[Docs] Sync docs_new with legacy docs and update migration redirects (#23337)

Co-authored-by: Mingyi <wisclmy0611@gmail.com>
2026-04-21 00:15:17 -07:00
parent f63def8510
commit 900aad5f72
179 changed files with 16014 additions and 8162 deletions
@@ -3,295 +3,255 @@ title: CLI reference
 sidebarTitle: CLI
 description: Run one-off generation tasks and launch the HTTP server from the command line.
 ---
+Use the CLI for one-off generation with `sglang generate` or to start a persistent HTTP server with `sglang serve`.

-The `sglang` CLI provides two main subcommands for diffusion inference:
+### Overlay repos for non-diffusers models

- **`sglang generate`** -- run a one-off generation without a persistent server
- **`sglang serve`** -- launch the OpenAI-compatible HTTP server
+If `--model-path` points to a supported non-diffusers source repo, SGLang can resolve it
+through a self-hosted overlay repo.

-## Prerequisites
+SGLang first checks a built-in overlay registry. Concrete built-in mappings can be added over time without changing the CLI surface.

-A working SGLang Diffusion installation with the `sglang` CLI available in your `$PATH`. See the [installation guide](../installation) for setup instructions.
+Override example:
+
+```bash Command
+export SGLANG_DIFFUSION_MODEL_OVERLAY_REGISTRY='{
+  "Wan-AI/Wan2.2-S2V-14B": {
+    "overlay_repo_id": "your-org/Wan2.2-S2V-14B-overlay",
+    "overlay_revision": "main"
+  }
+}'
+
+sglang generate \
+  --model-path Wan-AI/Wan2.2-S2V-14B \
+  --config configs/wan_s2v.yaml
+```
+
+The overlay repo should be a complete diffusers-style/componentized repo
+
+You can also pass the overlay repo itself as `--model-path` if it contains `_overlay/overlay_manifest.json`.
+
+Notes:
+1. `SGLANG_DIFFUSION_MODEL_OVERLAY_REGISTRY` is only an optional override for
+development and debugging. It accepts either a JSON object or a path to a JSON
+file, and can extend or replace built-in entries for the current process.
+2. On the first load, SGLang will:
+   - download overlay metadata from the overlay repo
+   - download the required files from the original source repo
+   - materialize a local standard component repo under `~/.cache/sgl_diffusion/materialized_models/`
+3. Later loads reuse the materialized local repo. The materialized repo is what the runtime loads as a normal componentized model directory.
+
+
+## Quick Start
+
+### Generate
+
+```bash Command
+sglang generate \
+  --model-path Qwen/Qwen-Image \
+  --prompt "A beautiful sunset over the mountains" \
+  --save-output
+```
+
+### Serve
+
+```bash Command
+sglang serve \
+  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+  --num-gpus 4 \
+  --ulysses-degree 2 \
+  --ring-degree 2 \
+  --port 30010
+```
+
+For request and response examples, see [OpenAI-Compatible API](./openai_api).
+
+<Tip>
+Use `sglang generate --help` and `sglang serve --help` for the full argument list. The CLI help output is the source of truth for exhaustive flags.
+</Tip>
+
+## Common Options
+
+### Model and runtime
+
+- `--model-path &#123;MODEL&#125;`: model path or Hugging Face model ID
+- `--lora-path &#123;PATH&#125;` and `--lora-nickname &#123;NAME&#125;`: load a LoRA adapter
+- `--num-gpus &#123;N&#125;`: number of GPUs to use
+- `--tp-size &#123;N&#125;`: tensor parallelism size, mainly for encoders
+- `--sp-degree &#123;N&#125;`: sequence parallelism size
+- `--ulysses-degree &#123;N&#125;` and `--ring-degree &#123;N&#125;`: USP parallelism controls
+- `--attention-backend &#123;BACKEND&#125;`: attention backend for native SGLang pipelines
+- `--attention-backend-config &#123;CONFIG&#125;`: attention backend configuration
+
+### Sampling and output
+
+- `--prompt &#123;PROMPT&#125;` and `--negative-prompt &#123;PROMPT&#125;`
+- `--image-path &#123;PATH&#125; [&#123;PATH&#125; ...]`: input image(s) for image-to-video or image-to-image generation
+- `--num-inference-steps &#123;STEPS&#125;` and `--seed &#123;SEED&#125;`
+- `--height &#123;HEIGHT&#125;`, `--width &#123;WIDTH&#125;`, `--num-frames &#123;N&#125;`, `--fps &#123;FPS&#125;`
+- `--output-path &#123;PATH&#125;`, `--output-file-name &#123;NAME&#125;`, `--save-output`, `--return-frames`
+
+For frame interpolation and upscaling, see [Post-Processing](./post_processing).
+
+### Quantized transformers
+
+For quantized transformer checkpoints, prefer:
+
+- `--model-path` for the base pipeline
+- `--transformer-path` for a quantized `transformers` transformer component folder
+- `--transformer-weights-path` for a quantized safetensors file, directory, or repo
+
+See [Quantization](../quantization) for supported quantization families and examples.
+
+## Configuration Files
+
+Use `--config` to load JSON or YAML configuration. Command-line flags override values from the config file.
+
+```bash Command
+sglang generate --config config.yaml
+```
+
+Example:
+
+```yaml Config
+model_path: FastVideo/FastHunyuan-diffusers
+prompt: A beautiful woman in a red dress walking down a street
+output_path: outputs/
+num_gpus: 2
+sp_size: 2
+tp_size: 1
+num_frames: 45
+height: 720
+width: 1280
+num_inference_steps: 6
+seed: 1024
+fps: 24
+precision: bf16
+vae_precision: fp16
+vae_tiling: true
+vae_sp: true
+enable_torch_compile: false
+```

 ## Generate

-Run a one-off generation task without launching a persistent server. Pass both server arguments and sampling parameters after the `generate` subcommand:
+`sglang generate` runs a single generation job and exits when the job finishes.

-```bash
-SERVER_ARGS=(
-  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers
-  --text-encoder-cpu-offload
-  --pin-cpu-memory
-  --num-gpus 4
-  --ulysses-degree=2
-  --ring-degree=2
-)
-
-SAMPLING_ARGS=(
-  --prompt "A curious raccoon"
-  --save-output
-  --output-path outputs
-  --output-file-name "A curious raccoon.mp4"
-)
-
-sglang generate "${SERVER_ARGS[@]}" "${SAMPLING_ARGS[@]}"
-```
-
-You can also enable Cache-DiT acceleration via an environment variable:
-
-```bash
-SGLANG_CACHE_DIT_ENABLED=true sglang generate "${SERVER_ARGS[@]}" "${SAMPLING_ARGS[@]}"
+```bash Command
+sglang generate \
+  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --text-encoder-cpu-offload \
+  --pin-cpu-memory \
+  --num-gpus 4 \
+  --ulysses-degree 2 \
+  --ring-degree 2 \
+  --prompt "A curious raccoon" \
+  --save-output \
+  --output-path outputs \
+  --output-file-name "a-curious-raccoon.mp4"
 ```

 <Note>
-HTTP server-related arguments are ignored in `generate` mode. The process shuts down automatically once generation completes.
+HTTP server-only arguments are ignored by `sglang generate`.
 </Note>

+For diffusers pipelines, Cache-DiT can be enabled with `SGLANG_CACHE_DIT_ENABLED=true` or `--cache-dit-config`. See [Cache-DiT](../cache_dit).
+
 ## Serve

-Launch the SGLang Diffusion HTTP server and interact through the OpenAI-compatible API.
+`sglang serve` starts the HTTP server and keeps the model loaded for repeated requests.

-```bash
-SERVER_ARGS=(
-  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers
-  --text-encoder-cpu-offload
-  --pin-cpu-memory
-  --num-gpus 4
-  --ulysses-degree=2
-  --ring-degree=2
-)
-
-sglang serve "${SERVER_ARGS[@]}"
-```
-
- `--model-path` -- which model to load (e.g. `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`)
- `--port` -- HTTP port to listen on (default: `30010`)
-
-For full API usage including image/video generation and LoRA management, see the [OpenAI API documentation](./openai-api).
-
---
-
-## Supported arguments
-
-### Server arguments
-
-<Accordion title="Server arguments reference">
-
-| Argument | Description |
-|:--|:--|
-| `--model-path MODEL_PATH` | Path to the model or HuggingFace model ID |
-| `--lora-path LORA_PATH` | Path to a LoRA adapter (local or HuggingFace ID). If omitted, LoRA is not applied |
-| `--lora-nickname NAME` | Nickname for the LoRA adapter (default: `default`) |
-| `--num-gpus NUM` | Number of GPUs to use |
-| `--tp-size SIZE` | Tensor parallelism size (encoder only; keep at most 1 when text encoder offload is enabled) |
-| `--sp-degree SIZE` | Sequence parallelism size (typically should match the number of GPUs) |
-| `--ulysses-degree SIZE` | DeepSpeed-Ulysses-style SP degree in USP |
-| `--ring-degree SIZE` | Ring attention-style SP degree in USP |
-| `--attention-backend BACKEND` | Attention backend. Native pipelines: `fa`, `torch_sdpa`, `sage_attn`, etc. Diffusers pipelines: `flash`, `_flash_3_hub`, `sage`, `xformers` |
-| `--attention-backend-config CONFIG` | Config for the attention backend. Accepts a JSON string, a JSON/YAML file path, or `key=value` pairs |
-| `--cache-dit-config PATH` | Path to a Cache-DiT YAML/JSON config (diffusers backend only) |
-| `--dit-precision DTYPE` | Precision for the DiT model (`fp32`, `fp16`, `bf16`) |
-| `--text-encoder-cpu-offload` | Offload text encoders to CPU |
-| `--pin-cpu-memory` | Pin CPU memory for faster transfers |
-
-</Accordion>
-
-### Sampling parameters
-
-<Accordion title="Generation parameters">
-
-| Argument | Description |
-|:--|:--|
-| `--prompt PROMPT` | Text description for the image or video to generate |
-| `--negative-prompt PROMPT` | Negative prompt to guide generation away from certain concepts |
-| `--num-inference-steps STEPS` | Number of denoising steps |
-| `--seed SEED` | Random seed for reproducible generation |
-
-</Accordion>
-
-<Accordion title="Image/video configuration">
-
-| Argument | Description |
-|:--|:--|
-| `--height HEIGHT` | Height of the generated output |
-| `--width WIDTH` | Width of the generated output |
-| `--num-frames NUM` | Number of frames to generate (video only) |
-| `--fps FPS` | Frames per second for the saved output (video only) |
-
-</Accordion>
-
-<Accordion title="Output options">
-
-| Argument | Description |
-|:--|:--|
-| `--save-output` | Save the image or video to disk |
-| `--output-path PATH` | Directory to save the generated output |
-| `--output-file-name NAME` | File name for the saved output |
-| `--return-frames` | Return the raw frames instead of saving |
-
-</Accordion>
-
-### Frame interpolation (video only)
-
-Frame interpolation is a post-processing step that synthesizes new frames between each pair of consecutive generated frames, producing smoother motion without re-running the diffusion model.
-
-The `--frame-interpolation-exp` flag controls how many rounds of interpolation to apply: each round inserts one new frame into every gap between adjacent frames, so the output frame count follows the formula:
-
-$$
-\text{output frames} = (N - 1) \times 2^{\text{exp}} + 1
-$$
-
-For example, 5 original frames with `exp=1` -> 4 gaps x 1 new frame + 5 originals = **9 frames**; with `exp=2` -> **17 frames**.
-
-| Argument | Description |
-|:--|:--|
-| `--enable-frame-interpolation` | Enable frame interpolation. Model weights are downloaded automatically on first use |
-| `--frame-interpolation-exp EXP` | Interpolation exponent -- `1` = 2x temporal resolution, `2` = 4x, etc. (default: `1`) |
-| `--frame-interpolation-scale SCALE` | RIFE inference scale; use `0.5` for high-resolution inputs to save memory (default: `1.0`) |
-| `--frame-interpolation-model-path PATH` | Local directory or HuggingFace repo ID containing RIFE `flownet.pkl` weights (default: `elfgum/RIFE-4.22.lite`, downloaded automatically) |
-
-**Example** -- generate a 5-frame video and interpolate to 9 frames ($(5 - 1) \times 2^1 + 1 = 9$):
-
-```bash
-sglang generate \
-  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
-  --prompt "A dog running through a park" \
-  --num-frames 5 \
-  --enable-frame-interpolation \
-  --frame-interpolation-exp 1 \
-  --save-output
-```
-
---
-
-## Configuration files
-
-Instead of passing every parameter on the command line, you can use a JSON or YAML config file. Command-line arguments take precedence over config values.
-
-```bash
-sglang generate --config config.json
-```
-
-<Tabs>
-  <Tab title="JSON">
-    ```json config.json
-    {
-      "model_path": "FastVideo/FastHunyuan-diffusers",
-      "prompt": "A beautiful woman in a red dress walking down a street",
-      "output_path": "outputs/",
-      "num_gpus": 2,
-      "sp_size": 2,
-      "tp_size": 1,
-      "num_frames": 45,
-      "height": 720,
-      "width": 1280,
-      "num_inference_steps": 6,
-      "seed": 1024,
-      "fps": 24,
-      "precision": "bf16",
-      "vae_precision": "fp16",
-      "vae_tiling": true,
-      "vae_sp": true,
-      "vae_config": {
-        "load_encoder": false,
-        "load_decoder": true,
-        "tile_sample_min_height": 256,
-        "tile_sample_min_width": 256
-      },
-      "text_encoder_precisions": ["fp16", "fp16"],
-      "mask_strategy_file_path": null,
-      "enable_torch_compile": false
-    }
-    ```
-  </Tab>
-  <Tab title="YAML">
-    ```yaml config.yaml
-    model_path: "FastVideo/FastHunyuan-diffusers"
-    prompt: "A beautiful woman in a red dress walking down a street"
-    output_path: "outputs/"
-    num_gpus: 2
-    sp_size: 2
-    tp_size: 1
-    num_frames: 45
-    height: 720
-    width: 1280
-    num_inference_steps: 6
-    seed: 1024
-    fps: 24
-    precision: "bf16"
-    vae_precision: "fp16"
-    vae_tiling: true
-    vae_sp: true
-    vae_config:
-      load_encoder: false
-      load_decoder: true
-      tile_sample_min_height: 256
-      tile_sample_min_width: 256
-    text_encoder_precisions:
-      - "fp16"
-      - "fp16"
-    mask_strategy_file_path: null
-    enable_torch_compile: false
-    ```
-  </Tab>
-</Tabs>
-
-To see all available options:
-
-```bash
-sglang generate --help
-```
-
---
-
-## Component path overrides
-
-You can override any pipeline component (e.g. `vae`, `transformer`, `text_encoder`) by specifying a custom checkpoint path with `--<component>-path`, where `<component>` matches the key in the model's `model_index.json`.
-
-### Example: FLUX.2-dev with Tiny AutoEncoder
-
-Replace the default VAE with a distilled tiny autoencoder for ~3x faster decoding:
-
-```bash
+```bash Command
 sglang serve \
-  --model-path=black-forest-labs/FLUX.2-dev \
-  --vae-path=fal/FLUX.2-Tiny-AutoEncoder
+  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+  --text-encoder-cpu-offload \
+  --pin-cpu-memory \
+  --num-gpus 4 \
+  --ulysses-degree 2 \
+  --ring-degree 2 \
+  --port 30010
 ```

-You can also use a local path:
+### Cloud Storage

-```bash
+SGLang Diffusion can upload generated images and videos to S3-compatible object storage after generation.
+
+```bash Command
+export SGLANG_CLOUD_STORAGE_TYPE=s3
+export SGLANG_S3_BUCKET_NAME=my-bucket
+export SGLANG_S3_ACCESS_KEY_ID=your-access-key
+export SGLANG_S3_SECRET_ACCESS_KEY=your-secret-key
+export SGLANG_S3_ENDPOINT_URL=https://minio.example.com
+```
+
+See [Environment Variables](../environment_variables) for the full set of storage options.
+
+## Component Path Overrides
+
+Override individual pipeline components such as `vae`, `transformer`, or `text_encoder` with `--<component>-path`.
+
+```bash Command
 sglang serve \
-  --model-path=black-forest-labs/FLUX.2-dev \
-  --vae-path=~/.cache/huggingface/hub/models--fal--FLUX.2-Tiny-AutoEncoder/snapshots/.../vae
+  --model-path black-forest-labs/FLUX.2-dev \
+  --vae-path fal/FLUX.2-Tiny-AutoEncoder
 ```

-<Warning>
-The component key must match the one in the model's `model_index.json` (e.g. `vae`).
-The path must be either a HuggingFace repo ID or point to a complete component folder containing `config.json` and safetensors files.
-</Warning>
+The component key must match the key in the model's `model_index.json`, and the path must be either a Hugging Face repo ID or a complete component directory.

---
+## Diffusers Backend

-## Diffusers backend
+Use `--backend diffusers` to force vanilla diffusers pipelines when no native SGLang implementation exists or when a model requires a custom pipeline class.

-SGLang Diffusion supports a diffusers backend that runs any diffusers-compatible model through SGLang's infrastructure using vanilla diffusers pipelines. This is useful for models without native SGLang implementations or models with custom pipeline classes.
+### Key Options

-### Backend arguments
+<table>
+  <thead>
+    <tr>
+      <th>Argument</th>
+      <th>Values</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>--backend</code></td>
+      <td><code>auto</code>, <code>sglang</code>, <code>diffusers</code></td>
+      <td>Choose native SGLang, force native, or force diffusers</td>
+    </tr>
+    <tr>
+      <td><code>--diffusers-attention-backend</code></td>
+      <td><code>flash</code>, <code>_flash_3_hub</code>, <code>sage</code>, <code>xformers</code>, <code>native</code></td>
+      <td>Attention backend for diffusers pipelines</td>
+    </tr>
+    <tr>
+      <td><code>--trust-remote-code</code></td>
+      <td>flag</td>
+      <td>Required for models with custom pipeline classes</td>
+    </tr>
+    <tr>
+      <td><code>--vae-tiling</code> and <code>--vae-slicing</code></td>
+      <td>flag</td>
+      <td>Lower memory usage for VAE decode</td>
+    </tr>
+    <tr>
+      <td><code>--dit-precision</code> and <code>--vae-precision</code></td>
+      <td><code>fp16</code>, <code>bf16</code>, <code>fp32</code></td>
+      <td>Precision controls</td>
+    </tr>
+    <tr>
+      <td><code>--enable-torch-compile</code></td>
+      <td>flag</td>
+      <td>Enable <code>torch.compile</code></td>
+    </tr>
+    <tr>
+      <td><code>--cache-dit-config</code></td>
+      <td><code>&#123;PATH&#125;</code></td>
+      <td>Cache-DiT config for diffusers pipelines</td>
+    </tr>
+  </tbody>
+</table>

-| Argument | Values | Description |
-|:--|:--|:--|
-| `--backend` | `auto` (default), `sglang`, `diffusers` | `auto`: prefer native SGLang, fallback to diffusers. `sglang`: force native (fails if unavailable). `diffusers`: force vanilla diffusers pipeline |
-| `--diffusers-attention-backend` | `flash`, `_flash_3_hub`, `sage`, `xformers`, `native` | Attention backend for diffusers pipelines |
-| `--trust-remote-code` | flag | Required for models with custom pipeline classes |
-| `--vae-tiling` | flag | Enable VAE tiling for large image support (decodes tile-by-tile) |
-| `--vae-slicing` | flag | Enable VAE slicing for lower memory usage (decodes slice-by-slice) |
-| `--dit-precision` | `fp16`, `bf16`, `fp32` | Precision for the diffusion transformer |
-| `--vae-precision` | `fp16`, `bf16`, `fp32` | Precision for the VAE |
-
-### Example: running Ovis-Image-7B
-
-[Ovis-Image-7B](https://huggingface.co/AIDC-AI/Ovis-Image-7B) is a 7B text-to-image model optimized for high-quality text rendering.
+### Example

