cd\u30c3\u30c8</a></h1>\n<p>\u3053\u308c\u306b\u3088\u308a\u3001\u4ee5\u4e0b\u306e U-Net \u304c\u5b9f\u88c5\u3055\u308c\u307e\u3059 <span translate=no>_^_0_^_</span></p>\n<p>\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u3092\u76f4\u63a5\u8aad\u307f\u8fbc\u3081\u308b\u3088\u3046\u306b\u3001<a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/Stable-Diffusion\u304b\u3089\u30e2\u30c7\u30eb\u5b9a\u7fa9\u3068\u547d\u540d\u3092\u5909\u66f4\u3057\u3066\u3044\u307e\u305b\u3093</a>\u3002</p>\n",
|
||||
"<h2>Create sinusoidal time step embeddings</h2>\n<ul><li><span translate=no>_^_0_^_</span> are the time steps of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> controls the minimum frequency of the embeddings.</li></ul>\n": "<h2>\u6b63\u5f26\u6ce2\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u57cb\u3081\u8fbc\u307f\u306e\u4f5c\u6210</h2>\n<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u306e\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n</ul><li><span translate=no>_^_2_^_</span>\u57cb\u3081\u8fbc\u307f\u306e\u6700\u5c0f\u983b\u5ea6\u3092\u5236\u5fa1\u3057\u307e\u3059\u3002</li>\n",
|
||||
"<h2>Down-sampling layer</h2>\n": "<h2>\u30c0\u30a6\u30f3\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30ec\u30a4\u30e4\u30fc</h2>\n",
|
||||
"<h2>ResNet Block</h2>\n": "<h2>\u30ea\u30cd\u30c3\u30c8\u30d6\u30ed\u30c3\u30af</h2>\n",
|
||||
"<h2>U-Net model</h2>\n": "<h2>U-\u30cd\u30c3\u30c8\u30e2\u30c7\u30eb</h2>\n",
|
||||
"<h3>Group normalization with float32 casting</h3>\n": "<h3>float32 \u30ad\u30e3\u30b9\u30c6\u30a3\u30f3\u30b0\u306b\u3088\u308b\u30b0\u30eb\u30fc\u30d7\u6b63\u898f\u5316</h3>\n",
|
||||
"<h3>Group normalization</h3>\n<p>This is a helper function, with fixed number of groups..</p>\n": "<h3>\u30b0\u30eb\u30fc\u30d7\u6b63\u898f\u5316</h3>\n<p>\u3053\u308c\u306f\u30b0\u30eb\u30fc\u30d7\u6570\u304c\u56fa\u5b9a\u3055\u308c\u305f\u30d8\u30eb\u30d1\u30fc\u95a2\u6570\u3067\u3059\u3002</p>\n",
|
||||
"<h3>Sequential block for modules with different inputs</h3>\n<p>This sequential module can compose of different modules such as <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> and <span translate=no>_^_2_^_</span> and calls them with the matching signatures</p>\n": "<h3>\u5165\u529b\u306e\u7570\u306a\u308b\u30e2\u30b8\u30e5\u30fc\u30eb\u7528\u306e\u30b7\u30fc\u30b1\u30f3\u30b7\u30e3\u30eb\u30d6\u30ed\u30c3\u30af</h3>\n<p>\u3053\u306e\u30b7\u30fc\u30b1\u30f3\u30b7\u30e3\u30eb\u30e2\u30b8\u30e5\u30fc\u30eb\u306f\u3001\u3001<span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u306a\u3069\u306e\u3055\u307e\u3056\u307e\u306a\u30e2\u30b8\u30e5\u30fc\u30eb\u3067\u69cb\u6210\u3067\u304d<span translate=no>_^_0_^_</span>\u3001\u305d\u308c\u3089\u3092\u5bfe\u5fdc\u3059\u308b\u30b7\u30b0\u30cd\u30c1\u30e3\u3067\u547c\u3073\u51fa\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002</p>\n",
|
||||
"<h3>Up-sampling layer</h3>\n": "<h3>\u30a2\u30c3\u30d7\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30ec\u30a4\u30e4\u30fc</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p> Test sinusoidal time step embeddings</p>\n": "<p>\u6b63\u5f26\u6ce2\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u57cb\u3081\u8fbc\u307f\u306e\u30c6\u30b9\u30c8</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u3068 <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution mapping </p>\n": "<p><span translate=no>_^_0_^_</span>\u30b3\u30f3\u30dc\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u30de\u30c3\u30d4\u30f3\u30b0</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution with stride length of <span translate=no>_^_1_^_</span> to down-sample by a factor of <span translate=no>_^_2_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u30b9\u30c8\u30e9\u30a4\u30c9\u306e\u9577\u3055\u304c\u306e\u7573\u307f\u8fbc\u307f\u304b\u3089\u3001<span translate=no>_^_1_^_</span>\u306e\u4fc2\u6570\u3067\u30c0\u30a6\u30f3\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u307e\u3059 <span translate=no>_^_2_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> not specified </p>\n": "<p><span translate=no>_^_0_^_</span>\u6307\u5b9a\u306a\u3057</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> mapping layer for residual connection </p>\n": "<p><span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>\u6b8b\u7559\u63a5\u7d9a\u7528\u306e\u30de\u30c3\u30d4\u30f3\u30b0\u30ec\u30a4\u30e4\u3078</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span>; half the channels are sin and the other half is cos, </p>\n": "<p><span translate=no>_^_0_^_</span>; \u30c1\u30e3\u30cd\u30eb\u306e\u534a\u5206\u306f\u7f6a\u3067\u3001\u6b8b\u308a\u306e\u534a\u5206\u306f\u30b3\u30b9</p>\n",
|
||||
"<p>Add skip connection </p>\n": "<p>\u30b9\u30ad\u30c3\u30d7\u63a5\u7d9a\u3092\u8ffd\u52a0</p>\n",
|
||||
"<p>Add the residual blocks and attentions </p>\n": "<p>\u6b8b\u7559\u30d6\u30ed\u30c3\u30af\u3068\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u3092\u8ffd\u52a0</p>\n",
|
||||
"<p>Add them to the input half of the U-Net and keep track of the number of channels of its output </p>\n": "<p>\u305d\u308c\u3089\u3092U-Net\u306e\u5165\u529b\u534a\u5206\u306b\u8ffd\u52a0\u3057\u3066\u3001\u305d\u306e\u51fa\u529b\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u3092\u8a18\u9332\u3057\u3066\u304a\u304d\u307e\u3059\u3002</p>\n",
|
||||
"<p>Add time step embeddings </p>\n": "<p>\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u57cb\u3081\u8fbc\u307f\u306e\u8ffd\u52a0</p>\n",
|
||||
"<p>Add to the output half of the U-Net </p>\n": "<p>U-Net\u306e\u51fa\u529b\u534a\u5206\u306b\u8ffd\u52a0</p>\n",
|
||||
"<p>Add transformer </p>\n": "<p>\u5909\u5727\u5668\u3092\u8ffd\u52a0</p>\n",
|
||||
"<p>Apply convolution </p>\n": "<p>\u30b3\u30f3\u30dc\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u3092\u9069\u7528</p>\n",
|
||||
"<p>Down sample at all levels except last </p>\n": "<p>\u6700\u5f8c\u306e\u30ec\u30d9\u30eb\u3092\u9664\u304f\u3059\u3079\u3066\u306e\u30ec\u30d9\u30eb\u3067\u30c0\u30a6\u30f3\u30b5\u30f3\u30d7\u30eb</p>\n",
|
||||
"<p>Final convolution </p>\n": "<p>\u6700\u7d42\u7573\u307f\u8fbc\u307f</p>\n",
|
||||
"<p>Final convolution layer </p>\n": "<p>\u6700\u7d42\u7573\u307f\u8fbc\u307f\u5c64</p>\n",
|
||||
"<p>Final normalization and <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u6700\u7d42\u7684\u306a\u6b63\u898f\u5316\u3068\u7573\u307f\u8fbc\u307f <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>First normalization and convolution </p>\n": "<p>\u6700\u521d\u306e\u6b63\u898f\u5316\u3068\u7573\u307f\u8fbc\u307f</p>\n",
|
||||
"<p>Get time step embeddings </p>\n": "<p>\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u306e\u57cb\u3081\u8fbc\u307f\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution that maps the input to <span translate=no>_^_1_^_</span>. The blocks are wrapped in <span translate=no>_^_2_^_</span> module because different modules have different forward function signatures; for example, convolution only accepts the feature map and residual blocks accept the feature map and time embedding. <span translate=no>_^_3_^_</span> calls them accordingly. </p>\n": "<p><span translate=no>_^_0_^_</span>\u5165\u529b\u3092\u306b\u30de\u30c3\u30d7\u3059\u308b\u521d\u671f\u7573\u307f\u8fbc\u307f\u3002<span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u30e2\u30b8\u30e5\u30fc\u30eb\u304c\u7570\u306a\u308c\u3070\u30d5\u30a9\u30ef\u30fc\u30c9\u95a2\u6570\u306e\u30b7\u30b0\u30cd\u30c1\u30e3\u3082\u7570\u306a\u308b\u305f\u3081\u3001\u30d6\u30ed\u30c3\u30af\u306f\u30e2\u30b8\u30e5\u30fc\u30eb\u3067\u30e9\u30c3\u30d7\u3055\u308c\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001\u7573\u307f\u8fbc\u307f\u306f\u7279\u5fb4\u30de\u30c3\u30d7\u306e\u307f\u3092\u53d7\u3051\u5165\u308c\u3001\u6b8b\u5dee\u30d6\u30ed\u30c3\u30af\u306f\u7279\u5fb4\u30de\u30c3\u30d7\u3068\u6642\u9593\u57cb\u3081\u8fbc\u307f\u3092\u53d7\u3051\u5165\u308c\u307e\u3059\u3002<span translate=no>_^_3_^_</span>\u305d\u308c\u306b\u5fdc\u3058\u3066\u547c\u3073\u51fa\u3057\u307e\u3059\u3002</p>\n",
|
||||
"<p>Initial convolution </p>\n": "<p>\u521d\u671f\u30b3\u30f3\u30dc\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3</p>\n",
|
||||
"<p>Input half of the U-Net </p>\n": "<p>U \u30cd\u30c3\u30c8\u306e\u534a\u5206\u3092\u5165\u529b</p>\n",
|
||||
"<p>Middle of the U-Net </p>\n": "<p>U-\u30cd\u30c3\u30c8\u306e\u771f\u3093\u4e2d</p>\n",
|
||||
"<p>Number of channels at each block in the input half of U-Net </p>\n": "<p>U-Net\u306e\u5165\u529b\u534a\u5206\u306e\u5404\u30d6\u30ed\u30c3\u30af\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</p>\n",
|
||||
"<p>Number of channels at each level </p>\n": "<p>\u5404\u30ec\u30d9\u30eb\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</p>\n",
|
||||
"<p>Number of levels </p>\n": "<p>\u30ec\u30d9\u30eb\u6570</p>\n",
|
||||
"<p>Output half of the U-Net </p>\n": "<p>U-\u30cd\u30c3\u30c8\u306e\u51fa\u529b\u534a\u5206</p>\n",
|
||||
"<p>Prepare levels </p>\n": "<p>\u30ec\u30d9\u30eb\u3092\u6e96\u5099</p>\n",
|
||||
"<p>Prepare levels in reverse order </p>\n": "<p>\u30ec\u30d9\u30eb\u3092\u9006\u306e\u9806\u5e8f\u3067\u6e96\u5099\u3059\u308b</p>\n",
|
||||
"<p>Residual block maps from previous number of channels plus the skip connections from the input half of U-Net to the number of channels in the current level. </p>\n": "<p>\u524d\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u306e\u6b8b\u5dee\u30d6\u30ed\u30c3\u30af\u30de\u30c3\u30d7\u306b U-Net \u306e\u5165\u529b\u534a\u5206\u304b\u3089\u306e\u30b9\u30ad\u30c3\u30d7\u63a5\u7d9a\u3092\u52a0\u3048\u305f\u3082\u306e\u304b\u3089\u73fe\u5728\u306e\u30ec\u30d9\u30eb\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u307e\u3067\u30de\u30c3\u30d7\u3055\u308c\u307e\u3059\u3002</p>\n",
|
||||
"<p>Residual block maps from previous number of channels to the number of channels in the current level </p>\n": "<p>\u6b8b\u5dee\u30d6\u30ed\u30c3\u30af\u306f\u3001\u524d\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u304b\u3089\u73fe\u5728\u306e\u30ec\u30d9\u30eb\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3055\u308c\u307e\u3059</p>\n",
|
||||
"<p>Second half of the U-Net </p>\n": "<p>U\u30cd\u30c3\u30c8\u5f8c\u534a</p>\n",
|
||||
"<p>Size time embeddings </p>\n": "<p>\u30b5\u30a4\u30ba\u30bf\u30a4\u30e0\u57cb\u3081\u8fbc\u307f</p>\n",
|
||||
"<p>The middle of the U-Net </p>\n": "<p>U\u30cd\u30c3\u30c8\u306e\u771f\u3093\u4e2d</p>\n",
|
||||
"<p>Time step embeddings </p>\n": "<p>\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u57cb\u3081\u8fbc\u307f</p>\n",
|
||||
"<p>To store the input half outputs for skip connections </p>\n": "<p>\u30b9\u30ad\u30c3\u30d7\u63a5\u7d9a\u306e\u5165\u529b\u30cf\u30fc\u30d5\u51fa\u529b\u3092\u4fdd\u5b58\u3059\u308b\u306b\u306f</p>\n",
|
||||
"<p>Up-sample at every level after last residual block except the last one. Note that we are iterating in reverse; i.e. <span translate=no>_^_0_^_</span> is the last. </p>\n": "<p>\u6700\u5f8c\u306e\u6b8b\u5dee\u30d6\u30ed\u30c3\u30af\u3092\u9664\u304f\u6700\u5f8c\u306e\u6b8b\u5dee\u30d6\u30ed\u30c3\u30af\u306e\u5f8c\u306e\u3059\u3079\u3066\u306e\u30ec\u30d9\u30eb\u3067\u30a2\u30c3\u30d7\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u307e\u3059\u3002\u9006\u306b\u7e70\u308a\u8fd4\u3057\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3064\u307e\u308a<span translate=no>_^_0_^_</span>\u3001\u6700\u5f8c\u3067\u3059</p>\u3002\n",
|
||||
"<p>Up-sample by a factor of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6b21\u306e\u500d\u307e\u3067\u306e\u30a2\u30c3\u30d7\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> are the time steps of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> conditioning of shape <span translate=no>_^_5_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u30b7\u30a7\u30a4\u30d7\u306e\u5165\u529b\u30d5\u30a3\u30fc\u30c1\u30e3\u30de\u30c3\u30d7\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u5f62\u72b6\u306e\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u3067\u3059 <span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u5f62\u72b6\u306e\u30b3\u30f3\u30c7\u30a3\u30b7\u30e7\u30cb\u30f3\u30b0 <span translate=no>_^_5_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map with shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the time step embeddings of shape <span translate=no>_^_3_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u4ed8\u304d\u306e\u5165\u529b\u30d5\u30a3\u30fc\u30c1\u30e3\u30de\u30c3\u30d7\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u5f62\u72b6\u306e\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_3_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u4ed8\u304d\u306e\u5165\u529b\u30d5\u30a3\u30fc\u30c1\u30e3\u30de\u30c3\u30d7\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the input feature map </li>\n<li><span translate=no>_^_1_^_</span> is the number of channels in the output feature map </li>\n<li><span translate=no>_^_2_^_</span> is the base channel count for the model </li>\n<li><span translate=no>_^_3_^_</span> number of residual blocks at each level </li>\n<li><span translate=no>_^_4_^_</span> are the levels at which attention should be performed </li>\n<li><span translate=no>_^_5_^_</span> are the multiplicative factors for number of channels for each level </li>\n<li><span translate=no>_^_6_^_</span> is the number of attention heads in the transformers </li>\n<li><span translate=no>_^_7_^_</span> is the number of transformer layers in the transformers </li>\n<li><span translate=no>_^_8_^_</span> is the size of the conditional embedding in the transformers</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u3001\u5165\u529b\u30d5\u30a3\u30fc\u30c1\u30e3\u30de\u30c3\u30d7\u306e\u30c1\u30e3\u30cd\u30eb\u6570\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u306f\u51fa\u529b\u30d5\u30a3\u30fc\u30c1\u30e3\u30de\u30c3\u30d7\u306e\u30c1\u30e3\u30cd\u30eb\u6570\u3067\u3059\u3002</li>\n<li><span translate=no>_^_2_^_</span>\u306f\u30e2\u30c7\u30eb\u306e\u30d9\u30fc\u30b9\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</li>\n<li><span translate=no>_^_3_^_</span>\u5404\u30ec\u30d9\u30eb\u306e\u6b8b\u5dee\u30d6\u30ed\u30c3\u30af\u6570</li>\n<li><span translate=no>_^_4_^_</span>\u6ce8\u610f\u3059\u3079\u304d\u30ec\u30d9\u30eb\u306f\u3069\u308c\u3050\u3089\u3044\u306e\u30ec\u30d9\u30eb\u304b</li>\n<li><span translate=no>_^_5_^_</span>\u306f\u5404\u30ec\u30d9\u30eb\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u306e\u4e57\u6cd5\u4fc2\u6570</li>\n<li><span translate=no>_^_6_^_</span>\u306f\u5909\u5727\u5668\u5185\u306e\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30d8\u30c3\u30c9\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_7_^_</span>\u306f\u5909\u5727\u5668\u5185\u306e\u5909\u5727\u5668\u5c64\u306e\u6570\u3067\u3059\u3002</li>\n<li><span translate=no>_^_8_^_</span>\u306f\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u5185\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u306e\u30b5\u30a4\u30ba\u3067\u3059</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u30c1\u30e3\u30cd\u30eb\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> the number of input channels </li>\n<li><span translate=no>_^_1_^_</span> the size of timestep embeddings </li>\n<li><span translate=no>_^_2_^_</span> is the number of out channels. defaults to `channels.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u5165\u529b\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</li>\n<li><span translate=no>_^_1_^_</span>\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u57cb\u3081\u8fbc\u307f\u306e\u30b5\u30a4\u30ba</li>\n</ul><li><span translate=no>_^_2_^_</span>\u306f\u51fa\u529b\u30c1\u30e3\u30f3\u30cd\u30eb\u306e\u6570\u3067\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306f `channels\u3067\u3059\u3002</li>\n",
|
||||
"Annotated PyTorch implementation/tutorial of the U-Net in stable diffusion.": "\u5b89\u5b9a\u7248\u62e1\u6563\u306b\u304a\u3051\u308bU-Net\u306e\u6ce8\u91c8\u4ed8\u304dPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3002",
|
||||
"U-Net for Stable Diffusion": "\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u3092\u5b9f\u73fe\u3059\u308bU\u30cd\u30c3\u30c8"
|
||||
}
|
||||
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"<h1>U-Net for <a href=\"../index.html\">Stable Diffusion</a></h1>\n<p>This implements the U-Net that gives <span translate=no>_^_0_^_</span></p>\n<p>We have kept to the model definition and naming unchanged from <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we can load the checkpoints directly.</p>\n": "<h1><a href=\"../index.html\">\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca</a></h1>\n<p>\u0db8\u0dd9\u0dba \u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0db1 \u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dbb\u0dba\u0dd2<span translate=no>_^_0_^_</span></p>\n<p>\u0d85\u0db4\u0dd2 \u0d86\u0daf\u0dbb\u0dca\u0dc1 \u0d85\u0dbb\u0dca\u0dae \u0daf\u0dd0\u0d9a\u0dca\u0dc0\u0dd3\u0db8 \u0dad\u0db6\u0dcf \u0d87\u0dad\u0dd2 \u0d85\u0dad\u0dbb <a href=\"https://github.com/CompVis/stable-diffusion\">\u0d9a\u0ddc\u0db8\u0dca\u0dc0\u0dd2\u0dc3\u0dca/\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab</a> \u0dc3\u0dd2\u0da7 \u0db1\u0ddc\u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0dc0 \u0db1\u0db8\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0d85\u0db4\u0da7 \u0db8\u0dd4\u0dbb\u0db4\u0ddc\u0dbd\u0dc0\u0dbd\u0dca \u0d9a\u0dd9\u0dbd\u0dd2\u0db1\u0dca\u0db8 \u0db4\u0dd0\u0da7\u0dc0\u0dd2\u0dba \u0dc4\u0dd0\u0d9a\u0dd2 \u0dc0\u0db1 \u0db4\u0dbb\u0dd2\u0daf\u0dd2.</p>\n",
|
||||
"<h2>Create sinusoidal time step embeddings</h2>\n<ul><li><span translate=no>_^_0_^_</span> are the time steps of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> controls the minimum frequency of the embeddings.</li></ul>\n": "<h2>\u0dc3\u0dba\u0dd2\u0db1\u0ddc\u0dc3\u0ddc\u0dba\u0dd2\u0da9\u0dbd\u0dca \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</h2>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0dc0\u0dda<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dbd \u0d85\u0dc0\u0db8 \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dad\u0dba \u0db4\u0dcf\u0dbd\u0db1\u0dba \u0d9a\u0dbb\u0dba\u0dd2.</li></ul>\n",
|
||||
"<h2>Down-sampling layer</h2>\n": "<h2>\u0db4\u0dc4\u0dc5-\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0dc3\u0dca\u0dae\u0dbb\u0dba</h2>\n",
|
||||
"<h2>ResNet Block</h2>\n": "<h2>\u0dbb\u0dd9\u0dc3\u0dca\u0db1\u0dd9\u0da7\u0dca \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca</h2>\n",
|
||||
"<h2>U-Net model</h2>\n": "<h2>\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba</h2>\n",
|
||||
"<h3>Group normalization with float32 casting</h3>\n": "<h3>float32 \u0dc0\u0dcf\u0dad\u0dca\u0dad\u0dd4 \u0dc3\u0db8\u0d9c \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba</h3>\n",
|
||||
"<h3>Group normalization</h3>\n<p>This is a helper function, with fixed number of groups..</p>\n": "<h3>\u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba</h3>\n<p>\u0db8\u0dd9\u0dba \u0d8b\u0db4\u0d9a\u0dcf\u0dbb\u0d9a \u0dc1\u0dca\u0dbb\u0dd2\u0dad\u0dba\u0d9a\u0dca \u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0d9a\u0dca \u0d87\u0dad..</p>\n",
|
||||
"<h3>Sequential block for modules with different inputs</h3>\n<p>This sequential module can compose of different modules suck as <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> and <span translate=no>_^_2_^_</span> and calls them with the matching signatures</p>\n": "<h3>\u0dc0\u0dd2\u0dc0\u0dd2\u0db0 \u0dba\u0dd9\u0daf\u0dc0\u0dd4\u0db8\u0dca \u0dc3\u0dc4\u0dd2\u0dad \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dd2\u0d9a \u0d9a\u0ddc\u0da7\u0dc3</h3>\n<p>\u0db8\u0dd9\u0db8 \u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dd2\u0d9a \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dba\u0da7 \u0dc0\u0dd2\u0dc0\u0dd2\u0db0 \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dba\u0db1\u0dca \u0d8b\u0dbb\u0dcf \u0db6\u0ddc\u0db1<span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span> \u0d85\u0dad\u0dbb \u0d9c\u0dd0\u0dbd\u0db4\u0dd9\u0db1 \u0d85\u0dad\u0dca\u0dc3\u0db1\u0dca \u0dc3\u0db8\u0d9f \u0d92\u0dc0\u0dcf \u0d85\u0db8\u0dad\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<h3>Up-sampling layer</h3>\n": "<h3>\u0daf\u0d9a\u0dca\u0dc0\u0dcf-\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8\u0dca \u0dc3\u0dca\u0dae\u0dbb\u0dba</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p> Test sinusoidal time step embeddings</p>\n": "<p>\u0dc3\u0dba\u0dd2\u0db1\u0ddc\u0dc3\u0ddc\u0dba\u0dd2\u0da9\u0dbd\u0dca \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0db4\u0dbb\u0dd3\u0d9a\u0dca\u0dc2\u0dcf \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u0dc3\u0dc4<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution mapping </p>\n": "<p><span translate=no>_^_0_^_</span>\u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd3\u0db8\u0dda \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca\u0d9a\u0dbb\u0dab\u0dba</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution with stride length of <span translate=no>_^_1_^_</span> to down-sample by a factor of <span translate=no>_^_2_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u0d9a \u0dc3\u0dcf\u0db0\u0d9a\u0dba\u0d9a\u0dca \u0dc0\u0dd2\u0dc3\u0dd2\u0db1\u0dca \u0db4\u0dc4\u0dc5-\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2<span translate=no>_^_1_^_</span> \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 stride \u0daf\u0dd2\u0d9c \u0dc3\u0db8\u0d9c convolution<span translate=no>_^_2_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> not specified </p>\n": "<p><span translate=no>_^_0_^_</span>\u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dc0 \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0db1\u0dd0\u0dad</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> mapping layer for residual connection </p>\n": "<p><span translate=no>_^_0_^_</span>\u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0\u0dad\u0dcf\u0dc0\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc3\u0dca\u0dad\u0dbb\u0dba<span translate=no>_^_1_^_</span> \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca\u0d9c\u0dad \u0d9a\u0dd2\u0dbb\u0dd3\u0db8</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span>; half the channels are sin and the other half is cos, </p>\n": "<p><span translate=no>_^_0_^_</span>; \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d85\u0da9\u0d9a\u0dca \u0db4\u0dcf\u0db4\u0dba \u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0d85\u0db1\u0dd9\u0d9a\u0dca \u0db7\u0dcf\u0d9c\u0dba \u0d9a\u0ddd\u0dc3\u0dca \u0dc0\u0dda,</p>\n",
|
||||
"<p>Add skip connection </p>\n": "<p>\u0db8\u0d9f \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0dda \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0\u0dad\u0dcf\u0dc0\u0dba \u0d91\u0d9a\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Add the residual blocks and attentions </p>\n": "<p>\u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d9a\u0dd4\u0da7\u0dca\u0da7\u0dd2 \u0dc3\u0dc4 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0d91\u0d9a\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Add them to the input half of the U-Net and keep track of the number of channels of its output </p>\n": "<p>\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0dc4\u0dd2 \u0d86\u0daf\u0dcf\u0db1 \u0db7\u0dcf\u0d9c\u0dba\u0da7 \u0d92\u0dc0\u0dcf \u0d91\u0d9a\u0dca \u0d9a\u0dbb \u0d91\u0dc4\u0dd2 \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0db1\u0dd2\u0dbb\u0dd3\u0d9a\u0dca\u0dc2\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Add time step embeddings </p>\n": "<p>\u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0d91\u0d9a\u0dad\u0dd4 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Add to the output half of the U-Net </p>\n": "<p>\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0dc4\u0dd2 \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1 \u0db7\u0dcf\u0d9c\u0dba\u0da7 \u0d91\u0d9a\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Add transformer </p>\n": "<p>\u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca \u0d91\u0d9a\u0dad\u0dd4 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Apply convolution </p>\n": "<p>\u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd2\u0db8 \u0dba\u0ddc\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Down sample at all levels except last </p>\n": "<p>\u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0dc0\u0dc1\u0dba\u0dd9\u0db1\u0dca \u0dc4\u0dd0\u0dbb \u0d85\u0db1\u0dd9\u0d9a\u0dca \u0dc3\u0dd2\u0dba\u0dbd\u0dd4\u0db8 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dca\u0dc0\u0dbd \u0db4\u0dc4\u0dc5 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba</p>\n",
|
||||
"<p>Final convolution </p>\n": "<p>\u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd2\u0db8</p>\n",
|
||||
"<p>Final convolution layer </p>\n": "<p>\u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd4\u0dab\u0dd4 \u0dc3\u0dca\u0dae\u0dbb\u0dba</p>\n",
|
||||
"<p>Final normalization and <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba \u0dc3\u0dc4<span translate=no>_^_0_^_</span> \u0d9a\u0dd0\u0da7\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8</p>\n",
|
||||
"<p>First normalization and convolution </p>\n": "<p>\u0db4\u0dc5\u0db8\u0dd4 \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba \u0dc3\u0dc4 \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd2\u0db8</p>\n",
|
||||
"<p>Get time step embeddings </p>\n": "<p>\u0d9a\u0dcf\u0dbd\u0dba \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution that maps the input to <span translate=no>_^_1_^_</span>. The blocks are wrapped in <span translate=no>_^_2_^_</span> module because different modules have different forward function signatures; for example, convolution only accepts the feature map and residual blocks accept the feature map and time embedding. <span translate=no>_^_3_^_</span> calls them accordingly. </p>\n": "<p>\u0d86\u0daf\u0dcf\u0db1\u0dba \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca<span translate=no>_^_0_^_</span> \u0d9c\u0dad \u0d9a\u0dbb\u0db1 \u0db8\u0dd6\u0dbd\u0dd2\u0d9a \u0dc0\u0dca\u0dba\u0dcf\u0dc0\u0da0\u0dca\u0da1\u0dcf\u0dc0<span translate=no>_^_1_^_</span>. \u0dc0\u0dd2\u0dc0\u0dd2\u0db0 \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dc0\u0dbd \u0dc0\u0dd2\u0dc0\u0dd2\u0db0 \u0d89\u0daf\u0dd2\u0dbb\u0dd2 \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0d9a\u0dcf\u0dbb\u0dd3 \u0d85\u0dad\u0dca\u0dc3\u0db1\u0dca \u0d87\u0dad\u0dd2 \u0db6\u0dd0\u0dc0\u0dd2\u0db1\u0dca \u0d9a\u0dd4\u0da7\u0dca\u0da7\u0dd2<span translate=no>_^_2_^_</span> \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dba\u0dda \u0d94\u0dad\u0dcf \u0d87\u0dad; \u0db1\u0dd2\u0daf\u0dc3\u0dd4\u0db1\u0d9a\u0dca \u0dbd\u0dd9\u0dc3, \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd3\u0db8\u0dda\u0daf\u0dd3 \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8 \u0db4\u0db8\u0dab\u0d9a\u0dca \u0db4\u0dd2\u0dc5\u0dd2\u0d9c\u0db1\u0dca\u0db1\u0dcf \u0d85\u0dad\u0dbb \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d9a\u0ddc\u0da7\u0dc3\u0dca \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8 \u0dc3\u0dc4 \u0dc0\u0dda\u0dbd\u0dcf\u0dc0 \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0db4\u0dd2\u0dc5\u0dd2\u0d9c\u0db1\u0dd3. <span translate=no>_^_3_^_</span>\u0d92 \u0d85\u0db1\u0dd4\u0dc0 \u0d94\u0dc0\u0dd4\u0db1\u0dca \u0d85\u0db8\u0dad\u0dba\u0dd2.</p>\n",
|
||||
"<p>Initial convolution </p>\n": "<p>\u0db8\u0dd6\u0dbd\u0dd2\u0d9a \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd2\u0db8</p>\n",
|
||||
"<p>Input half of the U-Net </p>\n": "<p>U-Net \u0d86\u0daf\u0dcf\u0db1 \u0d85\u0da9\u0d9a\u0dca</p>\n",
|
||||
"<p>Middle of the U-Net </p>\n": "<p>\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0db8\u0dd0\u0daf</p>\n",
|
||||
"<p>Number of channels at each block in the input half of U-Net </p>\n": "<p>\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0dc4\u0dd2 \u0d86\u0daf\u0dcf\u0db1 \u0db7\u0dcf\u0d9c\u0dba\u0dda \u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0d91\u0d9a\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</p>\n",
|
||||
"<p>Number of channels at each level </p>\n": "<p>\u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</p>\n",
|
||||
"<p>Number of levels </p>\n": "<p>\u0db8\u0da7\u0dca\u0da7\u0db8\u0dca \u0d9c\u0dab\u0db1</p>\n",
|
||||
"<p>Output half of the U-Net </p>\n": "<p>U-Net \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1 \u0d85\u0da9\u0d9a\u0dca</p>\n",
|
||||
"<p>Prepare levels </p>\n": "<p>\u0db8\u0da7\u0dca\u0da7\u0db8\u0dca \u0dc3\u0d9a\u0dc3\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Prepare levels in reverse order </p>\n": "<p>\u0db4\u0dca\u0dbb\u0dad\u0dd2\u0dbd\u0ddd\u0db8 \u0d85\u0db1\u0dd4\u0db4\u0dd2\u0dc5\u0dd2\u0dc0\u0dd9\u0dbd\u0dd2\u0db1\u0dca \u0db8\u0da7\u0dca\u0da7\u0db8\u0dca \u0dc3\u0d9a\u0dc3\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Residual block maps from previous number of channels plus the skip connections from the input half of U-Net to the number of channels in the current level. </p>\n": "<p>\u0db4\u0dd9\u0dbb \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0dd9\u0db1\u0dca \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca \u0dc3\u0dc4 \u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0dc4\u0dd2 \u0d86\u0daf\u0dcf\u0db1 \u0db7\u0dcf\u0d9c\u0dba\u0dda \u0dc3\u0dd2\u0da7 \u0dc0\u0dad\u0dca\u0db8\u0db1\u0dca \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0db8\u0d9f \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0dda \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0\u0dad\u0dcf.</p>\n",
|
||||
"<p>Residual block maps from previous number of channels to the number of channels in the current level </p>\n": "<p>\u0db4\u0dd9\u0dbb \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0dda \u0dc3\u0dd2\u0da7 \u0dc0\u0dbb\u0dca\u0dad\u0db8\u0dcf\u0db1 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0db6\u0dca\u0dbd\u0ddc\u0d9a\u0dca \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca</p>\n",
|
||||
"<p>Second half of the U-Net </p>\n": "<p>\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0dc4\u0dd2 \u0daf\u0dd9\u0dc0\u0db1 \u0db7\u0dcf\u0d9c\u0dba</p>\n",
|
||||
"<p>Size time embeddings </p>\n": "<p>\u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab \u0d9a\u0dcf\u0dbd \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca</p>\n",
|
||||
"<p>The middle of the U-Net </p>\n": "<p>\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0db8\u0dd0\u0daf</p>\n",
|
||||
"<p>Time step embeddings </p>\n": "<p>\u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca</p>\n",
|
||||
"<p>To store the input half outputs for skip connections </p>\n": "<p>\u0db8\u0d9f \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0dda \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0\u0dad\u0dcf \u0dc3\u0db3\u0dc4\u0dcf \u0d86\u0daf\u0dcf\u0db1 \u0d85\u0dbb\u0dca\u0db0 \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba\u0db1\u0dca \u0d9c\u0db6\u0da9\u0dcf \u0d9a\u0dd2\u0dbb\u0dd3\u0db8</p>\n",
|
||||
"<p>Up-sample at every level after last residual block except the last one. Note that we are iterating in reverse; i.e. <span translate=no>_^_0_^_</span> is the last. </p>\n": "<p>\u0d85\u0db1\u0dca\u0dad\u0dd2\u0db8 \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d9a\u0ddc\u0da7\u0dc3 \u0dc4\u0dd0\u0dbb \u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d9a\u0ddc\u0da7\u0dc3\u0dd2\u0db1\u0dca \u0db4\u0dc3\u0dd4 \u0dc3\u0dd1\u0db8 \u0db8\u0da7\u0dca\u0da7\u0db8\u0d9a\u0db8 \u0d89\u0dc4\u0dc5\u0da7 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba. \u0d85\u0db4\u0dd2 \u0d86\u0db4\u0dc3\u0dd4 \u0dc4\u0dd0\u0dbb\u0dc0\u0dd3\u0db8\u0da7 \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db1 \u0db6\u0dc0 \u0dc3\u0dbd\u0d9a\u0db1\u0dca\u0db1; i.e. \u0d85\u0dc0\u0dc3\u0dcf\u0db1<span translate=no>_^_0_^_</span> \u0dc0\u0dda.</p>\n",
|
||||
"<p>Up-sample by a factor of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc3\u0dcf\u0db0\u0d9a\u0dba\u0d9a\u0dca \u0d85\u0db1\u0dd4\u0dc0 \u0d89\u0dc4\u0dc5 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> are the time steps of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> conditioning of shape <span translate=no>_^_5_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d86\u0daf\u0dcf\u0db1 \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dba\u0dd2<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0dc0\u0dda<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d9a\u0db1\u0dca\u0da9\u0dd2\u0dc2\u0db1\u0dda\u0dc2\u0db1\u0dca<span translate=no>_^_5_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map with shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the time step embeddings of shape <span translate=no>_^_3_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0d86\u0daf\u0dcf\u0db1 \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dba\u0dd2<span translate=no>_^_1_^_</span></li>\n</ul><li><span translate=no>_^_2_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_3_^_</span></li>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba \u0dc3\u0dc4\u0dd2\u0dad \u0d86\u0daf\u0dcf\u0db1 \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dba\u0dd2<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the input feature map </li>\n<li><span translate=no>_^_1_^_</span> is the number of channels in the output feature map </li>\n<li><span translate=no>_^_2_^_</span> is the base channel count for the model </li>\n<li><span translate=no>_^_3_^_</span> number of residual blocks at each level </li>\n<li><span translate=no>_^_4_^_</span> are the levels at which attention should be performed </li>\n<li><span translate=no>_^_5_^_</span> are the multiplicative factors for number of channels for each level </li>\n<li><span translate=no>_^_6_^_</span> the number of attention heads in the transformers</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0d86\u0daf\u0dcf\u0db1 \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n<li><span translate=no>_^_1_^_</span>\u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1 \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n<li><span translate=no>_^_2_^_</span>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0db8\u0dd6\u0dbd\u0dd2\u0d9a \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n<li><span translate=no>_^_3_^_</span>\u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0db8\u0da7\u0dca\u0da7\u0db8\u0dda \u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d9a\u0dd4\u0da7\u0dca\u0da7\u0dd2 \u0d9c\u0dab\u0db1</li>\n<li><span translate=no>_^_4_^_</span>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4 \u0d9a\u0dc5 \u0dba\u0dd4\u0dad\u0dd4 \u0db8\u0da7\u0dca\u0da7\u0db8\u0dca \u0dc0\u0dda</li>\n<li><span translate=no>_^_5_^_</span>\u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0db8\u0da7\u0dca\u0da7\u0db8\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc3\u0db3\u0dc4\u0dcf \u0db6\u0dc4\u0dd4\u0d9a\u0dcf\u0dbb\u0dca\u0dba \u0dc3\u0dcf\u0db0\u0d9a \u0dc0\u0dda</li>\n<li><span translate=no>_^_6_^_</span>\u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca\u0dc0\u0dbd \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dc4\u0dd2\u0dc3\u0dca \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dba\u0db1\u0dd4 \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> the number of input channels </li>\n<li><span translate=no>_^_1_^_</span> the size of timestep embeddings </li>\n<li><span translate=no>_^_2_^_</span> is the number of out channels. defaults to `channels.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0d86\u0daf\u0dcf\u0db1 \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</li>\n<li><span translate=no>_^_1_^_</span>\u0d9a\u0dcf\u0dbd\u0dbb\u0dcf\u0db8\u0dd4 \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dbd \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba</li>\n<li><span translate=no>_^_2_^_</span>\u0db4\u0dd2\u0da7\u0dad\u0da7 \u0d87\u0dad\u0dd2 \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1 \u0dc0\u0dda. `\u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf\u0dc0\u0dbd\u0da7 \u0db4\u0dd9\u0dbb\u0db1\u0dd2\u0db8\u0dd2.</li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of the U-Net in stable diffusion.": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dad\u0dd4\u0dc5 \u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0dc4\u0dd2 PyTorch \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8/\u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1\u0dba.",
