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201 lines
20 KiB
Markdown
201 lines
20 KiB
Markdown
# Compatibility Matrix
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The table below shows every supported model and the optimizations supported for them.
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The symbols used have the following meanings:
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- ✅ = Full compatibility
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- ❌ = No compatibility
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- ⭕ = Does not apply to this model
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## Models x Optimization
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The `HuggingFace Model ID` can be passed directly to `from_pretrained()` methods, and sglang-diffusion will use the
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optimal
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default parameters when initializing and generating videos.
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### Video Generation Models
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| Model Name | Hugging Face Model ID | Resolutions | TeaCache | Sliding Tile Attn | Sage Attn | Video Sparse Attention (VSA) | Sparse Linear Attention (SLA) | Sage Sparse Linear Attention (SageSLA) | Sparse Video Gen 2 (SVG2) | Laser Attn | Block Sparse Attn | Rain Fusion Attn |
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|:-----------------------------|:--------------------------------------------------|:---------------------|:--------:|:-----------------:|:---------:|:----------------------------:|:-----------------------------:|:--------------------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
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| FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480p | ⭕ | ⭕ | ⭕ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| FastWan2.2 TI2V 5B Full Attn | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720p | ⭕ | ⭕ | ⭕ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720p | ⭕ | ⭕ | ✅ | ⭕ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ |
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| Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480p<br>720p | ❌ | ❌ | ✅ | ⭕ | ✅ | ❌ | ✅ |
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| Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480p<br>720p | ❌ | ❌ | ✅ | ⭕ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ |
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| HunyuanVideo | `hunyuanvideo-community/HunyuanVideo` | 720×1280<br>544×960 | ❌ | ✅ | ✅ | ⭕ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| FastHunyuan | `FastVideo/FastHunyuan-diffusers` | 720×1280<br>544×960 | ❌ | ✅ | ✅ | ⭕ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480p | ✅ | ✅ | ✅ | ⭕ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |
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| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480p, 720p | ✅ | ✅ | ✅ | ⭕ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |
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| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480p | ✅ | ✅ | ✅ | ⭕ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |
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| Wan2.1 I2V 720P | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | 720p | ✅ | ✅ | ✅ | ⭕ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |
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| TurboWan2.1 T2V 1.3B | `IPostYellow/TurboWan2.1-T2V-1.3B-Diffusers` | 480p | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ⭕ |
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| TurboWan2.1 T2V 14B | `IPostYellow/TurboWan2.1-T2V-14B-Diffusers` | 480p | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ⭕ | ❌ | ❌ | ❌ |
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| TurboWan2.1 T2V 14B 720P | `IPostYellow/TurboWan2.1-T2V-14B-720P-Diffusers` | 720p | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ⭕ |
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| TurboWan2.2 I2V A14B | `IPostYellow/TurboWan2.2-I2V-A14B-Diffusers` | 720p | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ⭕ |
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| Wan2.1 Fun 1.3B InP | `weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers` | 480p | ✅ | ✅ | ✅ | ⭕ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
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| Helios Base | `BestWishYsh/Helios-Base` | 720p | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| Helios Mid | `BestWishYsh/Helios-Mid` | 720p | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| Helios Distilled | `BestWishYsh/Helios-Distilled` | 720p | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| LTX-2 (one/two-stage/TI2V) | `Lightricks/LTX-2` | 768×512<br>1536×1024 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| LTX-2.3 (one/two-stage/TI2V/HQ) | `Lightricks/LTX-2.3` | 768×512<br>1536×1024<br>1920×1088 (HQ default) | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| Cosmos3-Nano (T2V / I2V / T2I) | `nvidia/Cosmos3-Nano` | 720p · 480p<br>1024×1024 (T2I) | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| Cosmos3-Super (T2V / I2V / T2I) | `nvidia/Cosmos3-Super` | 720p · 480p<br>1024×1024 (T2I) | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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**Note**:
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1. Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
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2. SageSLA is based on SpargeAttn. Install it first with `pip install git+https://github.com/thu-ml/SpargeAttn.git --no-build-isolation`
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3. LTX pipeline selection:
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- One-stage: `--pipeline-class-name LTX2Pipeline`
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- Two-stage: `--pipeline-class-name LTX2TwoStagePipeline`
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- Two-stage HQ: `--pipeline-class-name LTX2TwoStageHQPipeline` (HQ defaults to 1920×1088; you can still override `--width/--height`)
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- LTX-2 and LTX-2.3 support both T2V and TI2V (`--image-path`) on one-stage and two-stage pipelines (including HQ).