 ```bash
 sglang generate \
@@ -308,59 +268,4 @@ sglang generate \
  --output-file-name ovis_garden.png
 ```

-### Extra diffusers arguments
-
-For pipeline-specific parameters not exposed via CLI, use `diffusers_kwargs` in a config file:
-
-```json config.json
-{
-  "model_path": "AIDC-AI/Ovis-Image-7B",
-  "backend": "diffusers",
-  "prompt": "A beautiful landscape",
-  "diffusers_kwargs": {
-    "cross_attention_kwargs": {"scale": 0.5}
-  }
-}
-```
-
-```bash
-sglang generate --config config.json
-```
-
-### Cache-DiT acceleration
-
-Users on the diffusers backend can leverage Cache-DiT acceleration by loading custom cache configs from a YAML file. See the [Cache-DiT documentation](../cache-dit) for details.
-
---
-
-## Cloud storage support
-
-The server supports automatically uploading generated artifacts to S3-compatible cloud storage (AWS S3, MinIO, Alibaba Cloud OSS, Tencent Cloud COS).
-
-The workflow is: **Generate -> Upload -> Delete local file**. The API response returns the public URL of the uploaded object.
-
-1. **Install boto3**
-
-```bash
-pip install boto3
-```
-
-2. **Set environment variables**
-
-```bash
-export SGLANG_CLOUD_STORAGE_TYPE=s3
-export SGLANG_S3_BUCKET_NAME=my-bucket
-export SGLANG_S3_ACCESS_KEY_ID=your-access-key
-export SGLANG_S3_SECRET_ACCESS_KEY=your-secret-key
-
-# Optional: custom endpoint for MinIO/OSS/COS
-export SGLANG_S3_ENDPOINT_URL=https://minio.example.com
-```
-
-3. **Launch the server**
-
-```bash
-sglang serve --model-path MODEL_PATH
-```
-
-See the [environment variables reference](../environment-variables) for all storage-related variables.
+For pipeline-specific arguments not exposed in the CLI, pass `diffusers_kwargs` in a config file.
@@ -1,421 +0,0 @@
---
-title: OpenAI API
-sidebarTitle: OpenAI API
-description: Image and video generation endpoints with LoRA adapter management.
---
-
-The SGLang Diffusion HTTP server implements an OpenAI-compatible API for image and video generation, as well as dynamic LoRA adapter management.
-
-## Prerequisites
-
- Python 3.11+ if you plan to use the OpenAI Python SDK.
- A running SGLang Diffusion server (see the [CLI reference](./cli) for launch instructions).
-
-## Start the server
-
-```bash
-SERVER_ARGS=(
-  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers
-  --text-encoder-cpu-offload
-  --pin-cpu-memory
-  --num-gpus 4
-  --ulysses-degree=2
-  --ring-degree=2
-  --port 30010
-)
-
-sglang serve "${SERVER_ARGS[@]}"
-```
-
- `--model-path` -- path to the model or HuggingFace model ID
- `--port` -- HTTP port to listen on (default: `30000`)
-
-### Get model information
-
-**Endpoint:** `GET /models`
-
-Returns model path, task type, pipeline configuration, and precision settings.
-
-<CodeGroup>
-```bash curl
-curl -sS -X GET "http://localhost:30010/models"
-```
-</CodeGroup>
-
-**Response:**
-
-```json
-{
-  "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-  "task_type": "T2V",
-  "pipeline_name": "wan_pipeline",
-  "pipeline_class": "WanPipeline",
-  "num_gpus": 4,
-  "dit_precision": "bf16",
-  "vae_precision": "fp16"
-}
-```
-
---
-
-## Image generation
-
-The server implements an OpenAI-compatible Images API under the `/v1/images` namespace.
-
-### Create an image
-
-**Endpoint:** `POST /v1/images/generations`
-
-<CodeGroup>
-```python Python
-import base64
-from openai import OpenAI
-
-client = OpenAI(api_key="sk-proj-1234567890", base_url="http://localhost:30010/v1")
-
-img = client.images.generate(
-    prompt="A calico cat playing a piano on stage",
-    size="1024x1024",
-    n=1,
-    response_format="b64_json",
-)
-
-image_bytes = base64.b64decode(img.data[0].b64_json)
-with open("output.png", "wb") as f:
-    f.write(image_bytes)
-```
-
-```bash curl
-curl -sS -X POST "http://localhost:30010/v1/images/generations" \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer sk-proj-1234567890" \
-  -d '{
-        "prompt": "A calico cat playing a piano on stage",
-        "size": "1024x1024",
-        "n": 1,
-        "response_format": "b64_json"
-      }'
-```
-</CodeGroup>
-
-<Note>
-If `response_format=url` is used and cloud storage is not configured, the API returns a relative URL like `/v1/images/<IMAGE_ID>/content`.
-</Note>
-
-### Edit an image
-
-**Endpoint:** `POST /v1/images/edits`
-
-Accepts a multipart form upload with input images and a text prompt. Returns either a base64-encoded image or a URL.
-
-<Tabs>
-  <Tab title="b64_json response">
-    ```bash
-    curl -sS -X POST "http://localhost:30010/v1/images/edits" \
-      -H "Authorization: Bearer sk-proj-1234567890" \
-      -F "image=@local_input_image.png" \
-      -F "url=image_url.jpg" \
-      -F "prompt=A calico cat playing a piano on stage" \
-      -F "size=1024x1024" \
-      -F "response_format=b64_json"
-    ```
-  </Tab>
-  <Tab title="URL response">
-    ```bash
-    curl -sS -X POST "http://localhost:30010/v1/images/edits" \
-      -H "Authorization: Bearer sk-proj-1234567890" \
-      -F "image=@local_input_image.png" \
-      -F "url=image_url.jpg" \
-      -F "prompt=A calico cat playing a piano on stage" \
-      -F "size=1024x1024" \
-      -F "response_format=url"
-    ```
-  </Tab>
-</Tabs>
-
-### Download image content
-
-When `response_format=url` is used, the API returns a relative URL like `/v1/images/<IMAGE_ID>/content`.
-
-**Endpoint:** `GET /v1/images/{image_id}/content`
-
-```bash
-curl -sS -L "http://localhost:30010/v1/images/<IMAGE_ID>/content" \
-  -H "Authorization: Bearer sk-proj-1234567890" \
-  -o output.png
-```
-
---
-
-## Video generation
-
-The server implements a subset of the OpenAI Videos API under the `/v1/videos` namespace.
-
-### Create a video
-
-**Endpoint:** `POST /v1/videos`
-
-<CodeGroup>
-```python Python
-from openai import OpenAI
-
-client = OpenAI(api_key="sk-proj-1234567890", base_url="http://localhost:30010/v1")
-
-video = client.videos.create(
-    prompt="A calico cat playing a piano on stage",
-    size="1280x720"
-)
-print(f"Video ID: {video.id}, Status: {video.status}")
-```
-
-```bash curl
-curl -sS -X POST "http://localhost:30010/v1/videos" \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer sk-proj-1234567890" \
-  -d '{
-        "prompt": "A calico cat playing a piano on stage",
-        "size": "1280x720"
-      }'
-```
-</CodeGroup>
-
-### List videos
-
-**Endpoint:** `GET /v1/videos`
-
-<CodeGroup>
-```python Python
-videos = client.videos.list()
-for item in videos.data:
-    print(item.id, item.status)
-```
-
-```bash curl
-curl -sS -X GET "http://localhost:30010/v1/videos" \
-  -H "Authorization: Bearer sk-proj-1234567890"
-```
-</CodeGroup>
-
-### Download video content
-
-**Endpoint:** `GET /v1/videos/{video_id}/content`
-
-<CodeGroup>
-```python Python
-import time
-
-# Poll for completion
-while True:
-    page = client.videos.list()
-    item = next((v for v in page.data if v.id == video_id), None)
-    if item and item.status == "completed":
-        break
-    time.sleep(5)
-
-# Download content
-resp = client.videos.download_content(video_id=video_id)
-with open("output.mp4", "wb") as f:
-    f.write(resp.read())
-```
-
-```bash curl
-curl -sS -L "http://localhost:30010/v1/videos/<VIDEO_ID>/content" \
-  -H "Authorization: Bearer sk-proj-1234567890" \
-  -o output.mp4
-```
-</CodeGroup>
-
---
-
-## LoRA management
-
-The server supports dynamic loading, merging, and unmerging of LoRA adapters.
-
-<Info>
- **Mutual exclusion:** Only one LoRA can be merged (active) at a time.
- **Switching:** To switch LoRAs, you must first unmerge the current one, then set the new one.
- **Caching:** The server caches loaded LoRA weights in memory. Switching back to a previously loaded LoRA (same path) has negligible cost.
-</Info>
-
-### Set LoRA adapter
-
-Loads one or more LoRA adapters and merges their weights into the model. Supports both single LoRA (backward compatible) and multiple LoRA adapters.
-
-**Endpoint:** `POST /v1/set_lora`
-
-**Parameters:**
-
-| Parameter | Type | Description |
-|:--|:--|:--|
-| `lora_nickname` | string or list | A unique identifier for the LoRA adapter(s). Required |
-| `lora_path` | string or list | Path to `.safetensors` file(s) or HuggingFace repo ID(s). Required for first load; optional when re-activating a cached nickname |
-| `target` | string or list | Which transformer(s) to apply the LoRA to: `"all"` (default), `"transformer"`, `"transformer_2"`, `"critic"` |
-| `strength` | float or list | LoRA strength for merge (default: `1.0`). Values < 1.0 reduce the effect, > 1.0 amplify it |
-
-<Tabs>
-  <Tab title="Single LoRA">
-    ```bash
-    curl -X POST http://localhost:30010/v1/set_lora \
-      -H "Content-Type: application/json" \
-      -d '{
-            "lora_nickname": "lora_name",
-            "lora_path": "/path/to/lora.safetensors",
-            "target": "all",
-            "strength": 0.8
-          }'
-    ```
-  </Tab>
-  <Tab title="Multiple LoRAs">
-    ```bash
-    curl -X POST http://localhost:30010/v1/set_lora \
-      -H "Content-Type: application/json" \
-      -d '{
-            "lora_nickname": ["lora_1", "lora_2"],
-            "lora_path": ["/path/to/lora1.safetensors", "/path/to/lora2.safetensors"],
-            "target": ["transformer", "transformer_2"],
-            "strength": [0.8, 1.0]
-          }'
-    ```
-  </Tab>
-  <Tab title="Same target">
-    ```bash
-    curl -X POST http://localhost:30010/v1/set_lora \
-      -H "Content-Type: application/json" \
-      -d '{
-            "lora_nickname": ["style_lora", "character_lora"],
-            "lora_path": ["/path/to/style.safetensors", "/path/to/character.safetensors"],
-            "target": "all",
-            "strength": [0.7, 0.9]
-          }'
-    ```
-  </Tab>
-</Tabs>
-
-<Note>
-When using multiple LoRAs:
- All list parameters (`lora_nickname`, `lora_path`, `target`, `strength`) must have the same length.
- If `target` or `strength` is a single value, it will be applied to all LoRAs.
- Multiple LoRAs applied to the same target will be merged in order.
-</Note>
-
-### Merge LoRA weights
-
-Manually merges the currently set LoRA weights into the base model.
-
-**Endpoint:** `POST /v1/merge_lora_weights`
-
-| Parameter | Type | Description |
-|:--|:--|:--|
-| `target` | string | Which transformer(s) to merge: `"all"` (default), `"transformer"`, `"transformer_2"`, `"critic"` |
-| `strength` | float | LoRA strength for merge (default: `1.0`) |
-
-```bash
-curl -X POST http://localhost:30010/v1/merge_lora_weights \
-  -H "Content-Type: application/json" \
-  -d '{"strength": 0.8}'
-```
-
-<Tip>
-`set_lora` automatically performs a merge, so this endpoint is typically only needed if you have manually unmerged but want to re-apply the same LoRA without calling `set_lora` again.
-</Tip>
-
-### Unmerge LoRA weights
-
-Unmerges the currently active LoRA weights from the base model, restoring it to its original state. Call this before setting a different LoRA.
-
-**Endpoint:** `POST /v1/unmerge_lora_weights`
-
-```bash
-curl -X POST http://localhost:30010/v1/unmerge_lora_weights \
-  -H "Content-Type: application/json"
-```
-
-### List LoRA adapters
-
-Returns loaded LoRA adapters and current application status per module.
-
-**Endpoint:** `GET /v1/list_loras`
-
-```bash
-curl -sS -X GET "http://localhost:30010/v1/list_loras"
-```
-
-**Response:**
-
-```json
-{
-  "loaded_adapters": [
-    { "nickname": "lora_a", "path": "/weights/lora_a.safetensors" },
-    { "nickname": "lora_b", "path": "/weights/lora_b.safetensors" }
-  ],
-  "active": {
-    "transformer": [
-      {
-        "nickname": "lora2",
-        "path": "tarn59/pixel_art_style_lora_z_image_turbo",
-        "merged": true,
-        "strength": 1.0
-      }
-    ]
-  }
-}
-```
-
-### Example: switching LoRAs
-
-1. **Set LoRA A**
-
-```bash
-curl -X POST http://localhost:30010/v1/set_lora \
-  -d '{"lora_nickname": "lora_a", "lora_path": "path/to/A"}'
-```
-
-2. **Generate with LoRA A**
-
-Run your image or video generation requests.
-
-3. **Unmerge LoRA A**
-
-```bash
-curl -X POST http://localhost:30010/v1/unmerge_lora_weights
-```
-
-4. **Set LoRA B**
-
-```bash
-curl -X POST http://localhost:30010/v1/set_lora \
-  -d '{"lora_nickname": "lora_b", "lora_path": "path/to/B"}'
-```
-
-5. **Generate with LoRA B**
-
-Run your image or video generation requests with the new adapter.
-
---
-
-## Output quality
-
-Control output quality and compression for both image and video generation through the `output-quality` and `output-compression` parameters.
-
-### Parameters
-
-| Parameter | Type | Description |
-|:--|:--|:--|
-| `output-quality` | string | Preset quality level. Default: `"default"` |
-| `output-compression` | integer | Direct compression level override (0-100). When provided, takes precedence over `output-quality` |
-
-**Quality presets:**
-
-| Preset | Compression value |
-|:--|:--|
-| `"maximum"` | 100 |
-| `"high"` | 90 |
-| `"medium"` | 55 |
-| `"low"` | 35 |
-| `"default"` | Auto (50 for video, 75 for image) |
-
-<Warning>
- When both `output-quality` and `output-compression` are provided, `output-compression` takes precedence.
- Quality settings apply to JPEG and video formats. PNG uses lossless compression and ignores these settings.
- Lower compression values (or `"low"` quality preset) produce smaller files but may show visible artifacts.
-</Warning>
@@ -0,0 +1,450 @@
+---
+title: OpenAI API
+sidebarTitle: OpenAI API
+description: Image and video generation endpoints with LoRA adapter management.
+---
+The SGLang diffusion HTTP server implements an OpenAI-compatible API for image and video generation, as well as LoRA adapter management.
+
+## Prerequisites
+
+- Python 3.11+ if you plan to use the OpenAI Python SDK.
+
+## Serve
+
+Launch the server using the `sglang serve` command.
+
+### Start the server
+
+```bash
+SERVER_ARGS=(
+  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+  --text-encoder-cpu-offload
+  --pin-cpu-memory
+  --num-gpus 4
+  --ulysses-degree=2
+  --ring-degree=2
+  --port 30010
+)
+
+sglang serve "${SERVER_ARGS[@]}"
+```
+
+- **--model-path**: Path to the model or model ID.
+- **--port**: HTTP port to listen on (default: `30000`).
+
+**Get Model Information**
+
+**Endpoint:** `GET /models`
+
+Returns information about the model served by this server, including model path, task type, pipeline configuration, and precision settings.
+
+**Curl Example:**
+
+```bash curl
+curl -sS -X GET "http://localhost:30010/models"
+```
+
+**Response Example:**
+
+```json
+{
+  "model_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+  "task_type": "T2V",
+  "pipeline_name": "wan_pipeline",
+  "pipeline_class": "WanPipeline",
+  "num_gpus": 4,
+  "dit_precision": "bf16",
+  "vae_precision": "fp16"
+}
+```
+
+---
+
+## Endpoints
+
+### Image Generation
+
+The server implements an OpenAI-compatible Images API under the `/v1/images` namespace.
+
+**Create an image**
+
+**Endpoint:** `POST /v1/images/generations`
+
+**Python Example (b64_json response):**
+
+```python Python
+import base64
+from openai import OpenAI
+
+client = OpenAI(api_key="sk-proj-1234567890", base_url="http://localhost:30010/v1")
+
+img = client.images.generate(
+    prompt="A calico cat playing a piano on stage",
+    size="1024x1024",
+    n=1,
+    response_format="b64_json",
+)
+
+image_bytes = base64.b64decode(img.data[0].b64_json)
+with open("output.png", "wb") as f:
+    f.write(image_bytes)
+```
+
+**Curl Example:**
+
+```bash curl
+curl -sS -X POST "http://localhost:30010/v1/images/generations" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-proj-1234567890" \
+  -d '{
+        "prompt": "A calico cat playing a piano on stage",
+        "size": "1024x1024",
+        "n": 1,
+        "response_format": "b64_json"
+      }'
+```
+
+> **Note**
+> If `response_format=url` is used and cloud storage is not configured, the API returns
+> a relative URL like `/v1/images/<IMAGE_ID>/content`.
+
+**Edit an image**
+
+**Endpoint:** `POST /v1/images/edits`
+
+This endpoint accepts a multipart form upload with input images and a text prompt. The server can return either a base64-encoded image or a URL to download the image.
+
+**Curl Example (b64_json response):**
+
+```bash Command
+curl -sS -X POST "http://localhost:30010/v1/images/edits" \
+  -H "Authorization: Bearer sk-proj-1234567890" \
+  -F "image=@local_input_image.png" \
+  -F "url=image_url.jpg" \
+  -F "prompt=A calico cat playing a piano on stage" \
+  -F "size=1024x1024" \
+  -F "response_format=b64_json"
+```
+
+**Curl Example (URL response):**
+
+```bash Command
+curl -sS -X POST "http://localhost:30010/v1/images/edits" \
+  -H "Authorization: Bearer sk-proj-1234567890" \
+  -F "image=@local_input_image.png" \
+  -F "url=image_url.jpg" \
+  -F "prompt=A calico cat playing a piano on stage" \
+  -F "size=1024x1024" \
+  -F "response_format=url"
+```
+
+**Download image content**
+
+When `response_format=url` is used with `POST /v1/images/generations` or `POST /v1/images/edits`,
+the API returns a relative URL like `/v1/images/<IMAGE_ID>/content`.
+
+**Endpoint:** `GET /v1/images/&#123;image_id&#125;/content`
+
+**Curl Example:**
+
+```bash
+curl -sS -L "http://localhost:30010/v1/images/<IMAGE_ID>/content" \
+  -H "Authorization: Bearer sk-proj-1234567890" \
+  -o output.png
+```
+
+### Video Generation
+
+The server implements a subset of the OpenAI Videos API under the `/v1/videos` namespace.
+
+**Create a video (text-to-video)**
+
+**Endpoint:** `POST /v1/videos`
+
+**Python Example:**
+
+```python Python
+from openai import OpenAI
+
+client = OpenAI(api_key="sk-proj-1234567890", base_url="http://localhost:30010/v1")
+
+video = client.videos.create(
+    prompt="A calico cat playing a piano on stage",
+    size="1280x720"
+)
+print(f"Video ID: {video.id}, Status: {video.status}")
+```
+
+**Curl Example:**
+
+```bash curl
+curl -sS -X POST "http://localhost:30010/v1/videos" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-proj-1234567890" \
+  -d '{
+        "prompt": "A calico cat playing a piano on stage",
+        "size": "1280x720"
+      }'
+```
+
+**Create a video (image-to-video)**
+
+For I2V or TI2V models (e.g., Wan2.1 I2V, LTX-2.3 two-stage), pass an input image via multipart form upload or a reference URL.
+
+**Curl Example (multipart form upload):**
+
+```bash Command
+curl -sS -X POST "http://localhost:30010/v1/videos" \
+  -H "Authorization: Bearer sk-proj-1234567890" \
+  -F "prompt=A cat playing a piano" \
+  -F "input_reference=@input_image.png" \
+  -F "size=1280x720"
+```
+
+**Curl Example (reference URL):**
+
+```bash Command
+curl -sS -X POST "http://localhost:30010/v1/videos" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer sk-proj-1234567890" \
+  -d '{
+        "prompt": "A cat playing a piano",
+        "reference_url": "https://example.com/input_image.png",
+        "size": "1280x720"
+      }'
+```
+
+**List videos**
+
+**Endpoint:** `GET /v1/videos`
+
+**Python Example:**
+
+```python Python
+videos = client.videos.list()
+for item in videos.data:
+    print(item.id, item.status)
+```
+
+**Curl Example:**
+
+```bash curl
+curl -sS -X GET "http://localhost:30010/v1/videos" \
+  -H "Authorization: Bearer sk-proj-1234567890"
+```
+
+**Download video content**
+
+**Endpoint:** `GET /v1/videos/&#123;video_id&#125;/content`
+
+**Python Example:**
+
+```python Python
+import time
+
+# Poll for completion
+while True:
+    page = client.videos.list()
+    item = next((v for v in page.data if v.id == video_id), None)
+    if item and item.status == "completed":
+        break
+    time.sleep(5)
+
+# Download content
+resp = client.videos.download_content(video_id=video_id)
+with open("output.mp4", "wb") as f:
+    f.write(resp.read())
+```
+
+**Curl Example:**
+
+```bash curl
+curl -sS -L "http://localhost:30010/v1/videos/<VIDEO_ID>/content" \
+  -H "Authorization: Bearer sk-proj-1234567890" \
+  -o output.mp4
+```
+
+---
+
+### LoRA Management
+
+The server supports dynamic loading, merging, and unmerging of LoRA adapters.
+
+**Important Notes:**
+- Mutual Exclusion: Only one LoRA can be *merged* (active) at a time
+- Switching: To switch LoRAs, you must first `unmerge` the current one, then `set` the new one
+- Caching: The server caches loaded LoRA weights in memory. Switching back to a previously loaded LoRA (same path) has little cost
+
+**Set LoRA Adapter**
+
+Loads one or more LoRA adapters and merges their weights into the model. Supports both single LoRA (backward compatible) and multiple LoRA adapters.
+
+**Endpoint:** `POST /v1/set_lora`
+
+**Parameters:**
+- `lora_nickname` (string or list of strings, required): A unique identifier for the LoRA adapter(s). Can be a single string or a list of strings for multiple LoRAs
+- `lora_path` (string or list of strings/None, optional): Path to the `.safetensors` file(s) or Hugging Face repo ID(s). Required for the first load; optional if re-activating a cached nickname. If a list, must match the length of `lora_nickname`
+- `target` (string or list of strings, optional): Which transformer(s) to apply the LoRA to. If a list, must match the length of `lora_nickname`. Valid values:
+  - `"all"` (default): Apply to all transformers
+  - `"transformer"`: Apply only to the primary transformer (high noise for Wan2.2)
+  - `"transformer_2"`: Apply only to transformer_2 (low noise for Wan2.2)
+  - `"critic"`: Apply only to the critic model
+- `strength` (float or list of floats, optional): LoRA strength for merge, default 1.0. If a list, must match the length of `lora_nickname`. Values < 1.0 reduce the effect, values > 1.0 amplify the effect
+
+**Single LoRA Example:**
+
+```bash Command
+curl -X POST http://localhost:30010/v1/set_lora \
+  -H "Content-Type: application/json" \
+  -d '{
+        "lora_nickname": "lora_name",
+        "lora_path": "/path/to/lora.safetensors",
+        "target": "all",
+        "strength": 0.8
+      }'
+```
+
+**Multiple LoRA Example:**
+
+```bash Command
+curl -X POST http://localhost:30010/v1/set_lora \
+  -H "Content-Type: application/json" \
+  -d '{
+        "lora_nickname": ["lora_1", "lora_2"],
+        "lora_path": ["/path/to/lora1.safetensors", "/path/to/lora2.safetensors"],
+        "target": ["transformer", "transformer_2"],
+        "strength": [0.8, 1.0]
+      }'
+```
+
+**Multiple LoRA with Same Target:**
+
+```bash Command
+curl -X POST http://localhost:30010/v1/set_lora \
+  -H "Content-Type: application/json" \
+  -d '{
+        "lora_nickname": ["style_lora", "character_lora"],
+        "lora_path": ["/path/to/style.safetensors", "/path/to/character.safetensors"],
+        "target": "all",
+        "strength": [0.7, 0.9]
+      }'
+```
+
+> [!NOTE]
+> When using multiple LoRAs:
+> - All list parameters (`lora_nickname`, `lora_path`, `target`, `strength`) must have the same length
+> - If `target` or `strength` is a single value, it will be applied to all LoRAs
+> - Multiple LoRAs applied to the same target will be merged in order
+
+
+**Merge LoRA Weights**
+
+Manually merges the currently set LoRA weights into the base model.
+
+> [!NOTE]
+> `set_lora` automatically performs a merge, so this is typically only needed if you have manually unmerged but want to re-apply the same LoRA without calling `set_lora` again.*
+
+**Endpoint:** `POST /v1/merge_lora_weights`
+
+**Parameters:**
+- `target` (string, optional): Which transformer(s) to merge. One of "all" (default), "transformer", "transformer_2", "critic"
+- `strength` (float, optional): LoRA strength for merge, default 1.0. Values < 1.0 reduce the effect, values > 1.0 amplify the effect
+
+**Curl Example:**
+
+```bash
+curl -X POST http://localhost:30010/v1/merge_lora_weights \
+  -H "Content-Type: application/json" \
+  -d '{"strength": 0.8}'
+```
+
+
+**Unmerge LoRA Weights**
+
+Unmerges the currently active LoRA weights from the base model, restoring it to its original state. This **must** be called before setting a different LoRA.
+
+**Endpoint:** `POST /v1/unmerge_lora_weights`
+
+**Curl Example:**
+
+```bash
+curl -X POST http://localhost:30010/v1/unmerge_lora_weights \
+  -H "Content-Type: application/json"
+```
+
+**List LoRA Adapters**
+
+Returns loaded LoRA adapters and current application status per module.
+
+**Endpoint:** `GET /v1/list_loras`
+
+**Curl Example:**
+
+```bash
+curl -sS -X GET "http://localhost:30010/v1/list_loras"
+```
+
+**Response Example:**
+
+```json
+{
+  "loaded_adapters": [
+    { "nickname": "lora_a", "path": "/weights/lora_a.safetensors" },
+    { "nickname": "lora_b", "path": "/weights/lora_b.safetensors" }
+  ],
+  "active": {
+    "transformer": [
+      {
+        "nickname": "lora2",
+        "path": "tarn59/pixel_art_style_lora_z_image_turbo",
+        "merged": true,
+        "strength": 1.0
+      }
+    ]
+  }
+}
+```
+
+Notes:
+- If LoRA is not enabled for the current pipeline, the server will return an error.
+- `num_lora_layers_with_weights` counts only layers that have LoRA weights applied for the active adapter.
+
+### Example: Switching LoRAs
+
+1.  Set LoRA A:
+    ```bash Command
+    curl -X POST http://localhost:30010/v1/set_lora -d '{"lora_nickname": "lora_a", "lora_path": "path/to/A"}'
+    ```
+2.  Generate with LoRA A...
+3.  Unmerge LoRA A:
+    ```bash Command
+    curl -X POST http://localhost:30010/v1/unmerge_lora_weights
+    ```
+4.  Set LoRA B:
+    ```bash Command
+    curl -X POST http://localhost:30010/v1/set_lora -d '{"lora_nickname": "lora_b", "lora_path": "path/to/B"}'
+    ```
+5.  Generate with LoRA B...
+
+### Adjust Output Quality
+
+The server supports adjusting output quality and compression levels for both image and video generation through the `output-quality` and `output-compression` parameters.
+
+#### Parameters
+
+- **`output-quality`** (string, optional): Preset quality level that automatically sets compression. **Default is `"default"`**. Valid values:
+  - `"maximum"`: Highest quality (100)
+  - `"high"`: High quality (90)
+  - `"medium"`: Medium quality (55)
+  - `"low"`: Lower quality (35)
+  - `"default"`: Auto-adjust based on media type (50 for video, 75 for image)
+
+- **`output-compression`** (integer, optional): Direct compression level override (0-100). **Default is `None`**. When provided (not `None`), takes precedence over `output-quality`.
+  - `0`: Lowest quality, smallest file size
+  - `100`: Highest quality, largest file size
+
+#### Notes
+
+- **Precedence**: When both `output-quality` and `output-compression` are provided, `output-compression` takes precedence
+- **Format Support**: Quality settings apply to JPEG, and video formats. PNG uses lossless compression and ignores these settings
+- **File Size vs Quality**: Lower compression values (or "low" quality preset) produce smaller files but may show visible artifacts
@@ -0,0 +1,237 @@
+---
+title: "Post-Processing"
+metatags:
+    description: "Use SGLang Diffusion post-processing for frame interpolation and spatial upscaling after generation."
+---
+
+SGLang diffusion supports optional post-processing steps that run after
+generation to improve temporal smoothness (frame interpolation) or spatial
+resolution (upscaling). These steps are independent of the diffusion model and
+can be combined in a single run.
+
+When both are enabled, **frame interpolation runs first** (increasing the frame
+count), then **upscaling runs on every frame** (increasing the spatial
+resolution).
+
+---
+
+## Frame Interpolation (video only)
+
+Frame interpolation synthesizes new frames between each pair of consecutive
+generated frames, producing smoother motion without re-running the diffusion
+model.
+
+The `--frame-interpolation-exp` flag controls how many rounds of interpolation
+to apply: each round inserts one new frame into every gap between adjacent
+frames, so the output frame count follows the formula:
+
+> **(N − 1) × 2^exp + 1**
+>
+> e.g. 5 original frames with `exp=1` → 4 gaps × 1 new frame + 5 originals = **9** frames;
+> with `exp=2` → **17** frames.
+
+### CLI Arguments
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "50%"}} />
+    <col style={{width: "50%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Argument</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>--enable-frame-interpolation</code></td>
+      <td>Enable frame interpolation. Model weights are downloaded automatically on first use.</td>
+    </tr>
+    <tr>
+      <td><code>--frame-interpolation-exp &#123;EXP&#125;</code></td>
+      <td>Interpolation exponent — <code>1</code> = 2× temporal resolution, <code>2</code> = 4×, etc. (default: <code>1</code>)</td>
+    </tr>
+    <tr>
+      <td><code>--frame-interpolation-scale &#123;SCALE&#125;</code></td>
+      <td>RIFE inference scale; use <code>0.5</code> for high-resolution inputs to save memory (default: <code>1.0</code>)</td>
+    </tr>
+    <tr>
+      <td><code>--frame-interpolation-model-path &#123;PATH&#125;</code></td>
+      <td>Local directory or HuggingFace repo ID containing RIFE <code>flownet.pkl</code> weights (default: <code>elfgum/RIFE-4.22.lite</code>, downloaded automatically)</td>
+    </tr>
+  </tbody>
+</table>
+
+### Supported Models
+
+Frame interpolation uses the [RIFE](https://github.com/hzwer/Practical-RIFE)
+(Real-Time Intermediate Flow Estimation) architecture. Only **RIFE 4.22.lite**
+(`IFNet` with 4-scale `IFBlock` backbone) is supported. The network topology is
+hard-coded, so custom weights provided via `--frame-interpolation-model-path`
+must be a `flownet.pkl` checkpoint that is compatible with this architecture.
+
+Other RIFE versions (e.g., older `v4.x` variants with different block counts)
+or entirely different frame interpolation methods (FILM, AMT, etc.) are **not
+supported**.
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Weight</th>
+      <th>HuggingFace Repo</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>RIFE 4.22.lite *(default)*</td>
+      <td><a href="https://huggingface.co/elfgum/RIFE-4.22.lite"><code>elfgum/RIFE-4.22.lite</code></a></td>
+      <td>Lightweight model, downloaded automatically on first use</td>
+    </tr>
+  </tbody>
+</table>
+
+### Example
+
+Generate a 5-frame video and interpolate to 9 frames ((5 − 1) × 2¹ + 1 = 9):
+
+```bash
+sglang generate \
+  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --prompt "A dog running through a park" \
+  --num-frames 5 \
+  --enable-frame-interpolation \
+  --frame-interpolation-exp 1 \
+  --save-output
+```
+
+---
+
+## Upscaling (image and video)
+
+Upscaling increases the spatial resolution of generated images or video frames
+using [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN). The model weights
+are downloaded automatically on first use and cached for subsequent runs.
+
+### CLI Arguments
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "50%"}} />
+    <col style={{width: "50%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Argument</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>--enable-upscaling</code></td>
+      <td>Enable post-generation upscaling using Real-ESRGAN.</td>
+    </tr>
+    <tr>
+      <td><code>--upscaling-scale &#123;SCALE&#125;</code></td>
+      <td>Desired upscaling factor (default: <code>4</code>). The 4× model is used internally; if a different scale is requested, a bicubic resize is applied after the network output.</td>
+    </tr>
+    <tr>
+      <td><code>--upscaling-model-path &#123;PATH&#125;</code></td>
+      <td>Local <code>.pth</code> file, HuggingFace repo ID, or <code>repo_id:filename</code> for Real-ESRGAN weights (default: <code>ai-forever/Real-ESRGAN</code> with <code>RealESRGAN_x4.pth</code>, downloaded automatically). Use the <code>repo_id:filename</code> format to specify a custom weight file from a HuggingFace repo (e.g. <code>my-org/my-esrgan:weights.pth</code>).</td>
+    </tr>
+  </tbody>
+</table>
+
+### Supported Models
+
+Upscaling supports two Real-ESRGAN network architectures. The correct
+architecture is **auto-detected** from the checkpoint keys, so you only need to
+point `--upscaling-model-path` at a valid `.pth` file:
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Architecture</th>
+      <th>Example Weights</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>RRDBNet</strong></td>
+      <td><code>RealESRGAN_x4plus.pth</code></td>
+      <td>Heavier model with higher quality; best for photos</td>
+    </tr>
+    <tr>
+      <td><strong>SRVGGNetCompact</strong></td>
+      <td><code>RealESRGAN_x4.pth</code> *(default)*, <code>realesr-animevideov3.pth</code>, <code>realesr-general-x4v3.pth</code></td>
+      <td>Lightweight model; faster inference, good for video</td>
+    </tr>
+  </tbody>
+</table>
+
+The default weight file is
+[`ai-forever/Real-ESRGAN`](https://huggingface.co/ai-forever/Real-ESRGAN) with
+`RealESRGAN_x4.pth` (SRVGGNetCompact, 4× native scale).
+
+Other super-resolution models (e.g., SwinIR, HAT, BSRGAN) are **not supported**
+— only Real-ESRGAN checkpoints using the two architectures above are
+compatible.
+
+### Examples
+
+Generate a 1024×1024 image and upscale to 4096×4096:
+
+```bash
+sglang generate \
+  --model-path black-forest-labs/FLUX.2-dev \
+  --prompt "A cat sitting on a windowsill" \
+  --output-size 1024x1024 \
+  --enable-upscaling \
+  --save-output
+```
+
+Generate a video and upscale each frame by 4×:
+
+```bash
+sglang generate \
+  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+  --prompt "A curious raccoon" \
+  --enable-upscaling \
+  --upscaling-scale 4 \
+  --save-output
+```
+
+---
+
+## Combining Frame Interpolation and Upscaling
+
+Frame interpolation and upscaling can be combined in a single run.
+Interpolation is applied first (increasing the frame count), then upscaling is
+applied to every frame (increasing the spatial resolution).
+
+Example — generate 5 frames, interpolate to 9 frames, and upscale each frame
+by 4×:
+
+```bash
+sglang generate \
+  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+  --prompt "A curious raccoon" \
+  --num-frames 5 \
+  --enable-frame-interpolation \
+  --frame-interpolation-exp 1 \
+  --enable-upscaling \
+  --upscaling-scale 4 \
+  --save-output
+```
@@ -2,7 +2,6 @@
 title: "Attention Backends"
 description: "Select and configure attention backends for SGLang diffusion pipelines."
 ---
-
 This document describes the attention backends available in sglang diffusion (`sglang.multimodal_gen`) and how to select them.

 ## Overview
@@ -16,8 +15,10 @@ When using the diffusers backend, `--attention-backend` is passed through to dif

 - **CUDA**: prefers FlashAttention (FA3/FA4) when supported; otherwise falls back to PyTorch SDPA.
 - **ROCm**: uses FlashAttention when available; otherwise falls back to PyTorch SDPA.
+- **Intel XPU**: uses XPU Flash Attention backend (fp16/bf16, head sizes 64/96/128/192/256); otherwise falls back to PyTorch SDPA.
+- **MUSA**: uses FlashAttention when available; otherwise falls back to PyTorch SDPA.
 - **MPS**: always uses PyTorch SDPA.
- **NPU**: always uses PyTorch SDPA.
+- **NPU**: for ring attention uses FA otherwise uses PyTorch SDPA.

 ## Backend options

@@ -40,22 +41,22 @@ For SGLang-native pipelines, the CLI accepts the lowercase names of `AttentionBa
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`fa` / `fa3` / `fa4`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}>`FA`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>FlashAttention. `fa3/fa4` are normalized to `fa` during argument parsing (`ServerArgs.__post_init__`).</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>FlashAttention. <code>fa3/fa4</code> are normalized to <code>fa</code> during argument parsing (<code>ServerArgs.__post_init__</code>).</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`torch_sdpa`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}>`TORCH_SDPA`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>PyTorch `scaled_dot_product_attention`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>PyTorch <code>scaled_dot_product_attention</code>.</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`sliding_tile_attn`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}>`SLIDING_TILE_ATTN`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Sliding Tile Attention (STA). Requires `st_attn`. Configure via `--attention-backend-config`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Sliding Tile Attention (STA). Requires <code>st_attn</code>. Configure via <code>--attention-backend-config</code>.</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`sage_attn`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}>`SAGE_ATTN`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires `sageattention`. Upstream SageAttention CUDA extensions target SM80/SM86/SM89/SM90/SM120 (compute capability 8.0/8.6/8.9/9.0/12.0); see upstream `setup.py`: https://github.com/thu-ml/SageAttention/blob/main/setup.py.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires <code>sageattention</code>. Upstream SageAttention CUDA extensions target SM80/SM86/SM89/SM90/SM120 (compute capability 8.0/8.6/8.9/9.0/12.0); see upstream <code>setup.py</code>: https://github.com/thu-ml/SageAttention/blob/main/setup.py.</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`sage_attn_3`</td>
@@ -65,24 +66,39 @@ For SGLang-native pipelines, the CLI accepts the lowercase names of `AttentionBa
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`video_sparse_attn`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}>`VIDEO_SPARSE_ATTN`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires `vsa`. Configure `sparsity` via `--attention-backend-config`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires <code>vsa</code>. Configure <code>sparsity</code> via <code>--attention-backend-config</code>.</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`vmoba_attn`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}>`VMOBA_ATTN`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires `kernel.attn.vmoba_attn.vmoba`. Configure via `--attention-backend-config`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires <code>kernel.attn.vmoba_attn.vmoba</code>. Configure via <code>--attention-backend-config</code>.</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`aiter`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}>`AITER`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires `aiter`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires <code>aiter</code>.</td>
    </tr>
    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>aiter_sage</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}><code>AITER_SAGE</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires <code>aiter</code>.</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>sla_attn</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}><code>SLA_ATTN</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Sparse Linear Attention. Requires <code>SpargeAttn</code>. Install with <code>pip install git+https://github.com/thu-ml/SpargeAttn.git --no-build-isolation</code>.</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>sage_sla_attn</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}><code>SAGE_SLA_ATTN</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>SageAttention + Sparse Linear Attention. Requires <code>SpargeAttn</code> (same install as SLA).</td>
+    </tr>
+<tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`sparse_video_gen_2_attn`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)", whiteSpace: "nowrap"}}>`SPARSE_VIDEO_GEN_2_ATTN`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires `svg`. See installation instructions at https://github.com/svg-project/Sparse-VideoGen.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Requires <code>svg</code>. See installation instructions at https://github.com/svg-project/Sparse-VideoGen.</td>
    </tr>
-  </tbody>
+</tbody>
 </table>

 ## Selection priority
@@ -97,7 +113,7 @@ The selection order in `runtime/layers/attention/selector.py` is:

 Some backends require additional configuration. You can pass these parameters via `--attention-backend-config`. This argument accepts:
 - A path to a JSON or YAML configuration file.
- A JSON string (e.g., `'{"sparsity": 0.5}'`).
+- A JSON string (e.g., `'&#123;"sparsity": 0.5&#125;'`).
 - Key-value pairs (e.g., `"sparsity=0.5,enable_x=true"`).

 ### Supported Configuration Parameters
@@ -289,8 +305,10 @@ Some backends require additional configuration. You can pass these parameters vi
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Backend</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>ROCm</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>XPU</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>MUSA</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>MPS</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>NPU</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>NPU</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Notes</th>
    </tr>
  </thead>
@@ -299,9 +317,11 @@ Some backends require additional configuration. You can pass these parameters vi
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`fa`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA requires SM80+ and fp16/bf16. FlashAttention is only used when the required runtime is installed; otherwise it falls back to `torch_sdpa`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA requires SM80+ and fp16/bf16. XPU uses its own flash attention backend. FlashAttention is only used when the required runtime is installed; otherwise it falls back to <code>torch_sdpa</code>. No extra installations are required for NPU</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`torch_sdpa`</td>
@@ -309,6 +329,8 @@ Some backends require additional configuration. You can pass these parameters vi
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Most compatible option across platforms.</td>
    </tr>
    <tr>
@@ -317,7 +339,9 @@ Some backends require additional configuration. You can pass these parameters vi
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only. Requires `st_attn`. Configure via `--attention-backend-config`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only. Requires <code>st_attn</code>. Configure via <code>--attention-backend-config</code>.</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`sage_attn`</td>
@@ -325,6 +349,8 @@ Some backends require additional configuration. You can pass these parameters vi
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only (optional dependency).</td>
    </tr>
    <tr>
@@ -333,6 +359,8 @@ Some backends require additional configuration. You can pass these parameters vi
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only (optional dependency).</td>
    </tr>
    <tr>
@@ -341,33 +369,71 @@ Some backends require additional configuration. You can pass these parameters vi
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only. Requires `vsa`. Configure `sparsity` via `--attention-backend-config`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only. Requires <code>vsa</code>. Configure <code>sparsity</code> via <code>--attention-backend-config</code>.</td>
    </tr>
    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`vmoba_attn`</td>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>sla_attn</code></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only. Requires `kernel.attn.vmoba_attn.vmoba`. Configure via `--attention-backend-config`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only. Requires <code>SpargeAttn</code>.</td>
    </tr>
    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`aiter`</td>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>sage_sla_attn</code></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Requires `aiter`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only. Requires <code>SpargeAttn</code>.</td>
    </tr>
    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>vmoba_attn</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only. Requires <code>kernel.attn.vmoba_attn.vmoba</code>. Configure via <code>--attention-backend-config</code>.</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>aiter</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Requires <code>aiter</code>.</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>aiter_sage</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Requires <code>aiter</code>.</td>
+    </tr>
+<tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`sparse_video_gen_2_attn`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only. Requires `svg`.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>CUDA-only. Requires <code>svg</code>.</td>
    </tr>
-  </tbody>
+</tbody>
 </table>

 ## Usage
@@ -2,7 +2,6 @@
 title: "Cache-DiT Acceleration"
 description: "Configure Cache-DiT acceleration for diffusion inference."
 ---
-
 SGLang integrates [Cache-DiT](https://github.com/vipshop/cache-dit), a caching acceleration engine for Diffusion Transformers (DiT), to achieve up to **1.69x inference speedup** with minimal quality loss.

 ## Overview
@@ -33,6 +32,8 @@ flow requires cache-dit >= 1.2.0 (`cache_dit.load_configs`).

 Define a `cache.yaml` file that contains:

+- DBCache + TaylorSeer
+
 ```yaml
 cache_config:
  max_warmup_steps: 8
@@ -56,18 +57,54 @@ sglang generate \
  --prompt "A beautiful sunset over the mountains"
 ```

+- DBCache + TaylorSeer + SCM (Step Computation Mask)
+
+```yaml Config
+cache_config:
+  max_warmup_steps: 8
+  warmup_interval: 2
+  max_cached_steps: -1
+  max_continuous_cached_steps: 2
+  Fn_compute_blocks: 1
+  Bn_compute_blocks: 0
+  residual_diff_threshold: 0.12
+  enable_taylorseer: true
+  taylorseer_order: 1
+  # Must set the num_inference_steps for SCM. The SCM will automatically
+  # generate the steps computation mask based on the num_inference_steps.
+  # Reference: https://cache-dit.readthedocs.io/en/latest/user_guide/CACHE_API/#scm-steps-computation-masking
+  num_inference_steps: 28
+  steps_computation_mask: fast
+```
+
+- DBCache + TaylorSeer + SCM (Step Computation Mask) + Cache CFG
+
+```yaml Config
+cache_config:
+  max_warmup_steps: 8
+  warmup_interval: 2
+  max_cached_steps: -1
+  max_continuous_cached_steps: 2
+  Fn_compute_blocks: 1
+  Bn_compute_blocks: 0
+  residual_diff_threshold: 0.12
+  enable_taylorseer: true
+  taylorseer_order: 1
+  num_inference_steps: 28
+  steps_computation_mask: fast
+  enable_sperate_cfg: true # e.g, Qwen-Image, Wan, Chroma, Ovis-Image, etc.
+```
+
 ### Distributed inference

 - 1D Parallelism

 Define a parallelism only config yaml `parallel.yaml` file that contains:

-```yaml
+```yaml Config
 parallelism_config:
  ulysses_size: auto
-  parallel_kwargs:
-    attention_backend: native
-    extra_parallel_modules: ["text_encoder", "vae"]
+  attention_backend: native
 ```

