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89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
import gradio as gr
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import torch
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from diffusers import StableDiffusionPipeline # type: ignore
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from PIL import Image
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import os
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auth_token = os.getenv("HF_TOKEN")
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if not auth_token:
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print(
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"ERROR: No Hugging Face access token found.\n"
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"Please define an environment variable 'auth_token' before running.\n"
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"Example:\n"
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" export HF_TOKEN=XXXXXXXX\n"
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)
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model_id = "CompVis/stable-diffusion-v1-4"
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device = "cpu"
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pipe = StableDiffusionPipeline.from_pretrained(
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model_id, token=auth_token, variant="fp16", torch_dtype=torch.float16,
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)
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pipe = pipe.to(device)
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def infer(prompt, samples, steps, scale, seed):
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generator = torch.Generator(device=device).manual_seed(seed)
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images_list = pipe( # type: ignore
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[prompt] * samples,
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num_inference_steps=steps,
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guidance_scale=scale,
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generator=generator,
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)
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images = []
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safe_image = Image.open(r"unsafe.png")
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for i, image in enumerate(images_list["sample"]): # type: ignore
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if images_list["nsfw_content_detected"][i]: # type: ignore
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images.append(safe_image)
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else:
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images.append(image)
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return images
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block = gr.Blocks()
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with block:
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with gr.Group():
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with gr.Row():
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text = gr.Textbox(
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label="Enter your prompt",
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max_lines=1,
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placeholder="Enter your prompt",
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container=False,
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)
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btn = gr.Button("Generate image")
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gallery = gr.Gallery(
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label="Generated images",
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show_label=False,
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elem_id="gallery",
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columns=2,
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)
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advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
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with gr.Row(elem_id="advanced-options"):
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samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
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steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
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scale = gr.Slider(
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label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=2147483647,
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step=1,
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randomize=True,
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)
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gr.on(
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[text.submit, btn.click],
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infer,
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inputs=[text, samples, steps, scale, seed],
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outputs=gallery,
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)
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advanced_button.click(
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None,
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[],
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text,
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)
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block.launch()
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