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- The spatial upsampler and distilled LoRA are auto-resolved from the model snapshot by default, and can still be overridden with `--spatial-upsampler-path` and `--distilled-lora-path`.
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- For LTX models, the `Resolutions` column uses output video `width×height` semantics, matching `sglang generate --width ... --height ...`.
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4. LTX-2 / LTX-2.3 two-stage also supports `--ltx2-two-stage-device-mode {original,snapshot,resident}`:
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- `snapshot` is the default and recommended mode.
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- `resident` usually provides the best latency/throughput but uses much more VRAM.
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- `original` keeps official two-stage semantics without the premerged stage-2 transformer path.
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- Example (one prior run): `original` `154.67s`, `snapshot` `114.05s`, `resident` `75.71s`; peak VRAM trend is `original < snapshot < resident`.
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5. Cosmos3 ships in two sizes — `nvidia/Cosmos3-Nano` (8B) and
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`nvidia/Cosmos3-Super` (32B). Both share the same pipeline; the only
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difference is transformer depth and width, picked up from
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`transformer/config.json` at load time. A single checkpoint serves T2V,
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I2V (`--image-path`), and T2I (`--num-frames 1`).
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### Image Generation Models
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| Model Name | HuggingFace Model ID |
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|:--------------------------|:---------------------------------------------------------|
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| FLUX.1-dev | `black-forest-labs/FLUX.1-dev` |
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| FLUX.2-dev | `black-forest-labs/FLUX.2-dev` |
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| FLUX.2-dev-NVFP4 | `black-forest-labs/FLUX.2-dev-NVFP4` |
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| FLUX.2-Klein-4B | `black-forest-labs/FLUX.2-klein-4B` |
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| FLUX.2-Klein-9B | `black-forest-labs/FLUX.2-klein-9B` |
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| Z-Image | `Tongyi-MAI/Z-Image` |
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| Z-Image-Turbo | `Tongyi-MAI/Z-Image-Turbo` |
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| GLM-Image | `zai-org/GLM-Image` |
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| Qwen Image | `Qwen/Qwen-Image` |
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| Qwen Image 2512 | `Qwen/Qwen-Image-2512` |
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| Qwen Image Edit | `Qwen/Qwen-Image-Edit` |
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| Qwen Image Edit 2509 | `Qwen/Qwen-Image-Edit-2509` |
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| Qwen Image Edit 2511 | `Qwen/Qwen-Image-Edit-2511` |
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| Qwen Image Layered | `Qwen/Qwen-Image-Layered` |
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| SD3 Medium | `stabilityai/stable-diffusion-3-medium-diffusers` |
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| SD3.5 Medium | `stabilityai/stable-diffusion-3.5-medium-diffusers` |
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| SD3.5 Large | `stabilityai/stable-diffusion-3.5-large-diffusers` |
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| Hunyuan3D-2 | `tencent/Hunyuan3D-2` |
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| SANA 1.5 1.6B | `Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers` |
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| SANA 1.5 4.8B | `Efficient-Large-Model/SANA1.5_4.8B_1024px_diffusers` |
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| SANA 1600M 1024px | `Efficient-Large-Model/Sana_1600M_1024px_diffusers` |
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| SANA 600M 1024px | `Efficient-Large-Model/Sana_600M_1024px_diffusers` |
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| SANA 1600M 512px | `Efficient-Large-Model/Sana_1600M_512px_diffusers` |
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| SANA 600M 512px | `Efficient-Large-Model/Sana_600M_512px_diffusers` |
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| FireRed-Image-Edit 1.0 | `FireRedTeam/FireRed-Image-Edit-1.0` |
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| FireRed-Image-Edit 1.1 | `FireRedTeam/FireRed-Image-Edit-1.1` |
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| ERNIE-Image | `baidu/ERNIE-Image` |
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| ERNIE-Image-Turbo | `baidu/ERNIE-Image-Turbo` |
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## Supported Components
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SGLang Diffusion supports overriding individual pipeline components with
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`--<component>-path`. The value can be either a Hugging Face repo ID or a local
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component directory.
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The same overrides can also be provided in config files through
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`component_paths.<component>`.