 Then, apply the distributed inference acceleration config from yaml. `ulysses_size: auto` means that cache-dit will auto detect the `world_size` as the ulysses_size. Otherwise, you should manually set it as specific int number, e.g, 4.
@@ -87,13 +124,11 @@ sglang generate \

 You can also define a 2D parallelism config yaml `parallel_2d.yaml` file that contains:

-```yaml
+```yaml Config
 parallelism_config:
  ulysses_size: auto
  tp_size: 2
-  parallel_kwargs:
-    attention_backend: native
-    extra_parallel_modules: ["text_encoder", "vae"]
+  attention_backend: native
 ```
 Then, apply the 2D parallelism config from yaml. Here `tp_size: 2` means using tensor parallelism with size 2. The `ulysses_size: auto` means that cache-dit will auto detect the `world_size // tp_size` as the ulysses_size.

@@ -101,22 +136,66 @@ Then, apply the 2D parallelism config from yaml. Here `tp_size: 2` means using t

 You can also define a 3D parallelism config yaml `parallel_3d.yaml` file that contains:

-```yaml
+```yaml Config
 parallelism_config:
  ulysses_size: 2
  ring_size: 2
  tp_size: 2
-  parallel_kwargs:
-    attention_backend: native
-    extra_parallel_modules: ["text_encoder", "vae"]
+  attention_backend: native
 ```
 Then, apply the 3D parallelism config from yaml. Here `ulysses_size: 2`, `ring_size: 2`, `tp_size: 2` means using ulysses parallelism with size 2, ring parallelism with size 2 and tensor parallelism with size 2.

