Unifying CLI Argument Naming Style (#1309)

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

benchmarks/build_dataset/download_process_data_seedtts.md

@@ -56,7 +56,7 @@ python extract_tts_prompts.py -i meta.lst -o top100.txt -n 100
 **Options:**
 - `-i, --input`: Input metadata file (default: `meta.lst`)
 - `-o, --output`: Output prompts file (default: `prompts.txt`)
-- `-n, --num_lines`: Number of prompts to extract (required)
+- `-n, --num-lines`: Number of prompts to extract (required)
 
 ### 7. Clean Up (Optional)
 

benchmarks/build_dataset/extract_tts_prompts.py

@@ -56,7 +56,7 @@ def main():
         "-o", "--output", type=str, default="prompts.txt", help="Output txt file path (default: prompts.txt)"
     )
     parser.add_argument(
-        "-n", "--num_lines", type=int, required=True, help="Number of lines to extract from the beginning"
+        "-n", "--num-lines", type=int, required=True, help="Number of lines to extract from the beginning"
     )
 
     args = parser.parse_args()

benchmarks/qwen3-omni/README.md

@@ -36,7 +36,7 @@ bash benchmarks/qwen3-omni/transformers/eval_qwen3_moe_omni_transformers.sh
 ```
 
 What it does:
-- Runs `qwen3_omni_moe_transformers.py` over `top100.txt` with `--num_prompts 100`.
+- Runs `qwen3_omni_moe_transformers.py` over `top100.txt` with `--num-prompts 100`.
 - Outputs to `benchmarks/qwen3-omni/transformers/benchmark_results/`:
   - `perf_stats.json` — aggregated & per-prompt TPS/latency (thinker/talker/code2wav/overall).
   - `results.json` — per-prompt outputs and audio paths.

benchmarks/qwen3-omni/transformers/eval_qwen3_moe_omni_transformers.sh

@@ -16,7 +16,7 @@ if [[ ! -f "benchmarks/qwen3-omni/transformers/qwen3_omni_moe_transformers.py" ]
 else
     cd benchmarks/qwen3-omni/transformers
 
-    python qwen3_omni_moe_transformers.py --prompts_file ../../build_dataset/top100.txt --num_prompts 100
+    python qwen3_omni_moe_transformers.py --prompts-file ../../build_dataset/top100.txt --num-prompts 100
 
     echo "Logs and outputs are saved to $(pwd)/benchmark_results:"
     echo "  - perf_stats.json    Aggregated/per-prompt TPS and latency (thinker/talker/code2wav/overall)"

benchmarks/qwen3-omni/transformers/qwen3_omni_moe_transformers.py

@@ -227,17 +227,17 @@ def print_stats(stats: dict):
 def main():
     parser = argparse.ArgumentParser(description="Qwen3-Omni Benchmark Script")
     parser.add_argument(
-        "--prompts_file",
+        "--prompts-file",
         type=str,
         default="benchmark/build_dataset/top100.txt",
         help="Path to the prompts file (one prompt per line)",
     )
     parser.add_argument(
-        "--output_dir", type=str, default="benchmark_results", help="Directory to save benchmark results"
+        "--output-dir", type=str, default="benchmark_results", help="Directory to save benchmark results"
     )
-    parser.add_argument("--model_path", type=str, default=MODEL_PATH, help="Path to the model")
+    parser.add_argument("--model-path", type=str, default=MODEL_PATH, help="Path to the model")
     parser.add_argument("--speaker", type=str, default="Ethan", help="Speaker voice for audio output")
-    parser.add_argument("--num_prompts", type=int, default=None, help="Number of prompts to process (default: all)")
+    parser.add_argument("--num-prompts", type=int, default=None, help="Number of prompts to process (default: all)")
     args = parser.parse_args()
 
     # Load model and processor

docs/contributing/features/cfg_parallel.md

@@ -188,11 +188,11 @@ cd examples/offline_inference/text_to_image
 python text_to_image.py \
     --model Your-org/your-model \
     --prompt "a cup of coffee on the table" \
-    --negative_prompt "ugly, unclear" \
-    --cfg_scale 4.0 \
-    --num_inference_steps 50 \
+    --negative-prompt "ugly, unclear" \
+    --cfg-scale 4.0 \
+    --num-inference-steps 50 \
     --output "cfg_enabled.png" \
-    --cfg_parallel_size 2
+    --cfg-parallel-size 2
 ```
 
 **Verify:**
@@ -246,4 +246,4 @@ Adding CFG-Parallel support:
 
 1. ✅ **Create mixin** - Inherit from `CFGParallelMixin` and implement `diffuse()` method
 2. ✅ **(Optional) Customize** - Override `predict_noise()` or `cfg_normalize_function()` for custom behavior
-3. ✅ **Test** - Verify with `--cfg_parallel_size 2` and compare performance
+3. ✅ **Test** - Verify with `--cfg-parallel-size 2` and compare performance

docs/contributing/features/sequence_parallel.md

@@ -277,9 +277,9 @@ cd examples/offline_inference/text_to_image
 python text_to_image.py \
     --model Your-org/your-model \
     --prompt "a cup of coffee on the table" \
-    --num_inference_steps 50 \
-    --ulysses_degree 2 \
-    --ring_degree 2 \
+    --num-inference-steps 50 \
+    --ulysses-degree 2 \
+    --ring-degree 2 \
     --output sp_test_image_ulysses=2_ring=2.png
 ```
 
@@ -298,9 +298,9 @@ cd examples/offline_inference/text_to_image
 python text_to_image.py \
     --model Your-org/your-model \
     --prompt "a cup of coffee on the table" \
-    --num_inference_steps 50 \
-    --ulysses_degree 2 \
-    --tensor_parallel_size 2 \
+    --num-inference-steps 50 \
+    --ulysses-degree 2 \
+    --tensor-parallel-size 2 \
     --output sp_test_image_ulysses=2_tp=2.png
 ```
 

docs/contributing/features/tensor_parallel.md

@@ -181,11 +181,11 @@ cd examples/offline_inference/text_to_image
 python text_to_image.py \
     --model Your-org/your-model \
     --prompt "a cup of coffee on the table" \
-    --negative_prompt "ugly, unclear" \
-    --cfg_scale 4.0 \
-    --num_inference_steps 50 \
+    --negative-prompt "ugly, unclear" \
+    --cfg-scale 4.0 \
+    --num-inference-steps 50 \
     --output "tp_enabled.png" \
-    --tensor_parallel_size 2
+    --tensor-parallel-size 2
 ```
 
 **Verify:**

docs/contributing/profiling.md

@@ -106,7 +106,7 @@ python image_to_video.py \
     #    Reducing frames to the absolute minimum (2) keeps the
     #    tensor size small, ensuring the trace file doesn't become
     #    multi-gigabytes in size.
-    --num_frames 2 \
+    --num-frames 2 \
     \
     # Minimize Iteration Loop (Steps):
     #    This is the most critical setting for profiling.
@@ -114,12 +114,12 @@ python image_to_video.py \
     #    Profiling 2 steps gives you the exact same performance
     #    data as 50 steps, but saves minutes of runtime and
     #    prevents the trace viewer from freezing.
-    --num_inference_steps 2 \
+    --num-inference-steps 2 \
     \
-    --guidance_scale 5.0 \
-    --guidance_scale_high 6.0 \
-    --boundary_ratio 0.875 \
-    --flow_shift 12.0 \
+    --guidance-scale 5.0 \
+    --guidance-scale-high 6.0 \
+    --boundary-ratio 0.875 \
+    --flow-shift 12.0 \
     --fps 16 \
     --output i2v_output.mp4
 

docs/user_guide/diffusion/cache_dit_acceleration.md

@@ -188,8 +188,8 @@ cd examples/offline_inference/text_to_image
 python text_to_image.py \
     --model Qwen/Qwen-Image \
     --prompt "a cup of coffee on the table" \
-    --cache_backend cache_dit \
-    --num_inference_steps 50
+    --cache-backend cache_dit \
+    --num-inference-steps 50
 ```
 