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### Common Syntax
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CLI:
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```bash
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sglang generate \
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--model-path black-forest-labs/FLUX.2-dev \
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--vae-path black-forest-labs/FLUX.2-small-decoder \
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--transformer-path /models/flux2/transformer
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```
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Config file:
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```yaml
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model_path: black-forest-labs/FLUX.2-dev
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component_paths:
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vae: black-forest-labs/FLUX.2-small-decoder
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transformer: /models/flux2/transformer
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```
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Use the component name from the pipeline's `model_index.json` or the native pipeline's registered module name:
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| Component Type | Supported Keys | Notes |
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|:------------------|:---------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|
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| VAE | `vae`, `video_vae`, `audio_vae` | `vae` is the common image-generation override |
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| Transformer / DiT | `transformer`, `video_dit`, `audio_dit` | `transformer` is the standard override for the main denoiser |
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| Text / Preprocess | `text_encoder`, `text_encoder_2`, `tokenizer`, `processor`, `image_processor` | Replacement encoders often need matching preprocessing assets |
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| Auxiliary | `scheduler`, `spatial_upsampler`, `vocoder`, `connectors`, `dual_tower_bridge`, `image_encoder`, `vision_language_encoder` | Only valid for pipelines that expose these components |
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### Known Component Repos
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The table below lists concrete Hugging Face component repos that are already used in SGLang Diffusion docs or tests. It is not an exhaustive catalog of all compatible component repos.
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| Base Model | Override Key | Example Repo | Notes |
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|:-------------------------------|:--------------|:-----------------------------------------|:------------------------------------------|
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| `black-forest-labs/FLUX.2-dev` | `vae` | `black-forest-labs/FLUX.2-small-decoder` | Decoder-only FLUX.2 VAE override |
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| `black-forest-labs/FLUX.2-dev` | `vae` | `fal/FLUX.2-Tiny-AutoEncoder` | Existing tested custom VAE path |
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### VAE
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- `--vae-path` is the common image-generation override.
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- `--video-vae-path` and `--audio-vae-path` are only relevant for pipelines with separate video or audio VAEs.
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### Transformer / DiT
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- `--transformer-path` is the standard override for the main denoising transformer.
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- For quantized transformers, prefer `--transformer-path` or `--transformer-weights-path`; see `quantization.md`.
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- `--video-dit-path` and `--audio-dit-path` are only for pipelines that split denoisers by modality.
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### Text Encoders and Preprocessors
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- `--text-encoder-path` and `--text-encoder-2-path` override primary and secondary text encoders.
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- `--tokenizer-path`, `--processor-path`, and `--image-processor-path` are useful when the replacement encoder requires matching preprocessing assets.
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### Auxiliary Components
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- `--scheduler-path` is only relevant when the pipeline exposes a scheduler component.
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- `--spatial-upsampler-path` is mainly for two-stage pipelines such as `LTX2TwoStagePipeline`.
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- `--vocoder-path`, `--connectors-path`, `--dual-tower-bridge-path`, `--image-encoder-path`, and `--vision-language-encoder-path` are only valid for pipelines that expose those components.
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### Notes
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1. Component overrides are only valid when the target pipeline actually uses
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that component.
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2. The override key should match the component name in the pipeline's
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`model_index.json` or the native pipeline's registered module name.
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## Verified LoRA Examples
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This section lists example LoRAs that have been explicitly tested and verified with each base model in the **SGLang Diffusion** pipeline.
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> Important:
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> LoRAs that are not listed here are not necessarily incompatible.
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> In practice, most standard LoRAs are expected to work, especially those following common Diffusers or SD-style conventions.
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> The entries below simply reflect configurations that have been manually validated by the SGLang team.
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### Verified LoRAs by Base Model
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| Base Model | Supported LoRAs |
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|:----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|
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| Wan2.2 | `lightx2v/Wan2.2-Distill-Loras`<br>`Cseti/wan2.2-14B-Arcane_Jinx-lora-v1` |
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| Wan2.1 | `lightx2v/Wan2.1-Distill-Loras` |
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| Z-Image-Turbo | `tarn59/pixel_art_style_lora_z_image_turbo`<br>`wcde/Z-Image-Turbo-DeJPEG-Lora` |
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| Qwen-Image | `lightx2v/Qwen-Image-Lightning`<br>`flymy-ai/qwen-image-realism-lora`<br>`prithivMLmods/Qwen-Image-HeadshotX`<br>`starsfriday/Qwen-Image-EVA-LoRA` |
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| Qwen-Image-Edit | `ostris/qwen_image_edit_inpainting`<br>`lightx2v/Qwen-Image-Edit-2511-Lightning` |
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| Flux | `dvyio/flux-lora-simple-illustration`<br>`XLabs-AI/flux-furry-lora`<br>`XLabs-AI/flux-RealismLora` |
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## Special requirements
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### Sliding Tile Attention
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- Currently, only Hopper GPUs (H100s) are supported.
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