+- Ulysses Anything Attention
+
+To enable Ulysses Anything Attention, you can define a parallelism config yaml `parallel_uaa.yaml` file that contains:
+
+```yaml Config
+parallelism_config:
+  ulysses_size: auto
+  attention_backend: native
+  ulysses_anything: true
+```
+
+- Ulysses FP8 Communication
+
+For device that don't have NVLink support, you can enable Ulysses FP8 Communication to further reduce the communication overhead. You can define a parallelism config yaml `parallel_fp8.yaml` file that contains:
+
+```yaml Config
+parallelism_config:
+  ulysses_size: auto
+  attention_backend: native
+  ulysses_float8: true
+```
+
+- Async Ulysses CP
+
+You can also enable async ulysses CP to overlap the communication and computation. Define a parallelism config yaml `parallel_async.yaml` file that contains:
+
+```yaml Config
+parallelism_config:
+  ulysses_size: auto
+  attention_backend: native
+  ulysses_async: true # Now, only support for FLUX.1, Qwen-Image, Ovis-Image and Z-Image.
+```
+Then, apply the config from yaml. Here `ulysses_async: true` means enabling async ulysses CP.
+
+- TE-P and VAE-P
+
+You can also specify the extra parallel modules in the yaml config. For example, define a parallelism config yaml `parallel_extra.yaml` file that contains:
+
+```yaml Config
+parallelism_config:
+  ulysses_size: auto
+  attention_backend: native
+  extra_parallel_modules: ["text_encoder", "vae"]
+```
+
+
 ### Hybrid Cache and Parallelism

 Define a hybrid cache and parallel acceleration config yaml `hybrid.yaml` file that contains:

-```yaml
+```yaml Config
 cache_config:
  max_warmup_steps: 8
  warmup_interval: 2
@@ -129,9 +208,8 @@ cache_config:
  taylorseer_order: 1
 parallelism_config:
  ulysses_size: auto
-  parallel_kwargs:
-    attention_backend: native
-    extra_parallel_modules: ["text_encoder", "vae"]
+  attention_backend: native
+  extra_parallel_modules: ["text_encoder", "vae"]
 ```

 Then, apply the hybrid cache and parallel acceleration config from yaml.
@@ -145,6 +223,72 @@ sglang generate \
  --prompt "A beautiful sunset over the mountains"
 ```

+### Attention Backend
+
+In some cases, users may want to only specify the attention backend without any other optimization configs. In this case, you can define a yaml file `attention.yaml` that only contains:
+
+```yaml Config
+attention_backend: "flash" # '_flash_3' for Hopper
+```
+
+### Quantization
+
+You can also specify the quantization config in the yaml file, required `torchao>=0.16.0`. For example, define a yaml file `quantize.yaml` that contains:
+
+```yaml Config
+quantize_config: # quantization configuration for transformer modules
+  # float8 (DQ), float8_weight_only, float8_blockwise, int8 (DQ), int8_weight_only, etc.
+  quant_type: "float8"
+  # layers to exclude from quantization (transformer). layers that contains any of the
+  # keywords in the exclude_layers list will be excluded from quantization. This is useful
+  # for some sensitive layers that are not robust to quantization, e.g., embedding layers.
+  exclude_layers:
+    - "embedder"
+    - "embed"
+  verbose: false # whether to print verbose logs during quantization
+```
+Then, apply the quantization config from yaml. Please also enable torch.compile for better performance if you are using quantization. For example:
+
+```bash Command
+sglang generate \
+  --backend diffusers \
+  --model-path Qwen/Qwen-Image \
+  --warmup \
+  --cache-dit-config quantize.yaml \
+  --enable-torch-compile \
+  --dit-cpu-offload false \
+  --text-encoder-cpu-offload false \
+  --prompt "A beautiful sunset over the mountains"
+```
+
+### Combined Configs: Cache + Parallelism + Quantization
+
+You can also combine all the above configs together in a single yaml file `combined.yaml` that contains:
+
+```yaml Config
+cache_config:
+  max_warmup_steps: 8
+  warmup_interval: 2
+  max_cached_steps: -1
+  max_continuous_cached_steps: 2
+  Fn_compute_blocks: 1
+  Bn_compute_blocks: 0
+  residual_diff_threshold: 0.12
+  enable_taylorseer: true
+  taylorseer_order: 1
+parallelism_config:
+  ulysses_size: auto
+  attention_backend: native
+  extra_parallel_modules: ["text_encoder", "vae"]
+quantize_config:
+  quant_type: "float8"
+  exclude_layers:
+    - "embedder"
+    - "embed"
+  verbose: false
+```
+Then, apply the combined cache, parallelism and quantization config from yaml. Please also enable torch.compile for better performance if you are using quantization.
+
 ## Advanced Configuration

 ### DBCache Parameters
@@ -364,7 +508,7 @@ sglang generate --model-path Qwen/Qwen-Image \
 ## Environment Variables

 All Cache-DiT parameters can be configured via environment variables.
-See [Environment variables](./environment-variables) for the complete list.
+See [Environment Variables](./environment_variables) for the complete list.

 ## Supported Models

@@ -430,4 +574,4 @@ acceleration still works.
 ## References

 - [Cache-DiT](https://github.com/vipshop/cache-dit)
- [SGLang diffusion](../../sglang-diffusion/intro)
+- [SGLang Diffusion](./performance-optimization)
@@ -2,8 +2,7 @@
 title: "Caching Acceleration"
 description: "Compare caching acceleration strategies for diffusion models."
 ---
-
-SGLang provides multiple caching acceleration strategies for Diffusion Transformer (DiT) models. These strategies can significantly reduce inference time by skipping redundant computation.
+SGLang provides two complementary caching strategies for Diffusion Transformer (DiT) models. Both reduce denoising cost by skipping redundant computation, but they operate at different levels.

 ## Overview

@@ -40,13 +39,12 @@ SGLang supports two complementary caching approaches:
  </tbody>
 </table>

-
 ## Cache-DiT

 [Cache-DiT](https://github.com/vipshop/cache-dit) provides block-level caching with
 advanced strategies like DBCache and TaylorSeer. It can achieve up to **1.69x speedup**.

-See [Cache-DiT](./cache-dit) for detailed configuration.
+See [cache_dit.md](./cache_dit) for detailed configuration.

 ### Quick Start

@@ -66,7 +64,7 @@ sglang generate --model-path Qwen/Qwen-Image \

 TeaCache (Temporal similarity-based caching) accelerates diffusion inference by detecting when consecutive denoising steps are similar enough to skip computation entirely.

-See [TeaCache](./tea-cache) for detailed documentation.
+See [teacache.md](./teacache) for detailed documentation.

 ### Quick Overview

@@ -82,6 +80,7 @@ See [TeaCache](./tea-cache) for detailed documentation.

 For Flux and Qwen models, TeaCache is automatically disabled when CFG is enabled.

+
 ## References

 - [Cache-DiT Repository](https://github.com/vipshop/cache-dit)
@@ -2,10 +2,11 @@
 title: "CI Performance Baselines"
 description: "Generate and update diffusion performance baselines used in CI."
 ---
+## Perf Baseline Generation Script

 `python/sglang/multimodal_gen/test/scripts/gen_perf_baselines.py` starts a local diffusion server, issues requests for selected test cases, aggregates stage/denoise-step/E2E timings from the perf log, and writes the results back to the `scenarios` section of `perf_baselines.json`.

-## Usage
+### Usage

 Update a single case:

