[Doc] update offline inference doc and offline_inference examples (#274)

Signed-off-by: David Chen <530634352@qq.com>

docs/user_guide/examples/offline_inference/image_to_image.md

@@ -0,0 +1,30 @@
+# Qwen-Image-Edit Offline Inference
+
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_image>. Added in PR [#196](https://github.com/vllm-project/vllm-omni/pull/196).
+
+This example edits an input image with `Qwen/Qwen-Image-Edit` using the `image_edit.py` CLI.
+
+## Local CLI Usage
+
+```bash
+python image_edit.py \
+  --image qwen_bear.png \
+  --prompt "Let this mascot dance under the moon, surrounded by floating stars and poetic bubbles such as 'Be Kind'" \
+  --output output_image_edit.png \
+  --num_inference_steps 50 \
+  --cfg_scale 4.0
+```
+
+Key arguments:
+- `--image`: path to the source image (PNG/JPG, converted to RGB).
+- `--prompt` / `--negative_prompt`: text description (string).
+- `--cfg_scale`: true CFG scale for Qwen-Image-Edit (quality vs. fidelity).
+- `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
+- `--output`: path to save the generated PNG.
+
+## Example materials
+
+??? abstract "image_edit.py"
+    ``````py
+    --8<-- "examples/offline_inference/image_to_image/image_edit.py"
+    ``````

docs/user_guide/examples/offline_inference/text_to_image.md

@@ -1,9 +1,9 @@
 # Qwen-Image Offline Inference
 
-Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen_image>.
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_image>.
 
 
-This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Image` using vLLM-Omni:
+This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Image` `Tongyi-MAI/Z-Image-Turbo` using vLLM-Omni:
 
 - `text_to_image.py`: command-line script for single image generation.
 - `web_demo.py`: lightweight Gradio UI for interactive prompt/seed/CFG exploration.
@@ -13,6 +13,7 @@ This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Im
 
 ```bash
 python text_to_image.py \
+  --model Tongyi-MAI/Z-Image-Turbo \
   --prompt "a cup of coffee on the table" \
   --seed 42 \
   --cfg_scale 4.0 \
@@ -33,8 +34,6 @@ Key arguments:
 - `--height/--width`: output resolution (defaults 1024x1024).
 - `--output`: path to save the generated PNG.
 
-> ℹ️ Qwen-Image currently publishes best-effort presets at `1328x1328`, `1664x928`, `928x1664`, `1472x1140`, `1140x1472`, `1584x1056`, and `1056x1584`. Adjust `--height/--width` accordingly for the most reliable outcomes.
-
 ## Web UI Demo
 
 Launch the gradio demo:
@@ -49,9 +48,9 @@ Then open `http://localhost:7862/` on your local browser to interact with the we
 
 ??? abstract "gradio_demo.py"
     ``````py
-    --8<-- "examples/offline_inference/qwen_image/gradio_demo.py"
+    --8<-- "examples/offline_inference/text_to_image/gradio_demo.py"
     ``````
 ??? abstract "text_to_image.py"
     ``````py
-    --8<-- "examples/offline_inference/qwen_image/text_to_image.py"
+    --8<-- "examples/offline_inference/text_to_image/text_to_image.py"
     ``````

docs/user_guide/examples/offline_inference/text_to_video.md

@@ -0,0 +1,38 @@
+# Wan2.2 Text-to-Video Offline Inference
+
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_video>. Added in PR [#202](https://github.com/vllm-project/vllm-omni/pull/202).
+
+The `Wan-AI/Wan2.2-T2V-A14B-Diffusers` pipeline generates short videos from text prompts.
+
+## Local CLI Usage
+
+```bash
+python text_to_video.py \
+  --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
+  --negative_prompt "<optional quality filter>" \
+  --height 480 \
+  --width 640 \
+  --num_frames 32 \
+  --guidance_scale 4.0 \
+  --guidance_scale_high 3.0 \
+  --num_inference_steps 40 \
+  --fps 16 \
+  --output t2v_out.mp4
+```
+
+Key arguments:
+- `--prompt`: text description (string).
+- `--height/--width`: output resolution (defaults 720x1280). Dimensions should align with Wan VAE downsampling (multiples of 8).
+- `--num_frames`: Number of frames (Wan default is 81).
+- `--guidance_scale` and `--guidance_scale_high`: CFG scale (applied to low/high)..
+- `--negative_prompt`: optional list of artifacts to suppress (the PR demo used a long Chinese string).
+- `--boundary_ratio`: Boundary split ratio for low/high DiT.
+- `--fps`: frames per second for the saved MP4 (requires `diffusers` export_to_video).
+- `--output`: path to save the generated video.
+
+## Example materials
+
+??? abstract "text_to_video.py"
+    ``````py
+    --8<-- "examples/offline_inference/wan22/text_to_video.py"
+    ``````