 

docs/user_guide/diffusion/parallelism_acceleration.md

@@ -317,10 +317,10 @@ python image_edit.py \
   --model "Qwen/Qwen-Image-Edit" \
   --image "qwen_image_output.png" \
   --prompt "turn this cat to a dog" \
-  --negative_prompt "low quality, blurry" \
-  --cfg_scale 4.0 \
+  --negative-prompt "low quality, blurry" \
+  --cfg-scale 4.0 \
   --output "edited_image.png" \
-  --cfg_parallel_size 2
+  --cfg-parallel-size 2
 ```
 
 #### Online Serving

docs/user_guide/examples/offline_inference/image_to_image.md

@@ -22,8 +22,8 @@ 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
+  --num-inference-steps 50 \
+  --cfg-scale 4.0
 ```
 
 ### Multiple Image Editing (Qwen-Image-Edit-2509)
@@ -36,26 +36,26 @@ python image_edit.py \
   --image img1.png img2.png \
   --prompt "Combine these images into a single scene" \
   --output output_image_edit.png \
-  --num_inference_steps 50 \
-  --cfg_scale 4.0 \
-  --guidance_scale 1.0
+  --num-inference-steps 50 \
+  --cfg-scale 4.0 \
+  --guidance-scale 1.0
 ```
 
 Key arguments:
 
 - `--model`: model name or path. Use `Qwen/Qwen-Image-Edit-2509` or later for multiple image support.
 - `--image`: path(s) to the source image(s) (PNG/JPG, converted to RGB). Can specify multiple images.
-- `--prompt` / `--negative_prompt`: text description (string).
-- `--cfg_scale`: true classifier-free guidance scale (default: 4.0). Classifier-free guidance is enabled by setting cfg_scale > 1 and providing a negative_prompt. Higher guidance scale encourages images closely linked to the text prompt, usually at the expense of lower image quality.
-- `--guidance_scale`: guidance scale for guidance-distilled models (default: 1.0, disabled). Unlike classifier-free guidance (--cfg_scale), guidance-distilled models take the guidance scale directly as an input parameter. Enabled when guidance_scale > 1. Ignored when not using guidance-distilled models.
-- `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
+- `--prompt` / `--negative-prompt`: text description (string).
+- `--cfg-scale`: true classifier-free guidance scale (default: 4.0). Classifier-free guidance is enabled by setting cfg_scale > 1 and providing a negative_prompt. Higher guidance scale encourages images closely linked to the text prompt, usually at the expense of lower image quality.
+- `--guidance-scale`: guidance scale for guidance-distilled models (default: 1.0, disabled). Unlike classifier-free guidance (--cfg-scale), guidance-distilled models take the guidance scale directly as an input parameter. Enabled when guidance_scale > 1. Ignored when not using guidance-distilled models.
+- `--num-inference-steps`: diffusion sampling steps (more steps = higher quality, slower).
 - `--output`: path to save the generated PNG.
-- `--vae_use_slicing`: enable VAE slicing for memory optimization.
-- `--vae_use_tiling`: enable VAE tiling for memory optimization.
-- `--cfg_parallel_size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
+- `--vae-use-slicing`: enable VAE slicing for memory optimization.
+- `--vae-use-tiling`: enable VAE tiling for memory optimization.
+- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 
-> ℹ️ If you encounter OOM errors, try using `--vae_use_slicing` and `--vae_use_tiling` to reduce memory usage.
+> ℹ️ If you encounter OOM errors, try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage.
 
 ## Example materials
 

docs/user_guide/examples/offline_inference/image_to_video.md

@@ -20,15 +20,15 @@ python image_to_video.py \
   --model Wan-AI/Wan2.2-I2V-A14B-Diffusers \
   --image cherry_blossom.jpg \
   --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \
-  --negative_prompt "<optional quality filter>" \
+  --negative-prompt "<optional quality filter>" \
   --height 480 \
   --width 832 \
-  --num_frames 48 \
-  --guidance_scale 5.0 \
-  --guidance_scale_high 6.0 \
-  --num_inference_steps 40 \
-  --boundary_ratio 0.875 \
-  --flow_shift 12.0 \
+  --num-frames 48 \
+  --guidance-scale 5.0 \
+  --guidance-scale-high 6.0 \
+  --num-inference-steps 40 \
+  --boundary-ratio 0.875 \
+  --flow-shift 12.0 \
   --fps 16 \
   --output i2v_output.mp4
 ```
@@ -40,13 +40,13 @@ python image_to_video.py \
   --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
   --image cherry_blossom.jpg \
   --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \
-  --negative_prompt "<optional quality filter>" \
+  --negative-prompt "<optional quality filter>" \
   --height 480 \
   --width 832 \
-  --num_frames 48 \
-  --guidance_scale 4.0 \
-  --num_inference_steps 40 \
-  --flow_shift 12.0 \
+  --num-frames 48 \
+  --guidance-scale 4.0 \
+  --num-inference-steps 40 \
+  --flow-shift 12.0 \
   --fps 16 \
   --output i2v_output.mp4
 ```
@@ -57,20 +57,20 @@ Key arguments:
 - `--image`: Path to input image (required).
 - `--prompt`: Text description of desired motion/animation.
 - `--height/--width`: Output resolution (auto-calculated from image if not set). Dimensions should be multiples of 16.
-- `--num_frames`: Number of frames (default 81).
-- `--guidance_scale` and `--guidance_scale_high`: CFG scale (applied to low/high-noise stages for MoE).
-- `--negative_prompt`: Optional list of artifacts to suppress.
-- `--boundary_ratio`: Boundary split ratio for two-stage MoE models.
-- `--flow_shift`: Scheduler flow shift (5.0 for 720p, 12.0 for 480p).
-- `--num_inference_steps`: Number of denoising steps (default 50).
+- `--num-frames`: Number of frames (default 81).
+- `--guidance-scale` and `--guidance-scale-high`: CFG scale (applied to low/high-noise stages for MoE).
+- `--negative-prompt`: Optional list of artifacts to suppress.
+- `--boundary-ratio`: Boundary split ratio for two-stage MoE models.
+- `--flow-shift`: Scheduler flow shift (5.0 for 720p, 12.0 for 480p).
+- `--num-inference-steps`: Number of denoising steps (default 50).
 - `--fps`: Frames per second for the saved MP4 (requires `diffusers` export_to_video).
 - `--output`: Path to save the generated video.
-- `--vae_use_slicing`: Enable VAE slicing for memory optimization.
-- `--vae_use_tiling`: Enable VAE tiling for memory optimization.
-- `--cfg_parallel_size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
+- `--vae-use-slicing`: Enable VAE slicing for memory optimization.
+- `--vae-use-tiling`: Enable VAE tiling for memory optimization.
+- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 
-> ℹ️ If you encounter OOM errors, try using `--vae_use_slicing` and `--vae_use_tiling` to reduce memory usage.
+> ℹ️ If you encounter OOM errors, try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage.
 
 ## Example materials
 

docs/user_guide/examples/offline_inference/lora_inference.md

@@ -26,7 +26,7 @@ python -m examples.offline_inference.lora_inference.lora_inference \
     --prompt "A piece of cheesecake" \
     --lora-path /path/to/lora/ \
     --lora-scale 1.0 \
-    --num_inference_steps 50 \
+    --num-inference-steps 50 \
     --height 1024 \
     --width 1024 \
     --output output_preloaded.png
@@ -43,7 +43,7 @@ python -m examples.offline_inference.lora_inference.lora_inference \
     --prompt "A piece of cheesecake" \
     --lora-request-path /path/to/lora/ \
     --lora-scale 1.0 \
-    --num_inference_steps 50 \
+    --num-inference-steps 50 \
     --height 1024 \
     --width 1024 \
     --output output_per_request.png
@@ -56,7 +56,7 @@ If no LoRA request is provided, we will use the base model without any LoRA adap
 ```bash
 python -m examples.offline_inference.lora_inference.lora_inference \
     --prompt "A piece of cheesecake" \
-    --num_inference_steps 50 \
+    --num-inference-steps 50 \
     --height 1024 \
     --width 1024 \
     --output output_no_lora.png
@@ -77,7 +77,7 @@ python -m examples.offline_inference.lora_inference.lora_inference \
 - `--seed`: Random seed for reproducibility (default: 42)
 - `--height`: Image height in pixels (default: 1024)
 - `--width`: Image width in pixels (default: 1024)
-- `--num_inference_steps`: Number of denoising steps (default: 50)
+- `--num-inference-steps`: Number of denoising steps (default: 50)
 - `--output`: Output file path (default: `lora_output.png`)
 