|
||||
"U-Net for Stable Diffusion": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca"
|
||||
}
|
||||
@@ -0,0 +1,60 @@
|
||||
{
|
||||
"<h1>U-Net for <a href=\"../index.html\">Stable Diffusion</a></h1>\n<p>This implements the U-Net that gives <span translate=no>_^_0_^_</span></p>\n<p>We have kept to the model definition and naming unchanged from <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we can load the checkpoints directly.</p>\n": "<h1>U-Net \u7528\u4e8e<a href=\"../index.html\">\u7a33\u5b9a\u6269\u6563</a></h1>\n<p>\u8fd9\u5b9e\u73b0\u4e86 U-Net\uff0c\u5b83\u53ef\u4ee5\u7ed9\u51fa<span translate=no>_^_0_^_</span></p>\n<p>\u6211\u4eec\u4fdd\u6301\u4e86 <a href=\"https://github.com/CompVis/stable-diffusion\">compvis/Stable-Difusi</a> on \u7684\u6a21\u578b\u5b9a\u4e49\u548c\u547d\u540d\u4e0d\u53d8\uff0c\u8fd9\u6837\u6211\u4eec\u5c31\u53ef\u4ee5\u76f4\u63a5\u52a0\u8f7d\u68c0\u67e5\u70b9\u3002</p>\n",
|
||||
"<h2>Create sinusoidal time step embeddings</h2>\n<ul><li><span translate=no>_^_0_^_</span> are the time steps of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> controls the minimum frequency of the embeddings.</li></ul>\n": "<h2>\u521b\u5efa\u6b63\u5f26\u65f6\u95f4\u6b65\u957f\u5d4c\u5165</h2>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u5f62\u72b6\u7684\u65f6\u95f4\u6b65\u957f<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u63a7\u5236\u5d4c\u5165\u7684\u6700\u5c0f\u9891\u7387\u3002</li></ul>\n",
|
||||
"<h2>Down-sampling layer</h2>\n": "<h2>\u5411\u4e0b\u91c7\u6837\u5c42</h2>\n",
|
||||
"<h2>ResNet Block</h2>\n": "<h2>ResNet \u533a\u5757</h2>\n",
|
||||
"<h2>U-Net model</h2>\n": "<h2>U-Net \u6a21\u578b</h2>\n",
|
||||
"<h3>Group normalization with float32 casting</h3>\n": "<h3>\u4f7f\u7528 float32 \u5f3a\u5236\u8f6c\u6362\u8fdb\u884c\u5206\u7ec4\u5f52\u4e00\u5316</h3>\n",
|
||||
"<h3>Group normalization</h3>\n<p>This is a helper function, with fixed number of groups..</p>\n": "<h3>\u7fa4\u7ec4\u6807\u51c6\u5316</h3>\n<p>\u8fd9\u662f\u4e00\u4e2a\u8f85\u52a9\u51fd\u6570\uff0c\u5177\u6709\u56fa\u5b9a\u6570\u91cf\u7684\u7ec4\u3002</p>\n",
|
||||
"<h3>Sequential block for modules with different inputs</h3>\n<p>This sequential module can compose of different modules such as <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> and <span translate=no>_^_2_^_</span> and calls them with the matching signatures</p>\n": "<h3>\u7528\u4e8e\u5177\u6709\u4e0d\u540c\u8f93\u5165\u7684\u6a21\u5757\u7684\u987a\u5e8f\u6a21\u5757</h3>\n<p>\u8fd9\u4e2a\u987a\u5e8f\u6a21\u5757\u53ef\u4ee5\u7531\u4e0d\u540c\u7684\u6a21\u5757\uff08\u4f8b\u5982<span translate=no>_^_0_^_</span>\u3001<span translate=no>_^_1_^_</span>\u548c\uff09\u7ec4\u6210\uff0c<span translate=no>_^_2_^_</span>\u5e76\u4f7f\u7528\u5339\u914d\u7684\u7b7e\u540d\u8c03\u7528\u5b83\u4eec</p>\n",
|
||||
"<h3>Up-sampling layer</h3>\n": "<h3>\u5411\u4e0a\u91c7\u6837\u5c42</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p> Test sinusoidal time step embeddings</p>\n": "<p>\u6d4b\u8bd5\u6b63\u5f26\u65f6\u95f4\u6b65\u957f\u5d4c\u5165</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u548c<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution mapping </p>\n": "<p><span translate=no>_^_0_^_</span>\u5377\u79ef\u6620\u5c04</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> convolution with stride length of <span translate=no>_^_1_^_</span> to down-sample by a factor of <span translate=no>_^_2_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u5377\u79ef\uff0c\u6b65\u957f\u4e3a<span translate=no>_^_1_^_</span>\u5411\u4e0b\u91c7\u6837\u7684\u7cfb\u6570\u4e3a<span translate=no>_^_2_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> not specified </p>\n": "<p><span translate=no>_^_0_^_</span>\u672a\u6307\u5b9a</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> mapping layer for residual connection </p>\n": "<p><span translate=no>_^_0_^_</span>\u5230\u5269\u4f59\u8fde\u63a5\u7684<span translate=no>_^_1_^_</span>\u6620\u5c04\u5c42</p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span>; half the channels are sin and the other half is cos, </p>\n": "<p><span translate=no>_^_0_^_</span>; \u4e00\u534a\u7684\u9891\u9053\u662f\u7f6a\u6076\u53e6\u4e00\u534a\u662f cos\uff0c</p>\n",
|
||||
"<p>Add skip connection </p>\n": "<p>\u6dfb\u52a0\u8df3\u8fc7\u8fde\u63a5</p>\n",
|
||||
"<p>Add the residual blocks and attentions </p>\n": "<p>\u6dfb\u52a0\u6b8b\u7559\u65b9\u5757\u548c\u6ce8\u610f\u529b</p>\n",
|
||||
"<p>Add them to the input half of the U-Net and keep track of the number of channels of its output </p>\n": "<p>\u5c06\u5b83\u4eec\u52a0\u5230 U-Net \u7684\u8f93\u5165\u534a\u90e8\u5206\uff0c\u5e76\u8ddf\u8e2a\u5176\u8f93\u51fa\u7684\u901a\u9053\u6570</p>\n",
|
||||
"<p>Add time step embeddings </p>\n": "<p>\u6dfb\u52a0\u65f6\u95f4\u6b65\u957f\u5d4c\u5165</p>\n",
|
||||
"<p>Add to the output half of the U-Net </p>\n": "<p>\u5c06 U-Net \u7684\u4e00\u534a\u52a0\u5230\u8f93\u51fa\u4e2d</p>\n",
|
||||
"<p>Add transformer </p>\n": "<p>\u6dfb\u52a0\u53d8\u538b\u5668</p>\n",
|
||||
"<p>Apply convolution </p>\n": "<p>\u5e94\u7528\u5377\u79ef</p>\n",
|
||||
"<p>Down sample at all levels except last </p>\n": "<p>\u9664\u6700\u540e\u4e00\u4e2a\u5173\u5361\u5916\uff0c\u6240\u6709\u7ea7\u522b\u5747\u5411\u4e0b\u91c7\u6837</p>\n",
|
||||
"<p>Final convolution </p>\n": "<p>\u6700\u540e\u7684\u5377\u79ef</p>\n",
|
||||
"<p>Final convolution layer </p>\n": "<p>\u6700\u7ec8\u5377\u79ef\u5c42</p>\n",
|
||||
"<p>Final normalization and <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u6700\u7ec8\u6807\u51c6\u5316\u548c<span translate=no>_^_0_^_</span>\u5377\u79ef</p>\n",
|
||||
"<p>First normalization and convolution </p>\n": "<p>\u7b2c\u4e00\u6b21\u5f52\u4e00\u5316\u548c\u5377\u79ef</p>\n",
|
||||
"<p>Get time step embeddings </p>\n": "<p>\u83b7\u53d6\u65f6\u95f4\u6b65\u957f\u5d4c\u5165\u4fe1\u606f</p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution that maps the input to <span translate=no>_^_1_^_</span>. The blocks are wrapped in <span translate=no>_^_2_^_</span> module because different modules have different forward function signatures; for example, convolution only accepts the feature map and residual blocks accept the feature map and time embedding. <span translate=no>_^_3_^_</span> calls them accordingly. </p>\n": "\u5c06@@ <p>\u8f93\u5165\u6620\u5c04\u5230\u7684\u521d\u59cb<span translate=no>_^_0_^_</span>\u5377\u79ef<span translate=no>_^_1_^_</span>\u3002\u8fd9\u4e9b\u65b9\u5757\u88ab\u5c01\u88c5\u5728<span translate=no>_^_2_^_</span>\u6a21\u5757\u4e2d\uff0c\u56e0\u4e3a\u4e0d\u540c\u7684\u6a21\u5757\u5177\u6709\u4e0d\u540c\u7684\u6b63\u5411\u51fd\u6570\u7b7e\u540d\uff1b\u4f8b\u5982\uff0c\u5377\u79ef\u4ec5\u63a5\u53d7\u7279\u5f81\u56fe\uff0c\u800c\u5269\u4f59\u5757\u63a5\u53d7\u7279\u5f81\u56fe\u548c\u65f6\u95f4\u5d4c\u5165\u3002<span translate=no>_^_3_^_</span>\u76f8\u5e94\u5730\u7ed9\u4ed6\u4eec\u6253\u7535\u8bdd\u3002</p>\n",
|
||||
"<p>Initial convolution </p>\n": "<p>\u521d\u59cb\u5377\u79ef</p>\n",
|
||||
"<p>Input half of the U-Net </p>\n": "<p>\u8f93\u5165 U-Net \u7684\u4e00\u534a</p>\n",
|
||||
"<p>Middle of the U-Net </p>\n": "<p>U-Net \u7684\u4e2d\u95f4</p>\n",
|
||||
"<p>Number of channels at each block in the input half of U-Net </p>\n": "<p>U-Net \u8f93\u5165\u534a\u90e8\u5206\u4e2d\u6bcf\u4e2a\u6a21\u5757\u7684\u4fe1\u9053\u6570</p>\n",
|
||||
"<p>Number of channels at each level </p>\n": "<p>\u6bcf\u4e2a\u7ea7\u522b\u7684\u9891\u9053\u6570</p>\n",
|
||||
"<p>Number of levels </p>\n": "<p>\u5173\u5361\u6570</p>\n",
|
||||
"<p>Output half of the U-Net </p>\n": "<p>\u8f93\u51fa U-Net \u7684\u4e00\u534a</p>\n",
|
||||
"<p>Prepare levels </p>\n": "<p>\u51c6\u5907\u5173\u5361</p>\n",
|
||||
"<p>Prepare levels in reverse order </p>\n": "<p>\u6309\u76f8\u53cd\u7684\u987a\u5e8f\u51c6\u5907\u5173\u5361</p>\n",
|
||||
"<p>Residual block maps from previous number of channels plus the skip connections from the input half of U-Net to the number of channels in the current level. </p>\n": "<p>\u6b8b\u5dee\u65b9\u5757\u4ece\u5148\u524d\u7684\u4fe1\u9053\u6570\u52a0\u4e0a\u4ece U-Net \u7684\u8f93\u5165\u4e00\u534a\u7684\u8df3\u8fc7\u8fde\u63a5\u6620\u5c04\u5230\u5f53\u524d\u5173\u5361\u4e2d\u7684\u4fe1\u9053\u6570\u3002</p>\n",
|
||||
"<p>Residual block maps from previous number of channels to the number of channels in the current level </p>\n": "<p>\u6b8b\u5dee\u65b9\u5757\u4ece\u5148\u524d\u7684\u901a\u9053\u6570\u6620\u5c04\u5230\u5f53\u524d\u5173\u5361\u4e2d\u7684\u901a\u9053\u6570</p>\n",
|
||||
"<p>Second half of the U-Net </p>\n": "<p>U-Net \u7684\u540e\u534a\u90e8\u5206</p>\n",
|
||||
"<p>Size time embeddings </p>\n": "<p>\u8c03\u6574\u65f6\u95f4\u5d4c\u5165\u7684\u5927\u5c0f</p>\n",
|
||||
"<p>The middle of the U-Net </p>\n": "<p>U-Net \u7684\u4e2d\u95f4</p>\n",
|
||||
"<p>Time step embeddings </p>\n": "<p>\u65f6\u95f4\u6b65\u957f\u5d4c\u5165</p>\n",
|
||||
"<p>To store the input half outputs for skip connections </p>\n": "<p>\u5b58\u50a8\u8df3\u8fc7\u8fde\u63a5\u7684\u8f93\u5165\u534a\u8f93\u51fa</p>\n",
|
||||
"<p>Up-sample at every level after last residual block except the last one. Note that we are iterating in reverse; i.e. <span translate=no>_^_0_^_</span> is the last. </p>\n": "<p>\u5728\u6700\u540e\u4e00\u4e2a\u6b8b\u5dee\u65b9\u5757\u4e4b\u540e\u7684\u6bcf\u4e2a\u7b49\u7ea7\u4e0a\u91c7\u6837\uff0c\u6700\u540e\u4e00\u4e2a\u533a\u5757\u9664\u5916\u3002\u8bf7\u6ce8\u610f\uff0c\u6211\u4eec\u5728\u53cd\u5411\u8fed\u4ee3\uff1b<span translate=no>_^_0_^_</span>\u5373\u6700\u540e\u4e00\u6b21\u3002</p>\n",
|
||||
"<p>Up-sample by a factor of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6309\u7cfb\u6570\u5411\u4e0a\u91c7\u6837<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> are the time steps of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> conditioning of shape <span translate=no>_^_5_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5f62\u72b6\u7684\u8f93\u5165\u7279\u5f81\u56fe<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u5f62\u72b6\u7684\u65f6\u95f4\u6b65\u957f<span translate=no>_^_3_^_</span></li>\n</ul><li><span translate=no>_^_4_^_</span>\u5f62\u72b6\u8c03\u8282<span translate=no>_^_5_^_</span></li>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map with shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the time step embeddings of shape <span translate=no>_^_3_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5e26\u6709\u5f62\u72b6\u7684\u8f93\u5165\u8981\u7d20\u56fe<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u5f62\u72b6\u7684\u65f6\u95f4\u6b65\u957f\u5d4c\u5165<span translate=no>_^_3_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input feature map with shape <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5e26\u6709\u5f62\u72b6\u7684\u8f93\u5165\u8981\u7d20\u56fe<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the input feature map </li>\n<li><span translate=no>_^_1_^_</span> is the number of channels in the output feature map </li>\n<li><span translate=no>_^_2_^_</span> is the base channel count for the model </li>\n<li><span translate=no>_^_3_^_</span> number of residual blocks at each level </li>\n<li><span translate=no>_^_4_^_</span> are the levels at which attention should be performed </li>\n<li><span translate=no>_^_5_^_</span> are the multiplicative factors for number of channels for each level </li>\n<li><span translate=no>_^_6_^_</span> is the number of attention heads in the transformers </li>\n<li><span translate=no>_^_7_^_</span> is the number of transformer layers in the transformers </li>\n<li><span translate=no>_^_8_^_</span> is the size of the conditional embedding in the transformers</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u8f93\u5165\u7279\u5f81\u56fe\u4e2d\u7684\u901a\u9053\u6570</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u8f93\u51fa\u7279\u5f81\u56fe\u4e2d\u7684\u901a\u9053\u6570</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u6a21\u578b\u7684\u57fa\u672c\u4fe1\u9053\u6570</li>\n<li><span translate=no>_^_3_^_</span>\u6bcf\u4e2a\u7ea7\u522b\u7684\u5269\u4f59\u533a\u5757\u6570</li>\n<li><span translate=no>_^_4_^_</span>\u662f\u5e94\u8be5\u6ce8\u610f\u7684\u7ea7\u522b</li>\n<li><span translate=no>_^_5_^_</span>\u662f\u6bcf\u4e2a\u7ea7\u522b\u4fe1\u9053\u6570\u91cf\u7684\u4e58\u6cd5\u7cfb\u6570</li>\n<li><span translate=no>_^_6_^_</span>\u662f\u53d8\u5f62\u91d1\u521a\u4e2d\u7684\u6ce8\u610f\u529b\u5934\u6570\u91cf</li>\n<li><span translate=no>_^_7_^_</span>\u662f\u53d8\u538b\u5668\u4e2d\u7684\u53d8\u538b\u5668\u5c42\u6570</li>\n<li><span translate=no>_^_8_^_</span>\u662f\u53d8\u538b\u5668\u4e2d\u6761\u4ef6\u5d4c\u5165\u7684\u5927\u5c0f</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u9891\u9053\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> the number of input channels </li>\n<li><span translate=no>_^_1_^_</span> the size of timestep embeddings </li>\n<li><span translate=no>_^_2_^_</span> is the number of out channels. defaults to `channels.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u8f93\u5165\u901a\u9053\u7684\u6570\u91cf</li>\n<li><span translate=no>_^_1_^_</span>\u65f6\u95f4\u6b65\u5d4c\u5165\u7684\u5927\u5c0f</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u8f93\u51fa\u4fe1\u9053\u7684\u6570\u91cf\u3002\u9ed8\u8ba4\u4e3a `channels\u3002</li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of the U-Net in stable diffusion.": "\u5e26\u6ce8\u91ca\u7684 U-Net \u7a33\u5b9a\u6269\u6563\u7248 PyTorch \u5b9e\u73b0/\u6559\u7a0b\u3002",
|
||||
"U-Net for Stable Diffusion": "U-Net \u7528\u4e8e\u7a33\u5b9a\u6269\u6563"
|
||||
}
|
||||
@@ -0,0 +1,63 @@
|
||||
{
|
||||
"<h1>Transformer for Stable Diffusion <a href=\"unet.html\">U-Net</a></h1>\n<p>This implements the transformer module used in <a href=\"unet.html\">U-Net</a> that gives <span translate=no>_^_0_^_</span></p>\n<p>We have kept to the model definition and naming unchanged from <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we can load the checkpoints directly.</p>\n": "<h1><a href=\"unet.html\">\u5b89\u5b9a\u62e1\u6563\u7528\u5909\u5727\u5668 U-Net</a></h1>\n<p><a href=\"unet.html\">\u3053\u308c\u306fU-Net\u3067\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u30c8\u30e9\u30f3\u30b9\u30e2\u30b8\u30e5\u30fc\u30eb\u3092\u5b9f\u88c5\u3057\u305f\u3082\u306e\u3067</a>\u3001\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<span translate=no>_^_0_^_</span></p>\n<p>\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u3092\u76f4\u63a5\u8aad\u307f\u8fbc\u3081\u308b\u3088\u3046\u306b\u3001<a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/Stable-Diffusion\u304b\u3089\u30e2\u30c7\u30eb\u5b9a\u7fa9\u3068\u547d\u540d\u3092\u5909\u66f4\u3057\u3066\u3044\u307e\u305b\u3093</a>\u3002</p>\n",
|
||||
"<h2>Spatial Transformer</h2>\n": "<h2>\u7a7a\u9593\u5909\u5727\u5668</h2>\n",
|
||||
"<h3>Cross Attention Layer</h3>\n<p>This falls-back to self-attention when conditional embeddings are not specified.</p>\n": "<h3>\u30af\u30ed\u30b9\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30ec\u30a4\u30e4\u30fc</h3>\n<p>\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u304c\u6307\u5b9a\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408\u3001\u3053\u308c\u306f\u81ea\u52d5\u7684\u306b\u51e6\u7406\u3055\u308c\u307e\u3059\u3002</p>\n",
|
||||
"<h3>Feed-Forward Network</h3>\n": "<h3>\u30d5\u30a3\u30fc\u30c9\u30d5\u30a9\u30ef\u30fc\u30c9\u30cd\u30c3\u30c8\u30ef\u30fc\u30af</h3>\n",
|
||||
"<h3>GeGLU Activation</h3>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h3>GeGlu\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30b7\u30e7\u30f3</h3>\n<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<h3>Transformer Layer</h3>\n": "<h3>\u5909\u5727\u5668\u5c64</h3>\n",
|
||||
"<h4>Flash Attention</h4>\n<ul><li><span translate=no>_^_0_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_5_^_</span></li></ul>\n": "<h4>\u30d5\u30e9\u30c3\u30b7\u30e5\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3</h4>\n<ul><li><span translate=no>_^_0_^_</span>\u982d\u304c\u5206\u5272\u3055\u308c\u308b\u524d\u306e\u30af\u30a8\u30ea\u30d9\u30af\u30c8\u30eb\u3067\u3001\u5f62\u72b6\u304c\u5408\u3063\u3066\u3044\u308b\u304b <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u982d\u304c\u5206\u5272\u3055\u308c\u308b\u524d\u306e\u30af\u30a8\u30ea\u30d9\u30af\u30c8\u30eb\u3067\u3001\u5f62\u72b6\u304c\u5408\u3063\u3066\u3044\u308b\u304b <span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u982d\u304c\u5206\u5272\u3055\u308c\u308b\u524d\u306e\u30af\u30a8\u30ea\u30d9\u30af\u30c8\u30eb\u3067\u3001\u5f62\u72b6\u304c\u5408\u3063\u3066\u3044\u308b\u304b <span translate=no>_^_5_^_</span></li></ul>\n",
|
||||
"<h4>Normal Attention</h4>\n<ul><li><span translate=no>_^_0_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_5_^_</span></li></ul>\n": "<h4>\u30ce\u30fc\u30de\u30eb\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3</h4>\n<ul><li><span translate=no>_^_0_^_</span>\u982d\u304c\u5206\u5272\u3055\u308c\u308b\u524d\u306e\u30af\u30a8\u30ea\u30d9\u30af\u30c8\u30eb\u3067\u3001\u5f62\u72b6\u304c\u5408\u3063\u3066\u3044\u308b\u304b <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u982d\u304c\u5206\u5272\u3055\u308c\u308b\u524d\u306e\u30af\u30a8\u30ea\u30d9\u30af\u30c8\u30eb\u3067\u3001\u5f62\u72b6\u304c\u5408\u3063\u3066\u3044\u308b\u304b <span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u982d\u304c\u5206\u5272\u3055\u308c\u308b\u524d\u306e\u30af\u30a8\u30ea\u30d9\u30af\u30c8\u30eb\u3067\u3001\u5f62\u72b6\u304c\u5408\u3063\u3066\u3044\u308b\u304b <span translate=no>_^_5_^_</span></li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Add residual </p>\n": "<p>\u6b8b\u5dee\u3092\u8ffd\u52a0</p>\n",
|
||||
"<p>Apply the transformer layers </p>\n": "<p>\u30c8\u30e9\u30f3\u30b9\u30ec\u30a4\u30e4\u30fc\u3092\u9069\u7528</p>\n",
|
||||
"<p>Attention scaling factor </p>\n": "<p>\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u30d5\u30a1\u30af\u30bf\u30fc</p>\n",
|
||||
"<p>Calculate attention <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6ce8\u610f\u529b\u306e\u8a08\u7b97 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Combined linear projections <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u7dda\u5f62\u6295\u5f71\u6cd5\u306e\u7d44\u307f\u5408\u308f\u305b\u3068 <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Compute attention <span translate=no>_^_0_^_</span> This gives a tensor of shape <span translate=no>_^_1_^_</span> </p>\n": "<p>\u6ce8\u610f\u3092\u8a08\u7b97<span translate=no>_^_0_^_</span>:\u3053\u308c\u306b\u3088\u308a\u5f62\u72b6\u306e\u30c6\u30f3\u30bd\u30eb\u304c\u5f97\u3089\u308c\u307e\u3059 <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Compute attention output <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u51fa\u529b\u3092\u8a08\u7b97 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Compute softmax <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b3\u30f3\u30d4\u30e5\u30fc\u30c8\u30bd\u30d5\u30c8\u30de\u30c3\u30af\u30b9 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Cross attention layer and pre-norm layer </p>\n": "<p>\u30af\u30ed\u30b9\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30ec\u30a4\u30e4\u30fc\u3068\u30d7\u30ec\u30fb\u30ce\u30eb\u30e0\u30fb\u30ec\u30a4\u30e4\u30fc</p>\n",
|
||||
"<p>Cross-attention with conditioning </p>\n": "<p>\u30af\u30ed\u30b9\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u3068\u30b3\u30f3\u30c7\u30a3\u30b7\u30e7\u30cb\u30f3\u30b0</p>\n",
|
||||
"<p>Feed-forward network </p>\n": "<p>\u30d5\u30a3\u30fc\u30c9\u30d5\u30a9\u30ef\u30fc\u30c9\u30cd\u30c3\u30c8\u30ef\u30fc\u30af</p>\n",
|
||||
"<p>Feed-forward network and pre-norm layer </p>\n": "<p>\u30d5\u30a3\u30fc\u30c9\u30d5\u30a9\u30ef\u30fc\u30c9\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u30d7\u30ec\u30ce\u30eb\u30e0\u5c64</p>\n",
|
||||
"<p>Final <span translate=no>_^_0_^_</span> convolution </p>\n": "<p><span translate=no>_^_0_^_</span>\u6700\u7d42\u7573\u307f\u8fbc\u307f</p>\n",
|
||||
"<p>Final linear layer </p>\n": "<p>\u6700\u7d42\u7dda\u5f62\u30ec\u30a4\u30e4\u30fc</p>\n",
|
||||
"<p>Flash attention works for head sizes <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> and <span translate=no>_^_2_^_</span>, so we have to pad the heads to fit this size. </p>\n": "<p>\u30d5\u30e9\u30c3\u30b7\u30e5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u306f\u982d\u306e\u30b5\u30a4\u30ba\u306b\u5408\u3046\u306e\u3067<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u3001\u3053\u306e\u30b5\u30a4\u30ba\u306b\u5408\u3046\u3088\u3046\u306b\u982d\u3092\u30d1\u30c3\u30c9\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002</p>\n",
|
||||
"<p>For residual connection </p>\n": "<p>\u6b8b\u7559\u63a5\u7d9a\u7528</p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u53d6\u5f97\u3057\u3066 <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get batch size and number of elements along sequence axis (<span translate=no>_^_0_^_</span>) </p>\n": "<p>\u30b7\u30fc\u30b1\u30f3\u30b9\u8ef8\u306b\u6cbf\u3063\u305f\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u3068\u8981\u7d20\u6570\u3092\u53d6\u5f97 (<span translate=no>_^_0_^_</span>)</p>\n",
|
||||
"<p>Get query, key and value vectors </p>\n": "<p>\u30af\u30a8\u30ea\u3001\u30ad\u30fc\u3001\u5024\u306e\u30d9\u30af\u30c8\u30eb\u306e\u53d6\u5f97</p>\n",
|
||||
"<p>Get shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b7\u30a7\u30a4\u30d7\u3092\u53d6\u5f97 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>If <span translate=no>_^_0_^_</span> is <span translate=no>_^_1_^_</span> we perform self attention </p>\n": "<p><span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>\u30bb\u30eb\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u3092\u884c\u3046\u306a\u3089</p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution </p>\n": "<p><span translate=no>_^_0_^_</span>\u521d\u671f\u30b3\u30f3\u30dc\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3</p>\n",
|
||||
"<p>Initial group normalization </p>\n": "<p>\u521d\u671f\u30b0\u30eb\u30fc\u30d7\u6b63\u898f\u5316</p>\n",
|
||||
"<p>Map to <span translate=no>_^_0_^_</span> with a linear layer </p>\n": "<p><span translate=no>_^_0_^_</span>\u7dda\u5f62\u30ec\u30a4\u30e4\u30fc\u3067\u30de\u30c3\u30d4\u30f3\u30b0</p>\n",
|
||||
"<p>Normalize </p>\n": "<p>\u30ce\u30fc\u30de\u30e9\u30a4\u30ba</p>\n",
|
||||
"<p>Otherwise, fallback to normal attention </p>\n": "<p>\u305d\u308c\u4ee5\u5916\u306e\u5834\u5408\u306f\u3001\u901a\u5e38\u306e\u5bfe\u5fdc\u306b\u623b\u308b</p>\n",
|
||||
"<p>Pad the heads </p>\n": "<p>\u982d\u3092\u30d1\u30c3\u30c9\u30fb\u30b6\u30fb\u30d8\u30c3\u30c9</p>\n",
|
||||
"<p>Query, key and value mappings </p>\n": "<p>\u30af\u30a8\u30ea\u3001\u30ad\u30fc\u3001\u5024\u306e\u30de\u30c3\u30d4\u30f3\u30b0</p>\n",
|
||||
"<p>Reshape and transpose from <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u5f62\u72b6\u3092\u5909\u66f4\u3057\u3066\u304b\u3089\u306b\u8ee2\u7f6e <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Reshape to <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5f62\u72b6\u3092\u6b21\u306e\u5f62\u5f0f\u306b\u5909\u66f4 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Self attention </p>\n": "<p>\u30bb\u30eb\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3</p>\n",
|
||||
"<p>Self-attention layer and pre-norm layer </p>\n": "<p>\u30bb\u30eb\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u5c64\u3068\u30d7\u30ec\u30ce\u30eb\u30e0\u5c64</p>\n",
|
||||
"<p>Set the scale for scaled dot-product attention. </p>\n": "<p>\u30c9\u30c3\u30c8\u30d7\u30ed\u30c0\u30af\u30c8\u306e\u6ce8\u76ee\u5ea6\u3092\u9ad8\u3081\u308b\u305f\u3081\u306e\u30b9\u30b1\u30fc\u30eb\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002</p>\n",
|
||||
"<p>Set to <span translate=no>_^_0_^_</span> if it's not installed </p>\n": "<p><span translate=no>_^_0_^_</span>\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408\u306f\u306b\u8a2d\u5b9a</p>\n",
|
||||
"<p>Setup <a href=\"https://github.com/HazyResearch/flash-attention\">flash attention</a>. Flash attention is only used if it's installed and <span translate=no>_^_0_^_</span> is set to <span translate=no>_^_1_^_</span>. </p>\n": "<p><a href=\"https://github.com/HazyResearch/flash-attention\">\u30d5\u30e9\u30c3\u30b7\u30e5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u3092\u8a2d\u5b9a\u3057\u307e\u3059</a>\u3002\u30d5\u30e9\u30c3\u30b7\u30e5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u306f\u3001<span translate=no>_^_0_^_</span>\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u3066\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u308b\u5834\u5408\u306b\u306e\u307f\u4f7f\u7528\u3055\u308c\u307e\u3059<span translate=no>_^_1_^_</span>\u3002</p>\n",
|
||||
"<p>Split the heads </p>\n": "<p>\u30b9\u30d7\u30ea\u30c3\u30c8\u30fb\u30b6\u30fb\u30d8\u30c3\u30c9</p>\n",
|
||||
"<p>Split them to heads of shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u982d\u306e\u5f62\u306b\u5206\u3051\u308b <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Stack <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span>, <span translate=no>_^_2_^_</span> vectors for flash attention, to get a single tensor of shape <span translate=no>_^_3_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span><span translate=no>_^_2_^_</span>\u30d5\u30e9\u30c3\u30b7\u30e5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u7528\u306e\u30d9\u30af\u30c8\u30eb\u3092\u7a4d\u307f\u91cd\u306d\u3066\u3001\u5f62\u72b6\u306e\u30c6\u30f3\u30bd\u30eb\u30921\u3064\u306b\u3059\u308b <span translate=no>_^_1_^_</span> <span translate=no>_^_3_^_</span></p>\n",
|
||||
"<p>Transformer layers </p>\n": "<p>\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u5c64</p>\n",
|
||||
"<p>Transpose and reshape from <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u304b\u3089\u3078\u306e\u8ee2\u7f6e\u3068\u5f62\u72b6\u5909\u66f4 <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Truncate the extra head size </p>\n": "<p>\u4f59\u5206\u306a\u982d\u306e\u30b5\u30a4\u30ba\u306f\u5207\u308a\u6368\u3066\u3066\u304f\u3060\u3055\u3044</p>\n",
|
||||
"<p>Use flash attention if it's available and the head size is less than or equal to <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30d5\u30e9\u30c3\u30b7\u30e5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u3066\u3001\u982d\u306e\u30b5\u30a4\u30ba\u304c\u4ee5\u4e0b\u306e\u5834\u5408\u306f\u3001\u30d5\u30e9\u30c3\u30b7\u30e5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u3092\u4f7f\u7528\u3057\u3066\u304f\u3060\u3055\u3044 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>You can install flash attention by cloning their Github repo, <a href=\"https://github.com/HazyResearch/flash-attention\">https://github.com/HazyResearch/flash-attention</a> and then running <span translate=no>_^_0_^_</span> </p>\n": "<p><a href=\"https://github.com/HazyResearch/flash-attention\">\u30d5\u30e9\u30c3\u30b7\u30e5\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u306b\u306f\u3001\u5f7c\u3089\u306e Github \u30ea\u30dd\u30b8\u30c8\u30ea https://github.com/HazyResearch/flash-attention \u3092\u30af\u30ed\u30fc\u30f3\u3057\u3066\u304b\u3089\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044</a> <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> are the input embeddings of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings of shape <span translate=no>_^_3_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u306e\u5165\u529b\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u5f62\u72b6\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_3_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the feature map of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings of shape <span translate=no>_^_3_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u306e\u7279\u5fb4\u30de\u30c3\u30d7\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u5f62\u72b6\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_3_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input embedding size </li>\n<li><span translate=no>_^_1_^_</span> is multiplicative factor for the hidden layer size</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u5165\u529b\u57cb\u3081\u8fbc\u307f\u30b5\u30a4\u30ba\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u96a0\u308c\u30ec\u30a4\u30e4\u30fc\u30b5\u30a4\u30ba\u306e\u4e57\u6570\u3067\u3059</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input embedding size </li>\n<li><span translate=no>_^_1_^_</span> is the number of attention heads </li>\n<li><span translate=no>_^_2_^_</span> is the size of a attention head </li>\n<li><span translate=no>_^_3_^_</span> is the size of the conditional embeddings </li>\n<li><span translate=no>_^_4_^_</span> specifies whether to perform the attention softmax computation inplace to save memory</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u5165\u529b\u57cb\u3081\u8fbc\u307f\u30b5\u30a4\u30ba\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30d8\u30c3\u30c9\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_2_^_</span>\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30d8\u30c3\u30c9\u304f\u3089\u3044\u306e\u5927\u304d\u3055\u3067\u3059</li>\n<li><span translate=no>_^_3_^_</span>\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u306e\u30b5\u30a4\u30ba\u3067\u3059</li>\n<li><span translate=no>_^_4_^_</span>\u30e1\u30e2\u30ea\u3092\u7bc0\u7d04\u3059\u308b\u305f\u3081\u306b\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30bd\u30d5\u30c8\u30de\u30c3\u30af\u30b9\u8a08\u7b97\u3092\u30a4\u30f3\u30d7\u30ec\u30fc\u30b9\u3067\u5b9f\u884c\u3059\u308b\u304b\u3069\u3046\u304b\u3092\u6307\u5b9a\u3057\u307e\u3059</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input embedding size </li>\n<li><span translate=no>_^_1_^_</span> is the number of attention heads </li>\n<li><span translate=no>_^_2_^_</span> is the size of a attention head </li>\n<li><span translate=no>_^_3_^_</span> is the size of the conditional embeddings</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u5165\u529b\u57cb\u3081\u8fbc\u307f\u30b5\u30a4\u30ba\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30d8\u30c3\u30c9\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_2_^_</span>\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30d8\u30c3\u30c9\u304f\u3089\u3044\u306e\u5927\u304d\u3055\u3067\u3059</li>\n<li><span translate=no>_^_3_^_</span>\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u306e\u30b5\u30a4\u30ba\u3067\u3059</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the feature map </li>\n<li><span translate=no>_^_1_^_</span> is the number of attention heads </li>\n<li><span translate=no>_^_2_^_</span> is the number of transformer layers </li>\n<li><span translate=no>_^_3_^_</span> is the size of the conditional embedding</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u30d5\u30a3\u30fc\u30c1\u30e3\u30de\u30c3\u30d7\u5185\u306e\u30c1\u30e3\u30cd\u30eb\u6570\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30d8\u30c3\u30c9\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_2_^_</span>\u5909\u5727\u5668\u5c64\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_3_^_</span>\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u306e\u30b5\u30a4\u30ba</li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of the transformer for U-Net in stable diffusion.": "\u5b89\u5b9a\u62e1\u6563\u306b\u304a\u3051\u308bU-Net\u7528\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u306e\u6ce8\u91c8\u4ed8\u304dPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3002",
|
||||
"Transformer for Stable Diffusion U-Net": "\u5b89\u5b9a\u62e1\u6563\u7528\u5909\u5727\u5668 U-Net"
|
||||
}
|
||||
@@ -0,0 +1,63 @@
|
||||
{
|
||||
"<h1>Transformer for Stable Diffusion <a href=\"unet.html\">U-Net</a></h1>\n<p>This implements the transformer module used in <a href=\"unet.html\">U-Net</a> that gives <span translate=no>_^_0_^_</span></p>\n<p>We have kept to the model definition and naming unchanged from <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we can load the checkpoints directly.</p>\n": "<h1>\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba <a href=\"unet.html\">\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca</a> \u0dc3\u0db3\u0dc4\u0dcf \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca</h1>\n<p>\u0db8\u0dd9\u0dba \u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0db1 <a href=\"unet.html\">\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca</a> \u0dc4\u0dd2 \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1 \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca \u0db8\u0ddc\u0da9\u0dd2\u0dba\u0dd4\u0dbd\u0dba \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dbb\u0dba\u0dd2<span translate=no>_^_0_^_</span></p>\n<p>\u0d85\u0db4\u0dd2 \u0d86\u0daf\u0dbb\u0dca\u0dc1 \u0d85\u0dbb\u0dca\u0dae \u0daf\u0dd0\u0d9a\u0dca\u0dc0\u0dd3\u0db8 \u0dad\u0db6\u0dcf \u0d87\u0dad\u0dd2 \u0d85\u0dad\u0dbb <a href=\"https://github.com/CompVis/stable-diffusion\">\u0d9a\u0ddc\u0db8\u0dca\u0dc0\u0dd2\u0dc3\u0dca/\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab</a> \u0dc3\u0dd2\u0da7 \u0db1\u0ddc\u0dc0\u0dd9\u0db1\u0dc3\u0dca\u0dc0 \u0db1\u0db8\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0d85\u0db4\u0da7 \u0db8\u0dd4\u0dbb\u0db4\u0ddc\u0dbd\u0dc0\u0dbd\u0dca \u0d9a\u0dd9\u0dbd\u0dd2\u0db1\u0dca\u0db8 \u0db4\u0dd0\u0da7\u0dc0\u0dd2\u0dba \u0dc4\u0dd0\u0d9a\u0dd2 \u0dc0\u0db1 \u0db4\u0dbb\u0dd2\u0daf\u0dd2.</p>\n",
|
||||
"<h2>Spatial Transformer</h2>\n": "<h2>\u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dd3\u0dba \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca</h2>\n",
|
||||
"<h3>Cross Attention Layer</h3>\n<p>This falls-back to self-attention when conditional embeddings are not specified.</p>\n": "<h3>\u0dc4\u0dbb\u0dc3\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc3\u0dca\u0dae\u0dbb\u0dba</h3>\n<p>\u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dc0 \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2 \u0dc0\u0dd2\u0da7 \u0db8\u0dd9\u0dba \u0dc3\u0dca\u0dc0\u0dba\u0d82 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba\u0da7 \u0dba\u0ddc\u0db8\u0dd4 \u0dc0\u0dda.</p>\n",
|
||||
"<h3>Feed-Forward Network</h3>\n": "<h3>Feed-\u0d89\u0daf\u0dd2\u0dbb\u0dd2 \u0da2\u0dcf\u0dbd\u0dba</h3>\n",
|
||||
"<h3>GeGLU Activation</h3>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h3>Glu \u0dc3\u0d9a\u0dca\u0dbb\u0dd2\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8</h3>\n<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<h3>Transformer Layer</h3>\n": "<h3>\u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca \u0dc3\u0dca\u0dae\u0dbb\u0dba</h3>\n",
|
||||
"<h4>Flash Attention</h4>\n<ul><li><span translate=no>_^_0_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_5_^_</span></li></ul>\n": "<h4>\u0dc6\u0dca\u0dbd\u0dd1\u0dc2\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba</h4>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0db6\u0dd9\u0daf\u0dd3\u0db8\u0da7 \u0db4\u0dd9\u0dbb \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca \u0daf\u0ddb\u0dc1\u0dd2\u0d9a, \u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0db6\u0dd9\u0daf\u0dd3\u0db8\u0da7 \u0db4\u0dd9\u0dbb \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca \u0daf\u0ddb\u0dc1\u0dd2\u0d9a, \u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0db6\u0dd9\u0daf\u0dd3\u0db8\u0da7 \u0db4\u0dd9\u0dbb \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca \u0daf\u0ddb\u0dc1\u0dd2\u0d9a, \u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_5_^_</span></li></ul>\n",
|
||||