@@ -0,0 +1,631 @@
+---
+title: "Supported Models"
+description: "Check model compatibility across diffusion optimizations and backends."
+---
+The table below shows every supported model and the optimizations supported for them.
+
+The symbols used have the following meanings:
+
+- ✅ = Full compatibility
+- ❌ = No compatibility
+- ⭕ = Does not apply to this model
+
+## Models x Optimization
+
+The `HuggingFace Model ID` can be passed directly to `from_pretrained()` methods, and sglang-diffusion will use the
+optimal
+default parameters when initializing and generating videos.
+
+### Video Generation Models
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "17%"}} />
+    <col style={{width: "22%"}} />
+    <col style={{width: "8%"}} />
+    <col style={{width: "6%"}} />
+    <col style={{width: "8%"}} />
+    <col style={{width: "6%"}} />
+    <col style={{width: "8%"}} />
+    <col style={{width: "8%"}} />
+    <col style={{width: "9%"}} />
+    <col style={{width: "8%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model Name</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Hugging Face Model ID</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Resolutions</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>TeaCache</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Sliding Tile Attn</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Sage Attn</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Video Sparse Attention (VSA)</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Sparse Linear Attention (SLA)</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Sage Sparse Linear Attention (SageSLA)</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Sparse Video Gen 2 (SVG2)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FastWan2.1 T2V 1.3B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`FastVideo/FastWan2.1-T2V-1.3B-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FastWan2.2 TI2V 5B Full Attn</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.2 TI2V 5B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.2-TI2V-5B-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.2 T2V A14B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.2-T2V-A14B-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p&lt;br&gt;720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.2 I2V A14B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.2-I2V-A14B-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p&lt;br&gt;720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>HunyuanVideo</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`hunyuanvideo-community/HunyuanVideo`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720×1280&lt;br&gt;544×960</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FastHunyuan</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`FastVideo/FastHunyuan-diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720×1280&lt;br&gt;544×960</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1 T2V 1.3B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.1-T2V-1.3B-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1 T2V 14B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.1-T2V-14B-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p, 720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1 I2V 480P</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.1-I2V-14B-480P-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1 I2V 720P</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.1-I2V-14B-720P-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TurboWan2.1 T2V 1.3B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`IPostYellow/TurboWan2.1-T2V-1.3B-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>⭕</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TurboWan2.1 T2V 14B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`IPostYellow/TurboWan2.1-T2V-14B-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>⭕</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TurboWan2.1 T2V 14B 720P</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`IPostYellow/TurboWan2.1-T2V-14B-720P-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>⭕</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TurboWan2.2 I2V A14B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`IPostYellow/TurboWan2.2-I2V-A14B-Diffusers`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>⭕</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1 Fun 1.3B InP</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⭕</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Helios Base</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>BestWishYsh/Helios-Base</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Helios Mid</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>BestWishYsh/Helios-Mid</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Helios Distilled</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>BestWishYsh/Helios-Distilled</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>LTX-2 (one and two stages)</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Lightricks/LTX-2</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>768×512&lt;br&gt;1536×1024</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>LTX-2.3 (one and two stages)</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Lightricks/LTX-2.3</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>768×512&lt;br&gt;1536×1024</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
+    </tr>
+</tbody>
+</table>
+
+**Note**:
+
+1. Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
+2. SageSLA is based on SpargeAttn. Install it first with `pip install git+https://github.com/thu-ml/SpargeAttn.git --no-build-isolation`
+3. LTX-2 and LTX-2.3 two-stage generation uses `--pipeline-class-name LTX2TwoStagePipeline`. 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`.
+   - For LTX models, the `Resolutions` column uses output video `width×height` semantics, matching `sglang generate --width ... --height ...`.
+4. LTX-2.3 two-stage also supports `--ltx2-two-stage-device-mode &#123;legacy,snapshot,resident&#125;`:
+   - `snapshot` is the default and recommended mode.
+   - `resident` usually provides the best latency/throughput but uses much more VRAM.
+   - `legacy` preserves the historical switching path for fallback/debug.
+
+### Image Generation Models
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "22%"}} />
+    <col style={{width: "46%"}} />
+    <col style={{width: "32%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model Name</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>HuggingFace Model ID</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FLUX.1-dev</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`black-forest-labs/FLUX.1-dev`</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FLUX.2-dev</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`black-forest-labs/FLUX.2-dev`</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FLUX.2-dev-NVFP4</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>black-forest-labs/FLUX.2-dev-NVFP4</code></td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FLUX.2-Klein-4B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>black-forest-labs/FLUX.2-klein-4B</code></td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FLUX.2-Klein-9B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>black-forest-labs/FLUX.2-klein-9B</code></td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Z-Image</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Tongyi-MAI/Z-Image</code></td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Z-Image-Turbo</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Tongyi-MAI/Z-Image-Turbo</code></td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>GLM-Image</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>zai-org/GLM-Image</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen Image</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Qwen/Qwen-Image</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen Image 2512</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Qwen/Qwen-Image-2512</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen Image Edit</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Qwen/Qwen-Image-Edit`</td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen Image Edit 2509</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Qwen/Qwen-Image-Edit-2509</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen Image Edit 2511</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Qwen/Qwen-Image-Edit-2511</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen Image Layered</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Qwen/Qwen-Image-Layered</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SD3 Medium</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>stabilityai/stable-diffusion-3-medium-diffusers</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SD3.5 Medium</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>stabilityai/stable-diffusion-3.5-medium-diffusers</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SD3.5 Large</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>stabilityai/stable-diffusion-3.5-large-diffusers</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Hunyuan3D-2</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>tencent/Hunyuan3D-2</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SANA 1.5 1.6B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SANA 1.5 4.8B</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Efficient-Large-Model/SANA1.5_4.8B_1024px_diffusers</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SANA 1600M 1024px</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Efficient-Large-Model/Sana_1600M_1024px_diffusers</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SANA 600M 1024px</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Efficient-Large-Model/Sana_600M_1024px_diffusers</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SANA 1600M 512px</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Efficient-Large-Model/Sana_1600M_512px_diffusers</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SANA 600M 512px</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>Efficient-Large-Model/Sana_600M_512px_diffusers</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FireRed-Image-Edit 1.0</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>FireRedTeam/FireRed-Image-Edit-1.0</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FireRed-Image-Edit 1.1</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>FireRedTeam/FireRed-Image-Edit-1.1</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>ERNIE-Image</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>baidu/ERNIE-Image</code></td>
+    </tr>
+<tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>ERNIE-Image-Turbo</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>baidu/ERNIE-Image-Turbo</code></td>
+    </tr>
+</tbody>
+</table>
+
+## Supported Components
+
+SGLang Diffusion supports overriding individual pipeline components with
+`--<component>-path`. The value can be either a Hugging Face repo ID or a local
+component directory.
+
+The same overrides can also be provided in config files through
+`component_paths.<component>`.
+
+### Common Syntax
+
+CLI:
+
+```bash Command
+sglang generate \
+  --model-path black-forest-labs/FLUX.2-dev \
+  --vae-path black-forest-labs/FLUX.2-small-decoder \
+  --transformer-path /models/flux2/transformer
+```
+
+Config file:
+
+```yaml Config
+model_path: black-forest-labs/FLUX.2-dev
+component_paths:
+  vae: black-forest-labs/FLUX.2-small-decoder
+  transformer: /models/flux2/transformer
+```
+
+Use the component name from the pipeline's `model_index.json` or the native pipeline's registered module name:
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "20%"}} />
+    <col style={{width: "80%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Component Type</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Supported Keys</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>VAE</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>vae</code>, <code>video_vae</code>, <code>audio_vae</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>vae</code> is the common image-generation override</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Transformer / DiT</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>transformer</code>, <code>video_dit</code>, <code>audio_dit</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>transformer</code> is the standard override for the main denoiser</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Text / Preprocess</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>text_encoder</code>, <code>text_encoder_2</code>, <code>tokenizer</code>, <code>processor</code>, <code>image_processor</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Replacement encoders often need matching preprocessing assets</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Auxiliary</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>scheduler</code>, <code>spatial_upsampler</code>, <code>vocoder</code>, <code>connectors</code>, <code>dual_tower_bridge</code>, <code>image_encoder</code>, <code>vision_language_encoder</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Only valid for pipelines that expose these components</td>
+    </tr>
+  </tbody>
+</table>
+
+### Known Component Repos
+
+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.
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "24%"}} />
+    <col style={{width: "20%"}} />
+    <col style={{width: "28%"}} />
+    <col style={{width: "28%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Base Model</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Override Key</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Example Repo</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>black-forest-labs/FLUX.2-dev</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>vae</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>black-forest-labs/FLUX.2-small-decoder</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Decoder-only FLUX.2 VAE override</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>black-forest-labs/FLUX.2-dev</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>vae</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>fal/FLUX.2-Tiny-AutoEncoder</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Existing tested custom VAE path</td>
+    </tr>
+  </tbody>
+</table>
+
+### VAE
+
+- `--vae-path` is the common image-generation override.
+- `--video-vae-path` and `--audio-vae-path` are only relevant for pipelines with separate video or audio VAEs.
+
+### Transformer / DiT
+
+- `--transformer-path` is the standard override for the main denoising transformer.
+- For quantized transformers, prefer `--transformer-path` or `--transformer-weights-path`; see `quantization.md`.
+- `--video-dit-path` and `--audio-dit-path` are only for pipelines that split denoisers by modality.
+
+### Text Encoders and Preprocessors
+
+- `--text-encoder-path` and `--text-encoder-2-path` override primary and secondary text encoders.
+- `--tokenizer-path`, `--processor-path`, and `--image-processor-path` are useful when the replacement encoder requires matching preprocessing assets.
+
+### Auxiliary Components
+
+- `--scheduler-path` is only relevant when the pipeline exposes a scheduler component.
+- `--spatial-upsampler-path` is mainly for two-stage pipelines such as `LTX2TwoStagePipeline`.
+- `--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.
+
+### Notes
+
+1. Component overrides are only valid when the target pipeline actually uses
+   that component.
+2. The override key should match the component name in the pipeline's
+   `model_index.json` or the native pipeline's registered module name.
+
+## Verified LoRA Examples
+
+This section lists example LoRAs that have been explicitly tested and verified with each base model in the **SGLang Diffusion** pipeline.
+
+<Info>
+LoRAs that are not listed here are not necessarily incompatible.
+In practice, most standard LoRAs are expected to work, especially those following common Diffusers or SD-style conventions.
+The entries below simply reflect configurations that have been manually validated by the SGLang team.
+</Info>
+
+### Verified LoRAs by Base Model
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "20%"}} />
+    <col style={{width: "80%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Base Model</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Supported LoRAs</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.2</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lightx2v/Wan2.2-Distill-Loras`<br />`Cseti/wan2.2-14B-Arcane_Jinx-lora-v1`</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lightx2v/Wan2.1-Distill-Loras`</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Z-Image-Turbo</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`tarn59/pixel_art_style_lora_z_image_turbo`<br />`wcde/Z-Image-Turbo-DeJPEG-Lora`</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen-Image</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lightx2v/Qwen-Image-Lightning`<br />`flymy-ai/qwen-image-realism-lora`<br />`prithivMLmods/Qwen-Image-HeadshotX`<br />`starsfriday/Qwen-Image-EVA-LoRA`</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen-Image-Edit</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`ostris/qwen_image_edit_inpainting`<br />`lightx2v/Qwen-Image-Edit-2511-Lightning`</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Flux</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`dvyio/flux-lora-simple-illustration`<br />`XLabs-AI/flux-furry-lora`<br />`XLabs-AI/flux-RealismLora`</td>
+    </tr>
+  </tbody>
+</table>
+
+## Special requirements
+
+### Sliding Tile Attention
+
+- Currently, only Hopper GPUs (H100s) are supported.
@@ -0,0 +1,77 @@
+---
+title: "Contributing to SGLang Diffusion"
+metatags:
+    description: "This guide outlines the requirements for contributing to the SGLang Diffusion module (sglang.multimodalgen)."
+---
+
+This guide outlines the requirements for contributing to the SGLang Diffusion module (`sglang.multimodal_gen`).
+
+## Contributor Guides
+
+- [Support New Models](./support_new_models): implementation guide for adding new diffusion pipelines
+- [CI Performance](./ci_perf): update and regenerate perf baselines
+
+
+## On AI-Assisted ("Vibe Coding") PRs
+
+Vibe-coded PRs are welcome — we judge code quality, not how it was produced. The bar is the same for all PRs:
+
+- **No over-commenting.** If the name says it all, skip the docstring.
+- **No over-catching.** Don't guard against errors that virtually never happen in practice.
+- **Test before submitting.** AI-generated code can be subtly wrong — verify correctness end-to-end.
+
+## Commit Message Convention
+
+We follow a structured commit message format to maintain a clean history.
+
+**Format:**
+```text
+[diffusion] <scope>: <subject>
+```
+
+**Examples:**
+- `[diffusion] cli: add --perf-dump-path argument`
+- `[diffusion] scheduler: fix deadlock in batch processing`
+- `[diffusion] model: support Stable Diffusion 3.5`
+
+**Rules:**
+- **Prefix**: Always start with `[diffusion]`.
+- **Scope** (Optional): `cli`, `scheduler`, `model`, `pipeline`, `docs`, etc.
+- **Subject**: Imperative mood, short and clear (e.g., "add feature" not "added feature").
+
+## Performance Reporting
+
+For PRs that impact **latency**, **throughput**, or **memory usage**, you **should** provide a performance comparison report.
+
+### How to Generate a Report
+
+1.  **Baseline**: run the benchmark (for a single generation task)
+    ```bash
+    $ sglang generate --model-path <model> --prompt "A benchmark prompt" --perf-dump-path baseline.json
+    ```
+
+2.  **New**: run the same benchmark, without modifying any server_args or sampling_params
+    ```bash
+    $ sglang generate --model-path <model> --prompt "A benchmark prompt" --perf-dump-path new.json
+    ```
+
+3.  **Compare**: run the compare script, which will print a Markdown table to the console
+    ```bash
+    $ python python/sglang/multimodal_gen/benchmarks/compare_perf.py baseline.json new.json [new2.json ...]
+    ### Performance Comparison Report
+    ...
+    ```
+4. **Paste**: paste the table into the PR description
+
+## CI-Based Change Protection
+
+Consider adding tests to the `pr-test` or `nightly-test` suites to safeguard your changes, especially for PRs that:
+
+- support a new model
+    - add a testcase for this new model to `testcase_configs.py`
+- support or fix important features
+- significantly improve performance
+
+Please run the according testcase, then update/add the baseline to `perf_baselines.json` by following the instruction in console if applicable.
+
+See [test](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/test) for examples
@@ -0,0 +1,361 @@
+---
+title: "Disaggregated Diffusion Pipeline"
+metatags:
+    description: "Split SGLang Diffusion pipelines into independent encoder, denoiser, and decoder services for disaggregated serving."
+---
+
+Split a monolithic text-to-video/image pipeline into independent **Encoder**, **Denoiser**, and **Decoder** roles, each running on its own GPU(s). A central **DiffusionServer** routes requests through the pipeline.
+
+## Quick Start
+
+Disaggregation is controlled by a single flag: `--disagg-role`. Each component is launched independently, just like LLM PD disaggregation.
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "50%"}} />
+    <col style={{width: "50%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th><code>--disagg-role</code></th>
+      <th>What it runs</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>monolithic</code></td>
+      <td>(Default) Standard single-server mode</td>
+    </tr>
+    <tr>
+      <td><code>encoder</code></td>
+      <td>All stages with the default <code>RoleType.ENCODER</code> affinity: <code>InputValidationStage</code>, <code>TextEncodingStage</code> (plus <code>ImageEncodingStage</code> / <code>ImageVAEEncodingStage</code> for image-conditioned pipelines), <code>LatentPreparationStage</code>, <code>TimestepPreparationStage</code>, and any model-specific "before denoising" stage (e.g. <code>QwenImageLayeredBeforeDenoisingStage</code>, <code>GlmImageBeforeDenoisingStage</code>).</td>
+    </tr>
+    <tr>
+      <td><code>denoiser</code></td>
+      <td><code>DenoisingStage</code> (and its subclasses: <code>CausalDMDDenoisingStage</code>, <code>DmdDenoisingStage</code>, <code>LTX2AVDenoisingStage</code>, <code>LTX2RefinementStage</code>, <code>Hunyuan3DShapeDenoisingStage</code>, ...) — the DiT forward loop plus the scheduler stepping it drives.</td>
+    </tr>
+    <tr>
+      <td><code>decoder</code></td>
+      <td><code>DecodingStage</code> (VAE decode) and its subclasses (<code>LTX2AVDecodingStage</code>, <code>HeliosDecodingStage</code>, ...).</td>
+    </tr>
+    <tr>
+      <td><code>server</code></td>
+      <td>DiffusionServer head node + HTTP server (no GPU)</td>
+    </tr>
+  </tbody>
+</table>
+
+> Each stage declares its role via the `role_affinity` property on `PipelineStage` (default `ENCODER`). When `--disagg-role` is not `monolithic`, the pipeline only instantiates stages whose affinity matches, so the above table is the source of truth for what actually runs in each process.
+
+### Single-Machine Example (Verified)
+
+The following commands have been tested end-to-end on an 8×H200 machine with
+`Wan-AI/Wan2.1-T2V-1.3B-Diffusers`. Each role runs on a separate GPU via
+`--base-gpu-id`; the `server` head node requires no GPU.
+
+```bash
+# Terminal 1: Encoder (GPU 0)
+sglang serve --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --disagg-role encoder \
+    --disagg-server-addr tcp://127.0.0.1:19655 \
+    --scheduler-port 19000 \
+    --num-gpus 1 --base-gpu-id 0
+
+# Terminal 2: Denoiser (GPU 1)
+sglang serve --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --disagg-role denoiser \
+    --disagg-server-addr tcp://127.0.0.1:19655 \
+    --scheduler-port 19001 \
+    --num-gpus 1 --base-gpu-id 1
+
+# Terminal 3: Decoder (GPU 2)
+sglang serve --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --disagg-role decoder \
+    --disagg-server-addr tcp://127.0.0.1:19655 \
+    --scheduler-port 19002 \
+    --num-gpus 1 --base-gpu-id 2
+
+# Terminal 4: DiffusionServer head (no GPU, receives HTTP requests)
+sglang serve --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --disagg-role server \
+    --encoder-urls  "tcp://127.0.0.1:19000" \
+    --denoiser-urls "tcp://127.0.0.1:19001" \
+    --decoder-urls  "tcp://127.0.0.1:19002" \
+    --host 0.0.0.0 --port 22000 \
+    --scheduler-port 19655
+
+# Send request (video generation)
+curl http://127.0.0.1:22000/v1/videos \
+    -H "Content-Type: application/json" \
+    -d '{"model": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", "prompt": "A curious raccoon exploring a garden, cinematic", "size": "832x480"}'
+```
+
+> **Tested result (8×H200):**
+> Encoder 2.3 s (TextEncoding) → Denoiser 312.8 s (50 steps, layerwise offload) → Decoder 7.1 s (VAE decode).
+> Total ~322 s for 81-frame 1024×1024 video.
+
+> **Tip:** `--base-gpu-id` controls which physical GPU the role uses.
+> Encoder and Decoder can share a GPU (e.g. both `--base-gpu-id 0`) to save resources,
+> but make sure the combined GPU memory is sufficient.
+
+### Multi-Machine Example
+
+The exact same CLI pattern — just replace `127.0.0.1` with actual IPs and add
+RDMA flags for direct transfer:
+
+```bash
+# Machine A (10.0.0.1): Encoder
+sglang serve --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers \
+    --disagg-role encoder \
+    --disagg-server-addr tcp://10.0.0.4:19655 \
+    --scheduler-port 19000 \
+    --num-gpus 1 \
+    --disagg-p2p-hostname 10.0.0.1 --disagg-ib-device mlx5_0
+
+# Machine B (10.0.0.2): Denoiser (4 GPUs with SP)
+sglang serve --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers \
+    --disagg-role denoiser \
+    --disagg-server-addr tcp://10.0.0.4:19655 \
+    --scheduler-port 19001 \
+    --num-gpus 4 --denoiser-sp 4 --denoiser-ulysses 2 --denoiser-ring 2 \
+    --disagg-p2p-hostname 10.0.0.2 --disagg-ib-device mlx5_0
+
+# Machine C (10.0.0.3): Decoder
+sglang serve --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers \
+    --disagg-role decoder \
+    --disagg-server-addr tcp://10.0.0.4:19655 \
+    --scheduler-port 19002 \
+    --num-gpus 1 \
+    --disagg-p2p-hostname 10.0.0.3 --disagg-ib-device mlx5_0
+
+# Machine D (10.0.0.4): DiffusionServer head
+sglang serve --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers \
+    --disagg-role server \
+    --encoder-urls  "tcp://10.0.0.1:19000" \
+    --denoiser-urls "tcp://10.0.0.2:19001" \
+    --decoder-urls  "tcp://10.0.0.3:19002" \
+    --host 0.0.0.0 --port 30000 \
+    --scheduler-port 19655 \
+    --disagg-dispatch-policy max_free_slots
+```
+
+> ZMQ handles startup order gracefully — instances and head can start in any order.
+
+## Multiple Instances per Role
+
+Use semicolons in `--*-urls` to register multiple instances:
+
+```bash
+# 2 encoders + 2 denoisers (4-GPU SP each) + 1 decoder
+sglang serve --model-path ... --disagg-role server \
+    --encoder-urls  "tcp://10.0.0.1:35000;tcp://10.0.0.2:35000" \
+    --denoiser-urls "tcp://10.0.0.3:35000;tcp://10.0.0.4:35000" \
+    --decoder-urls  "tcp://10.0.0.5:35000"
+```
+
+## Port Convention
+
+Result endpoints are derived deterministically from the head node's `--scheduler-port` (default: 5555):
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "50%"}} />
+    <col style={{width: "50%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Socket</th>
+      <th>Port</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>DS frontend (ROUTER)</td>
+      <td><code>scheduler_port</code></td>
+    </tr>
+    <tr>
+      <td>Encoder result (PULL)</td>
+      <td><code>scheduler_port + 1</code></td>
+    </tr>
+    <tr>
+      <td>Denoiser result (PULL)</td>
+      <td><code>scheduler_port + 2</code></td>
+    </tr>
+    <tr>
+      <td>Decoder result (PULL)</td>
+      <td><code>scheduler_port + 3</code></td>
+    </tr>
+  </tbody>
+</table>
+
+Role instances derive their result endpoint automatically from `--disagg-server-addr`. No manual endpoint configuration needed.
+
+## Transfer Mechanism
+
+Tensor data between roles (encoder→denoiser, denoiser→decoder) is transferred via a P2P transfer engine. The DiffusionServer only routes lightweight control messages (alloc/push/ready); actual tensor data flows directly between instances.
+
+**mooncake-transfer-engine** is required for disaggregated diffusion. It provides RDMA for direct GPU-to-GPU data movement.
+
+```bash
+pip install mooncake-transfer-engine
+```
+
+### Transfer Flow
+
+1. **Sender** (encoder/denoiser) stages tensors: async copy to transfer buffer (GPU or CPU pinned, depending on GPUDirect support), overlapped with metadata JSON serialization.
+2. **Sender** sends `transfer_staged` control message to DiffusionServer (metadata only, no tensor data).
+3. **DiffusionServer** sends `transfer_alloc` to receiver → receiver allocates buffer slot → replies `transfer_allocated`.
+4. **DiffusionServer** sends `transfer_push` to receiver with sender's address info.
+5. **Receiver** pulls data via transfer engine (Mooncake RDMA or mock), sends `transfer_ready`.
+6. **Receiver** loads tensors async on a dedicated transfer stream, overlapped with the previous request's compute.
+
+Decoder results (final output) flow back through DiffusionServer as raw ZMQ frames to the HTTP client.
+
+### RDMA Flags
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Flag</th>
+      <th>Default</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>--disagg-p2p-hostname</code></td>
+      <td><code>127.0.0.1</code></td>
+      <td>RDMA-reachable hostname/IP of this instance</td>
+    </tr>
+    <tr>
+      <td><code>--disagg-ib-device</code></td>
+      <td><code>None</code></td>
+      <td>InfiniBand device (e.g., <code>mlx5_0</code>, <code>mlx5_roce0</code>)</td>
+    </tr>
+    <tr>
+      <td><code>--disagg-transfer-pool-size</code></td>
+      <td>256 MiB</td>
+      <td>Pinned memory pool per instance</td>
+    </tr>
+  </tbody>
+</table>
+
+Set `--disagg-p2p-hostname` to the actual IP on each machine. For multi-machine, `--disagg-ib-device` specifies the RDMA NIC.
+
+## Per-Role Parallelism
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "50%"}} />
+    <col style={{width: "50%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Flag</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>--encoder-tp</code></td>
+      <td>Encoder tensor parallelism</td>
+    </tr>
+    <tr>
+      <td><code>--denoiser-tp</code> / <code>--denoiser-sp</code> / <code>--denoiser-ulysses</code> / <code>--denoiser-ring</code></td>
+      <td>Denoiser parallelism</td>
+    </tr>
+    <tr>
+      <td><code>--decoder-tp</code></td>
+      <td>Decoder tensor parallelism</td>
+    </tr>
+  </tbody>
+</table>
+
+If not specified, parallelism is auto-derived from `--num-gpus`.
+
+## Other Options
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Flag</th>
+      <th>Default</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>--disagg-timeout</code></td>
+      <td><code>600</code></td>
+      <td>Timeout (seconds) for pending requests</td>
+    </tr>
+    <tr>
+      <td><code>--disagg-dispatch-policy</code></td>
+      <td><code>round_robin</code></td>
+      <td><code>round_robin</code> or <code>max_free_slots</code></td>
+    </tr>
+  </tbody>
+</table>
+
+## Python API
+
+For programmatic single-machine deployment, `launch_pool_disagg_server()` is available:
+
+```python
+from sglang.multimodal_gen.runtime.server_args import ServerArgs
+from sglang.multimodal_gen.runtime.launch_server import launch_pool_disagg_server
+
+server_args = ServerArgs.from_kwargs(
+    model_path="Wan-AI/Wan2.1-T2V-14B-Diffusers",
+    denoiser_sp=4, denoiser_ulysses=2, denoiser_ring=2,
+    disagg_ib_device="mlx5_0",
+)
+
+launch_pool_disagg_server(
+    server_args,
+    encoder_gpus=[[0]],
+    denoiser_gpus=[[1, 2, 3, 4], [5, 6, 7, 8]],
+    decoder_gpus=[[0]],
+)
+```
+
+## Architecture
+
+```
+Client ─── HTTP (port 30000) ──► FastAPI Server
+                                      │
+                                      ▼
+                              DiffusionServer (ROUTER, scheduler_port)
+                              ┌───────┼───────┐
+                   PUSH work  │       │       │  PUSH work
+                              ▼       │       ▼
+                    Encoder[0..N]     │    Decoder[0..K]
+                              │       │       ▲
+                   P2P tensor │       │       │ P2P tensor
+                   transfer   ▼       │       │ transfer
+                          Denoiser[0..M] ─────┘
+                                      │
+                    PULL results ◄────┘  (decoder → DS → client)
+```
+
+### Request State Machine
+
+```
+PENDING → ENCODER_WAITING → ENCODER_RUNNING → ENCODER_DONE
+                                                    │
+                        DENOISING_WAITING → DENOISING_RUNNING → DENOISING_DONE
+                                                                       │
+                                    DECODER_WAITING → DECODER_RUNNING → DONE
+```
+
+Any state can transition to `FAILED` or `TIMED_OUT`.
@@ -1,140 +0,0 @@
---
-title: "Environment Variables"
-description: "Configure SGLang diffusion behavior with environment variables."
---
-
-These variables configure caching acceleration for Diffusion Transformer (DiT) models.
-SGLang supports multiple caching strategies - see [performance optimization documentation](./performance-optimization) for an overview.
-
-See [Environment Variables](../references/environment_variables) for a list of all environment variables.
-
-## Cache-DiT configuration
-
-See [Cache-DiT documentation](./cache-dit) for detailed configuration.
-<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
-  <colgroup>
-    <col style={{width: "42%"}} />
-    <col style={{width: "16%"}} />
-    <col style={{width: "42%"}} />
-  </colgroup>
-  <thead>
-    <tr style={{borderBottom: "2px solid #d55816"}}>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Environment Variable</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Default</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_ENABLED`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>false</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Enable Cache-DiT acceleration</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_FN`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>First N blocks to always compute</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_BN`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Last N blocks to always compute</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_WARMUP`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>4</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Warmup steps before caching</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_RDT`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.24</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Residual difference threshold</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_MC`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>3</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Max continuous cached steps</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_TAYLORSEER`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>false</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Enable TaylorSeer calibrator</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_TS_ORDER`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>TaylorSeer order (1 or 2)</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_SCM_PRESET`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>none</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>SCM preset (none/slow/medium/fast/ultra)</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_SCM_POLICY`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>dynamic</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>SCM caching policy</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_SCM_COMPUTE_BINS`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Custom SCM compute bins</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_SCM_CACHE_BINS`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Custom SCM cache bins</td>
-    </tr>
-  </tbody>
-</table>
-
-## Cloud Storage
-
-These variables configure S3-compatible cloud storage for automatically uploading generated images and videos.
-
-<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
-  <colgroup>
-    <col style={{width: "35%"}} />
-    <col style={{width: "16%"}} />
-    <col style={{width: "49%"}} />
-  </colgroup>
-  <thead>
-    <tr style={{borderBottom: "2px solid #d55816"}}>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Environment Variable</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Default</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CLOUD_STORAGE_TYPE`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Set to `s3` to enable cloud storage</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_S3_BUCKET_NAME`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>The name of the S3 bucket</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_S3_ENDPOINT_URL`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Custom endpoint URL (for MinIO, OSS, etc.)</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_S3_REGION_NAME`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>us-east-1</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>AWS region name</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_S3_ACCESS_KEY_ID`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>AWS Access Key ID</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_S3_SECRET_ACCESS_KEY`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>AWS Secret Access Key</td>
-    </tr>
-  </tbody>
-</table>
@@ -0,0 +1,395 @@
+---
+title: "Environment Variables"
+description: "Configure SGLang diffusion behavior with environment variables."
+---
+## Runtime
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "42%"}} />
+    <col style={{width: "16%"}} />
+    <col style={{width: "42%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Environment Variable</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Default</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_TARGET_DEVICE</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>cuda</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Target device for inference (<code>cuda</code>, <code>rocm</code>, <code>xpu</code>, <code>npu</code>, <code>musa</code>, <code>mps</code>, <code>cpu</code>)</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_ATTENTION_BACKEND</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Override attention backend via env var (e.g. <code>fa</code>, <code>torch_sdpa</code>, <code>sage_attn</code>)</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_ATTENTION_CONFIG</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Path to attention backend configuration file (JSON/YAML)</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_STAGE_LOGGING</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>false</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Enable per-stage timing logs</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_SERVER_DEV_MODE</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>false</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Enable dev-only HTTP endpoints for debugging</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_TORCH_PROFILER_DIR</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Directory for torch profiler traces (absolute path). Enables profiling when set</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_CACHE_ROOT</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>~/.cache/sgl_diffusion</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Root directory for cache files</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_CONFIG_ROOT</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>~/.config/sgl_diffusion</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Root directory for configuration files</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_LOGGING_LEVEL</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>INFO</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Default logging level</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_WORKER_MULTIPROC_METHOD</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>fork</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Multiprocess context for workers (<code>fork</code> or <code>spawn</code>)</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_USE_RUNAI_MODEL_STREAMER</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>true</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Use Run:AI model streamer for model loading</td>
+    </tr>
+  </tbody>
+</table>
+
+## Platform-Specific
+
+### Apple MPS
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "35%"}} />
+    <col style={{width: "16%"}} />
+    <col style={{width: "49%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Environment Variable</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Default</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_USE_MLX</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Set to <code>1</code> to enable MLX fused Metal kernels for norm ops on MPS</td>
+    </tr>
+  </tbody>
+</table>
+
+### ROCm (AMD GPUs)
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Environment Variable</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Default</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_USE_ROCM_VAE</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>false</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Use AITer GroupNorm in VAE for improved performance on ROCm</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_USE_ROCM_CUDNN_BENCHMARK</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>false</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Enable MIOpen auto-tuning for VAE conv layers on ROCm</td>
+    </tr>
+  </tbody>
+</table>
+
+### Quantization
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Environment Variable</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Default</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>FlashInfer FP4 GEMM backend for generic NVFP4 fallback</td>
+    </tr>
+  </tbody>
+</table>
+
+## Caching Acceleration
+
+These variables configure caching acceleration for Diffusion Transformer (DiT) models.
+SGLang supports multiple caching strategies - see [caching documentation](./caching-acceleration) for an overview.
+
+### Cache-DiT Configuration
+
+See [cache-dit documentation](./cache_dit) for detailed configuration.
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "42%"}} />
+    <col style={{width: "16%"}} />
+    <col style={{width: "42%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Environment Variable</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Default</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_ENABLED`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>false</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Enable Cache-DiT acceleration</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_FN`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>First N blocks to always compute</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_BN`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Last N blocks to always compute</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_WARMUP`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>4</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Warmup steps before caching</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_RDT`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.24</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Residual difference threshold</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_MC`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>3</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Max continuous cached steps</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_TAYLORSEER`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>false</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Enable TaylorSeer calibrator</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_TS_ORDER`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>TaylorSeer order (1 or 2)</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_SCM_PRESET`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>none</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>SCM preset (none/slow/medium/fast/ultra)</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_SCM_POLICY`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>dynamic</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>SCM caching policy</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_SCM_COMPUTE_BINS`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Custom SCM compute bins</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CACHE_DIT_SCM_CACHE_BINS`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Custom SCM cache bins</td>
+    </tr>
+  </tbody>
+</table>
+
+### Cache-DiT Secondary Transformer
+
+For dual-transformer models (e.g., Wan2.2 with high/low-noise experts), these variables configure caching for the secondary transformer. Each falls back to its primary counterpart if not set.
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Environment Variable</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Default</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_CACHE_DIT_SECONDARY_FN</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>(from primary)</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>First N blocks to always compute</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_CACHE_DIT_SECONDARY_BN</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>(from primary)</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Last N blocks to always compute</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_CACHE_DIT_SECONDARY_WARMUP</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>(from primary)</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Warmup steps before caching</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_CACHE_DIT_SECONDARY_RDT</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>(from primary)</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Residual difference threshold</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_CACHE_DIT_SECONDARY_MC</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>(from primary)</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Max continuous cached steps</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_CACHE_DIT_SECONDARY_TAYLORSEER</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>(from primary)</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Enable TaylorSeer calibrator</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_CACHE_DIT_SECONDARY_TS_ORDER</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>(from primary)</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>TaylorSeer order (1 or 2)</td>
+    </tr>
+  </tbody>
+</table>
+
+## Cloud Storage
+
+These variables configure S3-compatible cloud storage for automatically uploading generated images and videos.
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "35%"}} />
+    <col style={{width: "16%"}} />
+    <col style={{width: "49%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Environment Variable</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Default</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_CLOUD_STORAGE_TYPE`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Set to `s3` to enable cloud storage</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_S3_BUCKET_NAME`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>The name of the S3 bucket</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_S3_ENDPOINT_URL`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Custom endpoint URL (for MinIO, OSS, etc.)</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_S3_REGION_NAME`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>us-east-1</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>AWS region name</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_S3_ACCESS_KEY_ID`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>AWS Access Key ID</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`SGLANG_S3_SECRET_ACCESS_KEY`</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>AWS Secret Access Key</td>
+    </tr>
+  </tbody>
+</table>
+
+## CUDA Crash Debugging
+
+These variables enable kernel API logging and optional input/output dumps around diffusion CUDA kernel call boundaries. They are useful when tracking down CUDA crashes such as illegal memory access, device-side assert, or shape mismatches in custom kernels.
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Environment Variable</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Default</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_KERNEL_API_LOGLEVEL</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>0</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Controls crash-debug kernel API logging. <code>1</code> logs API names, <code>3</code> logs tensor metadata, <code>5</code> adds tensor statistics, and <code>10</code> also writes dump snapshots.</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_KERNEL_API_LOGDEST</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>stdout</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Destination for crash-debug kernel API logs. Use <code>stdout</code>, <code>stderr</code>, or a file path. <code>%i</code> is replaced with the process PID.</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_KERNEL_API_DUMP_DIR</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>sglang_kernel_api_dumps</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Output directory for level-10 kernel API dumps. <code>%i</code> is replaced with the process PID.</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_KERNEL_API_DUMP_INCLUDE</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Comma-separated wildcard patterns for kernel API names to include in level-10 dumps.</td>
+    </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_KERNEL_API_DUMP_EXCLUDE</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>not set</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Comma-separated wildcard patterns for kernel API names to exclude from level-10 dumps.</td>
+    </tr>
+  </tbody>
+</table>
@@ -2,64 +2,54 @@
 title: SGLang Diffusion
 description: Accelerated image and video generation with diffusion models.
 ---
+SGLang Diffusion is a high-performance inference framework for image and video generation. It provides native SGLang pipelines, diffusers backend support, an OpenAI-compatible server, and an optimized kernel stack built on both precompiled `sgl-kernel` operators and JIT kernels for key inference paths.

-SGLang Diffusion is an inference framework for accelerated image and video generation using diffusion models. It provides an end-to-end unified pipeline with optimized kernels and an efficient scheduler loop.
+## Key Features

-## Key features
+- Broad model support across Wan, Hunyuan, Qwen-Image, FLUX, Z-Image, GLM-Image, and more
+- Fast inference with `sgl-kernel`, JIT kernels, scheduler improvements, and caching acceleration
+- Multiple interfaces: `sglang generate`, `sglang serve`, and an OpenAI-compatible API
+- Multi-platform support for NVIDIA, AMD, Intel XPU, Ascend, Apple Silicon, and Moore Threads

-* **Broad model support:** Wan series, FastWan series, Hunyuan, Qwen-Image, Qwen-Image-Edit, Flux, Z-Image, GLM-Image, and more
-* **Fast inference:** optimized kernels, efficient scheduler loop, and Cache-DiT acceleration
-* **Ease of use:** OpenAI-compatible API, CLI, and Python SDK
-* **Multi-platform:** NVIDIA GPUs (H100, H200, A100, B200, 4090), AMD GPUs (MI300X, MI325X), and Ascend NPU (A2, A3)
-
-## Quick start
-
-1. **Install SGLang Diffusion**
+## Quick Start

 ```bash
 uv pip install "sglang[diffusion]" --prerelease=allow
 ```

-See the [installation guide](./installation) for more installation methods and ROCm-specific instructions.
-
-2. **Run a one-off generation**
-
 ```bash
 sglang generate --model-path Qwen/Qwen-Image \
  --prompt "A beautiful sunset over the mountains" \
  --save-output
 ```

-3. **Serve with the OpenAI-compatible API**
-
 ```bash
 sglang serve --model-path Qwen/Qwen-Image --port 30010
 ```

-## CLI quick reference
+## Start Here

-### Generate (one-off generation)
+- [Installation](./installation): install SGLang Diffusion and platform dependencies
+- [Compatibility Matrix](./compatibility_matrix): check model, optimization, and component override support
+- [CLI](./api/cli): run one-off generation jobs or launch a persistent server
+- [OpenAI-Compatible API](./api/openai_api): send image and video requests to the HTTP server
+- [Attention Backends](./attention_backends): choose the best backend for your model and hardware
+- [Caching Acceleration](./caching-acceleration): use Cache-DiT or TeaCache to reduce denoising cost
+- [Quantization](./quantization): load quantized transformer checkpoints
+- [Contributing](./contributing): contribution workflow, adding new models, and CI perf baselines

-```bash
-sglang generate --model-path <MODEL> --prompt "<PROMPT>" --save-output
-```
+## Additional Documentation

-### Serve (HTTP server)
-
-```bash
-sglang serve --model-path <MODEL> --port 30010
-```
-
-### Enable Cache-DiT acceleration
-
-```bash
-SGLANG_CACHE_DIT_ENABLED=true sglang generate --model-path <MODEL> --prompt "<PROMPT>"
-```
+- [Post-Processing](./api/post_processing): frame interpolation and upscaling
+- [Performance Overview](./performance-optimization): overview of attention, caching, and profiling
+- [Environment Variables](./environment_variables): platform, caching, storage, and debugging configuration
+- [Support New Models](./support_new_models): implementation guide for new diffusion pipelines
+- [CI Performance](./ci_perf): performance baseline generation

 ## References

-* [SGLang GitHub](https://github.com/sgl-project/sglang)
-* [Cache-DiT](https://github.com/vipshop/cache-dit)
-* [FastVideo](https://github.com/hao-ai-lab/FastVideo)
-* [xDiT](https://github.com/xdit-project/xDiT)
-* [Diffusers](https://github.com/huggingface/diffusers)
+- [SGLang GitHub](https://github.com/sgl-project/sglang)
+- [Cache-DiT](https://github.com/vipshop/cache-dit)
+- [FastVideo](https://github.com/hao-ai-lab/FastVideo)
+- [xDiT](https://github.com/xdit-project/xDiT)
+- [Diffusers](https://github.com/huggingface/diffusers)
@@ -2,109 +2,129 @@
 title: Install SGLang Diffusion
 description: Install SGLang Diffusion on NVIDIA, AMD, MUSA, and Ascend platforms.
 ---
+You can install SGLang-Diffusion using one of the methods below. The standard installation already includes SGLang's optimized kernel stack, including both `sgl-kernel` and JIT kernels used by diffusion workloads.