 ## How LoRA Works

docs/user_guide/examples/offline_inference/text_to_audio.md

@@ -21,22 +21,22 @@ If you use a gated model (e.g., `stabilityai/stable-audio-open-1.0`), ensure you
 python text_to_audio.py \
   --model stabilityai/stable-audio-open-1.0 \
   --prompt "The sound of a hammer hitting a wooden surface" \
-  --negative_prompt "Low quality" \
+  --negative-prompt "Low quality" \
   --seed 42 \
-  --guidance_scale 7.0 \
-  --audio_length 10.0 \
-  --num_inference_steps 100 \
+  --guidance-scale 7.0 \
+  --audio-length 10.0 \
+  --num-inference-steps 100 \
   --output stable_audio_output.wav
 ```
 
 Key arguments:
 
 - `--prompt`: text description (string).
-- `--negative_prompt`: negative prompt for classifier-free guidance.
+- `--negative-prompt`: negative prompt for classifier-free guidance.
 - `--seed`: integer seed for deterministic generation.
-- `--guidance_scale`: classifier-free guidance scale.
-- `--audio_length`: audio duration in seconds.
-- `--num_inference_steps`: diffusion sampling steps.(more steps = higher quality, slower).
+- `--guidance-scale`: classifier-free guidance scale.
+- `--audio-length`: audio duration in seconds.
+- `--num-inference-steps`: diffusion sampling steps.(more steps = higher quality, slower).
 - `--output`: path to save the generated WAV file.
 
 ## Example materials

docs/user_guide/examples/offline_inference/text_to_image.md

@@ -82,9 +82,9 @@ 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 \
-  --num_images_per_prompt 1 \
-  --num_inference_steps 50 \
+  --cfg-scale 4.0 \
+  --num-images-per-prompt 1 \
+  --num-inference-steps 50 \
   --height 1024 \
   --width 1024 \
   --output outputs/coffee.png
@@ -94,17 +94,17 @@ Key arguments:
 
 - `--prompt`: text description (string).
 - `--seed`: integer seed for deterministic sampling.
-- `--cfg_scale`: true CFG scale (model-specific guidance strength).
-- `--num_images_per_prompt`: number of images to generate per prompt (saves as `output`, `output_1`, ...).
-- `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
+- `--cfg-scale`: true CFG scale (model-specific guidance strength).
+- `--num-images-per-prompt`: number of images to generate per prompt (saves as `output`, `output_1`, ...).
+- `--num-inference-steps`: diffusion sampling steps (more steps = higher quality, slower).
 - `--height/--width`: output resolution (defaults 1024x1024).
 - `--output`: path to save the generated PNG.
-- `--vae_use_slicing`: enable VAE slicing for memory optimization.
-- `--vae_use_tiling`: enable VAE tiling for memory optimization.
-- `--cfg_parallel_size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
+- `--vae-use-slicing`: enable VAE slicing for memory optimization.
+- `--vae-use-tiling`: enable VAE tiling for memory optimization.
+- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 
-> ℹ️ If you encounter OOM errors, try using `--vae_use_slicing` and `--vae_use_tiling` to reduce memory usage.
+> ℹ️ If you encounter OOM errors, try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage.
 
 > 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.
 

docs/user_guide/examples/offline_inference/text_to_video.md

@@ -10,14 +10,14 @@ The `Wan-AI/Wan2.2-T2V-A14B-Diffusers` pipeline generates short videos from text
 ```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>" \
+  --negative-prompt "<optional quality filter>" \
   --height 480 \
   --width 832 \
-  --num_frames 33 \
-  --guidance_scale 4.0 \
-  --guidance_scale_high 3.0 \
-  --flow_shift 12.0 \
-  --num_inference_steps 40 \
+  --num-frames 33 \
+  --guidance-scale 4.0 \
+  --guidance-scale-high 3.0 \
+  --flow-shift 12.0 \
+  --num-inference-steps 40 \
   --fps 16 \
   --output t2v_out.mp4
 ```
@@ -26,18 +26,18 @@ Key arguments:
 
 - `--prompt`: text description (string).
 - `--height/--width`: output resolution (defaults 480x832, i.e. 480P). 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. Default `0.875` uses both transformers for best quality. Set to `1.0` to load only the low-noise transformer (saves noticeable memory with good quality, recommended if memory is limited). Set to `0.0` loads only the high-noise transformer (not recommended, lower quality).
+- `--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. Default `0.875` uses both transformers for best quality. Set to `1.0` to load only the low-noise transformer (saves noticeable memory with good quality, recommended if memory is limited). Set to `0.0` loads only the high-noise transformer (not recommended, lower quality).
 - `--fps`: frames per second for the saved MP4 (requires `diffusers` export_to_video).
 - `--output`: path to save the generated video.
-- `--vae_use_slicing`: enable VAE slicing for memory optimization.
-- `--vae_use_tiling`: enable VAE tiling for memory optimization.
-- `--cfg_parallel_size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
+- `--vae-use-slicing`: enable VAE slicing for memory optimization.
+- `--vae-use-tiling`: enable VAE tiling for memory optimization.
+- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 
-> ℹ️ If you encounter OOM errors, try using `--vae_use_slicing` and `--vae_use_tiling` to reduce memory usage.
+> ℹ️ If you encounter OOM errors, try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage.
 
 ## Example materials
 

examples/offline_inference/bagel/end2end.py

@@ -19,7 +19,7 @@ def parse_args():
         default=None,
         help="Path to a .txt file with one prompt per line (preferred).",
     )
-    parser.add_argument("--prompt_type", default="text", choices=["text"])
+    parser.add_argument("--prompt-type", default="text", choices=["text"])
 
     parser.add_argument(
         "--modality",

examples/offline_inference/image_to_image/image_edit.py

@@ -9,34 +9,34 @@ Usage (single image):
         --image input.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 \
-        --guidance_scale 1.0
+        --num-inference-steps 50 \
+        --cfg-scale 4.0 \
+        --guidance-scale 1.0
 
 Usage (multiple images):
     python image_edit.py \
         --image input1.png input2.png input3.png \
         --prompt "Combine these images into a single scene" \
         --output output_image_edit.png \
-        --num_inference_steps 50 \
-        --cfg_scale 4.0 \
-        --guidance_scale 1.0
+        --num-inference-steps 50 \
+        --cfg-scale 4.0 \
+        --guidance-scale 1.0
 
 Usage (with cache-dit acceleration):
     python image_edit.py \
         --image input.png \
         --prompt "Edit description" \
-        --cache_backend cache_dit \
-        --cache_dit_max_continuous_cached_steps 3 \
-        --cache_dit_residual_diff_threshold 0.24 \
-        --cache_dit_enable_taylorseer
+        --cache-backend cache_dit \
+        --cache-dit-max-continuous-cached-steps 3 \
+        --cache-dit-residual-diff-threshold 0.24 \
+        --cache-dit-enable-taylorseer
 
 Usage (with tea_cache acceleration):
     python image_edit.py \
         --image input.png \
         --prompt "Edit description" \
-        --cache_backend tea_cache \
-        --tea_cache_rel_l1_thresh 0.25
+        --cache-backend tea_cache \
+        --tea-cache-rel-l1-thresh 0.25
 
 Usage (layered):
     python image_edit.py \
@@ -44,8 +44,8 @@ Usage (layered):
         --image input.png \