"<h4>Normal Attention</h4>\n<ul><li><span translate=no>_^_0_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_5_^_</span></li></ul>\n": "<h4>\u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba</h4>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0db6\u0dd9\u0daf\u0dd3\u0db8\u0da7 \u0db4\u0dd9\u0dbb \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca \u0daf\u0ddb\u0dc1\u0dd2\u0d9a, \u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0db6\u0dd9\u0daf\u0dd3\u0db8\u0da7 \u0db4\u0dd9\u0dbb \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca \u0daf\u0ddb\u0dc1\u0dd2\u0d9a, \u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0db6\u0dd9\u0daf\u0dd3\u0db8\u0da7 \u0db4\u0dd9\u0dbb \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca \u0daf\u0ddb\u0dc1\u0dd2\u0d9a, \u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_5_^_</span></li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Add residual </p>\n": "<p>\u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0d91\u0d9a\u0dad\u0dd4 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Apply the transformer layers </p>\n": "<p>\u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca \u0dc3\u0dca\u0dae\u0dbb \u0dba\u0ddc\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Attention scaling factor </p>\n": "<p>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab \u0dc3\u0dcf\u0db0\u0d9a\u0dba</p>\n",
|
||||
"<p>Calculate attention <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Combined linear projections <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0d92\u0d9a\u0dcf\u0db6\u0daf\u0dca\u0db0 \u0dbb\u0dda\u0d9b\u0dd3\u0dba \u0db4\u0dca\u0dbb\u0d9a\u0dca\u0dc2\u0dda\u0db4\u0dab<span translate=no>_^_0_^_</span> \u0dc3\u0dc4<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Compute attention <span translate=no>_^_0_^_</span> This gives a tensor of shape <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span> \u0db8\u0dd9\u0dba \u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d86\u0dad\u0dad\u0dd2\u0d9a\u0dba\u0d9a\u0dca \u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0dba\u0dd2<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Compute attention output <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba \u0d9c\u0dab\u0db1\u0dba<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Compute softmax <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc3\u0ddc\u0dc6\u0dca\u0da7\u0dca\u0db8\u0dd0\u0d9a\u0dca\u0dc3\u0dca \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Cross attention layer and pre-norm layer </p>\n": "<p>\u0dc4\u0dbb\u0dc3\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc3\u0dca\u0dae\u0dbb\u0dba \u0dc3\u0dc4 \u0db4\u0dd9\u0dbb-\u0dc3\u0db8\u0dca\u0db8\u0dad \u0dc3\u0dca\u0dae\u0dbb\u0dba</p>\n",
|
||||
"<p>Cross-attention with conditioning </p>\n": "<p>\u0d9a\u0db1\u0dca\u0da9\u0dd2\u0dc2\u0db1\u0dda\u0dc2\u0db1\u0dca \u0dc3\u0db8\u0d9f \u0dc4\u0dbb\u0dc3\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba</p>\n",
|
||||
"<p>Feed-forward network </p>\n": "<p>Feed-\u0d89\u0daf\u0dd2\u0dbb\u0dd2 \u0da2\u0dcf\u0dbd\u0dba</p>\n",
|
||||
"<p>Feed-forward network and pre-norm layer </p>\n": "<p>Feed-\u0d89\u0daf\u0dd2\u0dbb\u0dd2 \u0da2\u0dcf\u0dbd\u0dba \u0dc3\u0dc4 \u0db4\u0dd9\u0dbb-\u0dc3\u0db8\u0dca\u0db8\u0dad \u0dc3\u0dca\u0dae\u0dbb\u0dba</p>\n",
|
||||
"<p>Final <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u0d85\u0dc0\u0dc3\u0dcf\u0db1<span translate=no>_^_0_^_</span> \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd2\u0db8</p>\n",
|
||||
"<p>Final linear layer </p>\n": "<p>\u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0dbb\u0dda\u0d9b\u0dd3\u0dba \u0dc3\u0dca\u0dae\u0dbb\u0dba</p>\n",
|
||||
"<p>Flash attention works for head sizes <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> and <span translate=no>_^_2_^_</span>, so we have to pad the heads to fit this size. </p>\n": "<p>\u0dc6\u0dca\u0dbd\u0dd1\u0dc2\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc4\u0dd2\u0dc3\u0dca \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf \u0d9a\u0dbb\u0dba\u0dd2<span translate=no>_^_0_^_</span><span translate=no>_^_2_^_</span>,<span translate=no>_^_1_^_</span> \u0dc3\u0dc4, \u0d91\u0db6\u0dd0\u0dc0\u0dd2\u0db1\u0dca \u0db8\u0dd9\u0db8 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0da7 \u0dc3\u0dbb\u0dd2\u0dbd\u0db1 \u0db4\u0dbb\u0dd2\u0daf\u0dd2 \u0dc4\u0dd2\u0dc3\u0dca \u0db4\u0dd1\u0da9\u0dca \u0d9a\u0dc5 \u0dba\u0dd4\u0dad\u0dd4\u0dba.</p>\n",
|
||||
"<p>For residual connection </p>\n": "<p>\u0d85\u0dc0\u0dc1\u0dda\u0dc2 \u0dc3\u0db8\u0dca\u0db6\u0db1\u0dca\u0db0\u0dad\u0dcf\u0dc0\u0dba \u0dc3\u0db3\u0dc4\u0dcf</p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0dbd\u0db6\u0dcf<span translate=no>_^_0_^_</span> \u0d9c\u0db1\u0dca\u0db1<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get batch size and number of elements along sequence axis (<span translate=no>_^_0_^_</span>) </p>\n": "<p>\u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dd2\u0d9a \u0d85\u0d9a\u0dca\u0dc2\u0dba \u0d94\u0dc3\u0dca\u0dc3\u0dda \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dc3\u0dc4 \u0db8\u0dd6\u0dbd\u0daf\u0dca\u0dbb\u0dc0\u0dca\u0dba \u0d9c\u0dab\u0db1 \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1 (<span translate=no>_^_0_^_</span>)</p>\n",
|
||||
"<p>Get query, key and value vectors </p>\n": "<p>\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8, \u0dba\u0dad\u0dd4\u0dbb \u0dc3\u0dc4 \u0d85\u0d9c\u0dba \u0daf\u0ddb\u0dc1\u0dd2\u0d9a \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc4\u0dd0\u0da9\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>If <span translate=no>_^_0_^_</span> is <span translate=no>_^_1_^_</span> we perform self attention </p>\n": "<p><span translate=no>_^_1_^_</span>\u0d85\u0db4\u0dd2 \u0dc3\u0dca\u0dc0\u0dba\u0d82 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4<span translate=no>_^_0_^_</span> \u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dda \u0db1\u0db8\u0dca</p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u0db8\u0dd6\u0dbd\u0dd2\u0d9a<span translate=no>_^_0_^_</span> \u0d9a\u0dd0\u0da7\u0dd2 \u0d9c\u0dd0\u0dc3\u0dd2\u0db8</p>\n",
|
||||
"<p>Initial group normalization </p>\n": "<p>\u0d86\u0dbb\u0db8\u0dca\u0db7\u0d9a \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba\u0d9a\u0dbb\u0dab\u0dba</p>\n",
|
||||
"<p>Map to <span translate=no>_^_0_^_</span> with a linear layer </p>\n": "<p>\u0dbb\u0dda\u0d9b\u0dd3\u0dba \u0dc3\u0dca\u0dae\u0dbb\u0dba\u0d9a\u0dca<span translate=no>_^_0_^_</span> \u0dc3\u0db8\u0d9f \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8</p>\n",
|
||||
"<p>Normalize </p>\n": "<p>\u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u200d\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Otherwise, fallback to normal attention </p>\n": "<p>\u0d91\u0dc3\u0dda \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2 \u0db1\u0db8\u0dca, \u0dc3\u0dcf\u0db8\u0dcf\u0db1\u0dca\u0dba \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba\u0da7 \u0dc0\u0dd0\u0da7\u0dd3\u0db8</p>\n",
|
||||
"<p>Pad the heads </p>\n": "<p>\u0dc4\u0dd2\u0dc3\u0dca \u0db4\u0dd1\u0da9\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Query, key and value mappings </p>\n": "<p>\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca, \u0dba\u0dad\u0dd4\u0dbb \u0dc3\u0dc4 \u0d85\u0d9c\u0dba \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca</p>\n",
|
||||
"<p>Reshape and transpose from <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0db1\u0dd0\u0dc0\u0dad \u0dc4\u0dd0\u0da9\u0d9c\u0dc3\u0dca\u0dc0\u0dcf \u0dc3\u0dd2\u0da7 \u0dc3\u0db8\u0dca\u0db4\u0dca\u0dbb\u0dda\u0dc2\u0dab\u0dba<span translate=no>_^_0_^_</span> \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Reshape to <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db1\u0dd0\u0dc0\u0dad \u0dc4\u0dd0\u0da9\u0d9c\u0dc3\u0dca\u0dc0\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Self attention </p>\n": "<p>\u0dc3\u0dca\u0dc0\u0dba\u0d82 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba</p>\n",
|
||||
"<p>Self-attention layer and pre-norm layer </p>\n": "<p>\u0dc3\u0dca\u0dc0\u0dba\u0d82 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc3\u0dca\u0dae\u0dbb\u0dba \u0dc4\u0dcf \u0db4\u0dd9\u0dbb-\u0dc3\u0db8\u0dca\u0db8\u0dad \u0dc3\u0dca\u0dae\u0dbb\u0dba</p>\n",
|
||||
"<p>Set the scale for scaled dot-product attention. </p>\n": "<p>\u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab \u0dad\u0dd2\u0dad\u0dca \u0db1\u0dd2\u0dc2\u0dca\u0db4\u0dcf\u0daf\u0db1 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1.</p>\n",
|
||||
"<p>Set to <span translate=no>_^_0_^_</span> if it's not installed </p>\n": "<p>\u0d91\u0dba \u0dc3\u0dca\u0dae\u0dcf\u0db4\u0db1\u0dba \u0d9a\u0dbb \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2<span translate=no>_^_0_^_</span> \u0db1\u0db8\u0dca \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Setup <a href=\"https://github.com/HazyResearch/flash-attention\">flash attention</a>. Flash attention is only used if it's installed and <span translate=no>_^_0_^_</span> is set to <span translate=no>_^_1_^_</span>. </p>\n": "<p>\u0dc3\u0dd0\u0d9a\u0dc3\u0dd4\u0db8 <a href=\"https://github.com/HazyResearch/flash-attention\">\u0dc6\u0dca\u0dbd\u0dd1\u0dc2\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba</a>. \u0dc6\u0dca\u0dbd\u0dd1\u0dc2\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0db6\u0db1\u0dca\u0db1\u0dda \u0d91\u0dba \u0dc3\u0dca\u0dae\u0dcf\u0db4\u0db1\u0dba \u0d9a\u0dbb \u0d87\u0dad\u0dca\u0db1\u0db8\u0dca \u0dc3\u0dc4<span translate=no>_^_0_^_</span> \u0d91\u0dba \u0dc3\u0d9a\u0dc3\u0dcf \u0d87\u0dad\u0dca\u0db1\u0db8\u0dca \u0db4\u0db8\u0dab\u0dd2<span translate=no>_^_1_^_</span>.</p>\n",
|
||||
"<p>Split the heads </p>\n": "<p>\u0dc4\u0dd2\u0dc3\u0dca \u0db6\u0dd9\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Split them to heads of shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dbd\u0da7 \u0db6\u0dd9\u0daf\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Stack <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span>, <span translate=no>_^_2_^_</span> vectors for flash attention, to get a single tensor of shape <span translate=no>_^_3_^_</span> </p>\n": "<p>\u0dc6\u0dca\u0dbd\u0dd1\u0dc2\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc3\u0db3\u0dc4\u0dcf<span translate=no>_^_2_^_</span> \u0daf\u0ddb\u0dc1\u0dd2\u0d9a<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>, \u0dc4\u0dd0\u0da9\u0dba\u0dda \u0dad\u0db1\u0dd2 \u0da7\u0dd9\u0db1\u0dca\u0dc3\u0dbb\u0dba\u0d9a\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8\u0da7<span translate=no>_^_3_^_</span></p>\n",
|
||||
"<p>Transformer layers </p>\n": "<p>\u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca \u0dc3\u0dca\u0dae\u0dbb</p>\n",
|
||||
"<p>Transpose and reshape from <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0dc3\u0dd2\u0da7 \u0dc3\u0db8\u0dca\u0db4\u0dca\u0dbb\u0dda\u0dc2\u0dab\u0dba \u0d9a\u0dbb \u0db1\u0dd0\u0dc0\u0dad \u0dc4\u0dd0\u0da9\u0d9c\u0dc3\u0dca\u0dc0\u0dcf<span translate=no>_^_0_^_</span> \u0d9c\u0db1\u0dca\u0db1<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Truncate the extra head size </p>\n": "<p>\u0d85\u0db8\u0dad\u0dbb \u0dc4\u0dd2\u0dc3 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0db4\u0dcf</p>\n",
|
||||
"<p>Use flash attention if it's available and the head size is less than or equal to <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc6\u0dca\u0dbd\u0dd1\u0dc2\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0dad \u0dc4\u0dd0\u0d9a\u0dd2 \u0db1\u0db8\u0dca \u0dc3\u0dc4 \u0dc4\u0dd2\u0dc3 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0d85\u0da9\u0dd4 \u0dc4\u0ddd \u0dc3\u0db8\u0dcf\u0db1 \u0db1\u0db8\u0dca \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>You can install flash attention by cloning their Github repo, <a href=\"https://github.com/HazyResearch/flash-attention\">https://github.com/HazyResearch/flash-attention</a> and then running <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d9a\u0dca\u0dbd\u0ddd\u0db1\u0d9a\u0dbb\u0dab\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dd9\u0db1\u0dca \u0d94\u0db6\u0da7 \u0dc6\u0dca\u0dbd\u0dd1\u0dc2\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc3\u0dca\u0dae\u0dcf\u0db4\u0db1\u0dba \u0d9a\u0dc5 \u0dc4\u0dd0\u0d9a\u0dd2\u0dba Github repo, <a href=\"https://github.com/HazyResearch/flash-attention\">https://github.com/HazyResearch/flash-attention</a> \u0d89\u0db1\u0dca\u0db4\u0dc3\u0dd4 \u0db0\u0dcf\u0dc0\u0db1\u0dba<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> are the input embeddings of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings of shape <span translate=no>_^_3_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d86\u0daf\u0dcf\u0db1 \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_1_^_</span></li>\n</ul><li><span translate=no>_^_2_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_3_^_</span></li>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the feature map of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings of shape <span translate=no>_^_3_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dba\u0dd2<span translate=no>_^_1_^_</span></li>\n</ul><li><span translate=no>_^_2_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_3_^_</span></li>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input embedding size </li>\n<li><span translate=no>_^_1_^_</span> is multiplicative factor for the hidden layer size</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0d86\u0daf\u0dcf\u0db1 \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_1_^_</span>\u0dc3\u0dd0\u0d9f\u0dc0\u0dd4\u0dab\u0dd4 \u0dc3\u0dca\u0dae\u0dbb \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0db6\u0dc4\u0dd4\u0d9a\u0dcf\u0dbb\u0dca\u0dba \u0dc3\u0dcf\u0db0\u0d9a\u0dba\u0d9a\u0dd2</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input embedding size </li>\n<li><span translate=no>_^_1_^_</span> is the number of attention heads </li>\n<li><span translate=no>_^_2_^_</span> is the size of a attention head </li>\n<li><span translate=no>_^_3_^_</span> is the size of the conditional embeddings </li>\n<li><span translate=no>_^_4_^_</span> specifies whether to perform the attention softmax computation inplace to save memory</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0d86\u0daf\u0dcf\u0db1 \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_1_^_</span>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4 \u0db4\u0dca\u0dbb\u0db0\u0dcf\u0db1\u0dd3\u0db1\u0dca \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0 \u0dc0\u0dda</li>\n<li><span translate=no>_^_2_^_</span>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4 \u0dc4\u0dd2\u0dc3\u0dd9\u0dc4\u0dd2 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_3_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dbd \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_4_^_</span>\u0db8\u0dad\u0d9a\u0dba \u0d89\u0dad\u0dd2\u0dbb\u0dd2 \u0d9a\u0dbb \u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba softmax \u0d9c\u0dab\u0db1\u0dba inplace \u0d89\u0da7\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0dba\u0db1\u0dca\u0db1 \u0db1\u0dd2\u0dba\u0db8</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input embedding size </li>\n<li><span translate=no>_^_1_^_</span> is the number of attention heads </li>\n<li><span translate=no>_^_2_^_</span> is the size of a attention head </li>\n<li><span translate=no>_^_3_^_</span> is the size of the conditional embeddings</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0d86\u0daf\u0dcf\u0db1 \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dda \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_1_^_</span>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4 \u0db4\u0dca\u0dbb\u0db0\u0dcf\u0db1\u0dd3\u0db1\u0dca \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0 \u0dc0\u0dda</li>\n<li><span translate=no>_^_2_^_</span>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4 \u0dc4\u0dd2\u0dc3\u0dd9\u0dc4\u0dd2 \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_3_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dbd \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the feature map </li>\n<li><span translate=no>_^_1_^_</span> is the number of attention heads </li>\n<li><span translate=no>_^_2_^_</span> is the number of transformer layers </li>\n<li><span translate=no>_^_3_^_</span> is the size of the conditional embedding</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc0\u0dd2\u0dc1\u0dda\u0dc2\u0dcf\u0d82\u0d9c \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</li>\n<li><span translate=no>_^_1_^_</span>\u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4 \u0db4\u0dca\u0dbb\u0db0\u0dcf\u0db1\u0dd3\u0db1\u0dca \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0 \u0dc0\u0dda</li>\n<li><span translate=no>_^_2_^_</span>\u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca \u0dc3\u0dca\u0dae\u0dbb \u0d9c\u0dab\u0db1</li>\n<li><span translate=no>_^_3_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd2\u0dc0\u0dbd \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2</li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of the transformer for U-Net in stable diffusion.": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dad\u0dd4\u0dc5 \u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dba\u0dda PyTorch \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8/\u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1\u0dba.",
|
||||
"Transformer for Stable Diffusion U-Net": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0da7\u0dca\u0dbb\u0dcf\u0db1\u0dca\u0dc3\u0dca\u0dc6\u0ddd\u0db8\u0dbb\u0dca"
|
||||
}
|
||||
@@ -0,0 +1,63 @@
|
||||
{
|
||||
"<h1>Transformer for Stable Diffusion <a href=\"unet.html\">U-Net</a></h1>\n<p>This implements the transformer module used in <a href=\"unet.html\">U-Net</a> that gives <span translate=no>_^_0_^_</span></p>\n<p>We have kept to the model definition and naming unchanged from <a href=\"https://github.com/CompVis/stable-diffusion\">CompVis/stable-diffusion</a> so that we can load the checkpoints directly.</p>\n": "<h1>\u7528\u4e8e\u7a33\u5b9a\u6269\u6563 <a href=\"unet.html\">U-Net</a> \u7684\u53d8\u538b\u5668</h1>\n<p>\u8fd9\u5b9e\u73b0\u4e86 <a href=\"unet.html\">U-Net</a> \u4e2d\u4f7f\u7528\u7684\u53d8\u538b\u5668\u6a21\u5757\uff0c\u5b83\u63d0\u4f9b<span translate=no>_^_0_^_</span></p>\n<p>\u6211\u4eec\u4fdd\u6301\u4e86 <a href=\"https://github.com/CompVis/stable-diffusion\">compvis/Stable-Difusi</a> on \u7684\u6a21\u578b\u5b9a\u4e49\u548c\u547d\u540d\u4e0d\u53d8\uff0c\u8fd9\u6837\u6211\u4eec\u5c31\u53ef\u4ee5\u76f4\u63a5\u52a0\u8f7d\u68c0\u67e5\u70b9\u3002</p>\n",
|
||||
"<h2>Spatial Transformer</h2>\n": "<h2>\u7a7a\u95f4\u53d8\u538b\u5668</h2>\n",
|
||||
"<h3>Cross Attention Layer</h3>\n<p>This falls-back to self-attention when conditional embeddings are not specified.</p>\n": "<h3>\u4ea4\u53c9\u6ce8\u610f\u529b\u5c42</h3>\n<p>\u5f53\u672a\u6307\u5b9a\u6761\u4ef6\u5d4c\u5165\u65f6\uff0c\u8fd9\u4f1a\u56de\u5f52\u5230\u81ea\u6211\u6ce8\u610f\u529b\u3002</p>\n",
|
||||
"<h3>Feed-Forward Network</h3>\n": "<h3>\u524d\u9988\u7f51\u7edc</h3>\n",
|
||||
"<h3>GeGLU Activation</h3>\n<p><span translate=no>_^_0_^_</span></p>\n": "<h3>\u6fc0\u6d3b GegLU</h3>\n<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<h3>Transformer Layer</h3>\n": "<h3>\u53d8\u538b\u5668\u5c42</h3>\n",
|
||||
"<h4>Flash Attention</h4>\n<ul><li><span translate=no>_^_0_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_5_^_</span></li></ul>\n": "<h4>Flash \u6ce8\u610f</h4>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u5206\u5272\u5934\u90e8\u4e4b\u524d\u7684\u67e5\u8be2\u5411\u91cf\uff0c\u5f62\u72b6\u4e3a<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u5206\u5272\u5934\u90e8\u4e4b\u524d\u7684\u67e5\u8be2\u5411\u91cf\uff0c\u5f62\u72b6\u4e3a<span translate=no>_^_3_^_</span></li>\n</ul><li><span translate=no>_^_4_^_</span>\u662f\u5206\u5272\u5934\u90e8\u4e4b\u524d\u7684\u67e5\u8be2\u5411\u91cf\uff0c\u5f62\u72b6\u4e3a<span translate=no>_^_5_^_</span></li>\n",
|
||||
"<h4>Normal Attention</h4>\n<ul><li><span translate=no>_^_0_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> are the query vectors before splitting heads, of shape <span translate=no>_^_5_^_</span></li></ul>\n": "<h4>\u6b63\u5e38\u6ce8\u610f\u529b</h4>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u5206\u5272\u5934\u90e8\u4e4b\u524d\u7684\u67e5\u8be2\u5411\u91cf\uff0c\u5f62\u72b6\u4e3a<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u5206\u5272\u5934\u90e8\u4e4b\u524d\u7684\u67e5\u8be2\u5411\u91cf\uff0c\u5f62\u72b6\u4e3a<span translate=no>_^_3_^_</span></li>\n</ul><li><span translate=no>_^_4_^_</span>\u662f\u5206\u5272\u5934\u90e8\u4e4b\u524d\u7684\u67e5\u8be2\u5411\u91cf\uff0c\u5f62\u72b6\u4e3a<span translate=no>_^_5_^_</span></li>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Add residual </p>\n": "<p>\u6dfb\u52a0\u6b8b\u5dee</p>\n",
|
||||
"<p>Apply the transformer layers </p>\n": "<p>\u5e94\u7528\u53d8\u538b\u5668\u5c42</p>\n",
|
||||
"<p>Attention scaling factor </p>\n": "<p>\u6ce8\u610f\u529b\u7f29\u653e\u7cfb\u6570</p>\n",
|
||||
"<p>Calculate attention <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8ba1\u7b97\u6ce8\u610f\u529b<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Combined linear projections <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u7ec4\u5408\u7ebf\u6027\u6295\u5f71<span translate=no>_^_0_^_</span>\u548c<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Compute attention <span translate=no>_^_0_^_</span> This gives a tensor of shape <span translate=no>_^_1_^_</span> </p>\n": "<p>\u8ba1\u7b97\u6ce8\u610f\u529b<span translate=no>_^_0_^_</span>\u8fd9\u7ed9\u51fa\u4e86\u5f62\u72b6\u7684\u5f20\u91cf<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Compute attention output <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8ba1\u7b97\u6ce8\u610f\u529b\u8f93\u51fa<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Compute softmax <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8ba1\u7b97 softmax<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Cross attention layer and pre-norm layer </p>\n": "<p>\u4ea4\u53c9\u6ce8\u610f\u529b\u5c42\u548c\u9884\u89c4\u8303\u5c42</p>\n",
|
||||
"<p>Cross-attention with conditioning </p>\n": "<p>\u4ea4\u53c9\u6ce8\u610f\u529b\u4e0e\u8c03\u8282</p>\n",
|
||||
"<p>Feed-forward network </p>\n": "<p>\u524d\u9988\u7f51\u7edc</p>\n",
|
||||
"<p>Feed-forward network and pre-norm layer </p>\n": "<p>\u524d\u9988\u7f51\u7edc\u548c\u9884\u89c4\u8303\u5c42</p>\n",
|
||||
"<p>Final <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u6700\u540e\u7684<span translate=no>_^_0_^_</span>\u5377\u79ef</p>\n",
|
||||
"<p>Final linear layer </p>\n": "<p>\u6700\u540e\u7684\u7ebf\u6027\u5c42</p>\n",
|
||||
"<p>Flash attention works for head sizes <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> and <span translate=no>_^_2_^_</span>, so we have to pad the heads to fit this size. </p>\n": "<p>Flash \u6ce8\u610f\u529b\u9002\u7528\u4e8e\u5934\u90e8\u5c3a\u5bf8<span translate=no>_^_0_^_</span><span translate=no>_^_2_^_</span>\uff0c<span translate=no>_^_1_^_</span>\u800c\u4e14\uff0c\u56e0\u6b64\u6211\u4eec\u5fc5\u987b\u57ab\u4f4f\u5934\u90e8\u624d\u80fd\u9002\u5408\u8fd9\u4e2a\u5c3a\u5bf8\u3002</p>\n",
|
||||
"<p>For residual connection </p>\n": "<p>\u7528\u4e8e\u5269\u4f59\u8fde\u63a5</p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u83b7\u53d6<span translate=no>_^_0_^_</span>\u548c<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get batch size and number of elements along sequence axis (<span translate=no>_^_0_^_</span>) </p>\n": "<p>\u6cbf\u5e8f\u5217\u8f74\u83b7\u53d6\u6279\u91cf\u5927\u5c0f\u548c\u5143\u7d20\u6570\u91cf (<span translate=no>_^_0_^_</span>)</p>\n",
|
||||
"<p>Get query, key and value vectors </p>\n": "<p>\u83b7\u53d6\u67e5\u8be2\u5411\u91cf\u3001\u952e\u5411\u91cf\u548c\u503c\u5411\u91cf</p>\n",
|
||||
"<p>Get shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5851\u9020\u8eab\u5f62<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>If <span translate=no>_^_0_^_</span> is <span translate=no>_^_1_^_</span> we perform self attention </p>\n": "<p>\u5982\u679c<span translate=no>_^_0_^_</span>\u662f\uff0c<span translate=no>_^_1_^_</span>\u6211\u4eec\u8fdb\u884c\u81ea\u6211\u5173\u6ce8</p>\n",
|
||||
"<p>Initial <span translate=no>_^_0_^_</span> convolution </p>\n": "<p>\u521d\u59cb<span translate=no>_^_0_^_</span>\u5377\u79ef</p>\n",
|
||||
"<p>Initial group normalization </p>\n": "<p>\u521d\u59cb\u7fa4\u7ec4\u6807\u51c6\u5316</p>\n",
|
||||
"<p>Map to <span translate=no>_^_0_^_</span> with a linear layer </p>\n": "<p><span translate=no>_^_0_^_</span>\u4f7f\u7528\u7ebf\u6027\u56fe\u5c42\u6620\u5c04\u5230</p>\n",
|
||||
"<p>Normalize </p>\n": "<p>\u6807\u51c6\u5316</p>\n",
|
||||
"<p>Otherwise, fallback to normal attention </p>\n": "<p>\u5426\u5219\uff0c\u56de\u9000\u5230\u6b63\u5e38\u7684\u6ce8\u610f\u529b\u4e0a</p>\n",
|
||||
"<p>Pad the heads </p>\n": "<p>\u57ab\u4f4f\u5934\u90e8</p>\n",
|
||||
"<p>Query, key and value mappings </p>\n": "<p>\u67e5\u8be2\u3001\u952e\u548c\u503c\u6620\u5c04</p>\n",
|
||||
"<p>Reshape and transpose from <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u91cd\u5851\u5f62\u72b6\u5e76\u4ece\u53d8\u6362<span translate=no>_^_0_^_</span>\u4e3a<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Reshape to <span translate=no>_^_0_^_</span> </p>\n": "<p>\u91cd\u5851\u4e3a<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Self attention </p>\n": "<p>\u81ea\u6211\u6ce8\u610f\u529b</p>\n",
|
||||
"<p>Self-attention layer and pre-norm layer </p>\n": "<p>\u81ea\u6211\u6ce8\u610f\u529b\u5c42\u548c\u9884\u89c4\u8303\u5c42</p>\n",
|
||||
"<p>Set the scale for scaled dot-product attention. </p>\n": "<p>\u8bbe\u7f6e\u6309\u6bd4\u4f8b\u7f29\u653e\u70b9\u4ea7\u54c1\u6ce8\u610f\u529b\u7684\u6bd4\u4f8b\u3002</p>\n",
|
||||
"<p>Set to <span translate=no>_^_0_^_</span> if it's not installed </p>\n": "<p><span translate=no>_^_0_^_</span>\u5982\u679c\u672a\u5b89\u88c5\uff0c\u5219\u8bbe\u7f6e\u4e3a</p>\n",
|
||||
"<p>Setup <a href=\"https://github.com/HazyResearch/flash-attention\">flash attention</a>. Flash attention is only used if it's installed and <span translate=no>_^_0_^_</span> is set to <span translate=no>_^_1_^_</span>. </p>\n": "<p>\u8bbe\u7f6e<a href=\"https://github.com/HazyResearch/flash-attention\">\u95ea\u5149\u8b66\u793a</a>\u3002Flash \u6ce8\u610f\u53ea\u6709\u5728\u5b89\u88c5\u5e76\u8bbe\u7f6e<span translate=no>_^_0_^_</span>\u4e3a\u65f6\u624d\u4f1a\u4f7f\u7528<span translate=no>_^_1_^_</span>\u3002</p>\n",
|
||||
"<p>Split the heads </p>\n": "<p>\u5206\u5f00\u8111\u888b</p>\n",
|
||||
"<p>Split them to heads of shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5c06\u5b83\u4eec\u5206\u6210\u5f62\u72b6\u7684\u5934\u90e8<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Stack <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span>, <span translate=no>_^_2_^_</span> vectors for flash attention, to get a single tensor of shape <span translate=no>_^_3_^_</span> </p>\n": "<p>\u5806\u53e0<span translate=no>_^_0_^_</span>\u3001<span translate=no>_^_1_^_</span>\u3001<span translate=no>_^_2_^_</span>\u5411\u91cf\u4ee5\u83b7\u5f97\u95ea\u5149\u6ce8\u610f\u529b\uff0c\u4ee5\u83b7\u5f97\u5355\u4e2a\u5f62\u72b6\u5f20\u91cf<span translate=no>_^_3_^_</span></p>\n",
|
||||
"<p>Transformer layers </p>\n": "<p>\u53d8\u538b\u5668\u5c42</p>\n",
|
||||
"<p>Transpose and reshape from <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u4ece\u5230<span translate=no>_^_0_^_</span>\u8f6c\u7f6e\u548c\u91cd\u5851<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Truncate the extra head size </p>\n": "<p>\u622a\u65ad\u591a\u4f59\u7684\u5934\u90e8\u5c3a\u5bf8</p>\n",
|
||||
"<p>Use flash attention if it's available and the head size is less than or equal to <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5982\u679c\u95ea\u5149\u706f\u6ce8\u610f\u529b\u53ef\u7528\u4e14\u5934\u90e8\u5927\u5c0f\u5c0f\u4e8e\u6216\u7b49\u4e8e\uff0c\u8bf7\u4f7f\u7528\u95ea\u5149\u8b66\u793a<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>You can install flash attention by cloning their Github repo, <a href=\"https://github.com/HazyResearch/flash-attention\">https://github.com/HazyResearch/flash-attention</a> and then running <span translate=no>_^_0_^_</span> </p>\n": "<p>\u4f60\u53ef\u4ee5\u901a\u8fc7\u514b\u9686\u4ed6\u4eec\u7684 Github \u5b58\u50a8\u5e93 <a href=\"https://github.com/HazyResearch/flash-attention\">https://github.com/HazyResearch/flash-attention</a> \u7136\u540e\u8fd0\u884c\u6765\u5b89\u88c5 Flash \u6ce8\u610f\u529b<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> are the input embeddings of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings of shape <span translate=no>_^_3_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5f62\u72b6\u7684\u8f93\u5165\u5d4c\u5165<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u5f62\u72b6\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_3_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the feature map of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings of shape <span translate=no>_^_3_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5f62\u72b6\u7684\u7279\u5f81\u56fe<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u5f62\u72b6\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_3_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input embedding size </li>\n<li><span translate=no>_^_1_^_</span> is multiplicative factor for the hidden layer size</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u8f93\u5165\u5d4c\u5165\u5927\u5c0f</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u9690\u85cf\u5c42\u5927\u5c0f\u7684\u4e58\u6cd5\u56e0\u5b50</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input embedding size </li>\n<li><span translate=no>_^_1_^_</span> is the number of attention heads </li>\n<li><span translate=no>_^_2_^_</span> is the size of a attention head </li>\n<li><span translate=no>_^_3_^_</span> is the size of the conditional embeddings </li>\n<li><span translate=no>_^_4_^_</span> specifies whether to perform the attention softmax computation inplace to save memory</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u8f93\u5165\u5d4c\u5165\u5927\u5c0f</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u6ce8\u610f\u529b\u5934\u7684\u6570\u91cf</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u6ce8\u610f\u529b\u5934\u7684\u5927\u5c0f</li>\n<li><span translate=no>_^_3_^_</span>\u662f\u6761\u4ef6\u5d4c\u5165\u7684\u5927\u5c0f</li>\n<li><span translate=no>_^_4_^_</span>\u6307\u5b9a\u662f\u5426\u5c31\u5730\u6267\u884c\u6ce8\u610f\u529b softmax \u8ba1\u7b97\u4ee5\u8282\u7701\u5185\u5b58</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the input embedding size </li>\n<li><span translate=no>_^_1_^_</span> is the number of attention heads </li>\n<li><span translate=no>_^_2_^_</span> is the size of a attention head </li>\n<li><span translate=no>_^_3_^_</span> is the size of the conditional embeddings</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u8f93\u5165\u5d4c\u5165\u5927\u5c0f</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u6ce8\u610f\u529b\u5934\u7684\u6570\u91cf</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u6ce8\u610f\u529b\u5934\u7684\u5927\u5c0f</li>\n<li><span translate=no>_^_3_^_</span>\u662f\u6761\u4ef6\u5d4c\u5165\u7684\u5927\u5c0f</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the number of channels in the feature map </li>\n<li><span translate=no>_^_1_^_</span> is the number of attention heads </li>\n<li><span translate=no>_^_2_^_</span> is the number of transformer layers </li>\n<li><span translate=no>_^_3_^_</span> is the size of the conditional embedding</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u529f\u80fd\u56fe\u4e2d\u7684\u9891\u9053\u6570</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u6ce8\u610f\u529b\u5934\u7684\u6570\u91cf</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u53d8\u538b\u5668\u5c42\u6570</li>\n<li><span translate=no>_^_3_^_</span>\u662f\u6761\u4ef6\u5d4c\u5165\u7684\u5927\u5c0f</li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of the transformer for U-Net in stable diffusion.": "\u5e26\u6ce8\u91ca\u7684 U-Net \u7a33\u5b9a\u6269\u6563\u53d8\u538b\u5668\u7684 PyTorch \u5b9e\u73b0/\u6559\u7a0b\u3002",
|
||||
"Transformer for Stable Diffusion U-Net": "\u7528\u4e8e\u7a33\u5b9a\u6269\u6563 U-Net \u7684\u53d8\u538b\u5668"
|
||||
}
|
||||
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"<h1>Sampling algorithms for <a href=\"../index.html\">stable diffusion</a></h1>\n<p>We have implemented the following <a href=\"sampler/index.html\">sampling algorithms</a>:</p>\n<ul><li><a href=\"ddpm.html\">Denoising Diffusion Probabilistic Models (DDPM) Sampling</a> </li>\n<li><a href=\"ddim.html\">Denoising Diffusion Implicit Models (DDIM) Sampling</a></li></ul>\n": "<h1><a href=\"../index.html\">\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u306e\u305f\u3081\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0</a></h1>\n<p><a href=\"sampler/index.html\">\u4ee5\u4e0b\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u5b9f\u88c5\u3057\u307e\u3057\u305f</a>\u3002</p>\n<ul><li><a href=\"ddpm.html\">\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u78ba\u7387\u30e2\u30c7\u30eb (DDPM) \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0</a></li>\n</ul><li><a href=\"ddim.html\">\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u6697\u9ed9\u30e2\u30c7\u30eb (DDIM) \u30b5\u30f3\u30d7\u30ea\u30b7\u30c3\u30c8\u30b5\u30f3\u30d7\u30ea\u30b7\u30c3\u30c8</a></li>\n",
|
||||
"<h2>Base class for sampling algorithms</h2>\n": "<h2>\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u57fa\u672c\u30af\u30e9\u30b9</h2>\n",
|
||||
"<h2>Get <span translate=no>_^_0_^_</span></h2>\n<ul><li><span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is <span translate=no>_^_5_^_</span> of shape <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is the conditional embeddings <span translate=no>_^_8_^_</span> of shape <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the unconditional guidance scale <span translate=no>_^_11_^_</span>. This is used for <span translate=no>_^_12_^_</span> </li>\n<li><span translate=no>_^_13_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_14_^_</span></li></ul>\n": "<h2>\u53d6\u5f97 <span translate=no>_^_0_^_</span></h2>\n<ul><li><span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span><span translate=no>_^_5_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span><span translate=no>_^_8_^_</span>\u5f62\u72b6\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059 <span translate=no>_^_11_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_12_^_</span></li>\n<li><span translate=no>_^_13_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_14_^_</span></li></ul>\n",
|
||||
"<h3>Painting Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is <span translate=no>_^_1_^_</span> of shape <span translate=no>_^_2_^_</span> </li>\n<li><span translate=no>_^_3_^_</span> is the conditional embeddings <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the sampling step to start from, <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is the original image in latent page which we are in paining. </li>\n<li><span translate=no>_^_8_^_</span> is the mask to keep the original image. </li>\n<li><span translate=no>_^_9_^_</span> is fixed noise to be added to the original image. </li>\n<li><span translate=no>_^_10_^_</span> is the unconditional guidance scale <span translate=no>_^_11_^_</span>. This is used for <span translate=no>_^_12_^_</span> </li>\n<li><span translate=no>_^_13_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_14_^_</span></li></ul>\n": "<h3>\u30da\u30a4\u30f3\u30c6\u30a3\u30f3\u30b0\u30eb\u30fc\u30d7</h3>\n<ul><li><span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_2_^_</span></li>\n<li><span translate=no>_^_3_^_</span>\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u958b\u59cb\u3059\u308b\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30b9\u30c6\u30c3\u30d7\u3067\u3059 <span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span>\u73fe\u5728\u30da\u30a4\u30f3\u30c8\u4e2d\u306e\u6f5c\u5728\u30da\u30fc\u30b8\u306e\u30aa\u30ea\u30b8\u30ca\u30eb\u753b\u50cf\u3067\u3059\u3002</li>\n<li><span translate=no>_^_8_^_</span>\u5143\u306e\u753b\u50cf\u3092\u6b8b\u3059\u305f\u3081\u306e\u30de\u30b9\u30af\u3067\u3059\u3002</li>\n<li><span translate=no>_^_9_^_</span>\u5143\u306e\u753b\u50cf\u306b\u8ffd\u52a0\u3055\u308c\u308b\u56fa\u5b9a\u30ce\u30a4\u30ba\u3067\u3059\u3002</li>\n<li><span translate=no>_^_10_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059 <span translate=no>_^_11_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_12_^_</span></li>\n<li><span translate=no>_^_13_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_14_^_</span></li></ul>\n",