-You can install SGLang Diffusion using one of the methods below.
+## Standard Installation (NVIDIA GPUs)

-## Standard installation (NVIDIA GPUs)
+### Method 1: With pip or uv

-**Platform:** NVIDIA GPUs (CUDA)
+It is recommended to use uv for a faster installation:

-<Tabs>
-  <Tab title="Pip or uv">
-    Use `uv` for faster installation:
+```bash Command
+pip install --upgrade pip
+pip install uv
+uv pip install "sglang[diffusion]" --prerelease=allow
+```

-    ```bash
-    pip install --upgrade pip
-    pip install uv
-    uv pip install "sglang[diffusion]" --prerelease=allow
-    ```
-  </Tab>
+### Method 2: From source

-  <Tab title="From source">
-    ```bash
-    git clone https://github.com/sgl-project/sglang.git
-    cd sglang
-    pip install --upgrade pip
-    pip install -e "python[diffusion]"
-    ```
+```bash Command
+# Use the latest release branch
+git clone https://github.com/sgl-project/sglang.git
+cd sglang

-    Or with `uv`:
+# Install the Python packages
+pip install --upgrade pip
+pip install -e "python[diffusion]"

-    ```bash
-    uv pip install -e "python[diffusion]" --prerelease=allow
-    ```
-  </Tab>
+# With uv
+uv pip install -e "python[diffusion]" --prerelease=allow
+```

-  <Tab title="Docker">
-    The Docker images are available on Docker Hub at [lmsysorg/sglang](https://hub.docker.com/r/lmsysorg/sglang/tags), built from the [Dockerfile](https://github.com/sgl-project/sglang/blob/main/docker/Dockerfile). Replace `<secret>` below with your HuggingFace Hub [token](https://huggingface.co/docs/hub/en/security-tokens).
+### Method 3: Using Docker

-    ```bash
-    docker run --gpus all \
-        --shm-size 32g \
-        -p 30000:30000 \
-        -v ~/.cache/huggingface:/root/.cache/huggingface \
-        --env "HF_TOKEN=<secret>" \
-        --ipc=host \
-        lmsysorg/sglang:dev \
-        zsh -c '\
-            echo "Installing diffusion dependencies..." && \
-            pip install -e "python[diffusion]" && \
-            echo "Starting SGLang-Diffusion..." && \
-            sglang generate \
-                --model-path black-forest-labs/FLUX.1-dev \
-                --prompt "A logo With Bold Large text: SGL Diffusion" \
-                --save-output \
-        '
-    ```
-  </Tab>
-</Tabs>
+The Docker images are available on Docker Hub at [lmsysorg/sglang](https://hub.docker.com/r/lmsysorg/sglang), built from the [Dockerfile](https://github.com/sgl-project/sglang/blob/main/docker/Dockerfile).
+Replace `<secret>` below with your HuggingFace Hub [token](https://huggingface.co/docs/hub/en/security-tokens).

-## Platform-specific installs
+```bash Command
+docker run --gpus all \
+    --shm-size 32g \
+    -p 30000:30000 \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HF_TOKEN=<secret>" \
+    --ipc=host \
+    lmsysorg/sglang:dev \
+    zsh -c '\
+        echo "Installing diffusion dependencies..." && \
+        pip install -e "python[diffusion]" && \
+        echo "Starting SGLang-Diffusion..." && \
+        sglang generate \
+            --model-path black-forest-labs/FLUX.1-dev \
+            --prompt "A logo With Bold Large text: SGL Diffusion" \
+            --save-output \
+    '
+```

-Use the tab that matches your accelerator.
+## Platform-Specific: ROCm (AMD GPUs)

-<Tabs>
-  <Tab title="ROCm (AMD GPUs)">
-    **Platform:** AMD Instinct GPUs (ROCm)
+For AMD Instinct GPUs (e.g., MI300X), you can use the ROCm-enabled Docker image:

-    For AMD Instinct GPUs (for example, MI300X), use the ROCm-enabled Docker image:
+```bash Command
+docker run --device=/dev/kfd --device=/dev/dri --ipc=host \
+  -v ~/.cache/huggingface:/root/.cache/huggingface \
+  --env HF_TOKEN=<secret> \
+  lmsysorg/sglang:v0.5.5.post2-rocm700-mi30x \
+  sglang generate --model-path black-forest-labs/FLUX.1-dev --prompt "A logo With Bold Large text: SGL Diffusion" --save-output
+```

-    ```bash
-    docker run --device=/dev/kfd --device=/dev/dri --ipc=host \
-      -v ~/.cache/huggingface:/root/.cache/huggingface \
-      --env HF_TOKEN=<secret> \
-      lmsysorg/sglang:v0.5.9-rocm700-mi30x \
-      sglang generate --model-path black-forest-labs/FLUX.1-dev --prompt "A logo With Bold Large text: SGL Diffusion" --save-output
-    ```
+For detailed ROCm system configuration and installation from source, see [AMD GPUs](../hardware-platforms/amd_gpu).

-    For detailed ROCm system configuration and installation from source, see [AMD GPUs](../hardware-platforms/amd-gpus).
-  </Tab>
+## Platform-Specific: MUSA (Moore Threads GPUs)

-  <Tab title="MUSA (Moore Threads GPUs)">
-    **Platform:** Moore Threads GPUs (MUSA)
+For Moore Threads GPUs (MTGPU) with the MUSA software stack, please follow the instructions below to install from source:

-    For Moore Threads GPUs (MTGPU) with the MUSA software stack:
+```bash Command
+# Clone the repository
+git clone https://github.com/sgl-project/sglang.git
+cd sglang

-    ```bash
-    git clone https://github.com/sgl-project/sglang.git
-    cd sglang
-    pip install --upgrade pip
-    rm -f python/pyproject.toml && mv python/pyproject_other.toml python/pyproject.toml
-    pip install -e "python[all_musa]"
-    ```
-  </Tab>
+# Install the Python packages
+pip install --upgrade pip
+rm -f python/pyproject.toml && mv python/pyproject_other.toml python/pyproject.toml
+pip install -e "python[all_musa]"
+```

-  <Tab title="Ascend NPU">
-    **Platform:** Ascend NPU
+## Platform-Specific: Intel XPU

-    For Ascend NPU, follow the [NPU installation guide](../hardware-platforms/ascend-npus/SGLang-installation-with-NPUs-support).
+For Intel Data Center GPU Max or Arc GPUs, follow the [XPU installation guide](../hardware-platforms/xpu) to set up the base environment, then install diffusion dependencies:

-    Quick test:
+```bash Command
+pip install -e "python[diffusion]"
+```

-    ```bash
-    sglang generate --model-path black-forest-labs/FLUX.1-dev \
-        --prompt "A logo With Bold Large text: SGL Diffusion" \
-        --save-output
-    ```
-  </Tab>
-</Tabs>
+## Platform-Specific: Ascend NPU
+
+For Ascend NPU, please follow the [NPU installation guide](../hardware-platforms/ascend-npus/ascend_npu).
+
+Quick test:
+
+```bash Command
+sglang generate --model-path black-forest-labs/FLUX.1-dev \
+    --prompt "A logo With Bold Large text: SGL Diffusion" \
+    --save-output
+```
+
+## Platform-Specific: Apple MPS
+
+For Apple MPS, please follow the instructions below to install from source:
+
+```bash Command
+# Install ffmpeg
+brew install ffmpeg
+
+# Install uv
+brew install uv
+
+# Clone the repository
+git clone https://github.com/sgl-project/sglang.git
+cd sglang
+
+# Create and activate a virtual environment
+uv venv -p 3.11 sglang-diffusion
+source sglang-diffusion/bin/activate
+
+# Install the Python packages
+uv pip install --upgrade pip
+rm -f python/pyproject.toml && mv python/pyproject_other.toml python/pyproject.toml
+uv pip install -e "python[all_mps]"
+```
@@ -2,8 +2,7 @@
 title: "Performance Optimization"
 description: "Optimize SGLang diffusion performance with caching, kernels, and profiling."
 ---
-
-SGLang-Diffusion provides multiple performance optimization strategies to accelerate inference. This section covers all available performance tuning options.
+This section covers the main performance levers for SGLang Diffusion: attention backends, caching acceleration, and profiling.

 ## Overview

@@ -29,7 +28,7 @@ SGLang-Diffusion provides multiple performance optimization strategies to accele
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TeaCache</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Caching</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Timestep-level caching using L1 similarity</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Timestep-level caching based on temporal similarity</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Attention Backends</td>
@@ -44,60 +43,23 @@ SGLang-Diffusion provides multiple performance optimization strategies to accele
  </tbody>
 </table>

-## Caching Strategies
+## Start Here

-SGLang supports two complementary caching approaches:
+- Use [Attention Backends](./attention_backends) to choose the best backend for your model and hardware.
+- Use [Caching Acceleration](./caching-acceleration) to reduce denoising cost with Cache-DiT or TeaCache.
+- Use [Profiling](./profiling) when you need to diagnose a bottleneck rather than guess.

-### Cache-DiT
+## Caching at a Glance

-[Cache-DiT](https://github.com/vipshop/cache-dit) provides block-level caching with advanced strategies. It can achieve up to **1.69x speedup**.
+- [Cache-DiT](./cache_dit) is block-level caching for diffusers pipelines and higher speedup-oriented tuning.
+- [TeaCache](./teacache) is timestep-level caching built into SGLang model families.

-**Quick Start:**
-```bash
-SGLANG_CACHE_DIT_ENABLED=true \
-sglang generate --model-path Qwen/Qwen-Image \
-    --prompt "A beautiful sunset over the mountains"
-```

-**Key Features:**
- **DBCache**: Dynamic block-level caching based on residual differences
- **TaylorSeer**: Taylor expansion-based calibration for optimized caching
- **SCM**: Step-level computation masking for additional speedup
+## Current Baseline Snapshot

-See [Cache-DiT documentation](./cache-dit) for detailed configuration.
+For Ring SP benchmark details, see:

-### TeaCache
-
-TeaCache (Temporal similarity-based caching) accelerates diffusion inference by detecting when consecutive denoising steps are similar enough to skip computation entirely.
-
-**Quick Overview:**
- Tracks L1 distance between modulated inputs across timesteps
- When accumulated distance is below threshold, reuses cached residual
- Supports CFG with separate positive/negative caches
-
-**Supported Models:** Wan (wan2.1, wan2.2), Hunyuan (HunyuanVideo), Z-Image
-
-See [TeaCache documentation](./tea-cache) for detailed configuration.
-
-## Attention Backends
-
-Different attention backends offer varying performance characteristics depending on your hardware and model:
-
- **FlashAttention**: Fastest on NVIDIA GPUs with fp16/bf16
- **SageAttention**: Alternative optimized implementation
- **xformers**: Memory-efficient attention
- **SDPA**: PyTorch native scaled dot-product attention
-
-See [Attention backends](./attention-backends) for platform support and configuration options.
-
-## Profiling
-
-To diagnose performance bottlenecks, SGLang-Diffusion supports profiling tools:
-
- **PyTorch Profiler**: Built-in Python profiling
- **Nsight Systems**: GPU kernel-level analysis
-
-See [Profiling guide](./profiling) for detailed instructions.
+- [Ring SP Performance](./ring_sp_performance)

 ## References

@@ -2,7 +2,6 @@
 title: "Profiling"
 description: "Profile SGLang diffusion workloads with PyTorch Profiler and Nsight Systems."
 ---
-
 This guide covers profiling techniques for multimodal generation pipelines in SGLang.