         --prompt "" \
         --output "layered" \
-        --num_inference_steps 50 \
-        --cfg_scale 4.0 \
+        --num-inference-steps 50 \
+        --cfg-scale 4.0 \
         --layers 4 \
         --color-format "RGBA"
 
@@ -53,17 +53,17 @@ Usage (with CFG Parallel):
     python image_edit.py \
         --image input.png \
         --prompt "Edit description" \
-        --cfg_parallel_size 2 \
-        --num_inference_steps 50 \
-        --cfg_scale 4.0
+        --cfg-parallel-size 2 \
+        --num-inference-steps 50 \
+        --cfg-scale 4.0
 
 Usage (disable torch.compile):
     python image_edit.py \
         --image input.png \
         --prompt "Edit description" \
-        --enforce_eager \
-        --num_inference_steps 50 \
-        --cfg_scale 4.0
+        --enforce-eager \
+        --num-inference-steps 50 \
+        --cfg-scale 4.0
 
 For more options, run:
     python image_edit.py --help
@@ -109,7 +109,7 @@ def parse_args() -> argparse.Namespace:
         help="Text prompt describing the edit to make to the image.",
     )
     parser.add_argument(
-        "--negative_prompt",
+        "--negative-prompt",
         type=str,
         default=None,
         required=False,
@@ -121,7 +121,7 @@ def parse_args() -> argparse.Namespace:
         help="Random seed for deterministic results.",
     )
     parser.add_argument(
-        "--cfg_scale",
+        "--cfg-scale",
         type=float,
         default=4.0,
         help=(
@@ -132,12 +132,12 @@ def parse_args() -> argparse.Namespace:
         ),
     )
     parser.add_argument(
-        "--guidance_scale",
+        "--guidance-scale",
         type=float,
         default=1.0,
         help=(
             "Guidance scale for guidance-distilled models (default: 1.0, disabled). "
-            "Unlike classifier-free guidance (--cfg_scale), guidance-distilled models take the guidance scale "
+            "Unlike classifier-free guidance (--cfg-scale), guidance-distilled models take the guidance scale "
             "directly as an input parameter. Enabled when guidance_scale > 1. Ignored when not using guidance-distilled models."
         ),
     )
@@ -148,19 +148,19 @@ def parse_args() -> argparse.Namespace:
         help=("Path to save the edited image (PNG). Or prefix for Qwen-Image-Layered model save images(PNG)."),
     )
     parser.add_argument(
-        "--num_outputs_per_prompt",
+        "--num-outputs-per-prompt",
         type=int,
         default=1,
         help="Number of images to generate for the given prompt.",
     )
     parser.add_argument(
-        "--num_inference_steps",
+        "--num-inference-steps",
         type=int,
         default=50,
         help="Number of denoising steps for the diffusion sampler.",
     )
     parser.add_argument(
-        "--cache_backend",
+        "--cache-backend",
         type=str,
         default=None,
         choices=["cache_dit", "tea_cache"],
@@ -171,19 +171,19 @@ def parse_args() -> argparse.Namespace:
         ),
     )
     parser.add_argument(
-        "--ulysses_degree",
+        "--ulysses-degree",
         type=int,
         default=1,
         help="Number of GPUs used for ulysses sequence parallelism.",
     )
     parser.add_argument(
-        "--ring_degree",
+        "--ring-degree",
         type=int,
         default=1,
         help="Number of GPUs used for ring sequence parallelism.",
     )
     parser.add_argument(
-        "--tensor_parallel_size",
+        "--tensor-parallel-size",
         type=int,
         default=1,
         help="Number of GPUs used for tensor parallelism (TP) inside the DiT.",
@@ -205,56 +205,56 @@ def parse_args() -> argparse.Namespace:
 
     # Cache-DiT specific parameters
     parser.add_argument(
-        "--cache_dit_fn_compute_blocks",
+        "--cache-dit-fn-compute-blocks",
         type=int,
         default=1,
         help="[cache-dit] Number of forward compute blocks. Optimized for single-transformer models.",
     )
     parser.add_argument(
-        "--cache_dit_bn_compute_blocks",
+        "--cache-dit-bn-compute-blocks",
         type=int,
         default=0,
         help="[cache-dit] Number of backward compute blocks.",
     )
     parser.add_argument(
-        "--cache_dit_max_warmup_steps",
+        "--cache-dit-max-warmup-steps",
         type=int,
         default=4,
         help="[cache-dit] Maximum warmup steps (works for few-step models).",
     )
     parser.add_argument(
-        "--cache_dit_residual_diff_threshold",
+        "--cache-dit-residual-diff-threshold",
         type=float,
         default=0.24,
         help="[cache-dit] Residual diff threshold. Higher values enable more aggressive caching.",
     )
     parser.add_argument(
-        "--cache_dit_max_continuous_cached_steps",
+        "--cache-dit-max-continuous-cached-steps",
         type=int,
         default=3,
         help="[cache-dit] Maximum continuous cached steps to prevent precision degradation.",
     )
     parser.add_argument(
-        "--cache_dit_enable_taylorseer",
+        "--cache-dit-enable-taylorseer",
         action="store_true",
         default=False,
         help="[cache-dit] Enable TaylorSeer acceleration (not suitable for few-step models).",
     )
     parser.add_argument(
-        "--cache_dit_taylorseer_order",
+        "--cache-dit-taylorseer-order",
         type=int,
         default=1,
         help="[cache-dit] TaylorSeer polynomial order.",
     )
     parser.add_argument(
-        "--cache_dit_scm_steps_mask_policy",
+        "--cache-dit-scm-steps-mask-policy",
         type=str,
         default=None,
         choices=[None, "slow", "medium", "fast", "ultra"],
         help="[cache-dit] SCM mask policy: None (disabled), slow, medium, fast, ultra.",
     )
     parser.add_argument(
-        "--cache_dit_scm_steps_policy",
+        "--cache-dit-scm-steps-policy",
         type=str,
         default="dynamic",
         choices=["dynamic", "static"],
@@ -263,30 +263,30 @@ def parse_args() -> argparse.Namespace:
 
     # TeaCache specific parameters
     parser.add_argument(
-        "--tea_cache_rel_l1_thresh",
+        "--tea-cache-rel-l1-thresh",
         type=float,
         default=0.2,
         help="[tea_cache] Threshold for accumulated relative L1 distance.",
     )
     parser.add_argument(
-        "--cfg_parallel_size",
+        "--cfg-parallel-size",
         type=int,
         default=1,
         choices=[1, 2],
         help="Number of GPUs used for classifier free guidance parallel size.",
     )
     parser.add_argument(
-        "--enforce_eager",
+        "--enforce-eager",
         action="store_true",
         help="Disable torch.compile and force eager execution.",
     )
     parser.add_argument(
-        "--vae_use_slicing",
+        "--vae-use-slicing",
         action="store_true",
         help="Enable VAE slicing for memory optimization.",
     )
     parser.add_argument(
-        "--vae_use_tiling",
+        "--vae-use-tiling",
         action="store_true",
         help="Enable VAE tiling for memory optimization.",
     )

examples/offline_inference/image_to_image/image_to_image.md

@@ -19,8 +19,8 @@ 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
+  --num-inference-steps 50 \
+  --cfg-scale 4.0
 ```
 