|
||||
"<h3>Sample from <span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the time step <span translate=no>_^_5_^_</span> index </li>\n<li><span translate=no>_^_6_^_</span> is the noise, <span translate=no>_^_7_^_</span></li></ul>\n": "<h3>\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb <span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span><span translate=no>_^_5_^_</span>\u306f\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u30a4\u30f3\u30c7\u30c3\u30af\u30b9</li>\n<li><span translate=no>_^_6_^_</span>\u30ce\u30a4\u30ba\u306f\u3001<span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"<h3>Sampling Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the shape of the generated images in the form <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_5_^_</span> is <span translate=no>_^_6_^_</span>. If not provided random noise will be used. </li>\n<li><span translate=no>_^_7_^_</span> is the unconditional guidance scale <span translate=no>_^_8_^_</span>. This is used for <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_11_^_</span> </li>\n<li><span translate=no>_^_12_^_</span> is the number of time steps to skip.</li></ul>\n": "<h3>\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30eb\u30fc\u30d7</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u30d5\u30a9\u30fc\u30e0\u3067\u751f\u6210\u3055\u308c\u305f\u30a4\u30e1\u30fc\u30b8\u306e\u5f62\u72b6\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u306f\u30ce\u30a4\u30ba\u6e29\u5ea6 (\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba\u306b\u3053\u308c\u3092\u639b\u3051\u307e\u3059)</li>\n<li><span translate=no>_^_5_^_</span>\u3067\u3059<span translate=no>_^_6_^_</span>\u3002\u6307\u5b9a\u3057\u306a\u3044\u5834\u5408\u306f\u3001\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba\u304c\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002</li>\n<li><span translate=no>_^_7_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059 <span translate=no>_^_8_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_11_^_</span></li>\n<li><span translate=no>_^_12_^_</span>\u30b9\u30ad\u30c3\u30d7\u3059\u308b\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u306e\u6570\u3067\u3059\u3002</li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8a08\u7b97 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Concatenated <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u9023\u7d50\u3068 <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Duplicate <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u8907\u88fd\u3068 <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u53d6\u5f97\u3057\u3066 <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get number of steps the model was trained with <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u4f7f\u7528\u3057\u305f\u30b9\u30c6\u30c3\u30d7\u6570\u3092\u53d6\u5f97 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Set the model <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u8a2d\u5b9a\u3059\u308b <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>When the scale <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span> </p>\n": "<p>\u4f53\u91cd\u8a08\u306e\u3068\u304d <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model to predict noise <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u30ce\u30a4\u30ba\u3092\u4e88\u6e2c\u3059\u308b\u30e2\u30c7\u30eb\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of sampling algorithms for stable diffusion model.": "\u5b89\u5b9a\u62e1\u6563\u30e2\u30c7\u30eb\u7528\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u6ce8\u91c8\u4ed8\u304dPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3002",
|
||||
"Sampling algorithms for stable diffusion": "\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u306e\u305f\u3081\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0"
|
||||
}
|
||||
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"<h1>Sampling algorithms for <a href=\"../index.html\">stable diffusion</a></h1>\n<p>We have implemented the following <a href=\"sampler/index.html\">sampling algorithms</a>:</p>\n<ul><li><a href=\"ddpm.html\">Denoising Diffusion Probabilistic Models (DDPM) Sampling</a> </li>\n<li><a href=\"ddim.html\">Denoising Diffusion Implicit Models (DDIM) Sampling</a></li></ul>\n": "<h1><a href=\"../index.html\">\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba</a> \u0dc3\u0db3\u0dc4\u0dcf \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d87\u0dbd\u0dca\u0d9c\u0ddc\u0dbb\u0dd2\u0dad\u0db8</h1>\n<p>\u0d85\u0db4\u0dd2 \u0db4\u0dc4\u0dad \u0dc3\u0db3\u0dc4\u0db1\u0dca <a href=\"sampler/index.html\">\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d87\u0dbd\u0dca\u0d9c\u0ddc\u0dbb\u0dd2\u0dad\u0db8</a> \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dbb \u0d87\u0dad:</p>\n<ul><li><a href=\"ddpm.html\">Denoising \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db8\u0dca\u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf\u0dc0 \u0d86\u0d9a\u0dd8\u0dad\u0dd2 (DDPM) \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8</a></li>\n<li><a href=\"ddim.html\">Denoising \u0dc0\u0dd2\u0dc3\u0dbb\u0dab \u0dc0\u0dca\u0dba\u0d82\u0d9c \u0d86\u0d9a\u0dd8\u0dad\u0dd2 (DDIM) \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8</a></li></ul>\n",
|
||||
"<h2>Base class for sampling algorithms</h2>\n": "<h2>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d87\u0dbd\u0dca\u0d9c\u0ddc\u0dbb\u0dd2\u0dad\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0db8\u0dd6\u0dbd\u0dd2\u0d9a \u0db4\u0db1\u0dca\u0dad\u0dd2\u0dba</h2>\n",
|
||||
"<h2>Get <span translate=no>_^_0_^_</span></h2>\n<ul><li><span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is <span translate=no>_^_5_^_</span> of shape <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is the conditional embeddings <span translate=no>_^_8_^_</span> of shape <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the unconditional guidance scale <span translate=no>_^_11_^_</span>. This is used for <span translate=no>_^_12_^_</span> </li>\n<li><span translate=no>_^_13_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_14_^_</span></li></ul>\n": "<h2>\u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></h2>\n<ul><li><span translate=no>_^_1_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_2_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_5_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad<span translate=no>_^_8_^_</span> \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_11_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_12_^_</span></li>\n<li><span translate=no>_^_13_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc0\u0dda<span translate=no>_^_14_^_</span></li></ul>\n",
|
||||
"<h3>Painting Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is <span translate=no>_^_1_^_</span> of shape <span translate=no>_^_2_^_</span> </li>\n<li><span translate=no>_^_3_^_</span> is the conditional embeddings <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the sampling step to start from, <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is the original image in latent page which we are in paining. </li>\n<li><span translate=no>_^_8_^_</span> is the mask to keep the original image. </li>\n<li><span translate=no>_^_9_^_</span> is fixed noise to be added to the original image. </li>\n<li><span translate=no>_^_10_^_</span> is the unconditional guidance scale <span translate=no>_^_11_^_</span>. This is used for <span translate=no>_^_12_^_</span> </li>\n<li><span translate=no>_^_13_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_14_^_</span></li></ul>\n": "<h3>\u0db4\u0dd2\u0db1\u0dca\u0dad\u0dcf\u0dbb\u0dd4 \u0dbd\u0dd6\u0db4</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_1_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_2_^_</span></li>\n<li><span translate=no>_^_3_^_</span>\u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u0dc3\u0dd2\u0da7 \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0dc0\u0dda,<span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span>\u0dba\u0db1\u0dd4 \u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba\u0dba\u0dd2 \u0d9c\u0dd4\u0db4\u0dca\u0dad \u0db4\u0dd2\u0da7\u0dd4\u0dc0 \u0d85\u0db4\u0dd2 \u0db4\u0dd0\u0dbd\u0dca\u0dbd\u0db8\u0dca \u0d9a\u0dbb\u0db1.</li>\n<li><span translate=no>_^_8_^_</span>\u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba \u0dad\u0db6\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0dd9\u0dc3\u0dca\u0db8\u0dd4\u0dc4\u0dd4\u0dab \u0dc0\u0dda.</li>\n<li><span translate=no>_^_9_^_</span>\u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba\u0da7 \u0d91\u0d9a\u0dad\u0dd4 \u0d9a\u0dc5 \u0dba\u0dd4\u0dad\u0dd4 \u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc1\u0db6\u0dca\u0daf\u0dba.</li>\n<li><span translate=no>_^_10_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_11_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_12_^_</span></li>\n<li><span translate=no>_^_13_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc0\u0dda<span translate=no>_^_14_^_</span></li></ul>\n",
|
||||
"<h3>Sample from <span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the time step <span translate=no>_^_5_^_</span> index </li>\n<li><span translate=no>_^_6_^_</span> is the noise, <span translate=no>_^_7_^_</span></li></ul>\n": "<h3>\u0dc0\u0dd9\u0dad\u0dd2\u0db1\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_2_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb<span translate=no>_^_5_^_</span> \u0daf\u0dbb\u0dca\u0dc1\u0d9a\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_6_^_</span>\u0dc1\u0db6\u0dca\u0daf\u0dba,<span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"<h3>Sampling Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the shape of the generated images in the form <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_5_^_</span> is <span translate=no>_^_6_^_</span>. If not provided random noise will be used. </li>\n<li><span translate=no>_^_7_^_</span> is the unconditional guidance scale <span translate=no>_^_8_^_</span>. This is used for <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_11_^_</span> </li>\n<li><span translate=no>_^_12_^_</span> is the number of time steps to skip.</li></ul>\n": "<h3>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0dbd\u0dd6\u0db4</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc3\u0dca\u0dc0\u0dbb\u0dd6\u0db4\u0dba\u0dd9\u0db1\u0dca \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0dbb\u0dd6\u0db4\u0dc0\u0dbd \u0dc4\u0dd0\u0da9\u0dba<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u0dba\u0db1\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba\u0dda \u0d8b\u0dc2\u0dca\u0dab\u0dad\u0dca\u0dc0\u0dba (\u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db8\u0dd9\u0dba\u0dd2\u0db1\u0dca \u0d9c\u0dd4\u0dab \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0dd0\u0db6\u0dda)</li>\n<li><span translate=no>_^_5_^_</span>\u0dc0\u0dda<span translate=no>_^_6_^_</span>. \u0dc3\u0db4\u0dba\u0dcf \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2 \u0db1\u0db8\u0dca \u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1\u0dd4 \u0d87\u0dad.</li>\n<li><span translate=no>_^_7_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_8_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc0\u0dda<span translate=no>_^_11_^_</span></li>\n<li><span translate=no>_^_12_^_</span>\u0db8\u0d9f \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0da7 \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0dc0\u0dda.</li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Concatenated <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0dc3\u0d82\u0dba\u0dd4\u0d9a\u0dca\u0dad<span translate=no>_^_0_^_</span> \u0dc3\u0dc4<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Duplicate <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0d85\u0db1\u0dd4\u0db4\u0dd2\u0da7\u0db4\u0dad\u0dca<span translate=no>_^_0_^_</span> \u0dc3\u0dc4<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0dbd\u0db6\u0dcf<span translate=no>_^_0_^_</span> \u0d9c\u0db1\u0dca\u0db1<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get number of steps the model was trained with <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0db4\u0dd4\u0dc4\u0dd4\u0dab\u0dd4 \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Set the model <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>When the scale <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba \u0dc0\u0dd2\u0da7<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model to predict noise <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc1\u0db6\u0dca\u0daf\u0dba \u0db4\u0dd4\u0dbb\u0ddd\u0d9a\u0dae\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0dba\u0dd2<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of sampling algorithms for stable diffusion model.": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d87\u0dbd\u0dca\u0d9c\u0ddc\u0dbb\u0dd2\u0dad\u0db8 PyTorch \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8/\u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1\u0dba.",
|
||||
"Sampling algorithms for stable diffusion": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d87\u0dbd\u0dca\u0d9c\u0ddc\u0dbb\u0dd2\u0dad\u0db8"
|
||||
}
|
||||
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"<h1>Sampling algorithms for <a href=\"../index.html\">stable diffusion</a></h1>\n<p>We have implemented the following <a href=\"sampler/index.html\">sampling algorithms</a>:</p>\n<ul><li><a href=\"ddpm.html\">Denoising Diffusion Probabilistic Models (DDPM) Sampling</a> </li>\n<li><a href=\"ddim.html\">Denoising Diffusion Implicit Models (DDIM) Sampling</a></li></ul>\n": "<h1>\u7528\u4e8e<a href=\"../index.html\">\u7a33\u5b9a\u6269\u6563</a>\u7684\u91c7\u6837\u7b97\u6cd5</h1>\n<p>\u6211\u4eec\u5df2\u7ecf\u5b9e\u73b0\u4e86\u4ee5\u4e0b<a href=\"sampler/index.html\">\u91c7\u6837\u7b97\u6cd5</a>\uff1a</p>\n<ul><li><a href=\"ddpm.html\">\u53bb\u566a\u6269\u6563\u6982\u7387\u6a21\u578b (DDPM) \u91c7\u6837</a></li>\n<li><a href=\"ddim.html\">\u964d\u566a\u6269\u6563\u9690\u542b\u6a21\u578b (DDIM) \u91c7\u6837</a></li></ul>\n",
|
||||
"<h2>Base class for sampling algorithms</h2>\n": "<h2>\u91c7\u6837\u7b97\u6cd5\u7684\u57fa\u7c7b</h2>\n",
|
||||
"<h2>Get <span translate=no>_^_0_^_</span></h2>\n<ul><li><span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is <span translate=no>_^_5_^_</span> of shape <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is the conditional embeddings <span translate=no>_^_8_^_</span> of shape <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the unconditional guidance scale <span translate=no>_^_11_^_</span>. This is used for <span translate=no>_^_12_^_</span> </li>\n<li><span translate=no>_^_13_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_14_^_</span></li></ul>\n": "<h2>\u83b7\u53d6<span translate=no>_^_0_^_</span></h2>\n<ul><li><span translate=no>_^_1_^_</span>\u662f\u5f62<span translate=no>_^_2_^_</span>\u72b6\u7684<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u662f\u5f62<span translate=no>_^_5_^_</span>\u72b6\u7684<span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span>\u662f\u5f62\u72b6<span translate=no>_^_8_^_</span>\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_11_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_12_^_</span></li>\n<li><span translate=no>_^_13_^_</span>\u662f\u7a7a\u63d0\u793a\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_14_^_</span></li></ul>\n",
|
||||
"<h3>Painting Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is <span translate=no>_^_1_^_</span> of shape <span translate=no>_^_2_^_</span> </li>\n<li><span translate=no>_^_3_^_</span> is the conditional embeddings <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the sampling step to start from, <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is the original image in latent page which we are in paining. </li>\n<li><span translate=no>_^_8_^_</span> is the mask to keep the original image. </li>\n<li><span translate=no>_^_9_^_</span> is fixed noise to be added to the original image. </li>\n<li><span translate=no>_^_10_^_</span> is the unconditional guidance scale <span translate=no>_^_11_^_</span>. This is used for <span translate=no>_^_12_^_</span> </li>\n<li><span translate=no>_^_13_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_14_^_</span></li></ul>\n": "<h3>\u7ed8\u753b\u5faa\u73af</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u5f62<span translate=no>_^_1_^_</span>\u72b6\u7684<span translate=no>_^_2_^_</span></li>\n<li><span translate=no>_^_3_^_</span>\u662f\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u662f\u5f00\u59cb\u65f6\u7684\u91c7\u6837\u6b65\u9aa4\uff0c<span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span>\u662f\u6211\u4eec\u6b63\u5728\u7ed8\u5236\u7684\u6f5c\u5728\u9875\u9762\u4e2d\u7684\u539f\u59cb\u56fe\u50cf\u3002</li>\n<li><span translate=no>_^_8_^_</span>\u662f\u4fdd\u7559\u539f\u59cb\u56fe\u50cf\u7684\u63a9\u7801\u3002</li>\n<li><span translate=no>_^_9_^_</span>\u662f\u8981\u6dfb\u52a0\u5230\u539f\u59cb\u56fe\u50cf\u7684\u56fa\u5b9a\u566a\u70b9\u3002</li>\n<li><span translate=no>_^_10_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_11_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_12_^_</span></li>\n<li><span translate=no>_^_13_^_</span>\u662f\u7a7a\u63d0\u793a\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_14_^_</span></li></ul>\n",
|
||||
"<h3>Sample from <span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the time step <span translate=no>_^_5_^_</span> index </li>\n<li><span translate=no>_^_6_^_</span> is the noise, <span translate=no>_^_7_^_</span></li></ul>\n": "<h3>\u6837\u672c\u6765\u81ea<span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span>\u662f\u5f62<span translate=no>_^_2_^_</span>\u72b6\u7684<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u662f\u65f6\u95f4\u6b65\u957f<span translate=no>_^_5_^_</span>\u6307\u6570</li>\n<li><span translate=no>_^_6_^_</span>\u662f\u566a\u97f3\uff0c<span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"<h3>Sampling Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the shape of the generated images in the form <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_5_^_</span> is <span translate=no>_^_6_^_</span>. If not provided random noise will be used. </li>\n<li><span translate=no>_^_7_^_</span> is the unconditional guidance scale <span translate=no>_^_8_^_</span>. This is used for <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_11_^_</span> </li>\n<li><span translate=no>_^_12_^_</span> is the number of time steps to skip.</li></ul>\n": "<h3>\u91c7\u6837\u56de\u8def</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u8868\u5355\u4e2d\u751f\u6210\u7684\u56fe\u50cf\u7684\u5f62\u72b6<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u662f\u566a\u58f0\u6e29\u5ea6\uff08\u968f\u673a\u566a\u58f0\u4e58\u4ee5\u6b64\u503c\uff09</li>\n<li><span translate=no>_^_5_^_</span>\u662f<span translate=no>_^_6_^_</span>\u3002\u5982\u679c\u672a\u63d0\u4f9b\uff0c\u5c06\u4f7f\u7528\u968f\u673a\u566a\u58f0\u3002</li>\n<li><span translate=no>_^_7_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_8_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u662f\u7a7a\u63d0\u793a\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_11_^_</span></li>\n<li><span translate=no>_^_12_^_</span>\u662f\u8981\u8df3\u8fc7\u7684\u65f6\u95f4\u6b65\u6570\u3002</li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8ba1\u7b97<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Concatenated <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u4e32\u8054<span translate=no>_^_0_^_</span>\u548c<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Duplicate <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u590d\u5236<span translate=no>_^_0_^_</span>\u548c<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u83b7\u53d6<span translate=no>_^_0_^_</span>\u548c<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get number of steps the model was trained with <span translate=no>_^_0_^_</span> </p>\n": "<p>\u83b7\u53d6\u6a21\u578b\u8bad\u7ec3\u7684\u6b65\u6570<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Set the model <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8bbe\u7f6e\u6a21\u578b<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>When the scale <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span> </p>\n": "<p>\u5f53\u4f53\u91cd\u79e4\u65f6<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model to predict noise <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u9884\u6d4b\u566a\u58f0\u7684\u6a21\u578b<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of sampling algorithms for stable diffusion model.": "\u5e26\u6ce8\u91ca\u7684 PyTorch \u5b9e\u73b0/\u7a33\u5b9a\u6269\u6563\u6a21\u578b\u91c7\u6837\u7b97\u6cd5\u6559\u7a0b\u3002",
|
||||
"Sampling algorithms for stable diffusion": "\u7528\u4e8e\u7a33\u5b9a\u6269\u6563\u7684\u91c7\u6837\u7b97\u6cd5"
|
||||
}
|
||||
@@ -0,0 +1,38 @@
|
||||
{
|
||||
"<h1>Denoising Diffusion Implicit Models (DDIM) Sampling</h1>\n<p>This implements DDIM sampling from the paper <a href=\"https://arxiv.org/abs/2010.02502\">Denoising Diffusion Implicit Models</a></p>\n": "<h1>\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u6697\u9ed9\u30e2\u30c7\u30eb (DDIM) \u30b5\u30f3\u30d7\u30ea\u30b7\u30c3\u30c8\u30b5\u30f3\u30d7\u30ea\u30b7\u30c3\u30c8</h1>\n<p>\u3053\u308c\u306f\u3001\u8ad6\u6587\u300c<a href=\"https://arxiv.org/abs/2010.02502\">\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u6697\u9ed9\u30e2\u30c7\u30eb</a>\u300d\u304b\u3089\u306eDDIM\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u5b9f\u88c5\u3057\u3066\u3044\u307e\u3059\u3002</p>\n",
|
||||
"<h2>DDIM Sampler</h2>\n<p>This extends the <a href=\"index.html\"><span translate=no>_^_0_^_</span> base class</a>.</p>\n<p>DDPM samples images by repeatedly removing noise by sampling step by step using,</p>\n<span translate=no>_^_1_^_</span><p>where <span translate=no>_^_2_^_</span> is random noise, <span translate=no>_^_3_^_</span> is a subsequence of <span translate=no>_^_4_^_</span> of length <span translate=no>_^_5_^_</span>, and <span translate=no>_^_6_^_</span></p>\n<p>Note that, <span translate=no>_^_7_^_</span> in DDIM paper refers to <span translate=no>_^_8_^_</span> from <a href=\"ddpm.html\">DDPM</a>.</p>\n": "<h2>DDIM \u30b5\u30f3\u30d7\u30e9\u30fc</h2>\n<p><a href=\"index.html\"><span translate=no>_^_0_^_</span>\u3053\u308c\u306f\u57fa\u672c\u30af\u30e9\u30b9\u3092\u62e1\u5f35\u3057\u307e\u3059</a>\u3002</p>\n<p>DDPM\u306f\u3001\u4ee5\u4e0b\u3092\u4f7f\u7528\u3057\u3066\u6bb5\u968e\u7684\u306b\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3059\u308b\u3053\u3068\u306b\u3088\u308a\u3001\u30ce\u30a4\u30ba\u3092\u7e70\u308a\u8fd4\u3057\u9664\u53bb\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u753b\u50cf\u3092\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u307e\u3059\u3002</p>\n<span translate=no>_^_1_^_</span><p>\u3053\u3053\u3067\u3001<span translate=no>_^_2_^_</span>\u306f\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba\u3001<span translate=no>_^_3_^_</span><span translate=no>_^_4_^_</span>\u306f\u9577\u3055\u306e\u30b5\u30d6\u30b7\u30fc\u30b1\u30f3\u30b9<span translate=no>_^_5_^_</span>\u3001<span translate=no>_^_6_^_</span></p>\n<p><a href=\"ddpm.html\">\u306a\u304a\u3001<span translate=no>_^_7_^_</span> <span translate=no>_^_8_^_</span> DDIM\u306e\u8ad6\u6587\u3067\u306fDDPM\u306e\u3082\u306e\u3092\u6307\u3057\u3066\u3044\u307e\u3059\u3002</a></p>\n",
|
||||
"<h3>Painting Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is <span translate=no>_^_1_^_</span> of shape <span translate=no>_^_2_^_</span> </li>\n<li><span translate=no>_^_3_^_</span> is the conditional embeddings <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the sampling step to start from, <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is the original image in latent page which we are in paining. If this is not provided, it'll be an image to image transformation. </li>\n<li><span translate=no>_^_8_^_</span> is the mask to keep the original image. </li>\n<li><span translate=no>_^_9_^_</span> is fixed noise to be added to the original image. </li>\n<li><span translate=no>_^_10_^_</span> is the unconditional guidance scale <span translate=no>_^_11_^_</span>. This is used for <span translate=no>_^_12_^_</span> </li>\n<li><span translate=no>_^_13_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_14_^_</span></li></ul>\n": "<h3>\u30da\u30a4\u30f3\u30c6\u30a3\u30f3\u30b0\u30eb\u30fc\u30d7</h3>\n<ul><li><span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_2_^_</span></li>\n<li><span translate=no>_^_3_^_</span>\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u958b\u59cb\u3059\u308b\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30b9\u30c6\u30c3\u30d7\u3067\u3059 <span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span>\u73fe\u5728\u30da\u30a4\u30f3\u30c8\u4e2d\u306e\u6f5c\u5728\u30da\u30fc\u30b8\u306e\u30aa\u30ea\u30b8\u30ca\u30eb\u753b\u50cf\u3067\u3059\u3002\u3053\u308c\u304c\u6307\u5b9a\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408\u306f\u3001\u753b\u50cf\u304b\u3089\u753b\u50cf\u3078\u306e\u5909\u63db\u306b\u306a\u308a\u307e\u3059\u3002</li>\n<li><span translate=no>_^_8_^_</span>\u5143\u306e\u753b\u50cf\u3092\u6b8b\u3059\u305f\u3081\u306e\u30de\u30b9\u30af\u3067\u3059\u3002</li>\n<li><span translate=no>_^_9_^_</span>\u5143\u306e\u753b\u50cf\u306b\u8ffd\u52a0\u3055\u308c\u308b\u56fa\u5b9a\u30ce\u30a4\u30ba\u3067\u3059\u3002</li>\n<li><span translate=no>_^_10_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059 <span translate=no>_^_11_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_12_^_</span></li>\n<li><span translate=no>_^_13_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_14_^_</span></li></ul>\n",
|
||||
"<h3>Sample <span translate=no>_^_0_^_</span> given <span translate=no>_^_1_^_</span></h3>\n": "<h3><span translate=no>_^_0_^_</span>\u30b5\u30f3\u30d7\u30eb\u63d0\u4f9b <span translate=no>_^_1_^_</span></h3>\n",
|
||||
"<h3>Sample <span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the conditional embeddings <span translate=no>_^_5_^_</span> of shape <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is <span translate=no>_^_8_^_</span> of shape <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the step <span translate=no>_^_11_^_</span> as an integer </li>\n<li><span translate=no>_^_12_^_</span> is index <span translate=no>_^_13_^_</span> in the list <span translate=no>_^_14_^_</span> </li>\n<li><span translate=no>_^_15_^_</span> specified whether the noise should be same for all samples in the batch </li>\n<li><span translate=no>_^_16_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_17_^_</span> is the unconditional guidance scale <span translate=no>_^_18_^_</span>. This is used for <span translate=no>_^_19_^_</span> </li>\n<li><span translate=no>_^_20_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_21_^_</span></li></ul>\n": "<h3>[\u30b5\u30f3\u30d7\u30eb] <span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span><span translate=no>_^_5_^_</span>\u5f62\u72b6\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span><span translate=no>_^_8_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span><span translate=no>_^_11_^_</span>\u306f\u30b9\u30c6\u30c3\u30d7\u3092\u6574\u6570\u3067\u8868\u3057\u305f\u3082\u306e\u3067\u3059</li>\n<li><span translate=no>_^_12_^_</span><span translate=no>_^_13_^_</span>\u30ea\u30b9\u30c8\u5185\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3067\u3059 <span translate=no>_^_14_^_</span></li>\n<li><span translate=no>_^_15_^_</span>\u30d0\u30c3\u30c1\u5185\u306e\u3059\u3079\u3066\u306e\u30b5\u30f3\u30d7\u30eb\u3067\u30ce\u30a4\u30ba\u3092\u540c\u3058\u306b\u3059\u308b\u304b\u3069\u3046\u304b\u3092\u6307\u5b9a\u3057\u307e\u3057\u305f</li>\n<li><span translate=no>_^_16_^_</span>\u306f\u30ce\u30a4\u30ba\u6e29\u5ea6 (\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba\u306b\u3053\u308c\u3092\u639b\u3051\u307e\u3059)</li>\n<li><span translate=no>_^_17_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059 <span translate=no>_^_18_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_19_^_</span></li>\n<li><span translate=no>_^_20_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_21_^_</span></li></ul>\n",
|
||||
"<h3>Sample from <span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span> is <span translate=no>_^_3_^_</span> of shape <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the time step <span translate=no>_^_6_^_</span> index <span translate=no>_^_7_^_</span> </li>\n<li><span translate=no>_^_8_^_</span> is the noise, <span translate=no>_^_9_^_</span></li></ul>\n": "<h3>\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb <span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span><span translate=no>_^_3_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span><span translate=no>_^_6_^_</span>\u306f\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u30a4\u30f3\u30c7\u30c3\u30af\u30b9 <span translate=no>_^_7_^_</span></li>\n<li><span translate=no>_^_8_^_</span>\u30ce\u30a4\u30ba\u306f\u3001<span translate=no>_^_9_^_</span></li></ul>\n",
|
||||
"<h3>Sampling Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the shape of the generated images in the form <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_5_^_</span> is <span translate=no>_^_6_^_</span>. If not provided random noise will be used. </li>\n<li><span translate=no>_^_7_^_</span> is the unconditional guidance scale <span translate=no>_^_8_^_</span>. This is used for <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_11_^_</span> </li>\n<li><span translate=no>_^_12_^_</span> is the number of time steps to skip <span translate=no>_^_13_^_</span>. We start sampling from <span translate=no>_^_14_^_</span>. And <span translate=no>_^_15_^_</span> is then <span translate=no>_^_16_^_</span>.</li></ul>\n": "<h3>\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30eb\u30fc\u30d7</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u30d5\u30a9\u30fc\u30e0\u3067\u751f\u6210\u3055\u308c\u305f\u30a4\u30e1\u30fc\u30b8\u306e\u5f62\u72b6\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u306f\u30ce\u30a4\u30ba\u6e29\u5ea6 (\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba\u306b\u3053\u308c\u3092\u639b\u3051\u307e\u3059)</li>\n<li><span translate=no>_^_5_^_</span>\u3067\u3059<span translate=no>_^_6_^_</span>\u3002\u6307\u5b9a\u3057\u306a\u3044\u5834\u5408\u306f\u3001\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba\u304c\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002</li>\n<li><span translate=no>_^_7_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059\u3002<span translate=no>_^_8_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_11_^_</span></li>\n<li><span translate=no>_^_12_^_</span><span translate=no>_^_13_^_</span>\u30b9\u30ad\u30c3\u30d7\u3059\u308b\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u306e\u6570\u3067\u3059\u3002\u304b\u3089\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u958b\u59cb\u3057\u307e\u3059<span translate=no>_^_14_^_</span>\u3002\u305d\u3057\u3066\u3001<span translate=no>_^_15_^_</span>\u305d\u306e\u6642\u3067\u3059<span translate=no>_^_16_^_</span>\u3002</li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> and predicted <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u8a08\u7b97\u3068\u4e88\u6e2c <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> to be quadratically distributed across <span translate=no>_^_1_^_</span> </p>\n": "<p>2 <span translate=no>_^_0_^_</span> \u6b21\u5206\u5e03\u306b\u306a\u308b\u3088\u3046\u306b\u8a08\u7b97\u3059\u308b <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> to be uniformly distributed across <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u5168\u4f53\u306b\u5747\u7b49\u306b\u5206\u6563\u3059\u308b\u3088\u3046\u306b\u8a08\u7b97 <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Current prediction for <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u306e\u73fe\u5728\u306e\u4e88\u6e2c <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Different noise for each sample </p>\n": "<p>\u30b5\u30f3\u30d7\u30eb\u3054\u3068\u306b\u7570\u306a\u308b\u30ce\u30a4\u30ba</p>\n",
|
||||
"<p>Direction pointing to <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span> </p>\n": "<p>\u3092\u6307\u3059\u65b9\u5411 <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> </p>\n": "<p>\u53d6\u5f97 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Get batch size </p>\n": "<p>\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Get device and batch size </p>\n": "<p>\u30c7\u30d0\u30a4\u30b9\u3068\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u306e\u53d6\u5f97</p>\n",
|
||||
"<p>Get the <span translate=no>_^_0_^_</span> for original image in latent space </p>\n": "<p><span translate=no>_^_0_^_</span>\u6f5c\u5728\u7a7a\u9593\u306e\u30aa\u30ea\u30b8\u30ca\u30eb\u753b\u50cf\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>If same noise is used for all samples in the batch </p>\n": "<p>\u30d0\u30c3\u30c1\u5185\u306e\u3059\u3079\u3066\u306e\u30b5\u30f3\u30d7\u30eb\u306b\u540c\u3058\u30ce\u30a4\u30ba\u304c\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u5834\u5408</p>\n",
|
||||
"<p>Index <span translate=no>_^_0_^_</span> in the list <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u30ea\u30b9\u30c8\u5185\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9 <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Multiply noise by the temperature </p>\n": "<p>\u30ce\u30a4\u30ba\u306b\u6e29\u5ea6\u3092\u639b\u3051\u308b</p>\n",
|
||||
"<p>No noise is added, when <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6b21\u306e\u5834\u5408\u3001\u30ce\u30a4\u30ba\u306f\u8ffd\u52a0\u3055\u308c\u307e\u305b\u3093 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Number of steps, <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b9\u30c6\u30c3\u30d7\u6570\u3001<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Random noise, if noise is not specified </p>\n": "<p>\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba (\u30ce\u30a4\u30ba\u304c\u6307\u5b9a\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408)</p>\n",
|
||||
"<p>Replace the masked area </p>\n": "<p>\u30de\u30b9\u30af\u3055\u308c\u305f\u9818\u57df\u3092\u7f6e\u304d\u63db\u3048\u308b</p>\n",
|
||||
"<p>Replace the masked area with original image </p>\n": "<p>\u30de\u30b9\u30af\u3055\u308c\u305f\u9818\u57df\u3092\u5143\u306e\u753b\u50cf\u306b\u7f6e\u304d\u63db\u3048\u308b</p>\n",
|
||||
"<p>Return <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30ea\u30bf\u30fc\u30f3 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample <span translate=no>_^_0_^_</span> </p>\n": "<p>[\u30b5\u30f3\u30d7\u30eb] <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample from <span translate=no>_^_0_^_</span> </p>\n": "<p>\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time step <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time steps to sample at <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3059\u308b\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<span translate=no>_^_0_^_</span><p> </p>\n": "<span translate=no>_^_0_^_</span><p></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model to predict noise <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the number of DDIM sampling steps, <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> specifies how to extract <span translate=no>_^_5_^_</span> from <span translate=no>_^_6_^_</span>. It can be either <span translate=no>_^_7_^_</span> or <span translate=no>_^_8_^_</span>. </li>\n<li><span translate=no>_^_9_^_</span> is <span translate=no>_^_10_^_</span> used to calculate <span translate=no>_^_11_^_</span>. <span translate=no>_^_12_^_</span> makes the sampling process deterministic.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u30ce\u30a4\u30ba\u3092\u4e88\u6e2c\u3059\u308b\u30e2\u30c7\u30eb\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u306f DDIM \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30b9\u30c6\u30c3\u30d7\u306e\u6570\u3001<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span><span translate=no>_^_5_^_</span>\u304b\u3089\u306e\u62bd\u51fa\u65b9\u6cd5\u3092\u6307\u5b9a\u3057\u307e\u3059<span translate=no>_^_6_^_</span>\u3002<span translate=no>_^_7_^_</span>\u307e\u305f\u306f\u306e\u3069\u3061\u3089\u3067\u3082\u304b\u307e\u3044\u307e\u305b\u3093<span translate=no>_^_8_^_</span>\u3002</li>\n<li><span translate=no>_^_9_^_</span><span translate=no>_^_10_^_</span>\u8a08\u7b97\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059<span translate=no>_^_11_^_</span>\u3002<span translate=no>_^_12_^_</span>\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30d7\u30ed\u30bb\u30b9\u3092\u6c7a\u5b9a\u7684\u306b\u3057\u307e\u3059</li></ul>\u3002\n",