 ## PyTorch Profiler
@@ -0,0 +1,551 @@
+---
+title: "Quantization"
+metatags:
+    description: "SGLang-Diffusion supports quantized transformer checkpoints. In most cases, keep the base model and the quantized transformer override separate."
+---
+
+SGLang-Diffusion supports quantized transformer checkpoints. In most cases, keep
+the base model and the quantized transformer override separate.
+
+## Quick Reference
+
+Use these paths:
+
+- `--model-path`: the base or original model
+- `--transformer-path`: a quantized transformers-style transformer component directory that already contains its own `config.json`
+- `--transformer-weights-path`: quantized transformer weights provided as a single safetensors file, a sharded safetensors directory, a local path, or a Hugging Face repo ID
+
+Recommended example:
+
+```bash
+sglang generate \
+  --model-path black-forest-labs/FLUX.2-dev \
+  --transformer-weights-path black-forest-labs/FLUX.2-dev-NVFP4 \
+  --prompt "a curious pikachu"
+```
+
+For quantized transformers-style transformer component folders:
+
+```bash
+sglang generate \
+  --model-path /path/to/base-model \
+  --transformer-path /path/to/quantized-transformer \
+  --prompt "A Logo With Bold Large Text: SGL Diffusion"
+```
+
+NOTE: Some model-specific integrations also accept a quantized repo or local
+directory directly as `--model-path`, but that is a compatibility path. If a
+repo contains multiple candidate checkpoints, pass
+`--transformer-weights-path` explicitly.
+
+## Quant Families
+
+Here, `quant_family` means a checkpoint and loading family with shared CLI
+usage and loader behavior. It is not just the numeric precision or a kernel
+backend.
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "16.67%"}} />
+    <col style={{width: "16.67%"}} />
+    <col style={{width: "16.67%"}} />
+    <col style={{width: "16.67%"}} />
+    <col style={{width: "16.67%"}} />
+    <col style={{width: "16.67%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>quant_family</th>
+      <th>checkpoint form</th>
+      <th>canonical CLI</th>
+      <th>supported models</th>
+      <th>extra dependency</th>
+      <th>platform / notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>fp8</code></td>
+      <td>Quantized transformer component folder, or safetensors with <code>quantization_config</code> metadata</td>
+      <td><code>--transformer-path</code> or <code>--transformer-weights-path</code></td>
+      <td>ALL</td>
+      <td>None</td>
+      <td>Component-folder and single-file flows are both supported</td>
+    </tr>
+    <tr>
+      <td><code>modelopt-fp8</code></td>
+      <td>Converted ModelOpt FP8 transformer directory or repo with <code>config.json</code></td>
+      <td><code>--transformer-path</code></td>
+      <td>FLUX.1, FLUX.2, Wan2.2</td>
+      <td>None</td>
+      <td>Serialized config stays <code>quant_method=modelopt</code> with <code>quant_algo=FP8</code>; <code>dit_layerwise_offload</code> is supported and <code>dit_cpu_offload</code> stays disabled</td>
+    </tr>
+    <tr>
+      <td><code>modelopt-nvfp4</code></td>
+      <td>Mixed transformer directory/repo with <code>config.json</code>, or raw NVFP4 safetensors export/repo</td>
+      <td><code>--transformer-path</code> for mixed overrides; <code>--transformer-weights-path</code> for raw exports</td>
+      <td>FLUX.1, FLUX.2, Wan2.2</td>
+      <td>None</td>
+      <td>Mixed override repos keep the base model separate; raw exports such as <code>black-forest-labs/FLUX.2-dev-NVFP4</code> still use the weights-path flow</td>
+    </tr>
+    <tr>
+      <td><code>nunchaku-svdq</code></td>
+      <td>Pre-quantized Nunchaku transformer weights, usually named <code>svdq-&#123;int4\|fp4&#125;_r&#123;rank&#125;-...</code></td>
+      <td><code>--transformer-weights-path</code></td>
+      <td>Model-specific support such as Qwen-Image, FLUX, and Z-Image</td>
+      <td><code>nunchaku</code></td>
+      <td>SGLang can infer precision and rank from the filename and supports both <code>int4</code> and <code>nvfp4</code></td>
+    </tr>
+    <tr>
+      <td><code>msmodelslim</code></td>
+      <td>Pre-quantized msmodelslim transformer weights</td>
+      <td><code>--model-path</code></td>
+      <td>Wan2.2 family</td>
+      <td>None</td>
+      <td>Currently only compatible with the Ascend NPU family and supports both <code>w8a8</code> and <code>w4a4</code></td>
+    </tr>
+  </tbody>
+</table>
+
+## Validated ModelOpt Checkpoints
+
+This section is the canonical support matrix for the six diffusion ModelOpt
+checkpoints currently wired up in SGLang docs and B200 CI coverage.
+
+Published checkpoints keep the serialized quantization config as
+`quant_method=modelopt`; the FP8 vs NVFP4 split below is a documentation label
+derived from `quant_algo`.
+
+Five of the six repos live under `BBuf/*`. The FLUX.2 NVFP4 entry keeps the
+official `black-forest-labs/FLUX.2-dev-NVFP4` repo.
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "16.67%"}} />
+    <col style={{width: "16.67%"}} />
+    <col style={{width: "16.67%"}} />
+    <col style={{width: "16.67%"}} />
+    <col style={{width: "16.67%"}} />
+    <col style={{width: "16.67%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Quant Algo</th>
+      <th>Base Model</th>
+      <th>Preferred CLI</th>
+      <th>HF Repo</th>
+      <th>Current Scope</th>
+      <th>Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>FP8</code></td>
+      <td><code>black-forest-labs/FLUX.1-dev</code></td>
+      <td><code>--transformer-path</code></td>
+      <td><code>BBuf/flux1-dev-modelopt-fp8-sglang-transformer</code></td>
+      <td>single-transformer override, deterministic latent/image comparison, H100 benchmark, torch-profiler trace</td>
+      <td>SGLang converter keeps a validated BF16 fallback set for modulation and FF projection layers; use <code>--model-id FLUX.1-dev</code> for local mirrors</td>
+    </tr>
+    <tr>
+      <td><code>FP8</code></td>
+      <td><code>black-forest-labs/FLUX.2-dev</code></td>
+      <td><code>--transformer-path</code></td>
+      <td><code>BBuf/flux2-dev-modelopt-fp8-sglang-transformer</code></td>
+      <td>single-transformer override load and generation path</td>
+      <td>published SGLang-ready transformer override</td>
+    </tr>
+    <tr>
+      <td><code>FP8</code></td>
+      <td><code>Wan-AI/Wan2.2-T2V-A14B-Diffusers</code></td>
+      <td><code>--transformer-path</code></td>
+      <td><code>BBuf/wan22-t2v-a14b-modelopt-fp8-sglang-transformer</code></td>
+      <td>primary <code>transformer</code> quantized, <code>transformer_2</code> kept BF16</td>
+      <td>primary-transformer-only path; keep <code>transformer_2</code> on the base checkpoint, and do not describe this as dual-transformer full-model FP8 unless that path is validated separately</td>
+    </tr>
+    <tr>
+      <td><code>NVFP4</code></td>
+      <td><code>black-forest-labs/FLUX.1-dev</code></td>
+      <td><code>--transformer-path</code></td>
+      <td><code>BBuf/flux1-dev-modelopt-nvfp4-sglang-transformer</code></td>
+      <td>mixed BF16+NVFP4 transformer override, correctness validation, 4x RTX 5090 benchmark, torch-profiler trace</td>
+      <td>use <code>build_modelopt_nvfp4_transformer.py</code>; validated builder keeps selected FLUX.1 modules in BF16 and sets <code>swap_weight_nibbles=false</code></td>
+    </tr>
+    <tr>
+      <td><code>NVFP4</code></td>
+      <td><code>black-forest-labs/FLUX.2-dev</code></td>
+      <td><code>--transformer-weights-path</code></td>
+      <td><code>black-forest-labs/FLUX.2-dev-NVFP4</code></td>
+      <td>packed-QKV load path</td>
+      <td>official raw export repo; validated packed export detection and runtime layout handling</td>
+    </tr>
+    <tr>
+      <td><code>NVFP4</code></td>
+      <td><code>Wan-AI/Wan2.2-T2V-A14B-Diffusers</code></td>
+      <td><code>--transformer-path</code></td>
+      <td><code>BBuf/wan22-t2v-a14b-modelopt-nvfp4-sglang-transformer</code></td>
+      <td>primary <code>transformer</code> quantized with ModelOpt NVFP4, <code>transformer_2</code> kept BF16</td>
+      <td>primary-transformer-only path; keep <code>transformer_2</code> on the base checkpoint, and current B200/Blackwell bring-up uses <code>SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=cudnn</code></td>
+    </tr>
+  </tbody>
+</table>
+
+These six checkpoints are also the intended case set for the B200 diffusion CI
+job (`multimodal-gen-test-1-b200`).
+
+## ModelOpt FP8
+
+### Usage Examples
+
+Converted ModelOpt FP8 checkpoints should be loaded as transformer component
+overrides. If the repo or local directory already contains `config.json`, use
+`--transformer-path`.
+
+```bash
+sglang generate \
+  --model-path black-forest-labs/FLUX.2-dev \
+  --transformer-path BBuf/flux2-dev-modelopt-fp8-sglang-transformer \
+  --prompt "A Logo With Bold Large Text: SGL Diffusion" \
+  --save-output
+```
+
+```bash
+sglang generate \
+  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --transformer-path BBuf/wan22-t2v-a14b-modelopt-fp8-sglang-transformer \
+  --prompt "a fox walking through neon rain" \
+  --save-output
+```
+
+### Notes
+
+- `--transformer-path` is the canonical flag for converted ModelOpt FP8
+  transformer component repos or directories that already carry `config.json`.
+- If the override repo or local directory contains its own `config.json`,
+  SGLang reads the quantization config from that override instead of relying on
+  the base model config.
+- `--transformer-weights-path` still works when you intentionally point at raw
+  weight files or a directory that should be metadata-probed as weights first.
+- `dit_layerwise_offload` is supported for ModelOpt FP8 checkpoints.
+- `dit_cpu_offload` still stays disabled for ModelOpt FP8 checkpoints.
+- The layerwise offload path now preserves the non-contiguous FP8 weight stride
+  expected by the runtime FP8 GEMM path.
+- On disk, the quantization config stays `quant_method=modelopt` with
+  `quant_algo=FP8`; the `modelopt-fp8` label in this document is a support
+  family name, not a serialized config key.
+- To build the converted checkpoint yourself from a ModelOpt diffusers export,
+  use `python -m sglang.multimodal_gen.tools.build_modelopt_fp8_transformer`.
+
+## ModelOpt NVFP4
+
+### Usage Examples
+
+For mixed ModelOpt NVFP4 transformer overrides that already contain
+`config.json`, keep the base model and quantized transformer separate and use
+`--transformer-path`:
+
+```bash
+sglang generate \
+  --model-path black-forest-labs/FLUX.1-dev \
+  --transformer-path BBuf/flux1-dev-modelopt-nvfp4-sglang-transformer \
+  --prompt "A Logo With Bold Large Text: SGL Diffusion" \
+  --save-output
+```
+
+For raw NVFP4 exports such as the official FLUX.2 release, use
+`--transformer-weights-path`:
+
+```bash
+sglang generate \
+  --model-path black-forest-labs/FLUX.2-dev \
+  --transformer-weights-path black-forest-labs/FLUX.2-dev-NVFP4 \
+  --prompt "A Logo With Bold Large Text: SGL Diffusion" \
+  --save-output
+```
+
+SGLang also supports passing the NVFP4 repo or local directory directly as
+`--model-path`:
+
+```bash
+sglang generate \
+  --model-path black-forest-labs/FLUX.2-dev-NVFP4 \
+  --prompt "A Logo With Bold Large Text: SGL Diffusion" \
+  --save-output
+```
+
+For a dual-transformer Wan2.2 export where only the primary `transformer`
+was quantized:
+
+```bash
+SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=cudnn \
+sglang generate \
+  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --transformer-path BBuf/wan22-t2v-a14b-modelopt-nvfp4-sglang-transformer \
+  --prompt "a fox walking through neon rain" \
+  --save-output
+```
+
+### Notes
+
+- Use `--transformer-path` for mixed ModelOpt NVFP4 transformer repos or local
+  directories that already include `config.json`.
+- Use `--transformer-weights-path` for raw NVFP4 exports, individual
+  safetensors files, or repo layouts that should be treated as weights first.
+- For dual-transformer pipelines such as `Wan2.2-T2V-A14B-Diffusers`, the
+  primary `--transformer-path` override targets only `transformer`. Use a
+  per-component override such as `--transformer-2-path` only when you
+  intentionally want a non-default `transformer_2`.
+- On Blackwell, the validated Wan2.2 ModelOpt NVFP4 path currently prefers
+  FlashInfer FP4 GEMM via
+  `SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=cudnn`.
+- This environment-variable override is a current workaround for NVFP4 cases
+  where the default sglang JIT/CUTLASS `sm100` path rejects a large-M shape at
+  `can_implement()`. The intended long-term fix is to add a validated CUTLASS
+  fallback for those shapes rather than rely on the override.
+- Direct `--model-path` loading is a compatibility path for FLUX.2 NVFP4-style
+  repos or local directories.
+- If `--transformer-weights-path` is provided explicitly, it takes precedence
+  over the compatibility `--model-path` flow.
+- For local directories, SGLang first looks for `*-mixed.safetensors`, then
+  falls back to loading from the directory.
+- To force the generic diffusion ModelOpt FP4 path onto a specific FlashInfer
+  backend, set `SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND`. Supported values
+  include `flashinfer_cudnn`, `flashinfer_cutlass`, and `flashinfer_trtllm`.
+- On disk, the quantization config stays `quant_method=modelopt` with
+  `quant_algo=NVFP4`; the `modelopt-nvfp4` label here is again a documentation
+  family name rather than a serialized config key.
+
+## Nunchaku (SVDQuant)
+
+### Install
+
+Install the runtime dependency first:
+
+```bash
+pip install nunchaku
+```
+
+For platform-specific installation methods and troubleshooting, see the
+[Nunchaku installation guide](https://nunchaku.tech/docs/nunchaku/installation/installation.html).
+
+### File Naming and Auto-Detection
+
+For Nunchaku checkpoints, `--model-path` should still point to the original
+base model, while `--transformer-weights-path` points to the quantized
+transformer weights.
+
+If the basename of `--transformer-weights-path` contains the pattern
+`svdq-(int4|fp4)_r{rank}`, SGLang will automatically:
+- enable SVDQuant
+- infer `--quantization-precision`
+- infer `--quantization-rank`
+
+Examples:
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>checkpoint name fragment</th>
+      <th>inferred precision</th>
+      <th>inferred rank</th>
+      <th>notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>svdq-int4_r32</code></td>
+      <td><code>int4</code></td>
+      <td><code>32</code></td>
+      <td>Standard INT4 checkpoint</td>
+    </tr>
+    <tr>
+      <td><code>svdq-int4_r128</code></td>
+      <td><code>int4</code></td>
+      <td><code>128</code></td>
+      <td>Higher-quality INT4 checkpoint</td>
+    </tr>
+    <tr>
+      <td><code>svdq-fp4_r32</code></td>
+      <td><code>nvfp4</code></td>
+      <td><code>32</code></td>
+      <td><code>fp4</code> in the filename maps to CLI value <code>nvfp4</code></td>
+    </tr>
+    <tr>
+      <td><code>svdq-fp4_r128</code></td>
+      <td><code>nvfp4</code></td>
+      <td><code>128</code></td>
+      <td>Higher-quality NVFP4 checkpoint</td>
+    </tr>
+  </tbody>
+</table>
+
+Common filenames:
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>filename</th>
+      <th>precision</th>
+      <th>rank</th>
+      <th>typical use</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>svdq-int4_r32-qwen-image.safetensors</code></td>
+      <td><code>int4</code></td>
+      <td><code>32</code></td>
+      <td>Balanced default</td>
+    </tr>
+    <tr>
+      <td><code>svdq-int4_r128-qwen-image.safetensors</code></td>
+      <td><code>int4</code></td>
+      <td><code>128</code></td>
+      <td>Quality-focused</td>
+    </tr>
+    <tr>
+      <td><code>svdq-fp4_r32-qwen-image.safetensors</code></td>
+      <td><code>nvfp4</code></td>
+      <td><code>32</code></td>
+      <td>RTX 50-series / NVFP4 path</td>
+    </tr>
+    <tr>
+      <td><code>svdq-fp4_r128-qwen-image.safetensors</code></td>
+      <td><code>nvfp4</code></td>
+      <td><code>128</code></td>
+      <td>Quality-focused NVFP4</td>
+    </tr>
+    <tr>
+      <td><code>svdq-int4_r32-qwen-image-lightningv1.0-4steps.safetensors</code></td>
+      <td><code>int4</code></td>
+      <td><code>32</code></td>
+      <td>Lightning 4-step</td>
+    </tr>
+    <tr>
+      <td><code>svdq-int4_r128-qwen-image-lightningv1.1-8steps.safetensors</code></td>
+      <td><code>int4</code></td>
+      <td><code>128</code></td>
+      <td>Lightning 8-step</td>
+    </tr>
+  </tbody>
+</table>
+
+If your checkpoint name does not follow this convention, pass
+`--enable-svdquant`, `--quantization-precision`, and `--quantization-rank`
+explicitly.
+
+### Usage Examples
+
+Recommended auto-detected flow:
+
+```bash
+sglang generate \
+  --model-path Qwen/Qwen-Image \
+  --transformer-weights-path /path/to/svdq-int4_r32-qwen-image.safetensors \
+  --prompt "a beautiful sunset" \
+  --save-output
+```
+
+Manual override when the filename does not encode the quant settings:
+
+```bash
+sglang generate \
+  --model-path Qwen/Qwen-Image \
+  --transformer-weights-path /path/to/custom_nunchaku_checkpoint.safetensors \
+  --enable-svdquant \
+  --quantization-precision int4 \
+  --quantization-rank 128 \
+  --prompt "a beautiful sunset" \
+  --save-output
+```
+
+### Notes
+
+- `--transformer-weights-path` is the canonical flag for Nunchaku checkpoints.
+  Older config names such as `quantized_model_path` are treated as
+  compatibility aliases.
+- Auto-detection only happens when the checkpoint basename matches
+  `svdq-(int4|fp4)_r{rank}`.
+- The CLI values are `int4` and `nvfp4`. In filenames, the NVFP4 variant is
+  written as `fp4`.
+- Lightning checkpoints usually expect matching `--num-inference-steps`, such
+  as `4` or `8`.
+- Current runtime validation only allows Nunchaku on NVIDIA CUDA Ampere (SM8x)
+  or SM12x GPUs. Hopper (SM90) is currently rejected.
+
+## [ModelSlim](https://gitcode.com/Ascend/msmodelslim)
+MindStudio-ModelSlim (msModelSlim) is a model offline quantization compression tool launched by MindStudio and optimized for Ascend hardware.
+
+- **Installation**
+
+    ```bash
+    # Clone repo and install msmodelslim:
+    git clone https://gitcode.com/Ascend/msmodelslim.git
+    cd msmodelslim
+    bash install.sh
+    ```
+
+- **Multimodal_sd quantization**
+
+    Download the original floating-point weights of the large model. Taking Wan2.2-T2V-A14B as an example, you can go to [Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) to obtain the original model weights. Then install other dependencies (related to the model, refer to the modelscope model card).
+    > Note: You can find pre-quantized validated models on [modelscope/Eco-Tech](https://modelscope.cn/models/Eco-Tech).
+
+  Run quantization using one-click quantization (recommended):
+
+  ```bash
+  msmodelslim quant \
+    --model_path /path/to/wan2_2_float_weights \
+    --save_path /path/to/wan2_2_quantized_weights \
+    --device npu \
+    --model_type Wan2_2 \
+    --quant_type w8a8 \
+    --trust_remote_code True
+  ```
+
+  For more detailed examples of quantization of models, as well as information about their support, see the [examples](https://gitcode.com/Ascend/msmodelslim/blob/master/example/multimodal_sd/README.md) section in ModelSLim repo.
+
+  > Note: SGLang does not support quantized embeddings, please disable this option when quantizing using msmodelslim.
+
+- **Auto-Detection and different formats**
+
+    For msmodelslim checkpoints, it's enough to specify only ```--model-path```, the detection of quantization occurs automatically for each layer using parsing of      `quant_model_description.json` config.
+
+    In the case of `Wan2.2` only `Diffusers` weights storage format are supported, whereas modelslim saves the quantized model in the original `Wan2.2` format,
+    for conversion in use `python/sglang/multimodal_gen/tools/wan_repack.py` script:
+
+    ```bash
+    python wan_repack.py \
+      --input-path {path_to_quantized_model} \
+      --output-path {path_to_converted_model}
+    ```
+
+    After that, please copy all files from original `Diffusers` checkpoint (instead of `transformer`/`tranfsormer_2` folders)
+
+- **Usage Example**
+
+    With auto-detected flow:
+
+    ```bash
+    sglang generate \
+      --model-path Eco-Tech/Wan2.2-T2V-A14B-Diffusers-w8a8 \
+      --prompt "a beautiful sunset" \
+      --save-output
+    ```
+
+- **Available Quantization Methods**:
+    - [x]  ```W4A4_DYNAMIC``` linear with online quantization of activations
+    - [x]  ```W8A8``` linear with offline quantization of activations
+    - [x]  ```W8A8_DYNAMIC``` linear with online quantization of activations
+    - [ ]  ```mxfp8``` linear in progress
@@ -0,0 +1,158 @@
+---
+title: "Ring SP Benchmark: Wan2.2-TI2V-5B (u1r2 vs Baseline)"
+metatags:
+    description: "Review Ring-SP benchmark results for Wan2.2-TI2V-5B-Diffusers in SGLang Diffusion."
+---
+
+This page reports Ring-SP performance for `Wan2.2-TI2V-5B-Diffusers` using:
+
+- Parallel config: `sp=2, ulysses=1, ring=2` (short: `u1r2`)
+- Baseline config: `sp=1, ulysses=1, ring=1` (short: `u1r1`)
+
+## Benchmark Setup
+
+- Model: `Wan2.2-TI2V-5B-Diffusers`
+- GPU: `48G RTX40 series * 2`
+
+## Online Serving
+
+### Ring SP (`u1r2`)
+
+```bash
+sglang serve \
+  --model-type diffusion \
+  --model-path /model/HuggingFace/Wan-AI/Wan2.2-TI2V-5B-Diffusers \
+  --num-gpus 2 --sp-degree 2 --ulysses-degree 1 --ring-degree 2 \
+  --port 8898
+```
+
+### Baseline (`u1r1`)
+
+```bash
+sglang serve \
+  --model-type diffusion \
+  --model-path /model/HuggingFace/Wan-AI/Wan2.2-TI2V-5B-Diffusers \
+  --num-gpus 1 --sp-degree 1 --ulysses-degree 1 --ring-degree 1 \
+  --port 8898
+```
+
+## Benchmarks
+
+### Benchmark Disclaimer
+
+These benchmarks are provided for reference under one specific setup and command configuration. Actual performance may vary with model settings, runtime environment, and request patterns.
+
+### Stage Time Breakdown
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Stage / Metric</th>
+      <th><code>u1r2</code> (s)</th>
+      <th><code>u1r1</code> baseline (s)</th>
+      <th>Speedup</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>InputValidation</td>
+      <td>0.1060</td>
+      <td>0.1029</td>
+      <td>0.97x</td>
+    </tr>
+    <tr>
+      <td>TextEncoding</td>
+      <td>1.3965</td>
+      <td>2.2261</td>
+      <td>1.59x</td>
+    </tr>
+    <tr>
+      <td>LatentPreparation</td>
+      <td>0.0002</td>
+      <td>0.0002</td>
+      <td>1.00x</td>
+    </tr>
+    <tr>
+      <td>TimestepPreparation</td>
+      <td>0.0003</td>
+      <td>0.0004</td>
+      <td>1.33x</td>
+    </tr>
+    <tr>
+      <td>Denoising</td>
+      <td>52.6358</td>
+      <td>71.6785</td>
+      <td>1.36x</td>
+    </tr>
+    <tr>
+      <td>Decoding</td>
+      <td>7.6708</td>
+      <td>13.4314</td>
+      <td>1.75x</td>
+    </tr>
+    <tr>
+      <td><strong>Total</strong></td>
+      <td><strong>63.74</strong></td>
+      <td><strong>90.63</strong></td>
+      <td><strong>1.42x</strong></td>
+    </tr>
+  </tbody>
+</table>
+
+### Memory Usage
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Memory Metric</th>
+      <th><code>u1r2</code> (GB)</th>
+      <th><code>u1r1</code> baseline (GB)</th>
+      <th>Delta</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Peak GPU Memory</td>
+      <td>20.07</td>
+      <td>27.40</td>
+      <td>-7.33</td>
+    </tr>
+    <tr>
+      <td>Peak Allocated</td>
+      <td>13.35</td>
+      <td>20.40</td>
+      <td>-7.05</td>
+    </tr>
+    <tr>
+      <td>Memory Overhead</td>
+      <td>6.72</td>
+      <td>7.00</td>
+      <td>-0.28</td>
+    </tr>
+    <tr>
+      <td>Overhead Ratio</td>
+      <td>33.5%</td>
+      <td>25.6%</td>
+      <td>+7.9pp</td>
+    </tr>
+  </tbody>
+</table>
+
+## Summary
+
+- End-to-end latency improves from `90.63s` to `63.74s` (`1.42x`).
+- Main gains come from `Denoising` (`1.36x`) and `Decoding` (`1.75x`).
+- Absolute memory usage drops noticeably on Ring-SP (`Peak GPU Memory -7.33GB`, `Peak Allocated -7.05GB`).
+- Overhead ratio rises (`+7.9pp`), so future tuning can focus on reducing communication/runtime overhead while preserving the latency gain.
@@ -0,0 +1,601 @@
+---
+title: "How to Support New Diffusion Models"
+metatags:
+    description: "This document explains how to add support for new diffusion models in SGLang Diffusion."
+---
+
+This document explains how to add support for new diffusion models in SGLang Diffusion.
+
+## Architecture Overview
+
+SGLang Diffusion is engineered for both performance and flexibility, built upon a pipeline architecture. This
+design allows developers to construct pipelines for various diffusion models while keeping the core generation
+loop standardized for optimization.
+
+At its core, the architecture revolves around two key concepts, as highlighted in our [blog post](https://lmsys.org/blog/2025-11-07-sglang-diffusion/#architecture):
+
+-   **`ComposedPipeline`**: This class orchestrates a series of `PipelineStage`s to define the complete generation process for a specific model. It acts as the main entry point for a model and manages the data flow between the different stages of the diffusion process.
+-   **`PipelineStage`**: Each stage is a modular component that encapsulates a function within the diffusion process. Examples include prompt encoding, the denoising loop, or VAE decoding.
+
+### Two Pipeline Styles
+
+SGLang Diffusion supports two pipeline composition styles. Both are valid; choose the one that best fits your model.
+
+#### Style A: Hybrid Monolithic Pipeline (Recommended Default)
+
+The recommended default for most new models. Uses a three-stage structure:
+
+```
+BeforeDenoisingStage (model-specific)  →  DenoisingStage (standard)  →  DecodingStage (standard)
+```
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Stage</th>
+      <th>Ownership</th>
+      <th>Responsibility</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>&#123;Model&#125;BeforeDenoisingStage</code></td>
+      <td>Model-specific</td>
+      <td>All pre-processing: input validation, text/image encoding, latent preparation, timestep computation</td>
+    </tr>
+    <tr>
+      <td><code>DenoisingStage</code></td>
+      <td>Framework-standard</td>
+      <td>The denoising loop (DiT/UNet forward passes), shared across all models</td>
+    </tr>
+    <tr>
+      <td><code>DecodingStage</code></td>
+      <td>Framework-standard</td>
+      <td>VAE decoding from latent space to pixel space, shared across all models</td>
+    </tr>
+  </tbody>
+</table>
+
+**Why recommended?** Modern diffusion models often have highly heterogeneous pre-processing requirements — different text encoders, different latent formats, different conditioning mechanisms. The Hybrid approach keeps pre-processing isolated per model, avoids fragile shared stages with excessive conditional logic, and lets developers port Diffusers reference code quickly.
+
+#### Style B: Modular Composition Style
+
+Uses the framework's fine-grained standard stages (`TextEncodingStage`, `LatentPreparationStage`, `TimestepPreparationStage`, etc.) to build the pipeline by composition. Convenience methods like `add_standard_t2i_stages()` and `add_standard_ti2i_stages()` make this very concise.
+
+This style is appropriate when:
+- **The new model's pre-processing can largely reuse existing stages** — e.g., a model that uses standard CLIP/T5 text encoding + standard latent preparation with minimal customization.
+- **A model-specific optimization needs to be extracted as a standalone stage** — e.g., a specialized encoding or conditioning step that benefits from being a separate stage for profiling, parallelism control, or reuse across multiple pipeline variants.
+
+#### How to Choose
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "50%"}} />
+    <col style={{width: "50%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Situation</th>
+      <th>Recommended Style</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Model has unique/complex pre-processing (VLM captioning, AR token generation, custom latent packing, etc.)</td>
+      <td><strong>Hybrid</strong> — consolidate into a BeforeDenoisingStage</td>
+    </tr>
+    <tr>
+      <td>Model fits neatly into standard text-to-image or text+image-to-image pattern</td>
+      <td><strong>Modular</strong> — use <code>add_standard_t2i_stages()</code> / <code>add_standard_ti2i_stages()</code></td>
+    </tr>
+    <tr>
+      <td>Porting a Diffusers pipeline with many custom steps</td>
+      <td><strong>Hybrid</strong> — copy the <code>__call__</code> logic into a single stage</td>
+    </tr>
+    <tr>
+      <td>Adding a variant of an existing model that shares most logic</td>
+      <td><strong>Modular</strong> — reuse existing stages, customize via PipelineConfig callbacks</td>
+    </tr>
+    <tr>
