[Doc] refactor diffusion doc (#753)

Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>

docs/user_guide/diffusion/teacache.md

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+# TeaCache Configuration Guide
+
+TeaCache speeds up diffusion model inference by caching transformer computations when consecutive timesteps are similar. This typically provides **1.5x-2.0x speedup** with minimal quality loss.
+
+## Quick Start
+
+Enable TeaCache by setting `cache_backend` to `"tea_cache"`:
+
+```python
+from vllm_omni import Omni
+
+# Simple configuration - model_type is automatically extracted from pipeline.__class__.__name__
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    cache_backend="tea_cache",
+    cache_config={
+        "rel_l1_thresh": 0.2  # Optional, defaults to 0.2
+    }
+)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)
+```
+
+### Using Environment Variable
+
+You can also enable TeaCache via environment variable:
+
+```bash
+export DIFFUSION_CACHE_BACKEND=tea_cache
+```
+
+Then initialize without explicitly setting `cache_backend`:
+
+```python
+from vllm_omni import Omni
+
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    cache_config={"rel_l1_thresh": 0.2}  # Optional
+)
+```
+
+## Online Serving (OpenAI-Compatible)
+
+Enable TeaCache for online serving by passing `--cache-backend tea_cache` when starting the server:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni --port 8091 \
+  --cache-backend tea_cache \
+  --cache-config '{"rel_l1_thresh": 0.2}'
+```
+
+## Configuration Parameters
+
+### `rel_l1_thresh` (float, default: `0.2`)
+
+Controls the balance between speed and quality. Lower values prioritize quality, higher values prioritize speed.
+
+**Recommended values:**
+
+- `0.2` - **~1.5x speedup** with minimal quality loss (recommended)
+- `0.4` - **~1.8x speedup** with slight quality loss
+- `0.6` - **~2.0x speedup** with noticeable quality loss
+- `0.8` - **~2.25x speedup** with significant quality loss
+
+## Examples
+
+### Python API
+
+```python
+from vllm_omni import Omni
+
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    cache_backend="tea_cache",
+    cache_config={"rel_l1_thresh": 0.2}
+)
+outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)
+```
+
+## Performance Tuning
+
+Start with the default `rel_l1_thresh=0.2` and adjust based on your needs:
+
+- **Maximum quality**: Use `0.1-0.2`
+- **Balanced**: Use `0.2-0.4` (recommended)
+- **Maximum speed**: Use `0.6-0.8` (may reduce quality)
+
+## Troubleshooting
+
+### Quality Degradation
+
+If you notice quality issues, lower the threshold:
+
+```python
+cache_config={"rel_l1_thresh": 0.1}  # More conservative caching
+```
+
+## Supported Models
+
+### ImageGen
+
+<style>
+th {
+  white-space: nowrap;
+  min-width: 0 !important;
+}
+</style>
+
+| Architecture | Models | Example HF Models |
+|--------------|--------|-------------------|
+| `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` |
+| `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` |
+| `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` |
+| `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` |
+
+### VideoGen
+
+No VideoGen models are supported by TeaCache yet.
+
+### Coming Soon
+
+<style>
+th {
+  white-space: nowrap;
+  min-width: 0 !important;
+}
+</style>
+
+| Architecture | Models | Example HF Models |
+|--------------|--------|-------------------|
+| `FluxPipeline` | Flux | - |
+| `CogVideoXPipeline` | CogVideoX | - |