|
||||
"Annotated PyTorch implementation/tutorial of Denoising Diffusion Implicit Models (DDIM) Sampling for stable diffusion model.": "\u5b89\u5b9a\u62e1\u6563\u30e2\u30c7\u30eb\u306e\u305f\u3081\u306e\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u6697\u9ed9\u30e2\u30c7\u30eb\uff08DDIM\uff09\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306e\u6ce8\u91c8\u4ed8\u304dPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3002",
|
||||
"Denoising Diffusion Implicit Models (DDIM) Sampling": "\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u6697\u9ed9\u30e2\u30c7\u30eb (DDIM) \u30b5\u30f3\u30d7\u30ea\u30b7\u30c3\u30c8\u30b5\u30f3\u30d7\u30ea\u30b7\u30c3\u30c8"
|
||||
}
|
||||
@@ -0,0 +1,38 @@
|
||||
{
|
||||
"<h1>Denoising Diffusion Implicit Models (DDIM) Sampling</h1>\n<p>This implements DDIM sampling from the paper <a href=\"https://arxiv.org/abs/2010.02502\">Denoising Diffusion Implicit Models</a></p>\n": "<h1>Denoising \u0dc0\u0dd2\u0dc3\u0dbb\u0dab \u0dc0\u0dca\u0dba\u0d82\u0d9c \u0d86\u0d9a\u0dd8\u0dad\u0dd2 (DDIM) \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8</h1>\n<p>\u0db8\u0dd9\u0dba \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 \u0dc0\u0dbd\u0dd2\u0db1\u0dca DDIM \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8 \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dbb\u0dba\u0dd2 <a href=\"https://arxiv.org/abs/2010.02502\">Denoising Diffusion Implicit \u0d86\u0d9a\u0dd8\u0dad\u0dd2</a> Denoising</p>\n",
|
||||
"<h2>DDIM Sampler</h2>\n<p>This extends the <a href=\"index.html\"><span translate=no>_^_0_^_</span> base class</a>.</p>\n<p>DDPM samples images by repeatedly removing noise by sampling step by step using,</p>\n<span translate=no>_^_1_^_</span><p>where <span translate=no>_^_2_^_</span> is random noise, <span translate=no>_^_3_^_</span> is a subsequence of <span translate=no>_^_4_^_</span> of length <span translate=no>_^_5_^_</span>, and <span translate=no>_^_6_^_</span></p>\n<p>Note that, <span translate=no>_^_7_^_</span> in DDIM paper refers to <span translate=no>_^_8_^_</span> from <a href=\"ddpm.html\">DDPM</a>.</p>\n": "<h2>\u0da9\u0dd3\u0da9\u0dd3\u0d85\u0dba\u0dd2\u0d91\u0db8\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0d9a\u0dbb\u0dd4</h2>\n<p>\u0db8\u0dd9\u0dba <a href=\"index.html\"><span translate=no>_^_0_^_</span>\u0db8\u0dd6\u0dbd\u0dd2\u0d9a \u0db4\u0db1\u0dca\u0dad\u0dd2\u0dba</a> \u0db4\u0dd4\u0dc5\u0dd4\u0dbd\u0dca \u0d9a\u0dbb\u0dba\u0dd2.</p>\n<p>\u0da9\u0dd3\u0da9\u0dd3\u0db4\u0dd3\u0d91\u0db8\u0dca \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0dbb\u0dd6\u0db4 \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd2\u0db1\u0dca \u0db4\u0dd2\u0dba\u0dc0\u0dbb\u0dd9\u0db1\u0dca \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8\u0dd9\u0db1\u0dca \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db1\u0dd0\u0dc0\u0dad \u0db1\u0dd0\u0dc0\u0dad\u0dad\u0dca \u0d89\u0dc0\u0dad\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dd9\u0db1\u0dca,</p>\n<span translate=no>_^_1_^_</span><p>\u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba<span translate=no>_^_2_^_</span> \u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0dad\u0dd0\u0db1\u0daf, \u0daf\u0dd2\u0d9c<span translate=no>_^_3_^_</span> \u0d85\u0db1\u0dd4\u0d9a\u0dca\u0dbb\u0db8\u0dba\u0d9a\u0dd2<span translate=no>_^_5_^_</span>, \u0dc3\u0dc4<span translate=no>_^_4_^_</span><span translate=no>_^_6_^_</span></p>\n<p>\u0da9\u0dd3\u0da9\u0dd3\u0d85\u0dba\u0dd2\u0d91\u0db8\u0dca \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2<span translate=no>_^_7_^_</span> \u0dc0\u0dbd <a href=\"ddpm.html\">\u0da9\u0dd3\u0da9\u0dd3\u0db4\u0dd3\u0d91\u0db8\u0dca<span translate=no>_^_8_^_</span></a> \u0dc0\u0dd9\u0dad\u0dd2\u0db1\u0dca \u0dc3\u0db3\u0dc4\u0db1\u0dca \u0dc0\u0db1 \u0db6\u0dc0 \u0dc3\u0dbd\u0d9a\u0db1\u0dca\u0db1.</p>\n",
|
||||
"<h3>Painting Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is <span translate=no>_^_1_^_</span> of shape <span translate=no>_^_2_^_</span> </li>\n<li><span translate=no>_^_3_^_</span> is the conditional embeddings <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the sampling step to start from, <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is the original image in latent page which we are in paining. If this is not provided, it'll be an image to image transformation. </li>\n<li><span translate=no>_^_8_^_</span> is the mask to keep the original image. </li>\n<li><span translate=no>_^_9_^_</span> is fixed noise to be added to the original image. </li>\n<li><span translate=no>_^_10_^_</span> is the unconditional guidance scale <span translate=no>_^_11_^_</span>. This is used for <span translate=no>_^_12_^_</span> </li>\n<li><span translate=no>_^_13_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_14_^_</span></li></ul>\n": "<h3>\u0db4\u0dd2\u0db1\u0dca\u0dad\u0dcf\u0dbb\u0dd4 \u0dbd\u0dd6\u0db4</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_1_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_2_^_</span></li>\n<li><span translate=no>_^_3_^_</span>\u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u0dc3\u0dd2\u0da7 \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0dc0\u0dda,<span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span>\u0dba\u0db1\u0dd4 \u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba\u0dba\u0dd2 \u0d9c\u0dd4\u0db4\u0dca\u0dad \u0db4\u0dd2\u0da7\u0dd4\u0dc0 \u0d85\u0db4\u0dd2 \u0db4\u0dd0\u0dbd\u0dca\u0dbd\u0db8\u0dca \u0d9a\u0dbb\u0db1. \u0db8\u0dd9\u0dba \u0dc3\u0db4\u0dba\u0dcf \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2 \u0db1\u0db8\u0dca, \u0d91\u0dba \u0dbb\u0dd6\u0db4 \u0db4\u0dbb\u0dd2\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba\u0da7 \u0dbb\u0dd6\u0db4\u0dba\u0d9a\u0dca \u0dc0\u0db1\u0dd4 \u0d87\u0dad.</li>\n<li><span translate=no>_^_8_^_</span>\u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba \u0dad\u0db6\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0dd9\u0dc3\u0dca\u0db8\u0dd4\u0dc4\u0dd4\u0dab \u0dc0\u0dda.</li>\n<li><span translate=no>_^_9_^_</span>\u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba\u0da7 \u0d91\u0d9a\u0dad\u0dd4 \u0d9a\u0dc5 \u0dba\u0dd4\u0dad\u0dd4 \u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc1\u0db6\u0dca\u0daf\u0dba.</li>\n<li><span translate=no>_^_10_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_11_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_12_^_</span></li>\n<li><span translate=no>_^_13_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc0\u0dda<span translate=no>_^_14_^_</span></li></ul>\n",
|
||||
"<h3>Sample <span translate=no>_^_0_^_</span> given <span translate=no>_^_1_^_</span></h3>\n": "<h3><span translate=no>_^_0_^_</span>\u0dbd\u0db6\u0dcf \u0daf\u0dd3 \u0d87\u0dad\u0dd2 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_1_^_</span></h3>\n",
|
||||
"<h3>Sample <span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the conditional embeddings <span translate=no>_^_5_^_</span> of shape <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is <span translate=no>_^_8_^_</span> of shape <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the step <span translate=no>_^_11_^_</span> as an integer </li>\n<li><span translate=no>_^_12_^_</span> is index <span translate=no>_^_13_^_</span> in the list <span translate=no>_^_14_^_</span> </li>\n<li><span translate=no>_^_15_^_</span> specified whether the noise should be same for all samples in the batch </li>\n<li><span translate=no>_^_16_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_17_^_</span> is the unconditional guidance scale <span translate=no>_^_18_^_</span>. This is used for <span translate=no>_^_19_^_</span> </li>\n<li><span translate=no>_^_20_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_21_^_</span></li></ul>\n": "<h3>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_2_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad<span translate=no>_^_5_^_</span> \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_8_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u0dba\u0db1\u0dd4 \u0db4\u0dd6\u0dbb\u0dca\u0dab \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0dc0\u0d9a\u0dca<span translate=no>_^_11_^_</span> \u0dbd\u0dd9\u0dc3 \u0db4\u0dd2\u0dba\u0dc0\u0dbb\u0dba\u0dd2</li>\n<li><span translate=no>_^_12_^_</span><span translate=no>_^_13_^_</span>\u0dbd\u0dd0\u0dba\u0dd2\u0dc3\u0dca\u0dad\u0dd4\u0dc0\u0dda \u0daf\u0dbb\u0dca\u0dc1\u0d9a\u0dba \u0dc0\u0dda<span translate=no>_^_14_^_</span></li>\n<li><span translate=no>_^_15_^_</span>\u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dda \u0dc3\u0dd2\u0dba\u0dbd\u0dd4\u0db8 \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0dc3\u0db3\u0dc4\u0dcf \u0dc1\u0db6\u0dca\u0daf\u0dba \u0dc3\u0db8\u0dcf\u0db1 \u0dc0\u0dd2\u0dba \u0dba\u0dd4\u0dad\u0dd4\u0daf \u0dba\u0db1\u0dca\u0db1 \u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dc0 \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0d87\u0dad</li>\n<li><span translate=no>_^_16_^_</span>\u0dba\u0db1\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba\u0dda \u0d8b\u0dc2\u0dca\u0dab\u0dad\u0dca\u0dc0\u0dba (\u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db8\u0dd9\u0dba\u0dd2\u0db1\u0dca \u0d9c\u0dd4\u0dab \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0dd0\u0db6\u0dda)</li>\n<li><span translate=no>_^_17_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_18_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_19_^_</span></li>\n<li><span translate=no>_^_20_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc0\u0dda<span translate=no>_^_21_^_</span></li></ul>\n",
|
||||
"<h3>Sample from <span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span> is <span translate=no>_^_3_^_</span> of shape <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the time step <span translate=no>_^_6_^_</span> index <span translate=no>_^_7_^_</span> </li>\n<li><span translate=no>_^_8_^_</span> is the noise, <span translate=no>_^_9_^_</span></li></ul>\n": "<h3>\u0dc0\u0dd9\u0dad\u0dd2\u0db1\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_3_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb<span translate=no>_^_6_^_</span> \u0daf\u0dbb\u0dca\u0dc1\u0d9a\u0dba\u0dba\u0dd2<span translate=no>_^_7_^_</span></li>\n<li><span translate=no>_^_8_^_</span>\u0dc1\u0db6\u0dca\u0daf\u0dba,<span translate=no>_^_9_^_</span></li></ul>\n",
|
||||
"<h3>Sampling Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the shape of the generated images in the form <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_5_^_</span> is <span translate=no>_^_6_^_</span>. If not provided random noise will be used. </li>\n<li><span translate=no>_^_7_^_</span> is the unconditional guidance scale <span translate=no>_^_8_^_</span>. This is used for <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_11_^_</span> </li>\n<li><span translate=no>_^_12_^_</span> is the number of time steps to skip <span translate=no>_^_13_^_</span>. We start sampling from <span translate=no>_^_14_^_</span>. And <span translate=no>_^_15_^_</span> is then <span translate=no>_^_16_^_</span>.</li></ul>\n": "<h3>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0dbd\u0dd6\u0db4</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc3\u0dca\u0dc0\u0dbb\u0dd6\u0db4\u0dba\u0dd9\u0db1\u0dca \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0dbb\u0dd6\u0db4\u0dc0\u0dbd \u0dc4\u0dd0\u0da9\u0dba<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u0dba\u0db1\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba\u0dda \u0d8b\u0dc2\u0dca\u0dab\u0dad\u0dca\u0dc0\u0dba (\u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db8\u0dd9\u0dba\u0dd2\u0db1\u0dca \u0d9c\u0dd4\u0dab \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0dd0\u0db6\u0dda)</li>\n<li><span translate=no>_^_5_^_</span>\u0dc0\u0dda<span translate=no>_^_6_^_</span>. \u0dc3\u0db4\u0dba\u0dcf \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2 \u0db1\u0db8\u0dca \u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1\u0dd4 \u0d87\u0dad.</li>\n<li><span translate=no>_^_7_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_8_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc0\u0dda<span translate=no>_^_11_^_</span></li>\n<li><span translate=no>_^_12_^_</span>\u0db8\u0d9f \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0da7 \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1<span translate=no>_^_13_^_</span> \u0dc0\u0dda. \u0d85\u0db4\u0dd2 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8 \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db8\u0dd4<span translate=no>_^_14_^_</span>. \u0d91\u0dc0\u0dd2\u0da7<span translate=no>_^_15_^_</span> \u0dba<span translate=no>_^_16_^_</span>.</li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> and predicted <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0d9c\u0dab\u0db1\u0dba<span translate=no>_^_0_^_</span> \u0d9a\u0dbb \u0db4\u0dd4\u0dbb\u0ddd\u0d9a\u0dae\u0db1\u0dba \u0d9a\u0dbb \u0d87\u0dad<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> to be quadratically distributed across <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0da0\u0dad\u0dd4\u0dbb\u0dc3\u0dca\u0dbb\u0dcf\u0d9a\u0dcf\u0dbb \u0dbd\u0dd9\u0dc3 \u0db6\u0dd9\u0daf\u0dcf<span translate=no>_^_0_^_</span> \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0da7 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> to be uniformly distributed across <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0d92\u0d9a\u0dcf\u0d9a\u0dcf\u0dbb\u0dc0 \u0db6\u0dd9\u0daf\u0dcf<span translate=no>_^_0_^_</span> \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0da7 \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Current prediction for <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0dad\u0dca\u0db8\u0db1\u0dca \u0d85\u0db1\u0dcf\u0dc0\u0dd0\u0d9a\u0dd2\u0dba<span translate=no>_^_0_^_</span>,<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Different noise for each sample </p>\n": "<p>\u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0dd2\u0dc0\u0dd2\u0db0 \u0dc1\u0db6\u0dca\u0daf</p>\n",
|
||||
"<p>Direction pointing to <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span> </p>\n": "<p>\u0daf\u0dd2\u0dc1\u0dcf\u0dc0 \u0dba\u0ddc\u0db8\u0dd4 \u0d9a\u0dbb\u0dba\u0dd2<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Get batch size </p>\n": "<p>\u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get device and batch size </p>\n": "<p>\u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba \u0dc3\u0dc4 \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get the <span translate=no>_^_0_^_</span> for original image in latent space </p>\n": "<p>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba<span translate=no>_^_0_^_</span> \u0dc3\u0db3\u0dc4\u0dcf \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>If same noise is used for all samples in the batch </p>\n": "<p>\u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dda \u0dc3\u0dd2\u0dba\u0dbd\u0dd4\u0db8 \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0dc3\u0db3\u0dc4\u0dcf \u0d91\u0d9a\u0db8 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dda \u0db1\u0db8\u0dca</p>\n",
|
||||
"<p>Index <span translate=no>_^_0_^_</span> in the list <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u0dbd\u0dd0\u0dba\u0dd2\u0dc3\u0dca\u0dad\u0dd4\u0dc0\u0dda \u0daf\u0dbb\u0dca\u0dc1\u0d9a\u0dba<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Multiply noise by the temperature </p>\n": "<p>\u0d8b\u0dc2\u0dca\u0dab\u0dad\u0dca\u0dc0\u0dba \u0d85\u0db1\u0dd4\u0dc0 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0d9c\u0dd4\u0dab \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>No noise is added, when <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc1\u0db6\u0dca\u0daf\u0dba\u0d9a\u0dca \u0d91\u0d9a\u0dad\u0dd4 \u0db1\u0ddc\u0dc0\u0dda, \u0dc0\u0dd2\u0da7<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Number of steps, <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1,<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Random noise, if noise is not specified </p>\n": "<p>\u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba, \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dc0 \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2 \u0db1\u0db8\u0dca</p>\n",
|
||||
"<p>Replace the masked area </p>\n": "<p>\u0dc0\u0dd9\u0dc3\u0dca\u0d9c\u0dad\u0dca \u0db4\u0dca\u0dbb\u0daf\u0dda\u0dc1\u0dba \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0dc3\u0dca\u0dae\u0dcf\u0db4\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Replace the masked area with original image </p>\n": "<p>\u0dc0\u0dd9\u0dc3\u0dca \u0d9c\u0dad\u0dca \u0db4\u0dca\u0dbb\u0daf\u0dda\u0dc1\u0dba \u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba \u0dc3\u0db8\u0d9f \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0dc3\u0dca\u0dae\u0dcf\u0db4\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Return <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d86\u0db4\u0dc3\u0dd4<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample from <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc0\u0dd9\u0dad\u0dd2\u0db1\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time step <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0dc0\u0dda\u0dbd\u0dcf\u0dc0<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time steps to sample at <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<span translate=no>_^_0_^_</span><p> </p>\n": "<span translate=no>_^_0_^_</span><p></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model to predict noise <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the number of DDIM sampling steps, <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> specifies how to extract <span translate=no>_^_5_^_</span> from <span translate=no>_^_6_^_</span>. It can be either <span translate=no>_^_7_^_</span> or <span translate=no>_^_8_^_</span>. </li>\n<li><span translate=no>_^_9_^_</span> is <span translate=no>_^_10_^_</span> used to calculate <span translate=no>_^_11_^_</span>. <span translate=no>_^_12_^_</span> makes the sampling process deterministic.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc1\u0db6\u0dca\u0daf\u0dba \u0db4\u0dd4\u0dbb\u0ddd\u0d9a\u0dae\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0dba\u0dd2<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>DDIM \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1,<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span><span translate=no>_^_5_^_</span>\u0d8b\u0db4\u0dd4\u0da7\u0dcf \u0d9c\u0db1\u0dca\u0db1\u0dda \u0d9a\u0dd9\u0dc3\u0dda\u0daf \u0dba\u0db1\u0dca\u0db1 \u0db1\u0dd2\u0dba\u0db8 \u0d9a\u0dbb\u0dba\u0dd2<span translate=no>_^_6_^_</span>. \u0d91\u0dba \u0d91\u0d9a\u0dca\u0d9a\u0ddd<span translate=no>_^_7_^_</span> \u0dc4\u0ddd \u0dc0\u0dd2\u0dba \u0dc4\u0dd0\u0d9a\u0dd2\u0dba<span translate=no>_^_8_^_</span>.</li>\n<li><span translate=no>_^_9_^_</span>\u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7<span translate=no>_^_10_^_</span> \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_11_^_</span>. <span translate=no>_^_12_^_</span>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dc0\u0dbd\u0dd2\u0dba \u0dad\u0dd3\u0dbb\u0dab\u0dba \u0d9a\u0dbb\u0dba\u0dd2.</li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of Denoising Diffusion Implicit Models (DDIM) Sampling for stable diffusion model.": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0da9\u0dd9\u0db1\u0ddc\u0dba\u0dd2\u0dc3\u0dd2\u0d82 \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc0\u0dca\u0dba\u0dc0\u0dbb\u0dca\u0dae \u0d86\u0d9a\u0dd8\u0dad\u0dd2 (\u0da9\u0dd3\u0da9\u0dd3\u0d85\u0dba\u0dd2\u0d91\u0db8\u0dca) \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8 PyTorch \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8/\u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1\u0dba.",
|
||||
"Denoising Diffusion Implicit Models (DDIM) Sampling": "Denoising \u0dc0\u0dd2\u0dc3\u0dbb\u0dab \u0dc0\u0dca\u0dba\u0d82\u0d9c \u0d86\u0d9a\u0dd8\u0dad\u0dd2 (DDIM) \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8"
|
||||
}
|
||||
@@ -0,0 +1,38 @@
|
||||
{
|
||||
"<h1>Denoising Diffusion Implicit Models (DDIM) Sampling</h1>\n<p>This implements DDIM sampling from the paper <a href=\"https://arxiv.org/abs/2010.02502\">Denoising Diffusion Implicit Models</a></p>\n": "<h1>\u964d\u566a\u6269\u6563\u9690\u542b\u6a21\u578b (DDIM) \u91c7\u6837</h1>\n<p>\u8fd9\u5b9e\u73b0\u4e86\u6765\u81ea\u8bba\u6587 \u201c<a href=\"https://arxiv.org/abs/2010.02502\">\u964d\u566a\u6269\u6563\u9690\u5f0f\u6a21\u578b</a>\u201d \u7684 DDIM \u91c7\u6837</p>\n",
|
||||
"<h2>DDIM Sampler</h2>\n<p>This extends the <a href=\"index.html\"><span translate=no>_^_0_^_</span> base class</a>.</p>\n<p>DDIM samples images by repeatedly removing noise by sampling step by step using,</p>\n<span translate=no>_^_1_^_</span><p>where <span translate=no>_^_2_^_</span> is random noise, <span translate=no>_^_3_^_</span> is a subsequence of <span translate=no>_^_4_^_</span> of length <span translate=no>_^_5_^_</span>, and <span translate=no>_^_6_^_</span></p>\n<p>Note that, <span translate=no>_^_7_^_</span> in DDIM paper refers to <span translate=no>_^_8_^_</span> from <a href=\"ddpm.html\">DDPM</a>.</p>\n": "<h2>DDIM \u91c7\u6837\u5668</h2>\n<p>\u8fd9\u6269\u5c55\u4e86<a href=\"index.html\"><span translate=no>_^_0_^_</span>\u57fa\u7c7b</a>\u3002</p>\n<p>DDPM \u901a\u8fc7\u9010\u6b65\u91c7\u6837\u6765\u53cd\u590d\u6d88\u9664\u566a\u70b9\u6765\u5bf9\u56fe\u50cf\u8fdb\u884c\u91c7\u6837\uff0c</p>\n<span translate=no>_^_1_^_</span><p>\u5176\u4e2d<span translate=no>_^_2_^_</span>\uff0c\u662f\u968f\u673a\u566a\u58f0\uff0c<span translate=no>_^_3_^_</span>\u662f\u957f\u5ea6\u4e3a<span translate=no>_^_4_^_</span>\u7684\u5b50\u5e8f\u5217<span translate=no>_^_5_^_</span>\uff0c<span translate=no>_^_6_^_</span></p>\n<p>\u8bf7\u6ce8\u610f\uff0c<span translate=no>_^_7_^_</span>\u5728 DDIM \u8bba\u6587\u4e2d\uff0c\u6307\u7684\u662f\u6765<span translate=no>_^_8_^_</span>\u81ea <a href=\"ddpm.html\">DDPM</a> \u7684\u8bba\u6587\u3002</p>\n",
|
||||
"<h3>Painting Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is <span translate=no>_^_1_^_</span> of shape <span translate=no>_^_2_^_</span> </li>\n<li><span translate=no>_^_3_^_</span> is the conditional embeddings <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the sampling step to start from, <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is the original image in latent page which we are in paining. If this is not provided, it'll be an image to image transformation. </li>\n<li><span translate=no>_^_8_^_</span> is the mask to keep the original image. </li>\n<li><span translate=no>_^_9_^_</span> is fixed noise to be added to the original image. </li>\n<li><span translate=no>_^_10_^_</span> is the unconditional guidance scale <span translate=no>_^_11_^_</span>. This is used for <span translate=no>_^_12_^_</span> </li>\n<li><span translate=no>_^_13_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_14_^_</span></li></ul>\n": "<h3>\u7ed8\u753b\u5faa\u73af</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u5f62<span translate=no>_^_1_^_</span>\u72b6\u7684<span translate=no>_^_2_^_</span></li>\n<li><span translate=no>_^_3_^_</span>\u662f\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u662f\u5f00\u59cb\u65f6\u7684\u91c7\u6837\u6b65\u9aa4\uff0c<span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span>\u662f\u6211\u4eec\u6b63\u5728\u7ed8\u5236\u7684\u6f5c\u5728\u9875\u9762\u4e2d\u7684\u539f\u59cb\u56fe\u50cf\u3002\u5982\u679c\u672a\u63d0\u4f9b\uff0c\u5219\u5c06\u662f\u56fe\u50cf\u5230\u56fe\u50cf\u7684\u8f6c\u6362\u3002</li>\n<li><span translate=no>_^_8_^_</span>\u662f\u4fdd\u7559\u539f\u59cb\u56fe\u50cf\u7684\u63a9\u7801\u3002</li>\n<li><span translate=no>_^_9_^_</span>\u662f\u8981\u6dfb\u52a0\u5230\u539f\u59cb\u56fe\u50cf\u7684\u56fa\u5b9a\u566a\u70b9\u3002</li>\n<li><span translate=no>_^_10_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_11_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_12_^_</span></li>\n<li><span translate=no>_^_13_^_</span>\u662f\u7a7a\u63d0\u793a\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_14_^_</span></li></ul>\n",
|
||||
"<h3>Sample <span translate=no>_^_0_^_</span> given <span translate=no>_^_1_^_</span></h3>\n": "<h3><span translate=no>_^_0_^_</span>\u7ed9\u51fa\u7684\u6837\u672c<span translate=no>_^_1_^_</span></h3>\n",
|
||||
"<h3>Sample <span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> of shape <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the conditional embeddings <span translate=no>_^_5_^_</span> of shape <span translate=no>_^_6_^_</span> </li>\n<li><span translate=no>_^_7_^_</span> is <span translate=no>_^_8_^_</span> of shape <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the step <span translate=no>_^_11_^_</span> as an integer </li>\n<li><span translate=no>_^_12_^_</span> is index <span translate=no>_^_13_^_</span> in the list <span translate=no>_^_14_^_</span> </li>\n<li><span translate=no>_^_15_^_</span> specified whether the noise should be same for all samples in the batch </li>\n<li><span translate=no>_^_16_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_17_^_</span> is the unconditional guidance scale <span translate=no>_^_18_^_</span>. This is used for <span translate=no>_^_19_^_</span> </li>\n<li><span translate=no>_^_20_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_21_^_</span></li></ul>\n": "<h3>\u793a\u4f8b<span translate=no>_^_0_^_</span></h3>\n<ul><li><span translate=no>_^_1_^_</span>\u662f\u5f62<span translate=no>_^_2_^_</span>\u72b6\u7684<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u662f\u5f62\u72b6<span translate=no>_^_5_^_</span>\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_6_^_</span></li>\n<li><span translate=no>_^_7_^_</span>\u662f\u5f62<span translate=no>_^_8_^_</span>\u72b6\u7684<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u662f\u6574\u6570<span translate=no>_^_11_^_</span>\u5f62\u5f0f\u7684\u6b65\u957f</li>\n<li><span translate=no>_^_12_^_</span>\u662f\u5217\u8868<span translate=no>_^_13_^_</span>\u4e2d\u7684\u7d22\u5f15<span translate=no>_^_14_^_</span></li>\n<li><span translate=no>_^_15_^_</span>\u6307\u5b9a\u6279\u6b21\u4e2d\u6240\u6709\u6837\u672c\u7684\u566a\u58f0\u662f\u5426\u5e94\u76f8\u540c</li>\n<li><span translate=no>_^_16_^_</span>\u662f\u566a\u58f0\u6e29\u5ea6\uff08\u968f\u673a\u566a\u58f0\u4e58\u4ee5\u6b64\u503c\uff09</li>\n<li><span translate=no>_^_17_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_18_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_19_^_</span></li>\n<li><span translate=no>_^_20_^_</span>\u662f\u7a7a\u63d0\u793a\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_21_^_</span></li></ul>\n",
|
||||
"<h3>Sample from <span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span> is <span translate=no>_^_3_^_</span> of shape <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the time step <span translate=no>_^_6_^_</span> index <span translate=no>_^_7_^_</span> </li>\n<li><span translate=no>_^_8_^_</span> is the noise, <span translate=no>_^_9_^_</span></li></ul>\n": "<h3>\u6837\u672c\u6765\u81ea<span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span>\u662f\u5f62<span translate=no>_^_3_^_</span>\u72b6\u7684<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u662f\u65f6\u95f4\u6b65\u957f<span translate=no>_^_6_^_</span>\u6307\u6570<span translate=no>_^_7_^_</span></li>\n<li><span translate=no>_^_8_^_</span>\u662f\u566a\u97f3\uff0c<span translate=no>_^_9_^_</span></li></ul>\n",
|
||||
"<h3>Sampling Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the shape of the generated images in the form <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_5_^_</span> is <span translate=no>_^_6_^_</span>. If not provided random noise will be used. </li>\n<li><span translate=no>_^_7_^_</span> is the unconditional guidance scale <span translate=no>_^_8_^_</span>. This is used for <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_11_^_</span> </li>\n<li><span translate=no>_^_12_^_</span> is the number of time steps to skip <span translate=no>_^_13_^_</span>. We start sampling from <span translate=no>_^_14_^_</span>. And <span translate=no>_^_15_^_</span> is then <span translate=no>_^_16_^_</span>.</li></ul>\n": "<h3>\u91c7\u6837\u56de\u8def</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u8868\u5355\u4e2d\u751f\u6210\u7684\u56fe\u50cf\u7684\u5f62\u72b6<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u662f\u566a\u58f0\u6e29\u5ea6\uff08\u968f\u673a\u566a\u58f0\u4e58\u4ee5\u6b64\u503c\uff09</li>\n<li><span translate=no>_^_5_^_</span>\u662f<span translate=no>_^_6_^_</span>\u3002\u5982\u679c\u672a\u63d0\u4f9b\uff0c\u5c06\u4f7f\u7528\u968f\u673a\u566a\u58f0\u3002</li>\n<li><span translate=no>_^_7_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_8_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u662f\u7a7a\u63d0\u793a\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_11_^_</span></li>\n<li><span translate=no>_^_12_^_</span>\u662f\u8981\u8df3\u8fc7\u7684\u65f6\u95f4\u6b65\u6570<span translate=no>_^_13_^_</span>\u3002\u6211\u4eec\u4ece\u5f00\u59cb\u91c7\u6837<span translate=no>_^_14_^_</span>\u3002\u7136\u540e<span translate=no>_^_15_^_</span>\u5c31\u662f\u8fd9\u6837<span translate=no>_^_16_^_</span>\u3002</li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> and predicted <span translate=no>_^_1_^_</span> </p>\n": "<p>\u8ba1\u7b97<span translate=no>_^_0_^_</span>\u548c\u9884\u6d4b<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> to be quadratically distributed across <span translate=no>_^_1_^_</span> </p>\n": "<p>\u8ba1\u7b97<span translate=no>_^_0_^_</span>\u4ee5\u4e8c\u6b21\u5206\u5e03<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> to be uniformly distributed across <span translate=no>_^_1_^_</span> </p>\n": "<p>\u8ba1\u7b97<span translate=no>_^_0_^_</span>\u5f97\u5747\u5300\u5206\u5e03\u5728\u5404\u5904<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Current prediction for <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> </p>\n": "<p>\u76ee\u524d\u7684\u9884\u6d4b<span translate=no>_^_0_^_</span>\uff0c<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Different noise for each sample </p>\n": "<p>\u6bcf\u4e2a\u6837\u672c\u7684\u566a\u58f0\u4e0d\u540c</p>\n",
|
||||
"<p>Direction pointing to <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span> </p>\n": "<p>\u6307\u5411\u7684\u65b9\u5411<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> </p>\n": "<p>\u83b7\u53d6<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Get batch size </p>\n": "<p>\u83b7\u53d6\u6279\u6b21\u5927\u5c0f</p>\n",
|
||||
"<p>Get device and batch size </p>\n": "<p>\u83b7\u53d6\u8bbe\u5907\u548c\u6279\u6b21\u5927\u5c0f</p>\n",
|
||||
"<p>Get the <span translate=no>_^_0_^_</span> for original image in latent space </p>\n": "<p>\u5728\u6f5c\u5728\u7a7a\u95f4\u4e2d<span translate=no>_^_0_^_</span>\u83b7\u53d6\u539f\u59cb\u56fe\u50cf</p>\n",
|
||||
"<p>If same noise is used for all samples in the batch </p>\n": "<p>\u5982\u679c\u6279\u6b21\u4e2d\u7684\u6240\u6709\u6837\u54c1\u90fd\u4f7f\u7528\u76f8\u540c\u7684\u566a\u58f0</p>\n",
|
||||
"<p>Index <span translate=no>_^_0_^_</span> in the list <span translate=no>_^_1_^_</span> </p>\n": "<p>\u5217\u8868<span translate=no>_^_0_^_</span>\u4e2d\u7684\u7d22\u5f15<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Multiply noise by the temperature </p>\n": "<p>\u5c06\u566a\u58f0\u4e58\u4ee5\u6e29\u5ea6</p>\n",
|
||||
"<p>No noise is added, when <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5728\u4ee5\u4e0b\u60c5\u51b5\u4e0b\u4e0d\u6dfb\u52a0\u4efb\u4f55\u566a\u97f3<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Number of steps, <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6b65\u6570\uff0c<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Random noise, if noise is not specified </p>\n": "<p>\u5982\u679c\u672a\u6307\u5b9a\u566a\u58f0\uff0c\u5219\u4e3a\u968f\u673a\u566a\u58f0</p>\n",
|
||||
"<p>Replace the masked area </p>\n": "<p>\u66ff\u6362\u88ab\u5c4f\u853d\u7684\u533a\u57df</p>\n",
|
||||
"<p>Replace the masked area with original image </p>\n": "<p>\u5c06\u8499\u7248\u533a\u57df\u66ff\u6362\u4e3a\u539f\u59cb\u56fe\u50cf</p>\n",
|
||||
"<p>Return <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8fd4\u56de<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample <span translate=no>_^_0_^_</span> </p>\n": "<p>\u793a\u4f8b<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample from <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6837\u672c\u6765\u81ea<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time step <span translate=no>_^_0_^_</span> </p>\n": "<p>\u65f6\u95f4\u6b65\u957f<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time steps to sample at <span translate=no>_^_0_^_</span> </p>\n": "<p>\u91c7\u6837\u7684\u65f6\u95f4\u6b65\u957f<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<span translate=no>_^_0_^_</span><p> </p>\n": "<span translate=no>_^_0_^_</span><p></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model to predict noise <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the number of DDIM sampling steps, <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> specifies how to extract <span translate=no>_^_5_^_</span> from <span translate=no>_^_6_^_</span>. It can be either <span translate=no>_^_7_^_</span> or <span translate=no>_^_8_^_</span>. </li>\n<li><span translate=no>_^_9_^_</span> is <span translate=no>_^_10_^_</span> used to calculate <span translate=no>_^_11_^_</span>. <span translate=no>_^_12_^_</span> makes the sampling process deterministic.</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u9884\u6d4b\u566a\u58f0\u7684\u6a21\u578b<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f DDIM \u91c7\u6837\u6b65\u9aa4\u7684\u6570\u91cf\uff0c<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u6307\u5b9a\u5982\u4f55<span translate=no>_^_5_^_</span>\u4ece\u4e2d\u63d0\u53d6<span translate=no>_^_6_^_</span>\u3002\u53ef\u4ee5\u662f<span translate=no>_^_7_^_</span>\u6216<span translate=no>_^_8_^_</span>\u3002</li>\n<li><span translate=no>_^_9_^_</span><span translate=no>_^_10_^_</span>\u7528\u4e8e\u8ba1\u7b97<span translate=no>_^_11_^_</span>\u3002<span translate=no>_^_12_^_</span>\u4f7f\u91c7\u6837\u8fc7\u7a0b\u5177\u6709\u786e\u5b9a\u6027\u3002</li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of Denoising Diffusion Implicit Models (DDIM) Sampling for stable diffusion model.": "\u5e26\u6ce8\u91ca\u7684 PyTorch \u5b9e\u73b0/\u6559\u7a0b\uff0c\u7528\u4e8e\u7a33\u5b9a\u6269\u6563\u6a21\u578b\u7684\u964d\u566a\u6269\u6563\u9690\u5f0f\u6a21\u578b (DDIM) \u91c7\u6837\u3002",
|
||||
"Denoising Diffusion Implicit Models (DDIM) Sampling": "\u964d\u566a\u6269\u6563\u9690\u542b\u6a21\u578b (DDIM) \u91c7\u6837"
|
||||
}
|
||||
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"<h1>Denoising Diffusion Probabilistic Models (DDPM) Sampling</h1>\n<p>For a simpler DDPM implementation refer to our <a href=\"../../ddpm/index.html\">DDPM implementation</a>. We use same notations for <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> schedules, etc.</p>\n": "<h1>\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u78ba\u7387\u30e2\u30c7\u30eb (DDPM) \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0</h1>\n<p>\u3088\u308a\u30b7\u30f3\u30d7\u30eb\u306a DDPM \u5b9f\u88c5\u306b\u3064\u3044\u3066\u306f\u3001\u5f53\u793e\u306e <a href=\"../../ddpm/index.html\">DDPM</a> \u5b9f\u88c5\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<span translate=no>_^_1_^_</span>\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb\u306a\u3069\u306b\u3082\u540c\u3058\u8868\u8a18\u3092\u4f7f\u3044\u307e\u3059</p>\u3002<span translate=no>_^_0_^_</span>\n",