+      <td>A specific pre-processing step needs special parallelism or profiling isolation</td>
+      <td><strong>Modular</strong> — extract that step as a dedicated stage</td>
+    </tr>
+  </tbody>
+</table>
+
+## Key Components for Implementation
+
+To add support for a new diffusion model, you will need to define or configure the following components:
+
+1.  **`PipelineConfig`**: A dataclass holding static configurations for your model pipeline — precision settings, model architecture parameters, and callback methods used by the standard `DenoisingStage` and `DecodingStage`. Each model has its own subclass.
+
+2.  **`SamplingParams`**: A dataclass defining runtime generation parameters — `prompt`, `negative_prompt`, `guidance_scale`, `num_inference_steps`, `seed`, `height`, `width`, etc.
+
+3.  **Pre-processing stage(s)**: Either a single model-specific `{Model}BeforeDenoisingStage` (Hybrid style) or a combination of standard stages (Modular style). See [Two Pipeline Styles](#two-pipeline-styles) above.
+
+4.  **`ComposedPipeline`**: A class that wires together your pre-processing stage(s) with the standard `DenoisingStage` and `DecodingStage`. See base definitions:
+    - [`ComposedPipelineBase`](https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/runtime/pipelines_core/composed_pipeline_base.py)
+    - [`PipelineStage`](https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/runtime/pipelines_core/stages/base.py)
+    - [Central registry](https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/registry.py)
+
+5.  **Modules (model components)**: Each pipeline references modules loaded from the model repository (e.g., Diffusers `model_index.json`):
+    - `text_encoder`: Encodes text prompts into embeddings.
+    - `tokenizer`: Tokenizes raw text input for the text encoder(s).
+    - `processor`: Preprocesses images and extracts features; often used in image-to-image tasks.
+    - `image_encoder`: Specialized image feature extractor.
+    - `dit/transformer`: The core denoising network (DiT/UNet architecture) operating in latent space.
+    - `scheduler`: Controls the timestep schedule and denoising dynamics.
+    - `vae`: Variational Autoencoder for encoding/decoding between pixel space and latent space.
+
+## Pipeline Stages Reference
+
+### Core Stages (used by all pipelines)
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "50%"}} />
+    <col style={{width: "50%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Stage Class</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>DenoisingStage</code></td>
+      <td>Executes the main denoising loop, iteratively applying the model (DiT/UNet) to refine the latents.</td>
+    </tr>
+    <tr>
+      <td><code>DecodingStage</code></td>
+      <td>Decodes the final latent tensor back into pixel space using the VAE.</td>
+    </tr>
+    <tr>
+      <td><code>DmdDenoisingStage</code></td>
+      <td>A specialized denoising stage for DMD model architectures.</td>
+    </tr>
+    <tr>
+      <td><code>CausalDMDDenoisingStage</code></td>
+      <td>A specialized causal denoising stage for specific video models.</td>
+    </tr>
+  </tbody>
+</table>
+
+### Pre-processing Stages (for Modular Composition Style)
+
+The following fine-grained stages can be composed to build the pre-processing portion of a pipeline. They are best suited for models whose pre-processing largely fits the standard patterns. If your model requires significant customization, consider the Hybrid style with a single `BeforeDenoisingStage` instead.
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "50%"}} />
+    <col style={{width: "50%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Stage Class</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>InputValidationStage</code></td>
+      <td>Validates user-provided <code>SamplingParams</code>.</td>
+    </tr>
+    <tr>
+      <td><code>TextEncodingStage</code></td>
+      <td>Encodes text prompts into embeddings using one or more text encoders.</td>
+    </tr>
+    <tr>
+      <td><code>ImageEncodingStage</code></td>
+      <td>Encodes input images into embeddings, often used in image-to-image tasks.</td>
+    </tr>
+    <tr>
+      <td><code>ImageVAEEncodingStage</code></td>
+      <td>Encodes an input image into latent space using the VAE.</td>
+    </tr>
+    <tr>
+      <td><code>TimestepPreparationStage</code></td>
+      <td>Prepares the scheduler's timesteps for the diffusion process.</td>
+    </tr>
+    <tr>
+      <td><code>LatentPreparationStage</code></td>
+      <td>Creates the initial noisy latent tensor that will be denoised.</td>
+    </tr>
+  </tbody>
+</table>
+
+## Implementation Guide
+
+### Step 1: Obtain and Study the Reference Implementation
+
+Before writing any code, obtain the model's original implementation or Diffusers pipeline code:
+- The model's Diffusers pipeline source (e.g., the `pipeline_*.py` file from the `diffusers` library or HuggingFace repo)
+- Or the model's official reference implementation (e.g., from the model author's GitHub repo)
+- Or the HuggingFace model ID to look up `model_index.json` and the associated pipeline class
+
+Once you have the reference code, study it thoroughly:
+
+1. Find the model's `model_index.json` to identify required modules.
+2. Read the Diffusers pipeline's `__call__` method to understand:
+   - How text prompts are encoded
+   - How latents are prepared (shape, dtype, scaling)
+   - How timesteps/sigmas are computed
+   - What conditioning kwargs the DiT expects
+   - How the denoising loop works
+   - How VAE decoding is done
+
+### Step 2: Evaluate Reuse of Existing Pipelines and Stages
+
+Before creating any new files, check whether an existing pipeline or stage can be reused or extended. Only create new pipelines/stages when the existing ones would need substantial structural changes or when no architecturally similar implementation exists.
+
+- **Compare against existing pipelines** (Flux, Wan, Qwen-Image, GLM-Image, HunyuanVideo, LTX, etc.). If the new model shares most of its structure with an existing one, prefer adding a new config variant or reusing existing stages.
+- **Check existing stages** in `runtime/pipelines_core/stages/` and `stages/model_specific_stages/`.
+- **Check existing model components** — many models share VAEs (e.g., `AutoencoderKL`), text encoders (CLIP, T5), and schedulers. Reuse these directly.
+
+### Step 3: Implement Model Components
+
+Adapt the model's core components:
+
+- **DiT/Transformer**: Implement in [`runtime/models/dits/`](https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/runtime/models/dits/)
+- **Encoders**: Implement in [`runtime/models/encoders/`](https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/runtime/models/encoders/)
+- **VAEs**: Implement in [`runtime/models/vaes/`](https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/runtime/models/vaes/)
+- **Schedulers**: Implement in [`runtime/models/schedulers/`](https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/runtime/models/schedulers/) if needed
+
+Use SGLang's fused kernels where possible (see `LayerNormScaleShift`, `RMSNormScaleShift`, `apply_qk_norm`, etc.).
+
+**Tensor Parallel (TP) and Sequence Parallel (SP)**: For multi-GPU deployment, it is recommended to add TP/SP support to the DiT model. This can be done incrementally after the single-GPU implementation is verified. Reference implementations:
+- **Wan model** (`runtime/models/dits/wanvideo.py`) — Full TP + SP: `ColumnParallelLinear`/`RowParallelLinear` for attention, sequence dimension sharding via `get_sp_world_size()`
+- **Qwen-Image model** (`runtime/models/dits/qwen_image.py`) — SP via `USPAttention` (Ulysses + Ring Attention)
+
+### Step 4: Create Configs
+
+- **DiT Config**: `configs/models/dits/{model_name}.py`
+- **VAE Config**: `configs/models/vaes/{model_name}.py`
+- **SamplingParams**: `configs/sample/{model_name}.py`
+
+### Step 5: Create PipelineConfig
+
+The `PipelineConfig` provides callbacks that the standard `DenoisingStage` and `DecodingStage` use:
+
+```python
+# python/sglang/multimodal_gen/configs/pipeline_configs/my_model.py
+
+@dataclass
+class MyModelPipelineConfig(ImagePipelineConfig):
+    task_type: ModelTaskType = ModelTaskType.T2I
+    vae_precision: str = "bf16"
+    should_use_guidance: bool = True
+    dit_config: DiTConfig = field(default_factory=MyModelDitConfig)
+    vae_config: VAEConfig = field(default_factory=MyModelVAEConfig)
+
+    def get_freqs_cis(self, batch, device, rotary_emb, dtype):
+        """Prepare rotary position embeddings for the DiT."""
+        ...
+
+    def prepare_pos_cond_kwargs(self, batch, latent_model_input, t, **kwargs):
+        """Build positive conditioning kwargs for each denoising step."""
+        return {
+            "hidden_states": latent_model_input,
+            "encoder_hidden_states": batch.prompt_embeds[0],
+            "timestep": t,
+        }
+
+    def prepare_neg_cond_kwargs(self, batch, latent_model_input, t, **kwargs):
+        """Build negative conditioning kwargs for CFG."""
+        return {
+            "hidden_states": latent_model_input,
+            "encoder_hidden_states": batch.negative_prompt_embeds[0],
+            "timestep": t,
+        }
+
+    def get_decode_scale_and_shift(self):
+        """Return (scale, shift) for latent denormalization before VAE decode."""
+        ...
+```
+
+### Step 6: Implement Pre-processing
+
+Choose based on your model's needs (see [How to Choose](#how-to-choose)):
+
+#### Option A: BeforeDenoisingStage (Hybrid Style)
+
+Create a single stage that handles all pre-processing. Best when the model has custom/complex pre-processing logic.
+
+```python
+# python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/my_model.py
+
+class MyModelBeforeDenoisingStage(PipelineStage):
+    """Monolithic pre-processing stage for MyModel.
+
+    Consolidates: input validation, text/image encoding, latent
+    preparation, and timestep computation.
+    """
+
+    def __init__(self, vae, text_encoder, tokenizer, transformer, scheduler):
+        super().__init__()
+        self.vae = vae
+        self.text_encoder = text_encoder
+        self.tokenizer = tokenizer
+        self.transformer = transformer
+        self.scheduler = scheduler
+
+    @torch.no_grad()
+    def forward(self, batch: Req, server_args: ServerArgs) -> Req:
+        device = get_local_torch_device()
+
+        # 1. Encode prompt (model-specific logic)
+        prompt_embeds, negative_prompt_embeds = self._encode_prompt(...)
+
+        # 2. Prepare latents
+        latents = self._prepare_latents(...)
+
+        # 3. Prepare timesteps
+        timesteps, sigmas = self._prepare_timesteps(...)
+
+        # 4. Populate batch for DenoisingStage
+        batch.prompt_embeds = [prompt_embeds]
+        batch.negative_prompt_embeds = [negative_prompt_embeds]
+        batch.latents = latents
+        batch.timesteps = timesteps
+        batch.num_inference_steps = len(timesteps)
+        batch.sigmas = sigmas.tolist()
+        batch.generator = generator
+        batch.raw_latent_shape = latents.shape
+        return batch
+```
+
+#### Option B: Standard Stages (Modular Style)
+
+Skip creating a custom stage entirely — configure via `PipelineConfig` callbacks and use framework helpers. Best when the model fits standard patterns.
+
+(This option has no separate stage file; the pipeline class in Step 7 calls `add_standard_t2i_stages()` directly.)
+
+**Key batch fields that `DenoisingStage` expects** (regardless of which option you choose):
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Field</th>
+      <th>Type</th>
+      <th>Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><code>batch.latents</code></td>
+      <td><code>torch.Tensor</code></td>
+      <td>Initial noisy latent tensor</td>
+    </tr>
+    <tr>
+      <td><code>batch.timesteps</code></td>
+      <td><code>torch.Tensor</code></td>
+      <td>Timestep schedule</td>
+    </tr>
+    <tr>
+      <td><code>batch.num_inference_steps</code></td>
+      <td><code>int</code></td>
+      <td>Number of denoising steps</td>
+    </tr>
+    <tr>
+      <td><code>batch.sigmas</code></td>
+      <td><code>list[float]</code></td>
+      <td>Sigma schedule (must be a Python list, not numpy)</td>
+    </tr>
+    <tr>
+      <td><code>batch.prompt_embeds</code></td>
+      <td><code>list[torch.Tensor]</code></td>
+      <td>Positive prompt embeddings (wrapped in a list)</td>
+    </tr>
+    <tr>
+      <td><code>batch.negative_prompt_embeds</code></td>
+      <td><code>list[torch.Tensor]</code></td>
+      <td>Negative prompt embeddings (wrapped in a list)</td>
+    </tr>
+    <tr>
+      <td><code>batch.generator</code></td>
+      <td><code>torch.Generator</code></td>
+      <td>RNG generator for reproducibility</td>
+    </tr>
+    <tr>
+      <td><code>batch.raw_latent_shape</code></td>
+      <td><code>tuple</code></td>
+      <td>Original latent shape before any packing</td>
+    </tr>
+  </tbody>
+</table>
+
+### Step 7: Define the Pipeline Class
+
+#### Hybrid Style
+
+```python
+# python/sglang/multimodal_gen/runtime/pipelines/my_model.py
+
+class MyModelPipeline(LoRAPipeline, ComposedPipelineBase):
+    pipeline_name = "MyModelPipeline"  # Must match model_index.json _class_name
+
+    _required_config_modules = [
+        "text_encoder", "tokenizer", "vae", "transformer", "scheduler",
+    ]
+
+    def create_pipeline_stages(self, server_args: ServerArgs):
+        # 1. Monolithic pre-processing (model-specific)
+        self.add_stage(
+            MyModelBeforeDenoisingStage(
+                vae=self.get_module("vae"),
+                text_encoder=self.get_module("text_encoder"),
+                tokenizer=self.get_module("tokenizer"),
+                transformer=self.get_module("transformer"),
+                scheduler=self.get_module("scheduler"),
+            ),
+        )
+
+        # 2. Standard denoising loop (framework-provided)
+        self.add_stage(
+            DenoisingStage(
+                transformer=self.get_module("transformer"),
+                scheduler=self.get_module("scheduler"),
+            ),
+        )
+
+        # 3. Standard VAE decoding (framework-provided)
+        self.add_standard_decoding_stage()
+
+
+EntryClass = [MyModelPipeline]
+```
+
+#### Modular Style
+
+```python
+# python/sglang/multimodal_gen/runtime/pipelines/my_model.py
+
+class MyModelPipeline(LoRAPipeline, ComposedPipelineBase):
+    pipeline_name = "MyModelPipeline"
+
+    _required_config_modules = [
+        "text_encoder", "tokenizer", "vae", "transformer", "scheduler",
+    ]
+
+    def create_pipeline_stages(self, server_args: ServerArgs):
+        # All pre-processing + denoising + decoding in one call
+        self.add_standard_t2i_stages(
+            prepare_extra_timestep_kwargs=[prepare_mu],  # model-specific hooks
+        )
+
+
+EntryClass = [MyModelPipeline]
+```
+
+### Step 8: Register the Model
+
+Register your configs in [`registry.py`](https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/registry.py):
+
+```python
+register_configs(
+    model_family="my_model",
+    sampling_param_cls=MyModelSamplingParams,
+    pipeline_config_cls=MyModelPipelineConfig,
+    hf_model_paths=["org/my-model-name"],
+)
+```
+
+The `EntryClass` in your pipeline file is automatically discovered by the registry — no additional registration needed for the pipeline class itself.
+
+### Step 9: Verify Output Quality
+
+After implementation, verify that the generated output is not noise. A noisy or garbled output is the most common sign of an incorrect implementation. Common causes include:
+
+- Incorrect latent scale/shift factors
+- Wrong timestep/sigma schedule (order, dtype, or value range)
+- Mismatched conditioning kwargs
+- Rotary embedding style mismatch (`is_neox_style`)
+
+Debug by comparing intermediate tensor values against the Diffusers reference pipeline with the same seed.
+
+## Reference Implementations
+
+### Hybrid Style
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+    <col style={{width: "25%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Model</th>
+      <th>Pipeline</th>
+      <th>BeforeDenoisingStage</th>
+      <th>PipelineConfig</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>GLM-Image</td>
+      <td><code>runtime/pipelines/glm_image.py</code></td>
+      <td><code>stages/model_specific_stages/glm_image.py</code></td>
+      <td><code>configs/pipeline_configs/glm_image.py</code></td>
+    </tr>
+    <tr>
+      <td>Qwen-Image-Layered</td>
+      <td><code>runtime/pipelines/qwen_image.py</code></td>
+      <td><code>stages/model_specific_stages/qwen_image_layered.py</code></td>
+      <td><code>configs/pipeline_configs/qwen_image.py</code></td>
+    </tr>
+  </tbody>
+</table>
+
+### Modular Style
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+    <col style={{width: "33.33%"}} />
+  </colgroup>
+  <thead>
+    <tr>
+      <th>Model</th>
+      <th>Pipeline</th>
+      <th>Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Qwen-Image (T2I)</td>
+      <td><code>runtime/pipelines/qwen_image.py</code></td>
+      <td>Uses <code>add_standard_t2i_stages()</code></td>
+    </tr>
+    <tr>
+      <td>Qwen-Image-Edit</td>
+      <td><code>runtime/pipelines/qwen_image.py</code></td>
+      <td>Uses <code>add_standard_ti2i_stages()</code></td>
+    </tr>
+    <tr>
+      <td>Flux</td>
+      <td><code>runtime/pipelines/flux.py</code></td>
+      <td>Uses <code>add_standard_t2i_stages()</code> with custom <code>prepare_mu</code></td>
+    </tr>
+    <tr>
+      <td>Wan</td>
+      <td><code>runtime/pipelines/wan_pipeline.py</code></td>
+      <td>Uses <code>add_standard_ti2v_stages()</code></td>
+    </tr>
+  </tbody>
+</table>
+
+## Checklist
+
+Before submitting your implementation, verify:
+
+**Common (both styles):**
+- [ ] **Pipeline file** at `runtime/pipelines/{model_name}.py` with `EntryClass`
+- [ ] **PipelineConfig** at `configs/pipeline_configs/{model_name}.py`
+- [ ] **SamplingParams** at `configs/sample/{model_name}.py`
+- [ ] **DiT model** at `runtime/models/dits/{model_name}.py`
+- [ ] **Model configs** (DiT, VAE) at `configs/models/dits/` and `configs/models/vaes/`
+- [ ] **Registry entry** in `registry.py` via `register_configs()`
+- [ ] `pipeline_name` matches Diffusers `model_index.json` `_class_name`
+- [ ] `_required_config_modules` lists all modules from `model_index.json`
+- [ ] `PipelineConfig` callbacks (`prepare_pos_cond_kwargs`, etc.) match the DiT's `forward()` signature
+- [ ] Uses framework-standard `DenoisingStage` and `DecodingStage` (not custom denoising loops)
+- [ ] **TP/SP support** considered for DiT model (recommended; reference `wanvideo.py` for TP+SP, `qwen_image.py` for USPAttention)
+- [ ] **Output quality verified** — generated images/videos are not noise; compared against Diffusers reference output
+
+**Hybrid style only:**
+- [ ] **BeforeDenoisingStage** at `stages/model_specific_stages/{model_name}.py`
+- [ ] `BeforeDenoisingStage.forward()` populates all batch fields required by `DenoisingStage`
@@ -1,350 +0,0 @@
---
-title: "Supported Models"
-description: "Check model compatibility across diffusion optimizations and backends."
---
-
-The table below shows every supported model and the optimizations supported for them.
-
-Key:
-
- `Yes` = Compatible
- `No` = Incompatible
- `N/A` = Not applicable
-
-## Models x Optimization
-
-The `HuggingFace Model ID` can be passed directly to `from_pretrained()` methods, and sglang-diffusion will use the optimal default parameters when initializing and generating videos.
-
-### Video Generation Models
-
-<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
-  <colgroup>
-    <col style={{width: "17%"}} />
-    <col style={{width: "22%"}} />
-    <col style={{width: "8%"}} />
-    <col style={{width: "6%"}} />
-    <col style={{width: "8%"}} />
-    <col style={{width: "6%"}} />
-    <col style={{width: "8%"}} />
-    <col style={{width: "8%"}} />
-    <col style={{width: "9%"}} />
-    <col style={{width: "8%"}} />
-  </colgroup>
-  <thead>
-    <tr style={{borderBottom: "2px solid #d55816"}}>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model Name</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>HuggingFace Model ID</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Resolutions</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>TeaCache</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Sliding Tile Attn</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Sage Attn</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Video Sparse Attention (VSA)</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Sparse Linear Attention (SLA)</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Sage Sparse Linear Attention (SageSLA)</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Sparse Video Gen 2 (SVG2)</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FastWan2.1 T2V 1.3B</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`FastVideo/FastWan2.1-T2V-1.3B-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FastWan2.2 TI2V 5B Full Attn</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.2 TI2V 5B</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.2-TI2V-5B-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.2 T2V A14B</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.2-T2V-A14B-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p, 720p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.2 I2V A14B</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.2-I2V-A14B-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p, 720p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>HunyuanVideo</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`hunyuanvideo-community/HunyuanVideo`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720x1280, 544x960</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FastHunyuan</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`FastVideo/FastHunyuan-diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720x1280, 544x960</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1 T2V 1.3B</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.1-T2V-1.3B-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1 T2V 14B</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.1-T2V-14B-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p, 720p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1 I2V 480P</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.1-I2V-14B-480P-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1 I2V 720P</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Wan-AI/Wan2.1-I2V-14B-720P-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>N/A</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TurboWan2.1 T2V 1.3B</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`IPostYellow/TurboWan2.1-T2V-1.3B-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TurboWan2.1 T2V 14B</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`IPostYellow/TurboWan2.1-T2V-14B-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>480p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TurboWan2.1 T2V 14B 720P</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`IPostYellow/TurboWan2.1-T2V-14B-720P-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TurboWan2.2 I2V A14B</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`IPostYellow/TurboWan2.2-I2V-A14B-Diffusers`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>720p</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>No</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Yes</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>N/A</td>
-    </tr>
-  </tbody>
-</table>
-
-<Note>
-1. Wan2.2 TI2V 5B has known quality issues for some I2V workloads.
-2. SageSLA is based on SpargeAttn. Install SpargeAttn first with `pip install git+https://github.com/thu-ml/SpargeAttn.git --no-build-isolation`.
-</Note>
-
-### Image Generation Models
-
-<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
-  <colgroup>
-    <col style={{width: "22%"}} />
-    <col style={{width: "46%"}} />
-    <col style={{width: "32%"}} />
-  </colgroup>
-  <thead>
-    <tr style={{borderBottom: "2px solid #d55816"}}>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Model Name</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>HuggingFace Model ID</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Resolutions</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FLUX.1-dev</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`black-forest-labs/FLUX.1-dev`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Any resolution</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FLUX.2-dev</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`black-forest-labs/FLUX.2-dev`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Any resolution</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>FLUX.2-Klein</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`black-forest-labs/FLUX.2-klein-4B`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Any resolution</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Z-Image-Turbo</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Tongyi-MAI/Z-Image-Turbo`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Any resolution</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>GLM-Image</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`zai-org/GLM-Image`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Any resolution</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen Image</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Qwen/Qwen-Image`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Any resolution</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen Image 2512</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Qwen/Qwen-Image-2512`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Any resolution</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen Image Edit</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`Qwen/Qwen-Image-Edit`</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Any resolution</td>
-    </tr>
-  </tbody>
-</table>
-
-## Verified LoRA Examples
-
-This section lists example LoRAs that have been explicitly tested and verified with each base model in the **SGLang Diffusion** pipeline.
-
-<Info>
-LoRAs that are not listed here are not necessarily incompatible.
-In practice, most standard LoRAs are expected to work, especially those following common Diffusers or SD-style conventions.
-The entries below simply reflect configurations that have been manually validated by the SGLang team.
-</Info>
-
-### Verified LoRAs by Base Model
-
-<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
-  <colgroup>
-    <col style={{width: "20%"}} />
-    <col style={{width: "80%"}} />
-  </colgroup>
-  <thead>
-    <tr style={{borderBottom: "2px solid #d55816"}}>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Base Model</th>
-      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Supported LoRAs</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.2</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lightx2v/Wan2.2-Distill-Loras`<br />`Cseti/wan2.2-14B-Arcane_Jinx-lora-v1`</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan2.1</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lightx2v/Wan2.1-Distill-Loras`</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Z-Image-Turbo</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`tarn59/pixel_art_style_lora_z_image_turbo`<br />`wcde/Z-Image-Turbo-DeJPEG-Lora`</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen-Image</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lightx2v/Qwen-Image-Lightning`<br />`flymy-ai/qwen-image-realism-lora`<br />`prithivMLmods/Qwen-Image-HeadshotX`<br />`starsfriday/Qwen-Image-EVA-LoRA`</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen-Image-Edit</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`ostris/qwen_image_edit_inpainting`<br />`lightx2v/Qwen-Image-Edit-2511-Lightning`</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Flux</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`dvyio/flux-lora-simple-illustration`<br />`XLabs-AI/flux-furry-lora`<br />`XLabs-AI/flux-RealismLora`</td>
-    </tr>
-  </tbody>
-</table>
-
-## Special requirements
-
-### Sliding Tile Attention
-
- Currently, only Hopper GPUs (H100s) are supported.
@@ -4,7 +4,7 @@ description: "Configure TeaCache for temporal similarity-based diffusion acceler
 ---

 > **Note**: This is one of two caching strategies available in SGLang.
-> For an overview of all caching options, see [SGLang diffusion overview](../../sglang-diffusion/intro).
+> For an overview of all caching options, see [caching](./caching-acceleration).

 TeaCache (Temporal similarity-based caching) accelerates diffusion inference by detecting when consecutive denoising steps are similar enough to skip computation entirely.