 ### Multiple Image Editing (Qwen-Image-Edit-2509)
@@ -33,23 +33,23 @@ python image_edit.py \
   --image img1.png img2.png \
   --prompt "Combine these images into a single scene" \
   --output output_image_edit.png \
-  --num_inference_steps 50 \
-  --cfg_scale 4.0 \
-  --guidance_scale 1.0
+  --num-inference-steps 50 \
+  --cfg-scale 4.0 \
+  --guidance-scale 1.0
 ```
 
 Key arguments:
 
 - `--model`: model name or path. Use `Qwen/Qwen-Image-Edit-2509` or later for multiple image support.
 - `--image`: path(s) to the source image(s) (PNG/JPG, converted to RGB). Can specify multiple images.
-- `--prompt` / `--negative_prompt`: text description (string).
-- `--cfg_scale`: true classifier-free guidance scale (default: 4.0). Classifier-free guidance is enabled by setting cfg_scale > 1 and providing a negative_prompt. Higher guidance scale encourages images closely linked to the text prompt, usually at the expense of lower image quality.
-- `--guidance_scale`: guidance scale for guidance-distilled models (default: 1.0, disabled). Unlike classifier-free guidance (--cfg_scale), guidance-distilled models take the guidance scale directly as an input parameter. Enabled when guidance_scale > 1. Ignored when not using guidance-distilled models.
-- `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
+- `--prompt` / `--negative-prompt`: text description (string).
+- `--cfg-scale`: true classifier-free guidance scale (default: 4.0). Classifier-free guidance is enabled by setting cfg_scale > 1 and providing a negative_prompt. Higher guidance scale encourages images closely linked to the text prompt, usually at the expense of lower image quality.
+- `--guidance-scale`: guidance scale for guidance-distilled models (default: 1.0, disabled). Unlike classifier-free guidance (--cfg-scale), guidance-distilled models take the guidance scale directly as an input parameter. Enabled when guidance_scale > 1. Ignored when not using guidance-distilled models.
+- `--num-inference-steps`: diffusion sampling steps (more steps = higher quality, slower).
 - `--output`: path to save the generated PNG.
-- `--vae_use_slicing`: enable VAE slicing for memory optimization.
-- `--vae_use_tiling`: enable VAE tiling for memory optimization.
-- `--cfg_parallel_size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](../../../docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
+- `--vae-use-slicing`: enable VAE slicing for memory optimization.
+- `--vae-use-tiling`: enable VAE tiling for memory optimization.
+- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](../../../docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 
-> ℹ️ If you encounter OOM errors, try using `--vae_use_slicing` and `--vae_use_tiling` to reduce memory usage.
+> ℹ️ If you encounter OOM errors, try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage.

examples/offline_inference/image_to_image/run_qwen_image_edit_2511.sh

@@ -3,6 +3,6 @@ python image_edit.py \
     --image qwen_bear.png \
     --prompt "Add a white art board written with colorful text 'vLLM-Omni' on grassland. Add a paintbrush in the bear's hands. position the bear standing in front of the art board as if painting" \
     --output output_image_edit.png \
-    --num_inference_steps 50 \
-    --cfg_scale 4.0 \
-    --cache_backend  cache_dit \
+    --num-inference-steps 50 \
+    --cfg-scale 4.0 \
+    --cache-backend  cache_dit \

examples/offline_inference/image_to_video/README.md

@@ -17,15 +17,15 @@ python image_to_video.py \
   --model Wan-AI/Wan2.2-I2V-A14B-Diffusers \
   --image cherry_blossom.jpg \
   --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \
-  --negative_prompt "<optional quality filter>" \
+  --negative-prompt "<optional quality filter>" \
   --height 480 \
   --width 832 \
-  --num_frames 48 \
-  --guidance_scale 5.0 \
-  --guidance_scale_high 6.0 \
-  --num_inference_steps 40 \
-  --boundary_ratio 0.875 \
-  --flow_shift 12.0 \
+  --num-frames 48 \
+  --guidance-scale 5.0 \
+  --guidance-scale-high 6.0 \
+  --num-inference-steps 40 \
+  --boundary-ratio 0.875 \
+  --flow-shift 12.0 \
   --fps 16 \
   --output i2v_output.mp4
 ```
@@ -37,13 +37,13 @@ python image_to_video.py \
   --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
   --image cherry_blossom.jpg \
   --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \
-  --negative_prompt "<optional quality filter>" \
+  --negative-prompt "<optional quality filter>" \
   --height 480 \
   --width 832 \
-  --num_frames 48 \
-  --guidance_scale 4.0 \
-  --num_inference_steps 40 \
-  --flow_shift 12.0 \
+  --num-frames 48 \
+  --guidance-scale 4.0 \
+  --num-inference-steps 40 \
+  --flow-shift 12.0 \
   --fps 16 \
   --output i2v_output.mp4
 ```
@@ -54,17 +54,17 @@ Key arguments:
 - `--image`: Path to input image (required).
 - `--prompt`: Text description of desired motion/animation.
 - `--height/--width`: Output resolution (auto-calculated from image if not set). Dimensions should be multiples of 16.
-- `--num_frames`: Number of frames (default 81).
-- `--guidance_scale` and `--guidance_scale_high`: CFG scale (applied to low/high-noise stages for MoE).
-- `--negative_prompt`: Optional list of artifacts to suppress.
-- `--boundary_ratio`: Boundary split ratio for two-stage MoE models.
-- `--flow_shift`: Scheduler flow shift (5.0 for 720p, 12.0 for 480p).
-- `--num_inference_steps`: Number of denoising steps (default 50).
+- `--num-frames`: Number of frames (default 81).
+- `--guidance-scale` and `--guidance-scale-high`: CFG scale (applied to low/high-noise stages for MoE).
+- `--negative-prompt`: Optional list of artifacts to suppress.
+- `--boundary-ratio`: Boundary split ratio for two-stage MoE models.
+- `--flow-shift`: Scheduler flow shift (5.0 for 720p, 12.0 for 480p).