|
||||
"<h2>DDPM Sampler</h2>\n<p>This extends the <a href=\"index.html\"><span translate=no>_^_0_^_</span> base class</a>.</p>\n<p>DDPM samples images by repeatedly removing noise by sampling step by step from <span translate=no>_^_1_^_</span>,</p>\n<span translate=no>_^_2_^_</span>": "<h2>DPM \u30b5\u30f3\u30d7\u30e9\u30fc</h2>\n<p><a href=\"index.html\"><span translate=no>_^_0_^_</span>\u3053\u308c\u306f\u57fa\u672c\u30af\u30e9\u30b9\u3092\u62e1\u5f35\u3057\u307e\u3059</a>\u3002</p>\n<p>DDPM\u306f\u3001<span translate=no>_^_1_^_</span>\u4ee5\u4e0b\u304b\u3089\u6bb5\u968e\u7684\u306b\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3059\u308b\u3053\u3068\u306b\u3088\u308a\u3001\u30ce\u30a4\u30ba\u3092\u7e70\u308a\u8fd4\u3057\u9664\u53bb\u3057\u3066\u753b\u50cf\u3092\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u307e\u3059\u3002</p>\n<span translate=no>_^_2_^_</span>",
|
||||
"<h3>Sample <span translate=no>_^_0_^_</span> from <span translate=no>_^_1_^_</span></h3>\n<ul><li><span translate=no>_^_2_^_</span> is <span translate=no>_^_3_^_</span> of shape <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the conditional embeddings <span translate=no>_^_6_^_</span> of shape <span translate=no>_^_7_^_</span> </li>\n<li><span translate=no>_^_8_^_</span> is <span translate=no>_^_9_^_</span> of shape <span translate=no>_^_10_^_</span> </li>\n<li><span translate=no>_^_11_^_</span> is the step <span translate=no>_^_12_^_</span> as an integer :repeat_noise: specified whether the noise should be same for all samples in the batch </li>\n<li><span translate=no>_^_13_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_14_^_</span> is the unconditional guidance scale <span translate=no>_^_15_^_</span>. This is used for <span translate=no>_^_16_^_</span> </li>\n<li><span translate=no>_^_17_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_18_^_</span></li></ul>\n": "<h3><span translate=no>_^_0_^_</span>\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb <span translate=no>_^_1_^_</span></h3>\n<ul><li><span translate=no>_^_2_^_</span><span translate=no>_^_3_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span><span translate=no>_^_6_^_</span>\u5f62\u72b6\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_7_^_</span></li>\n<li><span translate=no>_^_8_^_</span><span translate=no>_^_9_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_10_^_</span></li>\n<li><span translate=no>_^_11_^_</span><span translate=no>_^_12_^_</span>\u306f\u30b9\u30c6\u30c3\u30d7\u3092\u6574\u6570\u3067\u8868\u3057\u305f\u3082\u306e:repeat_noise: \u30d0\u30c3\u30c1\u5185\u306e\u3059\u3079\u3066\u306e\u30b5\u30f3\u30d7\u30eb\u3067\u30ce\u30a4\u30ba\u3092\u540c\u3058\u306b\u3059\u308b\u304b\u3069\u3046\u304b\u3092\u6307\u5b9a\u3057\u307e\u3059</li>\n<li><span translate=no>_^_13_^_</span>\u306f\u30ce\u30a4\u30ba\u6e29\u5ea6 (\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba\u306b\u3053\u308c\u3092\u639b\u3051\u307e\u3059)</li>\n<li><span translate=no>_^_14_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059 <span translate=no>_^_15_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_16_^_</span></li>\n<li><span translate=no>_^_17_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_18_^_</span></li></ul>\n",
|
||||
"<h3>Sample from <span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span> is <span translate=no>_^_3_^_</span> of shape <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the time step <span translate=no>_^_6_^_</span> index </li>\n<li><span translate=no>_^_7_^_</span> is the noise, <span translate=no>_^_8_^_</span></li></ul>\n": "<h3>\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb <span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span><span translate=no>_^_3_^_</span>\u5f62\u304c\u5408\u3063\u3066\u3044\u308b <span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span><span translate=no>_^_6_^_</span>\u306f\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u30a4\u30f3\u30c7\u30c3\u30af\u30b9</li>\n<li><span translate=no>_^_7_^_</span>\u30ce\u30a4\u30ba\u306f\u3001<span translate=no>_^_8_^_</span></li></ul>\n",
|
||||
"<h3>Sampling Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the shape of the generated images in the form <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_5_^_</span> is <span translate=no>_^_6_^_</span>. If not provided random noise will be used. </li>\n<li><span translate=no>_^_7_^_</span> is the unconditional guidance scale <span translate=no>_^_8_^_</span>. This is used for <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_11_^_</span> </li>\n<li><span translate=no>_^_12_^_</span> is the number of time steps to skip <span translate=no>_^_13_^_</span>. We start sampling from <span translate=no>_^_14_^_</span>. And <span translate=no>_^_15_^_</span> is then <span translate=no>_^_16_^_</span>.</li></ul>\n": "<h3>\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30eb\u30fc\u30d7</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u30d5\u30a9\u30fc\u30e0\u3067\u751f\u6210\u3055\u308c\u305f\u30a4\u30e1\u30fc\u30b8\u306e\u5f62\u72b6\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u306f\u30ce\u30a4\u30ba\u6e29\u5ea6 (\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba\u306b\u3053\u308c\u3092\u639b\u3051\u307e\u3059)</li>\n<li><span translate=no>_^_5_^_</span>\u3067\u3059<span translate=no>_^_6_^_</span>\u3002\u6307\u5b9a\u3057\u306a\u3044\u5834\u5408\u306f\u3001\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba\u304c\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002</li>\n<li><span translate=no>_^_7_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059\u3002<span translate=no>_^_8_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u6761\u4ef6\u4ed8\u304d\u57cb\u3081\u8fbc\u307f\u3067\u3059 <span translate=no>_^_11_^_</span></li>\n<li><span translate=no>_^_12_^_</span><span translate=no>_^_13_^_</span>\u30b9\u30ad\u30c3\u30d7\u3059\u308b\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7\u306e\u6570\u3067\u3059\u3002\u304b\u3089\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u958b\u59cb\u3057\u307e\u3059<span translate=no>_^_14_^_</span>\u3002\u305d\u3057\u3066\u3001<span translate=no>_^_15_^_</span>\u305d\u306e\u6642\u3067\u3059<span translate=no>_^_16_^_</span>\u3002</li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p> <span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> schedule </p>\n": "<p><span translate=no>_^_0_^_</span>\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb</p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> with current <span translate=no>_^_1_^_</span></p>\n<p><span translate=no>_^_2_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u73fe\u5728\u306e\u5024\u3067\u8a08\u7b97 <span translate=no>_^_1_^_</span></p>\n<p><span translate=no>_^_2_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span> </p>\n": "<p>\u8a08\u7b97 <span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Clamped log of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30af\u30e9\u30f3\u30d4\u30f3\u30b0\u30fb\u30ed\u30b0\u30fb\u30aa\u30f3 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Different noise for each sample </p>\n": "<p>\u30b5\u30f3\u30d7\u30eb\u3054\u3068\u306b\u7570\u306a\u308b\u30ce\u30a4\u30ba</p>\n",
|
||||
"<p>Do not add noise when <span translate=no>_^_0_^_</span> (final step sampling process). Note that <span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> when <span translate=no>_^_3_^_</span>) </p>\n": "<p><span translate=no>_^_0_^_</span>\uff08\u6700\u7d42\u6bb5\u968e\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u51e6\u7406\uff09\u6642\u306f\u3001\u30ce\u30a4\u30ba\u3092\u52a0\u3048\u306a\u3044\u3067\u304f\u3060\u3055\u3044\u3002<span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span>\u305d\u306e\u6642\u3067\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044<span translate=no>_^_3_^_</span>\uff09</p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> </p>\n": "<p>\u53d6\u5f97 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Get batch size </p>\n": "<p>\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Get device and batch size </p>\n": "<p>\u30c7\u30d0\u30a4\u30b9\u3068\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u306e\u53d6\u5f97</p>\n",
|
||||
"<p>If same noise is used for all samples in the batch </p>\n": "<p>\u30d0\u30c3\u30c1\u5185\u306e\u3059\u3079\u3066\u306e\u30b5\u30f3\u30d7\u30eb\u306b\u540c\u3058\u30ce\u30a4\u30ba\u304c\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u5834\u5408</p>\n",
|
||||
"<p>Multiply noise by the temperature </p>\n": "<p>\u30ce\u30a4\u30ba\u306b\u6e29\u5ea6\u3092\u639b\u3051\u308b</p>\n",
|
||||
"<p>Random noise, if noise is not specified </p>\n": "<p>\u30e9\u30f3\u30c0\u30e0\u30ce\u30a4\u30ba (\u30ce\u30a4\u30ba\u304c\u6307\u5b9a\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408)</p>\n",
|
||||
"<p>Return <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30ea\u30bf\u30fc\u30f3 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample <span translate=no>_^_0_^_</span> </p>\n": "<p>[\u30b5\u30f3\u30d7\u30eb] <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample from <span translate=no>_^_0_^_</span> </p>\n": "<p>\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample from,</p>\n<p><span translate=no>_^_0_^_</span> </p>\n": "<p>\u304b\u3089\u306e\u30b5\u30f3\u30d7\u30eb</p>\n<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sampling loop </p>\n": "<p>\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30eb\u30fc\u30d7</p>\n",
|
||||
"<p>Sampling steps <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30b9\u30c6\u30c3\u30d7 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time step <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time steps to sample at <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3059\u308b\u30bf\u30a4\u30e0\u30b9\u30c6\u30c3\u30d7 <span translate=no>_^_0_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model to predict noise <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u30ce\u30a4\u30ba\u3092\u4e88\u6e2c\u3059\u308b\u30e2\u30c7\u30eb\u3067\u3059 <span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of Denoising Diffusion Probabilistic Models (DDPM) Sampling for stable diffusion model.": "\u5b89\u5b9a\u62e1\u6563\u30e2\u30c7\u30eb\u306e\u305f\u3081\u306e\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u78ba\u7387\u30e2\u30c7\u30eb\uff08DDPM\uff09\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306e\u6ce8\u91c8\u4ed8\u304dPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3002",
|
||||
"Denoising Diffusion Probabilistic Models (DDPM) Sampling": "\u30ce\u30a4\u30ba\u9664\u53bb\u62e1\u6563\u78ba\u7387\u30e2\u30c7\u30eb (DDPM) \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0"
|
||||
}
|
||||
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"<h1>Denoising Diffusion Probabilistic Models (DDPM) Sampling</h1>\n<p>For a simpler DDPM implementation refer to our <a href=\"../../ddpm/index.html\">DDPM implementation</a>. We use same notations for <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> schedules, etc.</p>\n": "<h1>Denoising \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db8\u0dca\u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf\u0dc0 \u0d86\u0d9a\u0dd8\u0dad\u0dd2 (DDPM) \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8</h1>\n<p>\u0dc3\u0dbb\u0dbd DDPM \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db4\u0d9c\u0dda <a href=\"../../ddpm/index.html\">DDPM \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8</a> \u0dc0\u0dd9\u0dad \u0dba\u0ddc\u0db8\u0dd4 \u0dc0\u0db1\u0dca\u0db1. <span translate=no>_^_1_^_</span>\u0d9a\u0dcf\u0dbd\u0dc3\u0da7\u0dc4\u0db1\u0dca<span translate=no>_^_0_^_</span> \u0d86\u0daf\u0dd2\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db4\u0dd2 \u0d91\u0d9a\u0db8 \u0d85\u0d82\u0d9a\u0db1 \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd4.</p>\n",
|
||||
"<h2>DDPM Sampler</h2>\n<p>This extends the <a href=\"index.html\"><span translate=no>_^_0_^_</span> base class</a>.</p>\n<p>DDPM samples images by repeatedly removing noise by sampling step by step from <span translate=no>_^_1_^_</span>,</p>\n<span translate=no>_^_2_^_</span>": "<h2>\u0da9\u0dd3\u0da9\u0dd3\u0db4\u0dd3\u0d91\u0db8\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2</h2>\n<p>\u0db8\u0dd9\u0dba <a href=\"index.html\"><span translate=no>_^_0_^_</span>\u0db8\u0dd6\u0dbd\u0dd2\u0d9a \u0db4\u0db1\u0dca\u0dad\u0dd2\u0dba</a> \u0db4\u0dd4\u0dc5\u0dd4\u0dbd\u0dca \u0d9a\u0dbb\u0dba\u0dd2.</p>\n<p>\u0db4\u0dd2\u0dba\u0dc0\u0dbb\u0dd9\u0db1\u0dca \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8\u0dd9\u0db1\u0dca \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db1\u0dd0\u0dc0\u0dad \u0db1\u0dd0\u0dc0\u0dad\u0dad\u0dca \u0d89\u0dc0\u0dad\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dd9\u0db1\u0dca \u0da9\u0dd3\u0da9\u0dd3\u0db4\u0dd3\u0d91\u0db8\u0dca \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0dbb\u0dd6\u0db4<span translate=no>_^_1_^_</span>,</p>\n<span translate=no>_^_2_^_</span>",
|
||||
"<h3>Sample <span translate=no>_^_0_^_</span> from <span translate=no>_^_1_^_</span></h3>\n<ul><li><span translate=no>_^_2_^_</span> is <span translate=no>_^_3_^_</span> of shape <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the conditional embeddings <span translate=no>_^_6_^_</span> of shape <span translate=no>_^_7_^_</span> </li>\n<li><span translate=no>_^_8_^_</span> is <span translate=no>_^_9_^_</span> of shape <span translate=no>_^_10_^_</span> </li>\n<li><span translate=no>_^_11_^_</span> is the step <span translate=no>_^_12_^_</span> as an integer :repeat_noise: specified whether the noise should be same for all samples in the batch </li>\n<li><span translate=no>_^_13_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_14_^_</span> is the unconditional guidance scale <span translate=no>_^_15_^_</span>. This is used for <span translate=no>_^_16_^_</span> </li>\n<li><span translate=no>_^_17_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_18_^_</span></li></ul>\n": "<h3><span translate=no>_^_0_^_</span>\u0dc0\u0dd9\u0dad\u0dd2\u0db1\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_1_^_</span></h3>\n<ul><li><span translate=no>_^_2_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_3_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad<span translate=no>_^_6_^_</span> \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_7_^_</span></li>\n<li><span translate=no>_^_8_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_9_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_10_^_</span></li>\n<li><span translate=no>_^_11_^_</span>\u0dba\u0db1\u0dd4 \u0dc3\u0d82\u0d9b\u0dca\u0dba\u0dcf\u0d82\u0d9a\u0dba\u0d9a\u0dca<span translate=no>_^_12_^_</span> \u0dbd\u0dd9\u0dc3 \u0db4\u0dd2\u0dba\u0dc0\u0dbb\u0dba\u0dd2: \u0db4\u0dd4\u0db1\u0dbb\u0dcf\u0dc0\u0dbb\u0dca\u0dad_\u0dc1\u0db6\u0dca\u0daf\u0dba: \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dda \u0dc3\u0dd2\u0dba\u0dbd\u0dd4\u0db8 \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0dc3\u0db3\u0dc4\u0dcf \u0dc1\u0db6\u0dca\u0daf\u0dba \u0dc3\u0db8\u0dcf\u0db1 \u0dc0\u0dd2\u0dba \u0dba\u0dd4\u0dad\u0dd4\u0daf \u0dba\u0db1\u0dca\u0db1 \u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dc0 \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0d87\u0dad</li>\n<li><span translate=no>_^_13_^_</span>\u0dba\u0db1\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba\u0dda \u0d8b\u0dc2\u0dca\u0dab\u0dad\u0dca\u0dc0\u0dba (\u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db8\u0dd9\u0dba\u0dd2\u0db1\u0dca \u0d9c\u0dd4\u0dab \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0dd0\u0db6\u0dda)</li>\n<li><span translate=no>_^_14_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_15_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_16_^_</span></li>\n<li><span translate=no>_^_17_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc0\u0dda<span translate=no>_^_18_^_</span></li></ul>\n",
|
||||
"<h3>Sample from <span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span> is <span translate=no>_^_3_^_</span> of shape <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the time step <span translate=no>_^_6_^_</span> index </li>\n<li><span translate=no>_^_7_^_</span> is the noise, <span translate=no>_^_8_^_</span></li></ul>\n": "<h3>\u0dc0\u0dd9\u0dad\u0dd2\u0db1\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dd9\u0db1\u0dca<span translate=no>_^_3_^_</span> \u0dba\u0dd4\u0d9a\u0dca\u0dad \u0dc0\u0dda<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb<span translate=no>_^_6_^_</span> \u0daf\u0dbb\u0dca\u0dc1\u0d9a\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_7_^_</span>\u0dc1\u0db6\u0dca\u0daf\u0dba,<span translate=no>_^_8_^_</span></li></ul>\n",
|
||||
"<h3>Sampling Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the shape of the generated images in the form <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_5_^_</span> is <span translate=no>_^_6_^_</span>. If not provided random noise will be used. </li>\n<li><span translate=no>_^_7_^_</span> is the unconditional guidance scale <span translate=no>_^_8_^_</span>. This is used for <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_11_^_</span> </li>\n<li><span translate=no>_^_12_^_</span> is the number of time steps to skip <span translate=no>_^_13_^_</span>. We start sampling from <span translate=no>_^_14_^_</span>. And <span translate=no>_^_15_^_</span> is then <span translate=no>_^_16_^_</span>.</li></ul>\n": "<h3>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0dbd\u0dd6\u0db4</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc3\u0dca\u0dc0\u0dbb\u0dd6\u0db4\u0dba\u0dd9\u0db1\u0dca \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0dbb\u0dd6\u0db4\u0dc0\u0dbd \u0dc4\u0dd0\u0da9\u0dba<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dc0\u0dda<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u0dba\u0db1\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba\u0dda \u0d8b\u0dc2\u0dca\u0dab\u0dad\u0dca\u0dc0\u0dba (\u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db8\u0dd9\u0dba\u0dd2\u0db1\u0dca \u0d9c\u0dd4\u0dab \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0dd0\u0db6\u0dda)</li>\n<li><span translate=no>_^_5_^_</span>\u0dc0\u0dda<span translate=no>_^_6_^_</span>. \u0dc3\u0db4\u0dba\u0dcf \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2 \u0db1\u0db8\u0dca \u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1\u0dd4 \u0d87\u0dad.</li>\n<li><span translate=no>_^_7_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_8_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc3\u0dc4\u0dd2\u0dad \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0dc0\u0dda<span translate=no>_^_11_^_</span></li>\n<li><span translate=no>_^_12_^_</span>\u0db8\u0d9f \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0da7 \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1<span translate=no>_^_13_^_</span> \u0dc0\u0dda. \u0d85\u0db4\u0dd2 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8 \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db8\u0dd4<span translate=no>_^_14_^_</span>. \u0d91\u0dc0\u0dd2\u0da7<span translate=no>_^_15_^_</span> \u0dba<span translate=no>_^_16_^_</span>.</li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p> <span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> schedule </p>\n": "<p><span translate=no>_^_0_^_</span>\u0d9a\u0dcf\u0dbd\u0dc3\u0da7\u0dc4\u0db1</p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> with current <span translate=no>_^_1_^_</span></p>\n<p><span translate=no>_^_2_^_</span> </p>\n": "<p>\u0db0\u0dcf\u0dbb\u0dcf\u0dc0<span translate=no>_^_0_^_</span> \u0dc3\u0db8\u0d9f \u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_1_^_</span></p>\n<p><span translate=no>_^_2_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span> </p>\n": "<p>\u0d9c\u0dab\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Clamped log of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d9a\u0dbd\u0db8\u0dca\u0db4 \u0dbd\u0d9d\u0dd4-\u0dc3\u0da7\u0dc4\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Different noise for each sample </p>\n": "<p>\u0d91\u0d9a\u0dca \u0d91\u0d9a\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0dd2\u0dc0\u0dd2\u0db0 \u0dc1\u0db6\u0dca\u0daf</p>\n",
|
||||
"<p>Do not add noise when <span translate=no>_^_0_^_</span> (final step sampling process). Note that <span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> when <span translate=no>_^_3_^_</span>) </p>\n": "<p><span translate=no>_^_0_^_</span>(\u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dc0\u0dbd\u0dd2\u0dba) \u0dc0\u0dd2\u0da7 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0d91\u0d9a\u0dad\u0dd4 \u0db1\u0ddc\u0d9a\u0dbb\u0db1\u0dca\u0db1. <span translate=no>_^_1_^_</span>\u0d91\u0dba<span translate=no>_^_2_^_</span> \u0d9a\u0dc0\u0daf\u0dcf\u0daf \u0dba\u0db1\u0dca\u0db1 \u0dc3\u0dbd\u0d9a\u0db1\u0dca\u0db1<span translate=no>_^_3_^_</span>)</p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Get batch size </p>\n": "<p>\u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get device and batch size </p>\n": "<p>\u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba \u0dc3\u0dc4 \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dca \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>If same noise is used for all samples in the batch </p>\n": "<p>\u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0dda \u0dc3\u0dd2\u0dba\u0dbd\u0dd4\u0db8 \u0dc3\u0dcf\u0db8\u0dca\u0db4\u0dbd \u0dc3\u0db3\u0dc4\u0dcf \u0d91\u0d9a\u0db8 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db1\u0dca\u0db1\u0dda \u0db1\u0db8\u0dca</p>\n",
|
||||
"<p>Multiply noise by the temperature </p>\n": "<p>\u0d8b\u0dc2\u0dca\u0dab\u0dad\u0dca\u0dc0\u0dba \u0d85\u0db1\u0dd4\u0dc0 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0d9c\u0dd4\u0dab \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Random noise, if noise is not specified </p>\n": "<p>\u0d85\u0dc4\u0db9\u0dd4 \u0dc1\u0db6\u0dca\u0daf\u0dba, \u0dc1\u0db6\u0dca\u0daf\u0dba \u0db1\u0dd2\u0dc1\u0dca\u0da0\u0dd2\u0dad\u0dc0 \u0daf\u0d9a\u0dca\u0dc0\u0dcf \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2 \u0db1\u0db8\u0dca</p>\n",
|
||||
"<p>Return <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0d86\u0db4\u0dc3\u0dd4<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample from <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc0\u0dd9\u0dad\u0dd2\u0db1\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample from,</p>\n<p><span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc0\u0dd9\u0dad\u0dd2\u0db1\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba,</p>\n<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sampling loop </p>\n": "<p>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0dbd\u0dd6\u0db4\u0dba</p>\n",
|
||||
"<p>Sampling steps <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0db4\u0dd2\u0dba\u0dc0\u0dbb<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time step <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0dc0\u0dda\u0dbd\u0dcf\u0dc0<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time steps to sample at <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d9a\u0dcf\u0dbd \u0db4\u0dd2\u0dba\u0dc0\u0dbb<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model to predict noise <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dc1\u0db6\u0dca\u0daf\u0dba \u0db4\u0dd4\u0dbb\u0ddd\u0d9a\u0dae\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba\u0dba\u0dd2<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of Denoising Diffusion Probabilistic Models (DDPM) Sampling for stable diffusion model.": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0dd2\u0dc0\u0dbb\u0dca\u0dad\u0db1 \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db8\u0dca\u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d86\u0d9a\u0dd8\u0dad\u0dd2 (\u0da9\u0dd3\u0da9\u0dd3\u0db4\u0dd3\u0d91\u0db8\u0dca) \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0dbd\u0db6\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8.",
|
||||
"Denoising Diffusion Probabilistic Models (DDPM) Sampling": "Denoising \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db8\u0dca\u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf\u0dc0 \u0d86\u0d9a\u0dd8\u0dad\u0dd2 (DDPM) \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd3\u0db8"
|
||||
}
|
||||
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"<h1>Denoising Diffusion Probabilistic Models (DDPM) Sampling</h1>\n<p>For a simpler DDPM implementation refer to our <a href=\"../../ddpm/index.html\">DDPM implementation</a>. We use same notations for <span translate=no>_^_0_^_</span>, <span translate=no>_^_1_^_</span> schedules, etc.</p>\n": "<h1>\u53bb\u566a\u6269\u6563\u6982\u7387\u6a21\u578b (DDPM) \u91c7\u6837</h1>\n<p>\u6709\u5173\u66f4\u7b80\u5355\u7684 DDPM \u5b9e\u73b0\uff0c\u8bf7\u53c2\u9605\u6211\u4eec\u7684 <a href=\"../../ddpm/index.html\">DDPM \u5b9e\u73b0</a>\u3002\u6211\u4eec\u5bf9<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>\u65f6\u95f4\u8868\u7b49\u4f7f\u7528\u76f8\u540c\u7684\u7b26\u53f7\u3002</p>\n",
|
||||
"<h2>DDPM Sampler</h2>\n<p>This extends the <a href=\"index.html\"><span translate=no>_^_0_^_</span> base class</a>.</p>\n<p>DDPM samples images by repeatedly removing noise by sampling step by step from <span translate=no>_^_1_^_</span>,</p>\n<span translate=no>_^_2_^_</span>": "<h2>DDPM \u91c7\u6837\u5668</h2>\n<p>\u8fd9\u6269\u5c55\u4e86<a href=\"index.html\"><span translate=no>_^_0_^_</span>\u57fa\u7c7b</a>\u3002</p>\n<p>DDPM \u901a\u8fc7\u9010\u6b65\u4ece<span translate=no>_^_1_^_</span>\u4e2d\u53cd\u590d\u6d88\u9664\u566a\u70b9\u6765\u5bf9\u56fe\u50cf\u8fdb\u884c\u91c7\u6837</p>\n<span translate=no>_^_2_^_</span>",
|
||||
"<h3>Sample <span translate=no>_^_0_^_</span> from <span translate=no>_^_1_^_</span></h3>\n<ul><li><span translate=no>_^_2_^_</span> is <span translate=no>_^_3_^_</span> of shape <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the conditional embeddings <span translate=no>_^_6_^_</span> of shape <span translate=no>_^_7_^_</span> </li>\n<li><span translate=no>_^_8_^_</span> is <span translate=no>_^_9_^_</span> of shape <span translate=no>_^_10_^_</span> </li>\n<li><span translate=no>_^_11_^_</span> is the step <span translate=no>_^_12_^_</span> as an integer :repeat_noise: specified whether the noise should be same for all samples in the batch </li>\n<li><span translate=no>_^_13_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_14_^_</span> is the unconditional guidance scale <span translate=no>_^_15_^_</span>. This is used for <span translate=no>_^_16_^_</span> </li>\n<li><span translate=no>_^_17_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_18_^_</span></li></ul>\n": "<h3>\u6837\u672c<span translate=no>_^_0_^_</span>\u6765\u81ea<span translate=no>_^_1_^_</span></h3>\n<ul><li><span translate=no>_^_2_^_</span>\u662f\u5f62<span translate=no>_^_3_^_</span>\u72b6\u7684<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u662f\u5f62\u72b6<span translate=no>_^_6_^_</span>\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_7_^_</span></li>\n<li><span translate=no>_^_8_^_</span>\u662f\u5f62<span translate=no>_^_9_^_</span>\u72b6\u7684<span translate=no>_^_10_^_</span></li>\n<li><span translate=no>_^_11_^_</span>\u662f\u6574\u6570\u5f62\u5f0f\u7684\u6b65<span translate=no>_^_12_^_</span>\u957f:repeat_noise: \u6307\u5b9a\u6279\u6b21\u4e2d\u6240\u6709\u6837\u672c\u7684\u566a\u58f0\u662f\u5426\u5e94\u76f8\u540c</li>\n<li><span translate=no>_^_13_^_</span>\u662f\u566a\u58f0\u6e29\u5ea6\uff08\u968f\u673a\u566a\u58f0\u4e58\u4ee5\u6b64\u503c\uff09</li>\n<li><span translate=no>_^_14_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_15_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_16_^_</span></li>\n<li><span translate=no>_^_17_^_</span>\u662f\u7a7a\u63d0\u793a\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_18_^_</span></li></ul>\n",
|
||||
"<h3>Sample from <span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span> is <span translate=no>_^_3_^_</span> of shape <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the time step <span translate=no>_^_6_^_</span> index </li>\n<li><span translate=no>_^_7_^_</span> is the noise, <span translate=no>_^_8_^_</span></li></ul>\n": "<h3>\u6837\u672c\u6765\u81ea<span translate=no>_^_0_^_</span></h3>\n<p><span translate=no>_^_1_^_</span></p>\n<ul><li><span translate=no>_^_2_^_</span>\u662f\u5f62<span translate=no>_^_3_^_</span>\u72b6\u7684<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u662f\u65f6\u95f4\u6b65\u957f<span translate=no>_^_6_^_</span>\u6307\u6570</li>\n<li><span translate=no>_^_7_^_</span>\u662f\u566a\u97f3\uff0c<span translate=no>_^_8_^_</span></li></ul>\n",
|
||||
"<h3>Sampling Loop</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the shape of the generated images in the form <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the conditional embeddings <span translate=no>_^_3_^_</span> </li>\n<li><span translate=no>_^_4_^_</span> is the noise temperature (random noise gets multiplied by this) </li>\n<li><span translate=no>_^_5_^_</span> is <span translate=no>_^_6_^_</span>. If not provided random noise will be used. </li>\n<li><span translate=no>_^_7_^_</span> is the unconditional guidance scale <span translate=no>_^_8_^_</span>. This is used for <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is the conditional embedding for empty prompt <span translate=no>_^_11_^_</span> </li>\n<li><span translate=no>_^_12_^_</span> is the number of time steps to skip <span translate=no>_^_13_^_</span>. We start sampling from <span translate=no>_^_14_^_</span>. And <span translate=no>_^_15_^_</span> is then <span translate=no>_^_16_^_</span>.</li></ul>\n": "<h3>\u91c7\u6837\u56de\u8def</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u8868\u5355\u4e2d\u751f\u6210\u7684\u56fe\u50cf\u7684\u5f62\u72b6<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_3_^_</span></li>\n<li><span translate=no>_^_4_^_</span>\u662f\u566a\u58f0\u6e29\u5ea6\uff08\u968f\u673a\u566a\u58f0\u4e58\u4ee5\u6b64\u503c\uff09</li>\n<li><span translate=no>_^_5_^_</span>\u662f<span translate=no>_^_6_^_</span>\u3002\u5982\u679c\u672a\u63d0\u4f9b\uff0c\u5c06\u4f7f\u7528\u968f\u673a\u566a\u58f0\u3002</li>\n<li><span translate=no>_^_7_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_8_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u662f\u7a7a\u63d0\u793a\u7684\u6761\u4ef6\u5d4c\u5165<span translate=no>_^_11_^_</span></li>\n<li><span translate=no>_^_12_^_</span>\u662f\u8981\u8df3\u8fc7\u7684\u65f6\u95f4\u6b65\u6570<span translate=no>_^_13_^_</span>\u3002\u6211\u4eec\u4ece\u5f00\u59cb\u91c7\u6837<span translate=no>_^_14_^_</span>\u3002\u7136\u540e<span translate=no>_^_15_^_</span>\u5c31\u662f\u8fd9\u6837<span translate=no>_^_16_^_</span>\u3002</li></ul>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p> <span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p><span translate=no>_^_0_^_</span> schedule </p>\n": "<p><span translate=no>_^_0_^_</span>\u65f6\u95f4\u8868</p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span> with current <span translate=no>_^_1_^_</span></p>\n<p><span translate=no>_^_2_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u7528\u7535\u6d41\u8ba1\u7b97<span translate=no>_^_1_^_</span></p>\n<p><span translate=no>_^_2_^_</span></p>\n",
|
||||
"<p>Calculate <span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span> </p>\n": "<p>\u8ba1\u7b97<span translate=no>_^_0_^_</span></p>\n<p><span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Clamped log of <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5c01\u95ed\u4e86\u65e5\u5fd7<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Different noise for each sample </p>\n": "<p>\u6bcf\u4e2a\u6837\u672c\u7684\u566a\u58f0\u4e0d\u540c</p>\n",
|
||||
"<p>Do not add noise when <span translate=no>_^_0_^_</span> (final step sampling process). Note that <span translate=no>_^_1_^_</span> is <span translate=no>_^_2_^_</span> when <span translate=no>_^_3_^_</span>) </p>\n": "<p><span translate=no>_^_0_^_</span>\uff08\u6700\u540e\u4e00\u6b65\u91c7\u6837\u8fc7\u7a0b\uff09\u65f6\u4e0d\u8981\u6dfb\u52a0\u566a\u97f3\u3002\u6ce8\u610f\u90a3<span translate=no>_^_1_^_</span>\u662f<span translate=no>_^_2_^_</span>\u65f6\u5019<span translate=no>_^_3_^_</span>\uff09</p>\n",
|
||||
"<p>Get <span translate=no>_^_0_^_</span> </p>\n": "<p>\u83b7\u53d6<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Get batch size </p>\n": "<p>\u83b7\u53d6\u6279\u6b21\u5927\u5c0f</p>\n",
|
||||
"<p>Get device and batch size </p>\n": "<p>\u83b7\u53d6\u8bbe\u5907\u548c\u6279\u6b21\u5927\u5c0f</p>\n",
|
||||
"<p>If same noise is used for all samples in the batch </p>\n": "<p>\u5982\u679c\u6279\u6b21\u4e2d\u7684\u6240\u6709\u6837\u54c1\u90fd\u4f7f\u7528\u76f8\u540c\u7684\u566a\u58f0</p>\n",
|
||||
"<p>Multiply noise by the temperature </p>\n": "<p>\u5c06\u566a\u58f0\u4e58\u4ee5\u6e29\u5ea6</p>\n",
|
||||
"<p>Random noise, if noise is not specified </p>\n": "<p>\u5982\u679c\u672a\u6307\u5b9a\u566a\u58f0\uff0c\u5219\u4e3a\u968f\u673a\u566a\u58f0</p>\n",
|
||||
"<p>Return <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8fd4\u56de<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample <span translate=no>_^_0_^_</span> </p>\n": "<p>\u793a\u4f8b<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample from <span translate=no>_^_0_^_</span> </p>\n": "<p>\u6837\u672c\u6765\u81ea<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sample from,</p>\n<p><span translate=no>_^_0_^_</span> </p>\n": "<p>\u6837\u672c\u6765\u81ea</p>\n<p><span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Sampling loop </p>\n": "<p>\u91c7\u6837\u56de\u8def</p>\n",
|
||||
"<p>Sampling steps <span translate=no>_^_0_^_</span> </p>\n": "<p>\u91c7\u6837\u6b65\u9aa4<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time step <span translate=no>_^_0_^_</span> </p>\n": "<p>\u65f6\u95f4\u6b65\u957f<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<p>Time steps to sample at <span translate=no>_^_0_^_</span> </p>\n": "<p>\u91c7\u6837\u7684\u65f6\u95f4\u6b65\u957f<span translate=no>_^_0_^_</span></p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the model to predict noise <span translate=no>_^_1_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u9884\u6d4b\u566a\u58f0\u7684\u6a21\u578b<span translate=no>_^_1_^_</span></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of Denoising Diffusion Probabilistic Models (DDPM) Sampling for stable diffusion model.": "\u5e26\u6ce8\u91ca\u7684 PyTorch \u5b9e\u73b0/\u6559\u7a0b\uff1a\u7528\u4e8e\u7a33\u5b9a\u6269\u6563\u6a21\u578b\u7684\u964d\u566a\u6269\u6563\u6982\u7387\u6a21\u578b (DDPM) \u91c7\u6837\u3002",
|
||||
"Denoising Diffusion Probabilistic Models (DDPM) Sampling": "\u53bb\u566a\u6269\u6563\u6982\u7387\u6a21\u578b (DDPM) \u91c7\u6837"
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"<h1>Scripts to show example usages <a href=\"../index.html\">stable diffusion</a></h1>\n<ul><li><a href=\"text_to_image.html\">Prompt to image diffusion</a> </li>\n<li><a href=\"image_to_image.html\">Image to image diffusion</a> </li>\n<li><a href=\"in_paint.html\">In-painting</a></li></ul>\n": "<h1><a href=\"../index.html\">\u5b89\u5b9a\u62e1\u6563\u306e\u4f7f\u7528\u4f8b\u3092\u793a\u3059\u30b9\u30af\u30ea\u30d7\u30c8</a></h1>\n<ul><li><a href=\"text_to_image.html\">\u753b\u50cf\u62e1\u6563\u3078\u306e\u30d7\u30ed\u30f3\u30d7\u30c8</a></li>\n<li><a href=\"image_to_image.html\">\u753b\u50cf\u304b\u3089\u753b\u50cf\u3078\u306e\u62e1\u6563</a></li>\n<li><a href=\"in_paint.html\">\u30a4\u30f3\u30da\u30a4\u30f3\u30c6\u30a3\u30f3\u30b0</a></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of example usages of stable diffusion": "\u5b89\u5b9a\u62e1\u6563\u306e\u4f7f\u7528\u4f8b\u306b\u95a2\u3059\u308b\u6ce8\u91c8\u4ed8\u304dPyTorch\u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb",
|
||||
"Scripts to show example usages stable diffusion": "\u5b89\u5b9a\u62e1\u6563\u306e\u4f7f\u7528\u4f8b\u3092\u793a\u3059\u30b9\u30af\u30ea\u30d7\u30c8"
