[skip ci][Doc] Refine the Diffusion Features User Guide (#1928)

Signed-off-by: Didan Deng <33117903+wtomin@users.noreply.github.com>

docs/user_guide/diffusion/cache_acceleration/teacache.md

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+# TeaCache Guide
+
+
+## Table of Content
+
+- [Overview](#overview)
+- [Quick Start](#quick-start)
+- [Example Script](#example-script)
+- [Configuration Parameters](#configuration-parameters)
+- [Best Practices](#best-practices)
+- [Troubleshooting](#troubleshooting)
+- [Summary](#summary)
+
+---
+
+## Overview
+
+TeaCache accelerates diffusion model inference by caching transformer computations when consecutive timesteps are similar, providing **1.5x-2.0x speedup** with minimal quality loss. It dynamically decides whether to reuse cached outputs based on input similarity, making it ideal for production deployments where inference speed matters without sacrificing generation quality.
+
+See supported models list in [Supported Models](../../diffusion_features.md#supported-models).
+
+---
+
+## Quick Start
+
+
+
+### Basic Usage
+
+
+```python
+from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    cache_backend="tea_cache",
+)
+
+outputs = omni.generate(
+    "A cat sitting on a windowsill",
+    OmniDiffusionSamplingParams(num_inference_steps=50),
+)
+```
+
+### Custom Configuration
+
+```python
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    cache_backend="tea_cache",
+    cache_config={
+        "rel_l1_thresh": 0.2,  # Controls speed/quality tradeoff
+    },
+)
+```
+
+### 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}
+)
+```
+
+---
+
+## Example Script
+
+### Offline Inference
+
+Use python script under `examples/offline_inference/text_to_image/` or `examples/offline_inference/image_to_image/` with CLI:
+
+```bash
+# Text-to-image example
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model Qwen/Qwen-Image \
+  --cache-backend tea_cache
+
+# Image-to-image example
+python examples/offline_inference/image_to_image/image_edit.py \
+  --model Qwen/Qwen-Image-Edit \
+  --image input.png \
+  --prompt "Edit description" \
+  --cache-backend tea_cache \
+  --tea-cache-rel-l1-thresh 0.25
+```
+
+See the [text_to_image.py](https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/text_to_image/text_to_image.py) or [image_edit.py](https://github.com/vllm-project/vllm-omni/blob/main/examples/offline_inference/image_to_image/image_edit.py) for detailed configuration options.
+
+### Online Serving
+
+```bash
+# Default configuration
+vllm serve Qwen/Qwen-Image --omni --port 8091 --cache-backend tea_cache
+
+# Custom configuration
+vllm serve Qwen/Qwen-Image --omni --port 8091 \
+  --cache-backend tea_cache \
+  --cache-config '{"rel_l1_thresh": 0.2}'
+```
+
+---
+
+## Configuration Parameters
+
+In `OmniDiffusionConfig`
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `rel_l1_thresh` | float | `0.2` | Similarity threshold for cache reuse. Lower values prioritize quality (less caching), higher values prioritize speed (more caching). Suggested range: 0.1-0.8 |
+| `coefficients` | list[float] \| None | `None` | Polynomial coefficients for rescaling L1 distance. Must contain exactly 5 elements if provided. If `None`, uses model-specific defaults based on transformer type. |
+
+Users can find the default model coefficients in [`vllm_omni/diffusion/cache/teacache/config.py`](https://github.com/vllm-project/vllm-omni/blob/main/vllm_omni/diffusion/cache/teacache/config.py), for example:
+
+```python
+_MODEL_COEFFICIENTS = {
+    # Qwen-Image transformer coefficients from ComfyUI-TeaCache
+    # Tuned specifically for Qwen's dual-stream transformer architecture
+    # Used for all Qwen-Image Family pipelines, in general
+    "QwenImageTransformer2DModel": [
+        -4.50000000e02,
+        2.80000000e02,
+        -4.50000000e01,
+        3.20000000e00,
+        -2.00000000e-02,
+    ],
+    ...
+}
+```
+
+---
+
+## Best Practices
+
+### When to Use
+
+**Good for:**
+
+- Production deployments requiring faster inference, tolerant of minimal quality loss
+- Scenarios where 1.5-2x speedup is valuable
+- Useful for single-card acceleration
+
+**Not for:**
+
+- Maximum quality requirements where no degradation is acceptable
+- Very short inference runs (< 20 steps) where caching overhead may outweigh benefits
+
+
+---
+
+## Troubleshooting
+
+### Common Issue 1: Quality Degradation
+
+**Symptoms**: Generated images show artifacts, reduced detail, or inconsistent quality compared to non-cached results
+
+**Solution**:
+
+```python
+# Lower the threshold for more conservative caching
+cache_config={"rel_l1_thresh": 0.1}
+```
+
+### Common Issue 2: Limited Speedup
+
+**Symptoms**: Actual speedup is less than expected (< 1.3x)
+
+**Solutions**:
+1. Increase the threshold to enable more aggressive caching:
+   ```python
+   cache_config={"rel_l1_thresh": 0.8}
+   ```
+2. Ensure you're using sufficient inference steps (35+ recommended)
+3. Check that your model architecture is supported (see Supported Models section)
+
+---
+
+
+## Summary
+
+1. ✅ **Enable TeaCache** - Set `cache_backend="tea_cache"` to get 1.5x-2.0x speedup with optimized defaults
+2. ✅ **(Optional) Customize** - Adjust thresholds and polynomial coefficients for specific speed/quality trade-offs