+- `--num-inference-steps`: Number of denoising steps (default 50).
 - `--fps`: Frames per second for the saved MP4 (requires `diffusers` export_to_video).
 - `--output`: Path to save the generated video.
-- `--vae_use_slicing`: Enable VAE slicing for memory optimization.
-- `--vae_use_tiling`: Enable VAE tiling for memory optimization.
-- `--cfg_parallel_size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](../../../docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
+- `--vae-use-slicing`: Enable VAE slicing for memory optimization.
+- `--vae-use-tiling`: Enable VAE tiling for memory optimization.
+- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](../../../docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 
-> ℹ️ If you encounter OOM errors, try using `--vae_use_slicing` and `--vae_use_tiling` to reduce memory usage.
+> ℹ️ If you encounter OOM errors, try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage.

examples/offline_inference/image_to_video/image_to_video.py

@@ -42,31 +42,31 @@ def parse_args() -> argparse.Namespace:
     )
     parser.add_argument("--image", required=True, help="Path to input image.")
     parser.add_argument("--prompt", default="", help="Text prompt describing the desired motion.")
-    parser.add_argument("--negative_prompt", default="", help="Negative prompt.")
+    parser.add_argument("--negative-prompt", default="", help="Negative prompt.")
     parser.add_argument("--seed", type=int, default=42, help="Random seed.")
-    parser.add_argument("--guidance_scale", type=float, default=5.0, help="CFG scale.")
+    parser.add_argument("--guidance-scale", type=float, default=5.0, help="CFG scale.")
     parser.add_argument(
-        "--guidance_scale_high", type=float, default=None, help="Optional separate CFG for high-noise (MoE only)."
+        "--guidance-scale-high", type=float, default=None, help="Optional separate CFG for high-noise (MoE only)."
     )
     parser.add_argument(
         "--height", type=int, default=None, help="Video height (auto-calculated from image if not set)."
     )
     parser.add_argument("--width", type=int, default=None, help="Video width (auto-calculated from image if not set).")
-    parser.add_argument("--num_frames", type=int, default=81, help="Number of frames.")
-    parser.add_argument("--num_inference_steps", type=int, default=50, help="Sampling steps.")
-    parser.add_argument("--boundary_ratio", type=float, default=0.875, help="Boundary split ratio for MoE models.")
+    parser.add_argument("--num-frames", type=int, default=81, help="Number of frames.")
+    parser.add_argument("--num-inference-steps", type=int, default=50, help="Sampling steps.")
+    parser.add_argument("--boundary-ratio", type=float, default=0.875, help="Boundary split ratio for MoE models.")
     parser.add_argument(
-        "--flow_shift", type=float, default=5.0, help="Scheduler flow_shift (5.0 for 720p, 12.0 for 480p)."
+        "--flow-shift", type=float, default=5.0, help="Scheduler flow_shift (5.0 for 720p, 12.0 for 480p)."
     )
     parser.add_argument("--output", type=str, default="i2v_output.mp4", help="Path to save the video (mp4).")
     parser.add_argument("--fps", type=int, default=16, help="Frames per second for the output video.")
     parser.add_argument(
-        "--vae_use_slicing",
+        "--vae-use-slicing",
         action="store_true",
         help="Enable VAE slicing for memory optimization.",
     )
     parser.add_argument(
-        "--vae_use_tiling",
+        "--vae-use-tiling",
         action="store_true",
         help="Enable VAE tiling for memory optimization.",
     )
@@ -87,14 +87,14 @@ def parse_args() -> argparse.Namespace:
         help="Number of ready layers (blocks) to keep on GPU during generation.",
     )
     parser.add_argument(
-        "--cfg_parallel_size",
+        "--cfg-parallel-size",
         type=int,
         default=1,
         choices=[1, 2],
         help="Number of GPUs used for classifier free guidance parallel size.",
     )
     parser.add_argument(
-        "--enforce_eager",
+        "--enforce-eager",
         action="store_true",
         help="Disable torch.compile and force eager execution.",
     )

examples/offline_inference/lora_inference/README.md

@@ -23,7 +23,7 @@ python -m examples.offline_inference.lora_inference.lora_inference \
     --prompt "A piece of cheesecake" \
     --lora-path /path/to/lora/ \
     --lora-scale 1.0 \
-    --num_inference_steps 50 \
+    --num-inference-steps 50 \
     --height 1024 \
     --width 1024 \
     --output output_preloaded.png
@@ -40,7 +40,7 @@ python -m examples.offline_inference.lora_inference.lora_inference \
     --prompt "A piece of cheesecake" \
     --lora-request-path /path/to/lora/ \
     --lora-scale 1.0 \
-    --num_inference_steps 50 \
+    --num-inference-steps 50 \
     --height 1024 \
     --width 1024 \
     --output output_per_request.png
@@ -53,7 +53,7 @@ If no LoRA request is provided, we will use the base model without any LoRA adap
 ```bash
 python -m examples.offline_inference.lora_inference.lora_inference \
     --prompt "A piece of cheesecake" \
-    --num_inference_steps 50 \
+    --num-inference-steps 50 \
     --height 1024 \
     --width 1024 \
     --output output_no_lora.png
@@ -74,7 +74,7 @@ python -m examples.offline_inference.lora_inference.lora_inference \
 - `--seed`: Random seed for reproducibility (default: 42)
 - `--height`: Image height in pixels (default: 1024)
 - `--width`: Image width in pixels (default: 1024)
-- `--num_inference_steps`: Number of denoising steps (default: 50)
+- `--num-inference-steps`: Number of denoising steps (default: 50)
 - `--output`: Output file path (default: `lora_output.png`)
 