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"<h1>Scripts to show example usages <a href=\"../index.html\">stable diffusion</a></h1>\n<ul><li><a href=\"text_to_image.html\">Prompt to image diffusion</a> </li>\n<li><a href=\"image_to_image.html\">Image to image diffusion</a> </li>\n<li><a href=\"in_paint.html\">In-painting</a></li></ul>\n": "<h1>\u0d8b\u0daf\u0dcf\u0dc4\u0dbb\u0dab\u0dba\u0d9a\u0dca \u0db4\u0dd9\u0db1\u0dca\u0dc0\u0dd3\u0db8\u0da7 \u0dc3\u0dca\u0d9a\u0dca\u0dbb\u0dd2\u0db4\u0dca\u0da7\u0dca <a href=\"../index.html\">\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba</a> \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0dba\u0dd2</h1>\n<ul><li><a href=\"text_to_image.html\">\u0dbb\u0dd6\u0db4 \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0dc0\u0dd2\u0db8\u0dc3\u0db1\u0dca\u0db1</a></li>\n<li><a href=\"image_to_image.html\">\u0dbb\u0dd6\u0db4 \u0dc3\u0dd2\u0da7 \u0dbb\u0dd6\u0db4 \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba</a></li>\n<li><a href=\"in_paint.html\">\u0daf\u0dd3-\u0dc3\u0dd2\u0dad\u0dd4\u0dc0\u0db8\u0dca</a></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of example usages of stable diffusion": "PyTorch \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8/\u0db1\u0dd2\u0db6\u0db1\u0dca\u0db0\u0db1\u0dba \u0d8b\u0daf\u0dcf\u0dc4\u0dbb\u0dab\u0dba\u0d9a\u0dca \u0dbd\u0dd9\u0dc3 \u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba\u0d9a \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dba\u0db1\u0dca",
|
||||
"Scripts to show example usages stable diffusion": "\u0d8b\u0daf\u0dcf\u0dc4\u0dbb\u0dab\u0dba\u0d9a\u0dca \u0db4\u0dd9\u0db1\u0dca\u0dc0\u0dd3\u0db8\u0da7 \u0dc3\u0dca\u0d9a\u0dca\u0dbb\u0dd2\u0db4\u0dca\u0da7\u0dca \u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0dba\u0dd2"
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"<h1>Scripts to show example usages <a href=\"../index.html\">stable diffusion</a></h1>\n<ul><li><a href=\"text_to_image.html\">Prompt to image diffusion</a> </li>\n<li><a href=\"image_to_image.html\">Image to image diffusion</a> </li>\n<li><a href=\"in_paint.html\">In-painting</a></li></ul>\n": "<h1>\u7528\u4e8e\u663e\u793a\u793a\u4f8b\u7528\u6cd5\u548c<a href=\"../index.html\">\u7a33\u5b9a\u6269\u6563</a>\u7684\u811a\u672c</h1>\n<ul><li><a href=\"text_to_image.html\">\u63d0\u793a\u56fe\u50cf\u6269\u6563</a></li>\n<li><a href=\"image_to_image.html\">\u56fe\u50cf\u5230\u56fe\u50cf\u7684\u6269\u6563</a></li>\n<li><a href=\"in_paint.html\">\u5185\u753b</a></li></ul>\n",
|
||||
"Annotated PyTorch implementation/tutorial of example usages of stable diffusion": "\u5e26\u6ce8\u91ca\u7684 PyTorch \u5b9e\u73b0/\u7a33\u5b9a\u6269\u6563\u793a\u4f8b\u7528\u6cd5\u6559\u7a0b",
|
||||
"Scripts to show example usages stable diffusion": "\u7528\u4e8e\u663e\u793a\u793a\u4f8b\u7528\u6cd5\u548c\u7a33\u5b9a\u6269\u6563\u7684\u811a\u672c"
|
||||
}
|
||||
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"<h1>Generate images using <a href=\"../index.html\">stable diffusion</a> with a prompt from a given image</h1>\n": "<h1><a href=\"../index.html\">\u4e0e\u3048\u3089\u308c\u305f\u753b\u50cf\u304b\u3089\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u5b89\u5b9a\u62e1\u6563\u3092\u7528\u3044\u305f\u753b\u50cf\u3092\u751f\u6210\u3057\u307e\u3059</a></h1>\n",
|
||||
"<h3>CLI</h3>\n": "<h3>CLI</h3>\n",
|
||||
"<h3>Image to image class</h3>\n": "<h3>\u753b\u50cf\u304b\u3089\u753b\u50cf\u3078\u306e\u30af\u30e9\u30b9</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>AMP auto casting </p>\n": "<p>AMP \u30aa\u30fc\u30c8\u30ad\u30e3\u30b9\u30c6\u30a3\u30f3\u30b0</p>\n",
|
||||
"<p>Add noise to the original image </p>\n": "<p>\u5143\u306e\u753b\u50cf\u306b\u30ce\u30a4\u30ba\u3092\u8ffd\u52a0</p>\n",
|
||||
"<p>Decode the image from the <a href=\"../model/autoencoder.html\">autoencoder</a> </p>\n": "<p><a href=\"../model/autoencoder.html\">\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u304b\u3089\u753b\u50cf\u3092\u30c7\u30b3\u30fc\u30c9\u3057\u307e\u3059</a></p>\n",
|
||||
"<p>Encode the image in the latent space and make <span translate=no>_^_0_^_</span> copies of it </p>\n": "<p>\u6f5c\u5728\u7a7a\u9593\u306b\u753b\u50cf\u3092\u30a8\u30f3\u30b3\u30fc\u30c9\u3057\u3001<span translate=no>_^_0_^_</span>\u305d\u306e\u30b3\u30d4\u30fc\u3092\u4f5c\u6210\u3057\u307e\u3059</p>\n",
|
||||
"<p>Get device </p>\n": "<p>\u30c7\u30d0\u30a4\u30b9\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Get the number of steps to diffuse the original </p>\n": "<p>\u30aa\u30ea\u30b8\u30ca\u30eb\u3092\u62e1\u6563\u3055\u305b\u308b\u307e\u3067\u306e\u30b9\u30c6\u30c3\u30d7\u6570\u3092\u6c42\u3081\u308b</p>\n",
|
||||
"<p>Get the prompt embeddings </p>\n": "<p>\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u57cb\u3081\u8fbc\u307f\u3092\u5165\u624b</p>\n",
|
||||
"<p>In unconditional scaling is not <span translate=no>_^_0_^_</span> get the embeddings for empty prompts (no conditioning). </p>\n": "<p>\u7121\u6761\u4ef6\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3067\u306f\u3001<span translate=no>_^_0_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u306f\u57cb\u3081\u8fbc\u307f\u306f\u53d6\u5f97\u3055\u308c\u307e\u305b\u3093 (\u6761\u4ef6\u306a\u3057)\u3002</p>\n",
|
||||
"<p>Initialize <a href=\"../sampler/ddim.html\">DDIM sampler</a> </p>\n": "<p><a href=\"../sampler/ddim.html\">DDIM \u30b5\u30f3\u30d7\u30e9\u30fc\u3092\u521d\u671f\u5316</a></p>\n",
|
||||
"<p>Load <a href=\"../latent_diffusion.html\">latent diffusion model</a> </p>\n": "<p><a href=\"../latent_diffusion.html\">\u8ca0\u8377\u6f5c\u4f0f\u62e1\u6563\u30e2\u30c7\u30eb</a></p>\n",
|
||||
"<p>Load image </p>\n": "<p>\u753b\u50cf\u3092\u8aad\u307f\u8fbc\u3080</p>\n",
|
||||
"<p>Make a batch of prompts </p>\n": "<p>\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u4e00\u62ec\u4f5c\u6210</p>\n",
|
||||
"<p>Move the model to device </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5</p>\n",
|
||||
"<p>Reconstruct from the noisy image </p>\n": "<p>\u30ce\u30a4\u30ba\u306e\u591a\u3044\u753b\u50cf\u304b\u3089\u306e\u518d\u69cb\u7bc9</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u753b\u50cf\u3092\u4fdd\u5b58</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path of the checkpoint </li>\n<li><span translate=no>_^_1_^_</span> is the number of sampling steps </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"../sampler/ddim.html\">DDIM sampling</a> <span translate=no>_^_3_^_</span> constant</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u306e\u30d1\u30b9\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u306f\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30b9\u30c6\u30c3\u30d7\u306e\u6570</li>\n<li><span translate=no>_^_2_^_</span><a href=\"../sampler/ddim.html\">DDIM</a> \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u5b9a\u6570\u3067\u3059 <span translate=no>_^_3_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path to store the generated images </li>\n<li><span translate=no>_^_1_^_</span> is the image to transform </li>\n<li><span translate=no>_^_2_^_</span> specifies how much of the original image should not be preserved </li>\n<li><span translate=no>_^_3_^_</span> is the number of images to generate in a batch </li>\n<li><span translate=no>_^_4_^_</span> is the prompt to generate images with </li>\n<li><span translate=no>_^_5_^_</span> is the unconditional guidance scale <span translate=no>_^_6_^_</span>. This is used for <span translate=no>_^_7_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u751f\u6210\u3055\u308c\u305f\u753b\u50cf\u3092\u4fdd\u5b58\u3059\u308b\u30d1\u30b9\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u5909\u63db\u3059\u308b\u753b\u50cf\u3067\u3059</li>\n<li><span translate=no>_^_2_^_</span>\u5143\u306e\u753b\u50cf\u306e\u3069\u306e\u7a0b\u5ea6\u4fdd\u5b58\u3057\u306a\u3044\u304b\u3092\u6307\u5b9a\u3057\u307e\u3059</li>\n<li><span translate=no>_^_3_^_</span>\u306f\u30d0\u30c3\u30c1\u3067\u751f\u6210\u3059\u308b\u753b\u50cf\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_4_^_</span>\u3067\u753b\u50cf\u3092\u751f\u6210\u3059\u308b\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u3059</li>\n<li><span translate=no>_^_5_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059 <span translate=no>_^_6_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"Generate images using stable diffusion with a prompt from a given image": "\u4e0e\u3048\u3089\u308c\u305f\u753b\u50cf\u304b\u3089\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u5b89\u5b9a\u62e1\u6563\u3092\u7528\u3044\u305f\u753b\u50cf\u3092\u751f\u6210\u3057\u307e\u3059"
|
||||
}
|
||||
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"<h1>Generate images using <a href=\"../index.html\">stable diffusion</a> with a prompt from a given image</h1>\n": "<h1>\u0daf\u0dd3 \u0d87\u0dad\u0dd2 \u0dbb\u0dd6\u0db4\u0dba\u0d9a\u0dd2\u0db1\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db8\u0d9f <a href=\"../index.html\">\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba</a> \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd2\u0db1\u0dca \u0dbb\u0dd6\u0db4 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</h1>\n",
|
||||
"<h3>CLI</h3>\n": "<h3>CLI</h3>\n",
|
||||
"<h3>Image to image class</h3>\n": "<h3>\u0dbb\u0dd6\u0db4 \u0db4\u0db1\u0dca\u0dad\u0dd2\u0dba\u0da7 \u0dbb\u0dd6\u0db4\u0dba</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>AMP auto casting </p>\n": "<p>AMP \u0dc0\u0dcf\u0dc4\u0db1 \u0dc0\u0dcf\u0dad\u0dca\u0dad\u0dd4</p>\n",
|
||||
"<p>Add noise to the original image </p>\n": "<p>\u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba\u0da7 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0d91\u0d9a\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Decode the image from the <a href=\"../model/autoencoder.html\">autoencoder</a> </p>\n": "<p><a href=\"../model/autoencoder.html\">\u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba\u0dd9\u0db1\u0dca \u0dbb\u0dd6\u0db4\u0dba \u0dc0\u0dd2\u0d9a\u0dda\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</a></p>\n",
|
||||
"<p>Encode the image in the latent space and make <span translate=no>_^_0_^_</span> copies of it </p>\n": "<p>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0dbb\u0dd6\u0db4\u0dba \u0d9a\u0dda\u0dad\u0db1\u0dba \u0d9a\u0dbb \u0d91\u0dc4\u0dd2<span translate=no>_^_0_^_</span> \u0db4\u0dd2\u0da7\u0db4\u0dad\u0dca \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get device </p>\n": "<p>\u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get the number of steps to diffuse the original </p>\n": "<p>\u0db8\u0dd4\u0dbd\u0dca \u0db4\u0dd2\u0da7\u0db4\u0dad \u0dc0\u0dd2\u0dc3\u0dd4\u0dbb\u0dd4\u0dc0\u0dcf \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0da7 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get the prompt embeddings </p>\n": "<p>\u0d9a\u0da9\u0dd2\u0db1\u0db8\u0dca \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>In unconditional scaling is not <span translate=no>_^_0_^_</span> get the embeddings for empty prompts (no conditioning). </p>\n": "<p>\u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba \u0dad\u0dd4\u0dc5 \u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd3\u0db8\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca<span translate=no>_^_0_^_</span> \u0dbd\u0db6\u0dcf \u0db1\u0ddc\u0d9c\u0db1\u0dd3 (\u0d9a\u0db1\u0dca\u0da9\u0dd2\u0dc2\u0db1\u0dda\u0dc2\u0db1\u0dca \u0db1\u0dd0\u0dad).</p>\n",
|
||||
"<p>Initialize <a href=\"../sampler/ddim.html\">DDIM sampler</a> </p>\n": "<p><a href=\"../sampler/ddim.html\">\u0da9\u0dd3\u0da9\u0dd3\u0d85\u0dba\u0dd2\u0d91\u0db8\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0d9a\u0dbb\u0dd4</a> \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Load <a href=\"../latent_diffusion.html\">latent diffusion model</a> </p>\n": "<p><a href=\"../latent_diffusion.html\">\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba</a> \u0db4\u0da7\u0dc0\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Load image </p>\n": "<p>\u0dbb\u0dd6\u0db4\u0dba \u0db4\u0da7\u0dc0\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Make a batch of prompts </p>\n": "<p>\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0d9a\u0dca \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Move the model to device </p>\n": "<p>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba\u0da7 \u0d9c\u0dd9\u0db1 \u0dba\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Reconstruct from the noisy image </p>\n": "<p>Is \u0ddd\u0dc2\u0dcf\u0d9a\u0dcf\u0dbb\u0dd3 \u0dbb\u0dd6\u0db4\u0dba\u0dd9\u0db1\u0dca \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0db1\u0dd2\u0dbb\u0dca\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u0db4\u0dd2\u0db1\u0dca\u0dad\u0dd6\u0dbb \u0dc3\u0dd4\u0dbb\u0d9a\u0dd2\u0db1\u0dca\u0db1</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path of the checkpoint </li>\n<li><span translate=no>_^_1_^_</span> is the number of sampling steps </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"../sampler/ddim.html\">DDIM sampling</a> <span translate=no>_^_3_^_</span> constant</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dba\u0db1\u0dd4 \u0db8\u0dd4\u0dbb\u0db4\u0ddc\u0dbd\u0dda \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_1_^_</span>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n<li><span translate=no>_^_2_^_</span><a href=\"../sampler/ddim.html\">DDIM \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2</a><span translate=no>_^_3_^_</span> \u0db1\u0dd2\u0dba\u0dad\u0dba</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path to store the generated images </li>\n<li><span translate=no>_^_1_^_</span> is the image to transform </li>\n<li><span translate=no>_^_2_^_</span> specifies how much of the original image should not be preserved </li>\n<li><span translate=no>_^_3_^_</span> is the number of images to generate in a batch </li>\n<li><span translate=no>_^_4_^_</span> is the prompt to generate images with </li>\n<li><span translate=no>_^_5_^_</span> is the unconditional guidance scale <span translate=no>_^_6_^_</span>. This is used for <span translate=no>_^_7_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0dbb\u0dd6\u0db4 \u0d9c\u0db6\u0da9\u0dcf \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_1_^_</span>\u0dba\u0db1\u0dd4 \u0db4\u0dbb\u0dd2\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0dbb\u0dd6\u0db4\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_2_^_</span>\u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba\u0dda \u0d9a\u0ddc\u0db4\u0db8\u0dab \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0d9a\u0dca \u0dc3\u0d82\u0dbb\u0d9a\u0dca\u0dc2\u0dab\u0dba \u0db1\u0ddc\u0d9a\u0dc5 \u0dba\u0dd4\u0dad\u0dd4\u0daf \u0dba\u0db1\u0dca\u0db1 \u0db1\u0dd2\u0dba\u0db8 \u0d9a\u0dbb\u0dba\u0dd2</li>\n<li><span translate=no>_^_3_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0d9a\u0dca \u0dad\u0dd4\u0dc5 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dc5 \u0dba\u0dd4\u0dad\u0dd4 \u0dbb\u0dd6\u0db4 \u0d9c\u0dab\u0db1</li>\n<li><span translate=no>_^_4_^_</span>\u0dc3\u0db8\u0d9f \u0dbb\u0dd6\u0db4 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8</li>\n<li><span translate=no>_^_5_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_6_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"Generate images using stable diffusion with a prompt from a given image": "\u0daf\u0dd3 \u0d87\u0dad\u0dd2 \u0dbb\u0dd6\u0db4\u0dba\u0d9a\u0dd2\u0db1\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db8\u0d9f \u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd2\u0db1\u0dca \u0dbb\u0dd6\u0db4 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1"
|
||||
}
|
||||
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"<h1>Generate images using <a href=\"../index.html\">stable diffusion</a> with a prompt from a given image</h1>\n": "<h1>\u6839\u636e\u7ed9\u5b9a\u56fe\u50cf\u7684\u63d0\u793a\uff0c\u4f7f\u7528<a href=\"../index.html\">\u7a33\u5b9a\u7684\u6269\u6563</a>\u751f\u6210\u56fe\u50cf</h1>\n",
|
||||
"<h3>CLI</h3>\n": "<h3>CLI</h3>\n",
|
||||
"<h3>Image to image class</h3>\n": "<h3>\u56fe\u50cf\u5230\u56fe\u50cf\u7c7b\u522b</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>AMP auto casting </p>\n": "<p>AMP \u81ea\u52a8\u6295\u5c04</p>\n",
|
||||
"<p>Add noise to the original image </p>\n": "<p>\u5411\u539f\u59cb\u56fe\u50cf\u6dfb\u52a0\u566a\u70b9</p>\n",
|
||||
"<p>Decode the image from the <a href=\"../model/autoencoder.html\">autoencoder</a> </p>\n": "<p>\u4ece<a href=\"../model/autoencoder.html\">\u81ea\u52a8\u7f16\u7801\u5668\u89e3\u7801</a>\u56fe\u50cf</p>\n",
|
||||
"<p>Encode the image in the latent space and make <span translate=no>_^_0_^_</span> copies of it </p>\n": "<p>\u5728\u6f5c\u5728\u7a7a\u95f4\u4e2d\u5bf9\u56fe\u50cf\u8fdb\u884c\u7f16\u7801\u5e76\u5236\u4f5c<span translate=no>_^_0_^_</span>\u526f\u672c</p>\n",
|
||||
"<p>Get device </p>\n": "<p>\u83b7\u53d6\u8bbe\u5907</p>\n",
|
||||
"<p>Get the number of steps to diffuse the original </p>\n": "<p>\u83b7\u53d6\u6f2b\u53cd\u5c04\u539f\u7a3f\u7684\u6b65\u6570</p>\n",
|
||||
"<p>Get the prompt embeddings </p>\n": "<p>\u83b7\u53d6\u63d0\u793a\u5d4c\u5165\u4fe1\u606f</p>\n",
|
||||
"<p>In unconditional scaling is not <span translate=no>_^_0_^_</span> get the embeddings for empty prompts (no conditioning). </p>\n": "<p>\u5728\u65e0\u6761\u4ef6\u7f29\u653e\u4e2d\uff0c\u65e0\u6cd5<span translate=no>_^_0_^_</span>\u83b7\u53d6\u7a7a\u63d0\u793a\u7684\u5d4c\u5165\u503c\uff08\u65e0\u6761\u4ef6\uff09\u3002</p>\n",
|
||||
"<p>Initialize <a href=\"../sampler/ddim.html\">DDIM sampler</a> </p>\n": "<p>\u521d\u59cb\u5316 <a href=\"../sampler/ddim.html\">DDIM \u91c7\u6837\u5668</a></p>\n",
|
||||
"<p>Load <a href=\"../latent_diffusion.html\">latent diffusion model</a> </p>\n": "<p>\u8f7d\u8377<a href=\"../latent_diffusion.html\">\u6f5c\u5728\u6269\u6563\u6a21\u578b</a></p>\n",
|
||||
"<p>Load image </p>\n": "<p>\u52a0\u8f7d\u56fe\u7247</p>\n",
|
||||
"<p>Make a batch of prompts </p>\n": "<p>\u505a\u4e00\u6279\u63d0\u793a</p>\n",
|
||||
"<p>Move the model to device </p>\n": "<p>\u5c06\u6a21\u578b\u79fb\u81f3\u8bbe\u5907</p>\n",
|
||||
"<p>Reconstruct from the noisy image </p>\n": "<p>\u4ece\u5608\u6742\u7684\u56fe\u50cf\u4e2d\u91cd\u5efa</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u4fdd\u5b58\u56fe\u7247</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path of the checkpoint </li>\n<li><span translate=no>_^_1_^_</span> is the number of sampling steps </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"../sampler/ddim.html\">DDIM sampling</a> <span translate=no>_^_3_^_</span> constant</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u68c0\u67e5\u70b9\u7684\u8def\u5f84</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u91c7\u6837\u6b65\u9aa4\u7684\u6570\u91cf</li>\n<li><span translate=no>_^_2_^_</span>\u662f <a href=\"../sampler/ddim.html\">DDIM \u91c7\u6837</a><span translate=no>_^_3_^_</span>\u5e38\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path to store the generated images </li>\n<li><span translate=no>_^_1_^_</span> is the image to transform </li>\n<li><span translate=no>_^_2_^_</span> specifies how much of the original image should not be preserved </li>\n<li><span translate=no>_^_3_^_</span> is the number of images to generate in a batch </li>\n<li><span translate=no>_^_4_^_</span> is the prompt to generate images with </li>\n<li><span translate=no>_^_5_^_</span> is the unconditional guidance scale <span translate=no>_^_6_^_</span>. This is used for <span translate=no>_^_7_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5b58\u50a8\u751f\u6210\u7684\u56fe\u50cf\u7684\u8def\u5f84</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u8981\u8f6c\u6362\u7684\u56fe\u50cf</li>\n<li><span translate=no>_^_2_^_</span>\u6307\u5b9a\u4e0d\u5e94\u4fdd\u7559\u539f\u59cb\u56fe\u50cf\u7684\u591a\u5c11</li>\n<li><span translate=no>_^_3_^_</span>\u662f\u6279\u91cf\u751f\u6210\u7684\u56fe\u50cf\u6570\u91cf</li>\n<li><span translate=no>_^_4_^_</span>\u662f\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u751f\u6210\u56fe\u50cf\u7684\u63d0\u793a</li>\n<li><span translate=no>_^_5_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_6_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"Generate images using stable diffusion with a prompt from a given image": "\u6839\u636e\u7ed9\u5b9a\u56fe\u50cf\u7684\u63d0\u793a\uff0c\u4f7f\u7528\u7a33\u5b9a\u7684\u6269\u6563\u751f\u6210\u56fe\u50cf"
|
||||
}
|
||||
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"<h1>In-paint images using <a href=\"../index.html\">stable diffusion</a> with a prompt</h1>\n": "<h1><a href=\"../index.html\">\u30d7\u30ed\u30f3\u30d7\u30c8\u4ed8\u304d\u306e\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u3092\u5229\u7528\u3057\u305f\u30da\u30a4\u30f3\u30c8\u4e2d\u306e\u753b\u50cf</a></h1>\n",
|
||||
"<h3>CLI</h3>\n": "<h3>CLI</h3>\n",
|
||||
"<h3>Image in-painting class</h3>\n": "<h3>\u30a4\u30e1\u30fc\u30b8\u30fb\u30a4\u30f3\u30fb\u30da\u30a4\u30f3\u30c6\u30a3\u30f3\u30b0\u30fb\u30af\u30e9\u30b9</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>AMP auto casting </p>\n": "<p>AMP \u30aa\u30fc\u30c8\u30ad\u30e3\u30b9\u30c6\u30a3\u30f3\u30b0</p>\n",
|
||||
"<p>Add noise to the original image </p>\n": "<p>\u5143\u306e\u753b\u50cf\u306b\u30ce\u30a4\u30ba\u3092\u8ffd\u52a0</p>\n",
|
||||
"<p>Decode the image from the <a href=\"../model/autoencoder.html\">autoencoder</a> </p>\n": "<p><a href=\"../model/autoencoder.html\">\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u304b\u3089\u753b\u50cf\u3092\u30c7\u30b3\u30fc\u30c9\u3057\u307e\u3059</a></p>\n",
|
||||
"<p>Encode the image in the latent space and make <span translate=no>_^_0_^_</span> copies of it </p>\n": "<p>\u6f5c\u5728\u7a7a\u9593\u306b\u753b\u50cf\u3092\u30a8\u30f3\u30b3\u30fc\u30c9\u3057\u3001<span translate=no>_^_0_^_</span>\u305d\u306e\u30b3\u30d4\u30fc\u3092\u4f5c\u6210\u3057\u307e\u3059</p>\n",
|
||||
"<p>Get device </p>\n": "<p>\u30c7\u30d0\u30a4\u30b9\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Get the number of steps to diffuse the original </p>\n": "<p>\u30aa\u30ea\u30b8\u30ca\u30eb\u3092\u62e1\u6563\u3055\u305b\u308b\u307e\u3067\u306e\u30b9\u30c6\u30c3\u30d7\u6570\u3092\u6c42\u3081\u308b</p>\n",
|
||||
"<p>Get the prompt embeddings </p>\n": "<p>\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u57cb\u3081\u8fbc\u307f\u3092\u5165\u624b</p>\n",
|
||||
"<p>If <span translate=no>_^_0_^_</span> is not provided, we set a sample mask to preserve the bottom half of the image </p>\n": "<p><span translate=no>_^_0_^_</span>\u63d0\u4f9b\u3055\u308c\u3066\u3044\u306a\u3044\u5834\u5408\u306f\u3001\u753b\u50cf\u306e\u4e0b\u534a\u5206\u3092\u4fdd\u5b58\u3059\u308b\u3088\u3046\u306b\u30b5\u30f3\u30d7\u30eb\u30de\u30b9\u30af\u3092\u8a2d\u5b9a\u3057\u307e\u3059</p>\n",
|
||||
"<p>In unconditional scaling is not <span translate=no>_^_0_^_</span> get the embeddings for empty prompts (no conditioning). </p>\n": "<p>\u7121\u6761\u4ef6\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3067\u306f\u3001<span translate=no>_^_0_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u306f\u57cb\u3081\u8fbc\u307f\u306f\u53d6\u5f97\u3055\u308c\u307e\u305b\u3093 (\u6761\u4ef6\u306a\u3057)\u3002</p>\n",
|
||||
"<p>Initialize <a href=\"../sampler/ddim.html\">DDIM sampler</a> </p>\n": "<p><a href=\"../sampler/ddim.html\">DDIM \u30b5\u30f3\u30d7\u30e9\u30fc\u3092\u521d\u671f\u5316</a></p>\n",
|
||||
"<p>Load <a href=\"../latent_diffusion.html\">latent diffusion model</a> </p>\n": "<p><a href=\"../latent_diffusion.html\">\u8ca0\u8377\u6f5c\u4f0f\u62e1\u6563\u30e2\u30c7\u30eb</a></p>\n",
|
||||
"<p>Load image </p>\n": "<p>\u753b\u50cf\u3092\u8aad\u307f\u8fbc\u3080</p>\n",
|
||||
"<p>Make a batch of prompts </p>\n": "<p>\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u4e00\u62ec\u4f5c\u6210</p>\n",
|
||||
"<p>Move the model to device </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5</p>\n",
|
||||
"<p>Noise diffuse the original image </p>\n": "<p>\u30ce\u30a4\u30ba\u306f\u5143\u306e\u753b\u50cf\u3092\u62e1\u6563\u3057\u307e\u3059</p>\n",
|
||||
"<p>Reconstruct from the noisy image, while preserving the masked area </p>\n": "<p>\u30de\u30b9\u30af\u3055\u308c\u305f\u9818\u57df\u306f\u305d\u306e\u307e\u307e\u306b\u3001\u30ce\u30a4\u30ba\u306e\u591a\u3044\u753b\u50cf\u304b\u3089\u518d\u69cb\u6210\u3057\u307e\u3059</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u753b\u50cf\u3092\u4fdd\u5b58</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path of the checkpoint </li>\n<li><span translate=no>_^_1_^_</span> is the number of sampling steps </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"../sampler/ddim.html\">DDIM sampling</a> <span translate=no>_^_3_^_</span> constant</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u306e\u30d1\u30b9\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u306f\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30b9\u30c6\u30c3\u30d7\u306e\u6570</li>\n<li><span translate=no>_^_2_^_</span><a href=\"../sampler/ddim.html\">DDIM</a> \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u5b9a\u6570\u3067\u3059 <span translate=no>_^_3_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path to store the generated images </li>\n<li><span translate=no>_^_1_^_</span> is the image to transform </li>\n<li><span translate=no>_^_2_^_</span> specifies how much of the original image should not be preserved </li>\n<li><span translate=no>_^_3_^_</span> is the number of images to generate in a batch </li>\n<li><span translate=no>_^_4_^_</span> is the prompt to generate images with </li>\n<li><span translate=no>_^_5_^_</span> is the unconditional guidance scale <span translate=no>_^_6_^_</span>. This is used for <span translate=no>_^_7_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u751f\u6210\u3055\u308c\u305f\u753b\u50cf\u3092\u4fdd\u5b58\u3059\u308b\u30d1\u30b9\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u5909\u63db\u3059\u308b\u753b\u50cf\u3067\u3059</li>\n<li><span translate=no>_^_2_^_</span>\u5143\u306e\u753b\u50cf\u306e\u3069\u306e\u7a0b\u5ea6\u4fdd\u5b58\u3057\u306a\u3044\u304b\u3092\u6307\u5b9a\u3057\u307e\u3059</li>\n<li><span translate=no>_^_3_^_</span>\u306f\u30d0\u30c3\u30c1\u3067\u751f\u6210\u3059\u308b\u753b\u50cf\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_4_^_</span>\u3067\u753b\u50cf\u3092\u751f\u6210\u3059\u308b\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u3059</li>\n<li><span translate=no>_^_5_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059 <span translate=no>_^_6_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"In-paint images using stable diffusion with a prompt": "\u30d7\u30ed\u30f3\u30d7\u30c8\u4ed8\u304d\u306e\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u3092\u5229\u7528\u3057\u305f\u30da\u30a4\u30f3\u30c8\u4e2d\u306e\u753b\u50cf"
|
||||
}
|
||||
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"<h1>In-paint images using <a href=\"../index.html\">stable diffusion</a> with a prompt</h1>\n": "<h1>\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db8\u0d9f <a href=\"../index.html\">\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba</a> \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd2\u0db1\u0dca \u0dad\u0dd3\u0db1\u0dca\u0dad \u0d86\u0dbd\u0dda\u0db4\u0db1 \u0dbb\u0dd6\u0db4</h1>\n",
|
||||
"<h3>CLI</h3>\n": "<h3>CLI</h3>\n",
|
||||
"<h3>Image in-painting class</h3>\n": "<h3>\u0dbb\u0dd6\u0db4\u0dba\u0dda \u0db4\u0dd2\u0db1\u0dca\u0dad\u0dcf\u0dbb\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0db4\u0db1\u0dca\u0dad\u0dd2\u0dba</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>AMP auto casting </p>\n": "<p>AMP \u0dc0\u0dcf\u0dc4\u0db1 \u0dc0\u0dcf\u0dad\u0dca\u0dad\u0dd4</p>\n",
|
||||
"<p>Add noise to the original image </p>\n": "<p>\u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba\u0da7 \u0dc1\u0db6\u0dca\u0daf\u0dba \u0d91\u0d9a\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Decode the image from the <a href=\"../model/autoencoder.html\">autoencoder</a> </p>\n": "<p><a href=\"../model/autoencoder.html\">\u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba\u0dd9\u0db1\u0dca \u0dbb\u0dd6\u0db4\u0dba \u0dc0\u0dd2\u0d9a\u0dda\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</a></p>\n",
|
||||
"<p>Encode the image in the latent space and make <span translate=no>_^_0_^_</span> copies of it </p>\n": "<p>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0dbb\u0dd6\u0db4\u0dba \u0d9a\u0dda\u0dad\u0db1\u0dba \u0d9a\u0dbb \u0d91\u0dc4\u0dd2<span translate=no>_^_0_^_</span> \u0db4\u0dd2\u0da7\u0db4\u0dad\u0dca \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get device </p>\n": "<p>\u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get the number of steps to diffuse the original </p>\n": "<p>\u0db8\u0dd4\u0dbd\u0dca \u0db4\u0dd2\u0da7\u0db4\u0dad \u0dc0\u0dd2\u0dc3\u0dd4\u0dbb\u0dd4\u0dc0\u0dcf \u0dc4\u0dd0\u0dbb\u0dd3\u0db8\u0da7 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get the prompt embeddings </p>\n": "<p>\u0d9a\u0da9\u0dd2\u0db1\u0db8\u0dca \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>If <span translate=no>_^_0_^_</span> is not provided, we set a sample mask to preserve the bottom half of the image </p>\n": "<p>\u0dc3\u0db4\u0dba\u0dcf \u0db1\u0ddc\u0db8\u0dd0\u0dad\u0dd2<span translate=no>_^_0_^_</span> \u0db1\u0db8\u0dca, \u0dbb\u0dd6\u0db4\u0dba\u0dda \u0db4\u0dc4\u0dc5 \u0db7\u0dcf\u0d9c\u0dba \u0d86\u0dbb\u0d9a\u0dca\u0dc2\u0dcf \u0d9a\u0dbb \u0d9c\u0dd0\u0db1\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d85\u0db4\u0dd2 \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0d86\u0dc0\u0dbb\u0dab\u0dba\u0d9a\u0dca \u0dc3\u0d9a\u0dc3\u0dca \u0d9a\u0dbb\u0db8\u0dd4</p>\n",
|
||||
"<p>In unconditional scaling is not <span translate=no>_^_0_^_</span> get the embeddings for empty prompts (no conditioning). </p>\n": "<p>\u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba \u0dad\u0dd4\u0dc5 \u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd3\u0db8\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca<span translate=no>_^_0_^_</span> \u0dbd\u0db6\u0dcf \u0db1\u0ddc\u0d9c\u0db1\u0dd3 (\u0d9a\u0db1\u0dca\u0da9\u0dd2\u0dc2\u0db1\u0dda\u0dc2\u0db1\u0dca \u0db1\u0dd0\u0dad).</p>\n",
|
||||
"<p>Initialize <a href=\"../sampler/ddim.html\">DDIM sampler</a> </p>\n": "<p><a href=\"../sampler/ddim.html\">\u0da9\u0dd3\u0da9\u0dd3\u0d85\u0dba\u0dd2\u0d91\u0db8\u0dca \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0d9a\u0dbb\u0dd4</a> \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Load <a href=\"../latent_diffusion.html\">latent diffusion model</a> </p>\n": "<p><a href=\"../latent_diffusion.html\">\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba</a> \u0db4\u0da7\u0dc0\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Load image </p>\n": "<p>\u0dbb\u0dd6\u0db4\u0dba \u0db4\u0da7\u0dc0\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Make a batch of prompts </p>\n": "<p>\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0d9a\u0dca \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Move the model to device </p>\n": "<p>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba\u0da7 \u0d9c\u0dd9\u0db1 \u0dba\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Noise diffuse the original image </p>\n": "<p>\u0dc1\u0db6\u0dca\u0daf\u0dba \u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba \u0dc0\u0dd2\u0dc3\u0dd4\u0dbb\u0dd4\u0dc0\u0dcf \u0dc4\u0dbb\u0dd2\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Reconstruct from the noisy image, while preserving the masked area </p>\n": "<p>\u0dc0\u0dd9\u0dc3\u0dca\u0d9c\u0dad\u0dca \u0db4\u0dca\u0dbb\u0daf\u0dda\u0dc1\u0dba \u0d86\u0dbb\u0d9a\u0dca\u0dc2\u0dcf \u0d9a\u0dbb \u0d9c\u0db1\u0dd2\u0db8\u0dd2\u0db1\u0dca is \u0ddd\u0dc2\u0dcf\u0d9a\u0dcf\u0dbb\u0dd3 \u0dbb\u0dd6\u0db4\u0dba\u0dd9\u0db1\u0dca \u0db4\u0dca\u0dbb\u0dad\u0dd2\u0db1\u0dd2\u0dbb\u0dca\u0db8\u0dcf\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u0db4\u0dd2\u0db1\u0dca\u0dad\u0dd6\u0dbb \u0dc3\u0dd4\u0dbb\u0d9a\u0dd2\u0db1\u0dca\u0db1</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path of the checkpoint </li>\n<li><span translate=no>_^_1_^_</span> is the number of sampling steps </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"../sampler/ddim.html\">DDIM sampling</a> <span translate=no>_^_3_^_</span> constant</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dba\u0db1\u0dd4 \u0db8\u0dd4\u0dbb\u0db4\u0ddc\u0dbd\u0dda \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_1_^_</span>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n<li><span translate=no>_^_2_^_</span><a href=\"../sampler/ddim.html\">DDIM \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2</a><span translate=no>_^_3_^_</span> \u0db1\u0dd2\u0dba\u0dad\u0dba</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path to store the generated images </li>\n<li><span translate=no>_^_1_^_</span> is the image to transform </li>\n<li><span translate=no>_^_2_^_</span> specifies how much of the original image should not be preserved </li>\n<li><span translate=no>_^_3_^_</span> is the number of images to generate in a batch </li>\n<li><span translate=no>_^_4_^_</span> is the prompt to generate images with </li>\n<li><span translate=no>_^_5_^_</span> is the unconditional guidance scale <span translate=no>_^_6_^_</span>. This is used for <span translate=no>_^_7_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0dbb\u0dd6\u0db4 \u0d9c\u0db6\u0da9\u0dcf \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_1_^_</span>\u0dba\u0db1\u0dd4 \u0db4\u0dbb\u0dd2\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0dbb\u0dd6\u0db4\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_2_^_</span>\u0db8\u0dd4\u0dbd\u0dca \u0dbb\u0dd6\u0db4\u0dba\u0dda \u0d9a\u0ddc\u0db4\u0db8\u0dab \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba\u0d9a\u0dca \u0dc3\u0d82\u0dbb\u0d9a\u0dca\u0dc2\u0dab\u0dba \u0db1\u0ddc\u0d9a\u0dc5 \u0dba\u0dd4\u0dad\u0dd4\u0daf \u0dba\u0db1\u0dca\u0db1 \u0db1\u0dd2\u0dba\u0db8 \u0d9a\u0dbb\u0dba\u0dd2</li>\n<li><span translate=no>_^_3_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0d9a\u0dca \u0dad\u0dd4\u0dc5 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dc5 \u0dba\u0dd4\u0dad\u0dd4 \u0dbb\u0dd6\u0db4 \u0d9c\u0dab\u0db1</li>\n<li><span translate=no>_^_4_^_</span>\u0dc3\u0db8\u0d9f \u0dbb\u0dd6\u0db4 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8</li>\n<li><span translate=no>_^_5_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_6_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"In-paint images using stable diffusion with a prompt": "\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db8\u0d9f \u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd2\u0db1\u0dca \u0dad\u0dd3\u0db1\u0dca\u0dad \u0d86\u0dbd\u0dda\u0db4\u0db1 \u0dbb\u0dd6\u0db4"
|
||||
}
|
||||
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"<h1>In-paint images using <a href=\"../index.html\">stable diffusion</a> with a prompt</h1>\n": "<h1>\u4f7f\u7528\u5e26\u6709\u63d0\u793a\u7684<a href=\"../index.html\">\u7a33\u5b9a\u6269\u6563</a>\u529f\u80fd\u586b\u5145\u56fe\u50cf</h1>\n",
|
||||
"<h3>CLI</h3>\n": "<h3>CLI</h3>\n",
|
||||
"<h3>Image in-painting class</h3>\n": "<h3>\u56fe\u50cf\u8865\u753b\u8bfe</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>AMP auto casting </p>\n": "<p>AMP \u81ea\u52a8\u6295\u5c04</p>\n",
|
||||
"<p>Add noise to the original image </p>\n": "<p>\u5411\u539f\u59cb\u56fe\u50cf\u6dfb\u52a0\u566a\u70b9</p>\n",
|
||||
"<p>Decode the image from the <a href=\"../model/autoencoder.html\">autoencoder</a> </p>\n": "<p>\u4ece<a href=\"../model/autoencoder.html\">\u81ea\u52a8\u7f16\u7801\u5668\u89e3\u7801</a>\u56fe\u50cf</p>\n",
|
||||
"<p>Encode the image in the latent space and make <span translate=no>_^_0_^_</span> copies of it </p>\n": "<p>\u5728\u6f5c\u5728\u7a7a\u95f4\u4e2d\u5bf9\u56fe\u50cf\u8fdb\u884c\u7f16\u7801\u5e76\u5236\u4f5c<span translate=no>_^_0_^_</span>\u526f\u672c</p>\n",
|
||||
"<p>Get device </p>\n": "<p>\u83b7\u53d6\u8bbe\u5907</p>\n",
|
||||
"<p>Get the number of steps to diffuse the original </p>\n": "<p>\u83b7\u53d6\u6f2b\u53cd\u5c04\u539f\u7a3f\u7684\u6b65\u6570</p>\n",
|
||||
"<p>Get the prompt embeddings </p>\n": "<p>\u83b7\u53d6\u63d0\u793a\u5d4c\u5165\u4fe1\u606f</p>\n",
|
||||
"<p>If <span translate=no>_^_0_^_</span> is not provided, we set a sample mask to preserve the bottom half of the image </p>\n": "<p>\u5982\u679c<span translate=no>_^_0_^_</span>\u672a\u63d0\u4f9b\uff0c\u6211\u4eec\u4f1a\u8bbe\u7f6e\u6837\u672c\u63a9\u7801\u4ee5\u4fdd\u7559\u56fe\u50cf\u7684\u4e0b\u534a\u90e8\u5206</p>\n",
|
||||
"<p>In unconditional scaling is not <span translate=no>_^_0_^_</span> get the embeddings for empty prompts (no conditioning). </p>\n": "<p>\u5728\u65e0\u6761\u4ef6\u7f29\u653e\u4e2d\uff0c\u65e0\u6cd5<span translate=no>_^_0_^_</span>\u83b7\u53d6\u7a7a\u63d0\u793a\u7684\u5d4c\u5165\u503c\uff08\u65e0\u6761\u4ef6\uff09\u3002</p>\n",
|
||||
"<p>Initialize <a href=\"../sampler/ddim.html\">DDIM sampler</a> </p>\n": "<p>\u521d\u59cb\u5316 <a href=\"../sampler/ddim.html\">DDIM \u91c7\u6837\u5668</a></p>\n",
|
||||
"<p>Load <a href=\"../latent_diffusion.html\">latent diffusion model</a> </p>\n": "<p>\u8f7d\u8377<a href=\"../latent_diffusion.html\">\u6f5c\u5728\u6269\u6563\u6a21\u578b</a></p>\n",
|
||||
"<p>Load image </p>\n": "<p>\u52a0\u8f7d\u56fe\u7247</p>\n",
|
||||
"<p>Make a batch of prompts </p>\n": "<p>\u505a\u4e00\u6279\u63d0\u793a</p>\n",
|
||||
"<p>Move the model to device </p>\n": "<p>\u5c06\u6a21\u578b\u79fb\u81f3\u8bbe\u5907</p>\n",
|
||||
"<p>Noise diffuse the original image </p>\n": "<p>\u566a\u70b9\u4f1a\u6f2b\u53cd\u5c04\u539f\u59cb\u56fe\u50cf</p>\n",
|
||||
"<p>Reconstruct from the noisy image, while preserving the masked area </p>\n": "<p>\u5728\u4fdd\u7559\u906e\u7f69\u533a\u57df\u7684\u540c\u65f6\uff0c\u4ece\u566a\u58f0\u56fe\u50cf\u4e2d\u91cd\u5efa</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u4fdd\u5b58\u56fe\u7247</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path of the checkpoint </li>\n<li><span translate=no>_^_1_^_</span> is the number of sampling steps </li>\n<li><span translate=no>_^_2_^_</span> is the <a href=\"../sampler/ddim.html\">DDIM sampling</a> <span translate=no>_^_3_^_</span> constant</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u68c0\u67e5\u70b9\u7684\u8def\u5f84</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u91c7\u6837\u6b65\u9aa4\u7684\u6570\u91cf</li>\n<li><span translate=no>_^_2_^_</span>\u662f <a href=\"../sampler/ddim.html\">DDIM \u91c7\u6837</a><span translate=no>_^_3_^_</span>\u5e38\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path to store the generated images </li>\n<li><span translate=no>_^_1_^_</span> is the image to transform </li>\n<li><span translate=no>_^_2_^_</span> specifies how much of the original image should not be preserved </li>\n<li><span translate=no>_^_3_^_</span> is the number of images to generate in a batch </li>\n<li><span translate=no>_^_4_^_</span> is the prompt to generate images with </li>\n<li><span translate=no>_^_5_^_</span> is the unconditional guidance scale <span translate=no>_^_6_^_</span>. This is used for <span translate=no>_^_7_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5b58\u50a8\u751f\u6210\u7684\u56fe\u50cf\u7684\u8def\u5f84</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u8981\u8f6c\u6362\u7684\u56fe\u50cf</li>\n<li><span translate=no>_^_2_^_</span>\u6307\u5b9a\u4e0d\u5e94\u4fdd\u7559\u539f\u59cb\u56fe\u50cf\u7684\u591a\u5c11</li>\n<li><span translate=no>_^_3_^_</span>\u662f\u6279\u91cf\u751f\u6210\u7684\u56fe\u50cf\u6570\u91cf</li>\n<li><span translate=no>_^_4_^_</span>\u662f\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u751f\u6210\u56fe\u50cf\u7684\u63d0\u793a</li>\n<li><span translate=no>_^_5_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_6_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"In-paint images using stable diffusion with a prompt": "\u4f7f\u7528\u5e26\u6709\u63d0\u793a\u7684\u7a33\u5b9a\u6269\u6563\u529f\u80fd\u586b\u5145\u56fe\u50cf"
|
||||
}
|
||||
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"<h1>Generate images using <a href=\"../index.html\">stable diffusion</a> with a prompt</h1>\n": "<h1><a href=\"../index.html\">\u30d7\u30ed\u30f3\u30d7\u30c8\u306b\u3088\u308b\u5b89\u5b9a\u62e1\u6563\u306b\u3088\u308b\u753b\u50cf\u751f\u6210</a></h1>\n",
|
||||
"<h3>CLI</h3>\n": "<h3>CLI</h3>\n",
|
||||
"<h3>Text to image class</h3>\n": "<h3>\u30c6\u30ad\u30b9\u30c8\u304b\u3089\u753b\u50cf\u3078\u306e\u30af\u30e9\u30b9</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><a href=\"../sampler/index.html\">Sample in the latent space</a>. <span translate=no>_^_0_^_</span> will be of shape <span translate=no>_^_1_^_</span> </p>\n": "<p><a href=\"../sampler/index.html\">\u6f5c\u4f0f\u7a7a\u9593\u3067\u30b5\u30f3\u30d7\u30eb\u3092\u63a1\u53d6\u3057\u307e\u3059</a>\u3002<span translate=no>_^_0_^_</span>\u5f62\u304c\u6574\u3044\u307e\u3059 <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>AMP auto casting </p>\n": "<p>AMP \u30aa\u30fc\u30c8\u30ad\u30e3\u30b9\u30c6\u30a3\u30f3\u30b0</p>\n",
|
||||
"<p>Decode the image from the <a href=\"../model/autoencoder.html\">autoencoder</a> </p>\n": "<p><a href=\"../model/autoencoder.html\">\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u304b\u3089\u753b\u50cf\u3092\u30c7\u30b3\u30fc\u30c9\u3057\u307e\u3059</a></p>\n",
|
||||
"<p>Get device </p>\n": "<p>\u30c7\u30d0\u30a4\u30b9\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Get the prompt embeddings </p>\n": "<p>\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u57cb\u3081\u8fbc\u307f\u3092\u5165\u624b</p>\n",
|
||||
"<p>Image to latent space resolution reduction </p>\n": "<p>\u753b\u50cf\u304b\u3089\u6f5c\u5728\u7a7a\u9593\u3078\u306e\u89e3\u50cf\u5ea6\u306e\u4f4e\u4e0b</p>\n",
|
||||
"<p>In unconditional scaling is not <span translate=no>_^_0_^_</span> get the embeddings for empty prompts (no conditioning). </p>\n": "<p>\u7121\u6761\u4ef6\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3067\u306f\u3001<span translate=no>_^_0_^_</span>\u7a7a\u306e\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u306f\u57cb\u3081\u8fbc\u307f\u306f\u53d6\u5f97\u3055\u308c\u307e\u305b\u3093 (\u6761\u4ef6\u306a\u3057)\u3002</p>\n",
|
||||
"<p>Initialize <a href=\"../sampler/index.html\">sampler</a> </p>\n": "<p><a href=\"../sampler/index.html\">\u30b5\u30f3\u30d7\u30e9\u30fc\u3092\u521d\u671f\u5316</a></p>\n",
|
||||
"<p>Load <a href=\"../latent_diffusion.html\">latent diffusion model</a> </p>\n": "<p><a href=\"../latent_diffusion.html\">\u8ca0\u8377\u6f5c\u4f0f\u62e1\u6563\u30e2\u30c7\u30eb</a></p>\n",
|
||||
"<p>Make a batch of prompts </p>\n": "<p>\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u4e00\u62ec\u4f5c\u6210</p>\n",
|
||||
"<p>Move the model to device </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5</p>\n",
|
||||
"<p>Number of channels in the image </p>\n": "<p>\u753b\u50cf\u5185\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u6570</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u753b\u50cf\u3092\u4fdd\u5b58</p>\n",
|
||||
"<p>Set flash attention </p>\n": "<p>\u30d5\u30e9\u30c3\u30b7\u30e5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u3092\u8a2d\u5b9a</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path of the checkpoint </li>\n<li><span translate=no>_^_1_^_</span> is the name of the <a href=\"../sampler/index.html\">sampler</a> </li>\n<li><span translate=no>_^_2_^_</span> is the number of sampling steps </li>\n<li><span translate=no>_^_3_^_</span> is the <a href=\"../sampler/ddim.html\">DDIM sampling</a> <span translate=no>_^_4_^_</span> constant</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u306e\u30d1\u30b9\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span><a href=\"../sampler/index.html\">\u30b5\u30f3\u30d7\u30e9\u30fc\u306e\u540d\u524d\u3067\u3059</a></li>\n<li><span translate=no>_^_2_^_</span>\u306f\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u30b9\u30c6\u30c3\u30d7\u306e\u6570</li>\n<li><span translate=no>_^_3_^_</span><a href=\"../sampler/ddim.html\">DDIM</a> \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u5b9a\u6570\u3067\u3059 <span translate=no>_^_4_^_</span></li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path to store the generated images </li>\n<li><span translate=no>_^_1_^_</span> is the number of images to generate in a batch </li>\n<li><span translate=no>_^_2_^_</span> is the prompt to generate images with </li>\n<li><span translate=no>_^_3_^_</span> is the height of the image </li>\n<li><span translate=no>_^_4_^_</span> is the width of the image </li>\n<li><span translate=no>_^_5_^_</span> is the unconditional guidance scale <span translate=no>_^_6_^_</span>. This is used for <span translate=no>_^_7_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u751f\u6210\u3055\u308c\u305f\u753b\u50cf\u3092\u4fdd\u5b58\u3059\u308b\u30d1\u30b9\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u306f\u30d0\u30c3\u30c1\u3067\u751f\u6210\u3059\u308b\u753b\u50cf\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_2_^_</span>\u3067\u753b\u50cf\u3092\u751f\u6210\u3059\u308b\u30d7\u30ed\u30f3\u30d7\u30c8\u3067\u3059</li>\n<li><span translate=no>_^_3_^_</span>\u753b\u50cf\u306e\u9ad8\u3055\u3067\u3059</li>\n<li><span translate=no>_^_4_^_</span>\u753b\u50cf\u306e\u5e45\u3067\u3059</li>\n<li><span translate=no>_^_5_^_</span>\u7121\u6761\u4ef6\u30ac\u30a4\u30c0\u30f3\u30b9\u30b9\u30b1\u30fc\u30eb\u3067\u3059 <span translate=no>_^_6_^_</span>\u3053\u308c\u306f\u6b21\u306e\u7528\u9014\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 <span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"Generate images using stable diffusion with a prompt": "\u30d7\u30ed\u30f3\u30d7\u30c8\u306b\u3088\u308b\u5b89\u5b9a\u62e1\u6563\u306b\u3088\u308b\u753b\u50cf\u751f\u6210"
|
||||
}
|
||||
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"<h1>Generate images using <a href=\"../index.html\">stable diffusion</a> with a prompt</h1>\n": "<h1>\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db8\u0d9f <a href=\"../index.html\">\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba</a> \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd2\u0db1\u0dca \u0dbb\u0dd6\u0db4 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</h1>\n",
|
||||
"<h3>CLI</h3>\n": "<h3>CLI</h3>\n",
|
||||
"<h3>Text to image class</h3>\n": "<h3>\u0dbb\u0dd6\u0db4 \u0db4\u0db1\u0dca\u0dad\u0dd2\u0dba\u0da7 \u0db4\u0dd9\u0dc5</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><a href=\"../sampler/index.html\">Sample in the latent space</a>. <span translate=no>_^_0_^_</span> will be of shape <span translate=no>_^_1_^_</span> </p>\n": "<p><a href=\"../sampler/index.html\">\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0dda \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0dba</a>. <span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba \u0dc0\u0db1\u0dd4 \u0d87\u0dad<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>AMP auto casting </p>\n": "<p>AMP \u0dc0\u0dcf\u0dc4\u0db1 \u0dc0\u0dcf\u0dad\u0dca\u0dad\u0dd4</p>\n",
|
||||
"<p>Decode the image from the <a href=\"../model/autoencoder.html\">autoencoder</a> </p>\n": "<p><a href=\"../model/autoencoder.html\">\u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba\u0dd9\u0db1\u0dca \u0dbb\u0dd6\u0db4\u0dba \u0dc0\u0dd2\u0d9a\u0dda\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</a></p>\n",
|
||||
"<p>Get device </p>\n": "<p>\u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Get the prompt embeddings </p>\n": "<p>\u0d9a\u0da9\u0dd2\u0db1\u0db8\u0dca \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Image to latent space resolution reduction </p>\n": "<p>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0d85\u0db7\u0dca\u0dba\u0dc0\u0d9a\u0dcf\u0dc1 \u0dc0\u0dd2\u0db7\u0dda\u0daf\u0db1 \u0d85\u0da9\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0dbb\u0dd6\u0db4\u0dba</p>\n",
|
||||
"<p>In unconditional scaling is not <span translate=no>_^_0_^_</span> get the embeddings for empty prompts (no conditioning). </p>\n": "<p>\u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba \u0dad\u0dd4\u0dc5 \u0dc4\u0dd2\u0dc3\u0dca \u0dc0\u0dd2\u0db8\u0dc3\u0dd3\u0db8\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8\u0dca<span translate=no>_^_0_^_</span> \u0dbd\u0db6\u0dcf \u0db1\u0ddc\u0d9c\u0db1\u0dd3 (\u0d9a\u0db1\u0dca\u0da9\u0dd2\u0dc2\u0db1\u0dda\u0dc2\u0db1\u0dca \u0db1\u0dd0\u0dad).</p>\n",
|
||||
"<p>Initialize <a href=\"../sampler/index.html\">sampler</a> </p>\n": "<p><a href=\"../sampler/index.html\">\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0d9a\u0dbb\u0dd4</a> \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Load <a href=\"../latent_diffusion.html\">latent diffusion model</a> </p>\n": "<p><a href=\"../latent_diffusion.html\">\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba</a> \u0db4\u0da7\u0dc0\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Make a batch of prompts </p>\n": "<p>\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0dca \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0d9a\u0dca \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Move the model to device </p>\n": "<p>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0d8b\u0db4\u0dcf\u0d82\u0d9c\u0dba\u0da7 \u0d9c\u0dd9\u0db1 \u0dba\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Number of channels in the image </p>\n": "<p>\u0dbb\u0dd6\u0db4\u0dba\u0dda \u0db1\u0dcf\u0dbd\u0dd2\u0d9a\u0dcf \u0d9c\u0dab\u0db1</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u0db4\u0dd2\u0db1\u0dca\u0dad\u0dd6\u0dbb \u0dc3\u0dd4\u0dbb\u0d9a\u0dd2\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Set flash attention </p>\n": "<p>\u0dc6\u0dca\u0dbd\u0dd1\u0dc2\u0dca \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path of the checkpoint </li>\n<li><span translate=no>_^_1_^_</span> is the name of the <a href=\"../sampler/index.html\">sampler</a> </li>\n<li><span translate=no>_^_2_^_</span> is the number of sampling steps </li>\n<li><span translate=no>_^_3_^_</span> is the <a href=\"../sampler/ddim.html\">DDIM sampling</a> <span translate=no>_^_4_^_</span> constant</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0dba\u0db1\u0dd4 \u0db8\u0dd4\u0dbb\u0db4\u0ddc\u0dbd\u0dda \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_1_^_</span><a href=\"../sampler/index.html\">\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2\u0d9a\u0dbb\u0dd4\u0d9c\u0dda \u0db1\u0db8\u0dba\u0dd2</a></li>\n<li><span translate=no>_^_2_^_</span>\u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2 \u0db4\u0dd2\u0dba\u0dc0\u0dbb \u0d9c\u0dab\u0db1 \u0dc0\u0dda</li>\n<li><span translate=no>_^_3_^_</span><a href=\"../sampler/ddim.html\">DDIM \u0db1\u0dd2\u0dba\u0dd0\u0daf\u0dd2</a><span translate=no>_^_4_^_</span> \u0db1\u0dd2\u0dba\u0dad\u0dba</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path to store the generated images </li>\n<li><span translate=no>_^_1_^_</span> is the number of images to generate in a batch </li>\n<li><span translate=no>_^_2_^_</span> is the prompt to generate images with </li>\n<li><span translate=no>_^_3_^_</span> is the height of the image </li>\n<li><span translate=no>_^_4_^_</span> is the width of the image </li>\n<li><span translate=no>_^_5_^_</span> is the unconditional guidance scale <span translate=no>_^_6_^_</span>. This is used for <span translate=no>_^_7_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1 \u0dbd\u0daf \u0dbb\u0dd6\u0db4 \u0d9c\u0db6\u0da9\u0dcf \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_1_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0dab\u0dca\u0da9\u0dcf\u0dba\u0db8\u0d9a\u0dca \u0dad\u0dd4\u0dc5 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dc5 \u0dba\u0dd4\u0dad\u0dd4 \u0dbb\u0dd6\u0db4 \u0d9c\u0dab\u0db1</li>\n<li><span translate=no>_^_2_^_</span>\u0dc3\u0db8\u0d9f \u0dbb\u0dd6\u0db4 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8</li>\n<li><span translate=no>_^_3_^_</span>\u0dba\u0db1\u0dd4 \u0dbb\u0dd6\u0db4\u0dba\u0dda \u0d8b\u0dc3</li>\n<li><span translate=no>_^_4_^_</span>\u0dbb\u0dd6\u0db4\u0dba\u0dda \u0db4\u0dc5\u0dbd \u0dc0\u0dda</li>\n<li><span translate=no>_^_5_^_</span>\u0dba\u0db1\u0dd4 \u0d9a\u0ddc\u0db1\u0dca\u0daf\u0dda\u0dc3\u0dd2 \u0dc0\u0dd2\u0dbb\u0dc4\u0dd2\u0dad \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0ddd\u0db4\u0daf\u0dda\u0dc1<span translate=no>_^_6_^_</span> \u0db4\u0dbb\u0dd2\u0db8\u0dcf\u0dab\u0dba\u0dba\u0dd2. \u0db8\u0dd9\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0dc0\u0dda<span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"Generate images using stable diffusion with a prompt": "\u0dc0\u0dd2\u0db8\u0dc3\u0dd4\u0db8\u0d9a\u0dca \u0dc3\u0db8\u0d9f \u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0db7\u0dcf\u0dc0\u0dd2\u0dad\u0dcf \u0d9a\u0dbb\u0db8\u0dd2\u0db1\u0dca \u0dbb\u0dd6\u0db4 \u0da2\u0db1\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1"
|
||||
}
|
||||
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"<h1>Generate images using <a href=\"../index.html\">stable diffusion</a> with a prompt</h1>\n": "<h1>\u5728\u63d0\u793a\u4e0b\u4f7f\u7528<a href=\"../index.html\">\u7a33\u5b9a\u7684\u6269\u6563</a>\u751f\u6210\u56fe\u50cf</h1>\n",
|
||||
"<h3>CLI</h3>\n": "<h3>CLI</h3>\n",
|
||||
"<h3>Text to image class</h3>\n": "<h3>\u6587\u672c\u8f6c\u56fe\u50cf\u7c7b\u522b</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p><a href=\"../sampler/index.html\">Sample in the latent space</a>. <span translate=no>_^_0_^_</span> will be of shape <span translate=no>_^_1_^_</span> </p>\n": "<p><a href=\"../sampler/index.html\">\u5728\u6f5c\u5728\u7a7a\u95f4\u4e2d\u53d6\u6837</a>\u3002<span translate=no>_^_0_^_</span>\u4f1a\u53d8\u5f62<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>AMP auto casting </p>\n": "<p>AMP \u81ea\u52a8\u6295\u5c04</p>\n",
|
||||
"<p>Decode the image from the <a href=\"../model/autoencoder.html\">autoencoder</a> </p>\n": "<p>\u4ece<a href=\"../model/autoencoder.html\">\u81ea\u52a8\u7f16\u7801\u5668\u89e3\u7801</a>\u56fe\u50cf</p>\n",
|
||||
"<p>Get device </p>\n": "<p>\u83b7\u53d6\u8bbe\u5907</p>\n",
|
||||
"<p>Get the prompt embeddings </p>\n": "<p>\u83b7\u53d6\u63d0\u793a\u5d4c\u5165\u4fe1\u606f</p>\n",
|
||||
"<p>Image to latent space resolution reduction </p>\n": "<p>\u964d\u4f4e\u56fe\u50cf\u5230\u6f5c\u5728\u7a7a\u95f4\u7684\u5206\u8fa8\u7387</p>\n",
|
||||
"<p>In unconditional scaling is not <span translate=no>_^_0_^_</span> get the embeddings for empty prompts (no conditioning). </p>\n": "<p>\u5728\u65e0\u6761\u4ef6\u7f29\u653e\u4e2d\uff0c\u65e0\u6cd5<span translate=no>_^_0_^_</span>\u83b7\u53d6\u7a7a\u63d0\u793a\u7684\u5d4c\u5165\u503c\uff08\u65e0\u6761\u4ef6\uff09\u3002</p>\n",
|
||||
"<p>Initialize <a href=\"../sampler/index.html\">sampler</a> </p>\n": "<p>\u521d\u59cb\u5316<a href=\"../sampler/index.html\">\u91c7\u6837\u5668</a></p>\n",
|
||||
"<p>Load <a href=\"../latent_diffusion.html\">latent diffusion model</a> </p>\n": "<p>\u8f7d\u8377<a href=\"../latent_diffusion.html\">\u6f5c\u5728\u6269\u6563\u6a21\u578b</a></p>\n",
|
||||
"<p>Make a batch of prompts </p>\n": "<p>\u505a\u4e00\u6279\u63d0\u793a</p>\n",
|
||||
"<p>Move the model to device </p>\n": "<p>\u5c06\u6a21\u578b\u79fb\u81f3\u8bbe\u5907</p>\n",
|
||||
"<p>Number of channels in the image </p>\n": "<p>\u56fe\u50cf\u4e2d\u7684\u901a\u9053\u6570</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u4fdd\u5b58\u56fe\u7247</p>\n",
|
||||
"<p>Set flash attention </p>\n": "<p>\u8bbe\u7f6e\u95ea\u5149\u706f\u6ce8\u610f\u529b</p>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path of the checkpoint </li>\n<li><span translate=no>_^_1_^_</span> is the name of the <a href=\"../sampler/index.html\">sampler</a> </li>\n<li><span translate=no>_^_2_^_</span> is the number of sampling steps </li>\n<li><span translate=no>_^_3_^_</span> is the <a href=\"../sampler/ddim.html\">DDIM sampling</a> <span translate=no>_^_4_^_</span> constant</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u68c0\u67e5\u70b9\u7684\u8def\u5f84</li>\n<li><span translate=no>_^_1_^_</span>\u662f<a href=\"../sampler/index.html\">\u91c7\u6837</a>\u5668\u7684\u540d\u5b57</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u91c7\u6837\u6b65\u9aa4\u7684\u6570\u91cf</li>\n<li><span translate=no>_^_3_^_</span>\u662f <a href=\"../sampler/ddim.html\">DDIM \u91c7\u6837</a><span translate=no>_^_4_^_</span>\u5e38\u6570</li></ul>\n",
|
||||
"<ul><li><span translate=no>_^_0_^_</span> is the path to store the generated images </li>\n<li><span translate=no>_^_1_^_</span> is the number of images to generate in a batch </li>\n<li><span translate=no>_^_2_^_</span> is the prompt to generate images with </li>\n<li><span translate=no>_^_3_^_</span> is the height of the image </li>\n<li><span translate=no>_^_4_^_</span> is the width of the image </li>\n<li><span translate=no>_^_5_^_</span> is the unconditional guidance scale <span translate=no>_^_6_^_</span>. This is used for <span translate=no>_^_7_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u662f\u5b58\u50a8\u751f\u6210\u7684\u56fe\u50cf\u7684\u8def\u5f84</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u6279\u91cf\u751f\u6210\u7684\u56fe\u50cf\u6570\u91cf</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u751f\u6210\u56fe\u50cf\u7684\u63d0\u793a</li>\n<li><span translate=no>_^_3_^_</span>\u662f\u56fe\u50cf\u7684\u9ad8\u5ea6</li>\n<li><span translate=no>_^_4_^_</span>\u662f\u56fe\u50cf\u7684\u5bbd\u5ea6</li>\n<li><span translate=no>_^_5_^_</span>\u662f\u65e0\u6761\u4ef6\u6307\u5bfc\u91cf\u8868<span translate=no>_^_6_^_</span>\u3002\u8fd9\u7528\u4e8e<span translate=no>_^_7_^_</span></li></ul>\n",
|
||||
"Generate images using stable diffusion with a prompt": "\u5728\u63d0\u793a\u4e0b\u4f7f\u7528\u7a33\u5b9a\u7684\u6269\u6563\u751f\u6210\u56fe\u50cf"
|
||||
}
|
||||
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"<h1>Utility functions for <a href=\"index.html\">stable diffusion</a></h1>\n": "<h1><a href=\"index.html\">\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u306e\u305f\u3081\u306e\u30e6\u30fc\u30c6\u30a3\u30ea\u30c6\u30a3\u95a2\u6570</a></h1>\n",
|
||||
"<h3>Load <a href=\"latent_diffusion.html\"><span translate=no>_^_0_^_</span> model</a></h3>\n": "<h3><a href=\"latent_diffusion.html\"><span translate=no>_^_0_^_</span>\u30e2\u30c7\u30eb\u3092\u8aad\u307f\u8fbc\u3080</a></h3>\n",
|
||||
"<h3>Load an image</h3>\n<p>This loads an image from a file and returns a PyTorch tensor.</p>\n<ul><li><span translate=no>_^_0_^_</span> is the path of the image</li></ul>\n": "<h3>\u753b\u50cf\u3092\u8aad\u307f\u8fbc\u3080</h3>\n<p>\u3053\u308c\u306f\u30d5\u30a1\u30a4\u30eb\u304b\u3089\u753b\u50cf\u3092\u30ed\u30fc\u30c9\u3057\u3001PyTorch \u30c6\u30f3\u30bd\u30eb\u3092\u8fd4\u3057\u307e\u3059\u3002</p>\n<ul><li><span translate=no>_^_0_^_</span>\u753b\u50cf\u306e\u30d1\u30b9\u3067\u3059</li></ul>\n",
|
||||
"<h3>Save a images</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the tensor with images of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the folder to save images in </li>\n<li><span translate=no>_^_3_^_</span> is the prefix to add to file names </li>\n<li><span translate=no>_^_4_^_</span> is the image format</li></ul>\n": "<h3>\u753b\u50cf\u3092\u4fdd\u5b58\u3059\u308b</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u5f62\u72b6\u306e\u753b\u50cf\u3092\u542b\u3080\u30c6\u30f3\u30bd\u30eb\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u753b\u50cf\u3092\u4fdd\u5b58\u3059\u308b\u30d5\u30a9\u30eb\u30c0\u30fc\u3067\u3059</li>\n<li><span translate=no>_^_3_^_</span>\u30d5\u30a1\u30a4\u30eb\u540d\u306b\u8ffd\u52a0\u3059\u308b\u30d7\u30ec\u30d5\u30a3\u30c3\u30af\u30b9\u3067\u3059</li>\n<li><span translate=no>_^_4_^_</span>\u306f\u753b\u50cf\u5f62\u5f0f\u3067\u3059</li></ul>\n",
|
||||
"<h3>Set random seeds</h3>\n": "<h3>\u30e9\u30f3\u30c0\u30e0\u30b7\u30fc\u30c9\u3092\u8a2d\u5b9a</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>Convert to numpy and map to <span translate=no>_^_0_^_</span> for <span translate=no>_^_1_^_</span> </p>\n": "<p>numpy \u306b\u5909\u63db\u3057\u3066 for \u306b\u30de\u30c3\u30d7\u3059\u308b <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Convert to torch </p>\n": "<p>\u30c8\u30fc\u30c1\u306b\u5909\u63db</p>\n",
|
||||
"<p>Create the destination folder </p>\n": "<p>\u4fdd\u5b58\u5148\u30d5\u30a9\u30eb\u30c0\u30fc\u306e\u4f5c\u6210</p>\n",
|
||||
"<p>Debugging output </p>\n": "<p>\u30c7\u30d0\u30c3\u30b0\u51fa\u529b</p>\n",
|
||||
"<p>Get image size </p>\n": "<p>\u753b\u50cf\u30b5\u30a4\u30ba\u3092\u53d6\u5f97</p>\n",
|
||||
"<p>Initialize the CLIP text embedder </p>\n": "<p>CLIP \u30c6\u30ad\u30b9\u30c8\u30a8\u30f3\u30d9\u30c0\u30fc\u3092\u521d\u671f\u5316</p>\n",
|
||||
"<p>Initialize the Latent Diffusion model </p>\n": "<p>\u6f5c\u5728\u62e1\u6563\u30e2\u30c7\u30eb\u3092\u521d\u671f\u5316</p>\n",
|
||||
"<p>Initialize the U-Net </p>\n": "<p>U-Net \u3092\u521d\u671f\u5316\u3057\u307e\u3059</p>\n",
|
||||
"<p>Initialize the autoencoder </p>\n": "<p>\u30aa\u30fc\u30c8\u30a8\u30f3\u30b3\u30fc\u30c0\u3092\u521d\u671f\u5316</p>\n",
|
||||
"<p>Load the checkpoint </p>\n": "<p>\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u3092\u30ed\u30fc\u30c9</p>\n",
|
||||
"<p>Map images to <span translate=no>_^_0_^_</span> space and clip </p>\n": "<p><span translate=no>_^_0_^_</span>\u753b\u50cf\u3092\u30b9\u30da\u30fc\u30b9\u306b\u30de\u30c3\u30d7\u3057\u3066\u30af\u30ea\u30c3\u30d7\u3059\u308b</p>\n",
|
||||
"<p>Open Image </p>\n": "<p>[\u30a4\u30e1\u30fc\u30b8\u3092\u958b\u304f]</p>\n",
|
||||
"<p>Resize to a multiple of 32 </p>\n": "<p>32 \u306e\u500d\u6570\u306b\u30ea\u30b5\u30a4\u30ba</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u753b\u50cf\u3092\u4fdd\u5b58</p>\n",
|
||||
"<p>Set model state </p>\n": "<p>\u30e2\u30c7\u30eb\u30b9\u30c6\u30fc\u30c8\u306e\u8a2d\u5b9a</p>\n",
|
||||
"<p>Transpose to <span translate=no>_^_0_^_</span> and convert to numpy </p>\n": "<p><span translate=no>_^_0_^_</span>numpy\u3078\u306e\u8ee2\u7f6e\u3068numpy\u3078\u306e\u5909\u63db</p>\n",
|
||||
"<p>Transpose to shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u30b7\u30a7\u30a4\u30d7\u306b\u8ee2\u7f6e <span translate=no>_^_0_^_</span></p>\n",
|
||||
"Utility functions for stable diffusion": "\u5b89\u5b9a\u3057\u305f\u62e1\u6563\u306e\u305f\u3081\u306e\u30e6\u30fc\u30c6\u30a3\u30ea\u30c6\u30a3\u95a2\u6570"
|
||||
}
|
||||
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"<h1>Utility functions for <a href=\"index.html\">stable diffusion</a></h1>\n": "<h1><a href=\"index.html\">\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba</a> \u0dc3\u0db3\u0dc4\u0dcf \u0d8b\u0db4\u0dba\u0ddd\u0d9c\u0dd2\u0dad\u0dcf \u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba\u0db1\u0dca</h1>\n",
|
||||
"<h3>Load <a href=\"latent_diffusion.html\"><span translate=no>_^_0_^_</span> model</a></h3>\n": "<h3>\u0db4\u0dd0\u0da7\u0dc0\u0dd3\u0db8\u0dda <a href=\"latent_diffusion.html\"><span translate=no>_^_0_^_</span>\u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba</a></h3>\n",
|
||||
"<h3>Load an image</h3>\n<p>This loads an image from a file and returns a PyTorch tensor.</p>\n<ul><li><span translate=no>_^_0_^_</span> is the path of the image</li></ul>\n": "<h3>\u0dbb\u0dd6\u0db4\u0dba\u0d9a\u0dca \u0db4\u0dd6\u0dbb\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</h3>\n<p>\u0db8\u0dd9\u0dba \u0d9c\u0ddc\u0db1\u0dd4\u0dc0\u0d9a\u0dd2\u0db1\u0dca \u0dbb\u0dd6\u0db4\u0dba\u0d9a\u0dca \u0db4\u0da7\u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0db4\u0dba\u0dd2\u0da7\u0ddd\u0da0\u0dca \u0da7\u0dd9\u0db1\u0dca\u0dc3\u0dbb\u0dba\u0d9a\u0dca \u0db1\u0dd0\u0dc0\u0dad \u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0dba\u0dd2.</p>\n<ul><li><span translate=no>_^_0_^_</span>\u0dba\u0db1\u0dd4 \u0dbb\u0dd6\u0db4\u0dba\u0dda \u0db8\u0dcf\u0dbb\u0dca\u0d9c\u0dba\u0dba\u0dd2</li></ul>\n",
|
||||
"<h3>Save a images</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the tensor with images of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the folder to save images in </li>\n<li><span translate=no>_^_3_^_</span> is the prefix to add to file names </li>\n<li><span translate=no>_^_4_^_</span> is the image format</li></ul>\n": "<h3>\u0db4\u0dd2\u0db1\u0dca\u0dad\u0dd6\u0dbb \u0dc3\u0dd4\u0dbb\u0d9a\u0dd2\u0db1\u0dca\u0db1</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u0dc4\u0dd0\u0da9\u0dba\u0dda \u0dbb\u0dd6\u0db4 \u0dc3\u0dc4\u0dd2\u0dad \u0da7\u0dd9\u0db1\u0dca\u0dc3\u0dbb\u0dba\u0dba\u0dd2<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u0db4\u0dd2\u0db1\u0dca\u0dad\u0dd6\u0dbb \u0dc3\u0dd4\u0dbb\u0dd0\u0d9a\u0dd3\u0db8\u0da7 \u0dc6\u0ddd\u0dbd\u0dca\u0da9\u0dbb\u0dba\u0dba\u0dd2</li>\n<li><span translate=no>_^_3_^_</span>\u0d9c\u0ddc\u0db1\u0dd4 \u0db1\u0dcf\u0db8 \u0dc0\u0dbd\u0da7 \u0d91\u0d9a\u0dad\u0dd4 \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0d8b\u0db4\u0dc3\u0dbb\u0dca\u0d9c\u0dba \u0dc0\u0dda</li>\n<li><span translate=no>_^_4_^_</span>\u0dbb\u0dd6\u0db4 \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0dc0\u0dda</li></ul>\n",
|
||||
"<h3>Set random seeds</h3>\n": "<h3>\u0d85\u0dc4\u0db9\u0dd4 \u0db6\u0dd3\u0da2 \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>Convert to numpy and map to <span translate=no>_^_0_^_</span> for <span translate=no>_^_1_^_</span> </p>\n": "<p><span translate=no>_^_0_^_</span>\u0dc3\u0db3\u0dc4\u0dcf \u0db1\u0ddc\u0db8\u0dca\u0db8\u0dbb \u0d91\u0d9a\u0dda \u0dc3\u0dc4 \u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8 \u0db6\u0dc0\u0da7 \u0db4\u0dbb\u0dd2\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Convert to torch </p>\n": "<p>\u0db4\u0db1\u0dca\u0daf\u0db8 \u0db6\u0dc0\u0da7 \u0db4\u0dbb\u0dd2\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Create the destination folder </p>\n": "<p>\u0d9c\u0db8\u0db1\u0dcf\u0db1\u0dca\u0dad \u0dc6\u0ddd\u0dbd\u0dca\u0da9\u0dbb\u0dba \u0dc3\u0dcf\u0daf\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Debugging output </p>\n": "<p>\u0db4\u0dca\u0dbb\u0dad\u0dd2\u0daf\u0dcf\u0db1\u0dba \u0db1\u0dd2\u0daf\u0ddc\u0dc3\u0dca\u0d9a\u0dbb\u0dab\u0dba</p>\n",
|
||||
"<p>Get image size </p>\n": "<p>\u0dbb\u0dd6\u0db4\u0dba\u0dda \u0db4\u0dca\u0dbb\u0db8\u0dcf\u0dab\u0dba \u0dbd\u0db6\u0dcf \u0d9c\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Initialize the CLIP text embedder </p>\n": "<p>CLIP \u0db4\u0dd9\u0dc5 \u0d9a\u0dcf\u0dc0\u0dd0\u0daf\u0dca\u0daf\u0dd3\u0db8 \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Initialize the Latent Diffusion model </p>\n": "<p>\u0d9c\u0dd4\u0db4\u0dca\u0dad \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0d86\u0d9a\u0dd8\u0dad\u0dd2\u0dba \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Initialize the U-Net </p>\n": "<p>\u0dba\u0dd6-\u0db1\u0dd9\u0da7\u0dca \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Initialize the autoencoder </p>\n": "<p>\u0dc3\u0dca\u0dc0\u0dba\u0d82\u0d9a\u0dca\u0dbb\u0dd3\u0dba \u0d91\u0db1\u0dca\u0d9a\u0ddd\u0da9\u0dbb\u0dba \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Load the checkpoint </p>\n": "<p>\u0db8\u0dd4\u0dbb\u0db4\u0ddc\u0dbd \u0db4\u0dd6\u0dbb\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Map images to <span translate=no>_^_0_^_</span> space and clip </p>\n": "<p>\u0dc3\u0dd2\u0dad\u0dd2\u0dba\u0db8\u0dca\u0d9c\u0dad \u0dbb\u0dd6\u0db4<span translate=no>_^_0_^_</span> \u0d85\u0dc0\u0d9a\u0dcf\u0dc1\u0dba\u0da7 \u0dc3\u0dc4 \u0d9a\u0dca\u0dbd\u0dd2\u0db4\u0dca</p>\n",
|
||||
"<p>Open Image </p>\n": "<p>\u0dc0\u0dd2\u0dc0\u0dd8\u0dad \u0dbb\u0dd6\u0db4\u0dba</p>\n",
|
||||
"<p>Resize to a multiple of 32 </p>\n": "<p>32 \u0d9a \u0db6\u0dc4\u0dd4\u0dba\u0d9a\u0da7 \u0dc0\u0dd9\u0db1\u0dc3\u0dca \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u0db4\u0dd2\u0db1\u0dca\u0dad\u0dd6\u0dbb \u0dc3\u0dd4\u0dbb\u0d9a\u0dd2\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Set model state </p>\n": "<p>\u0d86\u0daf\u0dbb\u0dca\u0dc1 \u0dad\u0dad\u0dca\u0dc0\u0dba \u0dc3\u0d9a\u0dc3\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Transpose to <span translate=no>_^_0_^_</span> and convert to numpy </p>\n": "<p>\u0dc0\u0dd9\u0dad \u0dc3\u0db8\u0dca\u0db4\u0dca\u0dbb\u0dda\u0dc2\u0dab\u0dba<span translate=no>_^_0_^_</span> \u0d9a\u0dbb \u0d85\u0d82\u0d9a\u0dba\u0da7 \u0db4\u0dbb\u0dd2\u0dc0\u0dbb\u0dca\u0dad\u0db1\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1</p>\n",
|
||||
"<p>Transpose to shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u0dc4\u0dd0\u0da9\u0dba\u0da7 \u0dc3\u0db8\u0dca\u0db4\u0dca\u0dbb\u0dda\u0dc2\u0dab\u0dba \u0d9a\u0dbb\u0db1\u0dca\u0db1<span translate=no>_^_0_^_</span></p>\n",
|
||||
"Utility functions for stable diffusion": "\u0dc3\u0dca\u0dae\u0dcf\u0dc0\u0dbb \u0dc0\u0dd2\u0dc3\u0dbb\u0dab\u0dba \u0dc3\u0db3\u0dc4\u0dcf \u0d8b\u0db4\u0dba\u0ddd\u0d9c\u0dd2\u0dad\u0dcf \u0d9a\u0dcf\u0dbb\u0dca\u0dba\u0dba\u0db1\u0dca"
|
||||
}
|
||||
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"<h1>Utility functions for <a href=\"index.html\">stable diffusion</a></h1>\n": "<h1>\u7528\u4e8e<a href=\"index.html\">\u7a33\u5b9a\u6269\u6563</a>\u7684\u5b9e\u7528\u51fd\u6570</h1>\n",
|
||||
"<h3>Load <a href=\"latent_diffusion.html\"><span translate=no>_^_0_^_</span> model</a></h3>\n": "<h3>\u52a0\u8f7d<a href=\"latent_diffusion.html\"><span translate=no>_^_0_^_</span>\u6a21\u578b</a></h3>\n",
|
||||
"<h3>Load an image</h3>\n<p>This loads an image from a file and returns a PyTorch tensor.</p>\n<ul><li><span translate=no>_^_0_^_</span> is the path of the image</li></ul>\n": "<h3>\u52a0\u8f7d\u56fe\u7247</h3>\n<p>\u8fd9\u5c06\u4ece\u6587\u4ef6\u52a0\u8f7d\u56fe\u50cf\u5e76\u8fd4\u56de PyTorch \u5f20\u91cf\u3002</p>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u56fe\u50cf\u7684\u8def\u5f84</li></ul>\n",
|
||||
"<h3>Save a images</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the tensor with images of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the folder to save images in </li>\n<li><span translate=no>_^_3_^_</span> is the prefix to add to file names </li>\n<li><span translate=no>_^_4_^_</span> is the image format</li></ul>\n": "<h3>\u4fdd\u5b58\u56fe\u50cf</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u5e26\u6709\u5f62\u72b6\u56fe\u50cf\u7684\u5f20\u91cf<span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span>\u662f\u4fdd\u5b58\u56fe\u50cf\u7684\u6587\u4ef6\u5939</li>\n<li><span translate=no>_^_3_^_</span>\u662f\u6dfb\u52a0\u5230\u6587\u4ef6\u540d\u7684\u524d\u7f00</li>\n<li><span translate=no>_^_4_^_</span>\u662f\u56fe\u50cf\u683c\u5f0f</li></ul>\n",
|
||||
"<h3>Set random seeds</h3>\n": "<h3>\u8bbe\u7f6e\u968f\u673a\u79cd\u5b50</h3>\n",
|
||||
"<p> </p>\n": "<p></p>\n",
|
||||
"<p>Convert to numpy and map to <span translate=no>_^_0_^_</span> for <span translate=no>_^_1_^_</span> </p>\n": "<p>\u8f6c\u6362\u4e3a numpy \u5e76\u6620\u5c04\u5230 fo<span translate=no>_^_0_^_</span> r<span translate=no>_^_1_^_</span></p>\n",
|
||||
"<p>Convert to torch </p>\n": "<p>\u8f6c\u6362\u4e3a torch</p>\n",
|
||||
"<p>Create the destination folder </p>\n": "<p>\u521b\u5efa\u76ee\u6807\u6587\u4ef6\u5939</p>\n",
|
||||
"<p>Debugging output </p>\n": "<p>\u8c03\u8bd5\u8f93\u51fa</p>\n",
|
||||
"<p>Get image size </p>\n": "<p>\u83b7\u53d6\u56fe\u50cf\u5927\u5c0f</p>\n",
|
||||
"<p>Initialize the CLIP text embedder </p>\n": "<p>\u521d\u59cb\u5316 CLIP \u6587\u672c\u5d4c\u5165\u5668</p>\n",
|
||||
"<p>Initialize the Latent Diffusion model </p>\n": "<p>\u521d\u59cb\u5316\u6f5c\u5728\u6269\u6563\u6a21\u578b</p>\n",
|
||||
"<p>Initialize the U-Net </p>\n": "<p>\u521d\u59cb\u5316 U-Net</p>\n",
|
||||
"<p>Initialize the autoencoder </p>\n": "<p>\u521d\u59cb\u5316\u81ea\u52a8\u7f16\u7801\u5668</p>\n",
|
||||
"<p>Load the checkpoint </p>\n": "<p>\u52a0\u8f7d\u68c0\u67e5\u70b9</p>\n",
|
||||
"<p>Map images to <span translate=no>_^_0_^_</span> space and clip </p>\n": "<p>\u5c06\u56fe\u50cf\u6620\u5c04\u5230<span translate=no>_^_0_^_</span>\u7a7a\u95f4\u5e76\u526a\u8f91</p>\n",
|
||||
"<p>Open Image </p>\n": "<p>\u6253\u5f00\u56fe\u7247</p>\n",
|
||||
"<p>Resize to a multiple of 32 </p>\n": "<p>\u8c03\u6574\u4e3a 32 \u7684\u500d\u6570</p>\n",
|
||||
"<p>Save images </p>\n": "<p>\u4fdd\u5b58\u56fe\u7247</p>\n",
|
||||
"<p>Set model state </p>\n": "<p>\u8bbe\u7f6e\u6a21\u578b\u72b6\u6001</p>\n",
|
||||
"<p>Transpose to <span translate=no>_^_0_^_</span> and convert to numpy </p>\n": "<p>\u8f6c\u7f6e\u4e3a numpy<span translate=no>_^_0_^_</span> \u5e76\u8f6c\u6362\u4e3a numpy</p>\n",
|
||||
"<p>Transpose to shape <span translate=no>_^_0_^_</span> </p>\n": "<p>\u8f6c\u7f6e\u6210\u5f62\u72b6<span translate=no>_^_0_^_</span></p>\n",
|
||||
"Utility functions for stable diffusion": "\u7528\u4e8e\u7a33\u5b9a\u6269\u6563\u7684\u5b9e\u7528\u51fd\u6570"
|
||||
}
|
||||
Reference in New Issue
Block a user