 ## How LoRA Works

examples/offline_inference/lora_inference/lora_inference.py

@@ -18,7 +18,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--height", type=int, default=1024, help="Height of generated image.")
     parser.add_argument("--width", type=int, default=1024, help="Width of generated image.")
     parser.add_argument(
-        "--num_inference_steps",
+        "--num-inference-steps",
         type=int,
         default=50,
         help="Number of denoising steps for the diffusion sampler.",

examples/offline_inference/text_to_audio/README.md

@@ -18,20 +18,20 @@ If you use a gated model (e.g., `stabilityai/stable-audio-open-1.0`), ensure you
 python text_to_audio.py \
   --model stabilityai/stable-audio-open-1.0 \
   --prompt "The sound of a hammer hitting a wooden surface" \
-  --negative_prompt "Low quality" \
+  --negative-prompt "Low quality" \
   --seed 42 \
-  --guidance_scale 7.0 \
-  --audio_length 10.0 \
-  --num_inference_steps 100 \
+  --guidance-scale 7.0 \
+  --audio-length 10.0 \
+  --num-inference-steps 100 \
   --output stable_audio_output.wav
 ```
 
 Key arguments:
 
 - `--prompt`: text description (string).
-- `--negative_prompt`: negative prompt for classifier-free guidance.
+- `--negative-prompt`: negative prompt for classifier-free guidance.
 - `--seed`: integer seed for deterministic generation.
-- `--guidance_scale`: classifier-free guidance scale.
-- `--audio_length`: audio duration in seconds.
-- `--num_inference_steps`: diffusion sampling steps.(more steps = higher quality, slower).
+- `--guidance-scale`: classifier-free guidance scale.
+- `--audio-length`: audio duration in seconds.
+- `--num-inference-steps`: diffusion sampling steps.(more steps = higher quality, slower).
 - `--output`: path to save the generated WAV file.

examples/offline_inference/text_to_audio/text_to_audio.py

@@ -9,8 +9,8 @@ the Stable Audio Open model with vLLM-Omni.
 
 Usage:
     python text_to_audio.py --prompt "The sound of a dog barking"
-    python text_to_audio.py --prompt "A piano playing a gentle melody" --audio_length 10.0
-    python text_to_audio.py --prompt "Thunder and rain sounds" --negative_prompt "Low quality"
+    python text_to_audio.py --prompt "A piano playing a gentle melody" --audio-length 10.0
+    python text_to_audio.py --prompt "Thunder and rain sounds" --negative-prompt "Low quality"
 """
 
 import argparse
@@ -38,7 +38,7 @@ def parse_args() -> argparse.Namespace:
         help="Text prompt for audio generation.",
     )
     parser.add_argument(
-        "--negative_prompt",
+        "--negative-prompt",
         default="Low quality.",
         help="Negative prompt for classifier-free guidance.",
     )
@@ -49,31 +49,31 @@ def parse_args() -> argparse.Namespace:
         help="Random seed for deterministic results.",
     )
     parser.add_argument(
-        "--guidance_scale",
+        "--guidance-scale",
         type=float,
         default=7.0,
         help="Classifier-free guidance scale.",
     )
     parser.add_argument(
-        "--audio_start",
+        "--audio-start",
         type=float,
         default=0.0,
         help="Audio start time in seconds.",
     )
     parser.add_argument(
-        "--audio_length",
+        "--audio-length",
         type=float,
         default=10.0,
         help="Audio length in seconds (max ~47s for stable-audio-open-1.0).",
     )
     parser.add_argument(
-        "--num_inference_steps",
+        "--num-inference-steps",
         type=int,
         default=100,
         help="Number of denoising steps for the diffusion sampler.",
     )
     parser.add_argument(
-        "--num_waveforms",
+        "--num-waveforms",
         type=int,
         default=1,
         help="Number of audio waveforms to generate for the given prompt.",
@@ -85,7 +85,7 @@ def parse_args() -> argparse.Namespace:
         help="Path to save the generated audio (WAV format).",
     )
     parser.add_argument(
-        "--sample_rate",
+        "--sample-rate",
         type=int,
         default=44100,
         help="Sample rate for output audio (Stable Audio uses 44100 Hz).",

examples/offline_inference/text_to_image/README.md

@@ -79,9 +79,9 @@ 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 \
-  --num_images_per_prompt 1 \
-  --num_inference_steps 50 \
+  --cfg-scale 4.0 \
+  --num-images-per-prompt 1 \
+  --num-inference-steps 50 \
   --height 1024 \
   --width 1024 \
   --output outputs/coffee.png
@@ -91,17 +91,17 @@ Key arguments:
 
 - `--prompt`: text description (string).
 - `--seed`: integer seed for deterministic sampling.
-- `--cfg_scale`: true CFG scale (model-specific guidance strength).
-- `--num_images_per_prompt`: number of images to generate per prompt (saves as `output`, `output_1`, ...).
-- `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
+- `--cfg-scale`: true CFG scale (model-specific guidance strength).
+- `--num-images-per-prompt`: number of images to generate per prompt (saves as `output`, `output_1`, ...).
+- `--num-inference-steps`: diffusion sampling steps (more steps = higher quality, slower).
 - `--height/--width`: output resolution (defaults 1024x1024).
 - `--output`: path to save the generated PNG.
-- `--vae_use_slicing`: enable VAE slicing for memory optimization.
-- `--vae_use_tiling`: enable VAE tiling for memory optimization.
-- `--cfg_parallel_size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](../../../docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
+- `--vae-use-slicing`: enable VAE slicing for memory optimization.
+- `--vae-use-tiling`: enable VAE tiling for memory optimization.
+- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](../../../docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 
-> ℹ️ If you encounter OOM errors, try using `--vae_use_slicing` and `--vae_use_tiling` to reduce memory usage.
+> ℹ️ If you encounter OOM errors, try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage.
 
 > 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.
 

examples/offline_inference/text_to_image/gradio_demo.py

@@ -40,7 +40,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--default-seed", type=int, default=42, help="Initial seed shown in UI.")
     parser.add_argument("--default-cfg-scale", type=float, default=4.0, help="Initial CFG scale shown in UI.")
     parser.add_argument(
-        "--num_inference_steps",
+        "--num-inference-steps",
         type=int,
         default=50,
         help="Default number of denoising steps shown in the UI.",

examples/offline_inference/text_to_image/text_to_image.py

@@ -25,19 +25,19 @@ def parse_args() -> argparse.Namespace:
     )
     parser.add_argument("--prompt", default="a cup of coffee on the table", help="Text prompt for image generation.")
     parser.add_argument(
-        "--negative_prompt",
+        "--negative-prompt",
         default=None,
         help="negative prompt for classifier-free conditional guidance.",
     )
     parser.add_argument("--seed", type=int, default=142, help="Random seed for deterministic results.")
     parser.add_argument(
-        "--cfg_scale",
+        "--cfg-scale",
         type=float,
         default=4.0,
         help="True classifier-free guidance scale specific to Qwen-Image.",
     )
     parser.add_argument(
-        "--guidance_scale",
+        "--guidance-scale",
         type=float,
         default=1.0,
         help="Classifier-free guidance scale.",
@@ -51,19 +51,19 @@ def parse_args() -> argparse.Namespace:
         help="Path to save the generated image (PNG).",
     )
     parser.add_argument(
-        "--num_images_per_prompt",
+        "--num-images-per-prompt",
         type=int,
         default=1,
         help="Number of images to generate for the given prompt.",
     )
     parser.add_argument(
-        "--num_inference_steps",
+        "--num-inference-steps",
         type=int,
         default=50,
         help="Number of denoising steps for the diffusion sampler.",
     )
     parser.add_argument(
-        "--cache_backend",
+        "--cache-backend",
         type=str,
         default=None,
         choices=["cache_dit", "tea_cache"],
@@ -79,26 +79,26 @@ def parse_args() -> argparse.Namespace:
         help="Enable cache-dit summary logging after diffusion forward passes.",
     )
     parser.add_argument(
-        "--ulysses_degree",
+        "--ulysses-degree",
         type=int,
         default=1,
         help="Number of GPUs used for ulysses sequence parallelism.",
     )
     parser.add_argument(
-        "--ring_degree",
+        "--ring-degree",
         type=int,
         default=1,
         help="Number of GPUs used for ring sequence parallelism.",
     )
     parser.add_argument(
-        "--cfg_parallel_size",
+        "--cfg-parallel-size",
         type=int,
         default=1,
         choices=[1, 2],
         help="Number of GPUs used for classifier free guidance parallel size.",
     )
     parser.add_argument(
-        "--enforce_eager",
+        "--enforce-eager",
         action="store_true",
         help="Disable torch.compile and force eager execution.",
     )
@@ -119,23 +119,23 @@ def parse_args() -> argparse.Namespace:
         help="Number of ready layers (blocks) to keep on GPU during generation.",
     )
     parser.add_argument(
-        "--vae_use_slicing",
+        "--vae-use-slicing",
         action="store_true",
         help="Enable VAE slicing for memory optimization.",
     )
     parser.add_argument(
-        "--vae_use_tiling",
+        "--vae-use-tiling",
         action="store_true",
         help="Enable VAE tiling for memory optimization.",
     )
     parser.add_argument(
-        "--tensor_parallel_size",
+        "--tensor-parallel-size",
         type=int,
         default=1,
         help="Number of GPUs used for tensor parallelism (TP) inside the DiT.",
     )
     parser.add_argument(
-        "--vae_patch_parallel_size",
+        "--vae-patch-parallel-size",
         type=int,
         default=1,
         help="Number of ranks used for VAE patch/tile parallelism (decode/encode).",

examples/offline_inference/text_to_video/text_to_video.md

@@ -7,14 +7,14 @@ The `Wan-AI/Wan2.2-T2V-A14B-Diffusers` pipeline generates short videos from text
 ```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>" \
+  --negative-prompt "<optional quality filter>" \
   --height 480 \
   --width 832 \
-  --num_frames 33 \
-  --guidance_scale 4.0 \
-  --guidance_scale_high 3.0 \
-  --flow_shift 12.0 \
-  --num_inference_steps 40 \
+  --num-frames 33 \
+  --guidance-scale 4.0 \
+  --guidance-scale-high 3.0 \
+  --flow-shift 12.0 \
+  --num-inference-steps 40 \
   --fps 16 \
   --output t2v_out.mp4
 ```
@@ -23,15 +23,15 @@ Key arguments:
 
 - `--prompt`: text description (string).
 - `--height/--width`: output resolution (defaults 480x832, i.e. 480P). 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. Default `0.875` uses both transformers for best quality. Set to `1.0` to load only the low-noise transformer (saves noticeable memory with good quality, recommended if memory is limited). Set to `0.0` loads only the high-noise transformer (not recommended, lower quality).
+- `--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. Default `0.875` uses both transformers for best quality. Set to `1.0` to load only the low-noise transformer (saves noticeable memory with good quality, recommended if memory is limited). Set to `0.0` loads only the high-noise transformer (not recommended, lower quality).
 - `--fps`: frames per second for the saved MP4 (requires `diffusers` export_to_video).
 - `--output`: path to save the generated video.
-- `--vae_use_slicing`: enable VAE slicing for memory optimization.
-- `--vae_use_tiling`: enable VAE tiling for memory optimization.
-- `--cfg_parallel_size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](../../../docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
+- `--vae-use-slicing`: enable VAE slicing for memory optimization.
+- `--vae-use-tiling`: enable VAE tiling for memory optimization.
+- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](../../../docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 
-> ℹ️ If you encounter OOM errors, try using `--vae_use_slicing` and `--vae_use_tiling` to reduce memory usage.
+> ℹ️ If you encounter OOM errors, try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage.

examples/offline_inference/text_to_video/text_to_video.py

@@ -24,25 +24,25 @@ def parse_args() -> argparse.Namespace:
         help="Diffusers Wan2.2 model ID or local path.",
     )
     parser.add_argument("--prompt", default="A serene lakeside sunrise with mist over the water.", help="Text prompt.")
-    parser.add_argument("--negative_prompt", default="", help="Negative prompt.")
+    parser.add_argument("--negative-prompt", default="", help="Negative prompt.")
     parser.add_argument("--seed", type=int, default=42, help="Random seed.")
-    parser.add_argument("--guidance_scale", type=float, default=4.0, help="CFG scale (applied to low/high).")
-    parser.add_argument("--guidance_scale_high", type=float, default=None, help="Optional separate CFG for high-noise.")
+    parser.add_argument("--guidance-scale", type=float, default=4.0, help="CFG scale (applied to low/high).")
+    parser.add_argument("--guidance-scale-high", type=float, default=None, help="Optional separate CFG for high-noise.")
     parser.add_argument("--height", type=int, default=720, help="Video height.")
     parser.add_argument("--width", type=int, default=1280, help="Video width.")
-    parser.add_argument("--num_frames", type=int, default=81, help="Number of frames (Wan default is 81).")
-    parser.add_argument("--num_inference_steps", type=int, default=40, help="Sampling steps.")
+    parser.add_argument("--num-frames", type=int, default=81, help="Number of frames (Wan default is 81).")
+    parser.add_argument("--num-inference-steps", type=int, default=40, help="Sampling steps.")
     parser.add_argument(
-        "--boundary_ratio",
+        "--boundary-ratio",
         type=float,
         default=0.875,
         help="Boundary split ratio for low/high DiT. Default 0.875 uses both transformers for best quality. Set to 1.0 to load only the low-noise transformer (saves noticeable memory with good quality, recommended if memory is limited).",
     )
     parser.add_argument(
-        "--flow_shift", type=float, default=5.0, help="Scheduler flow_shift (5.0 for 720p, 12.0 for 480p)."
+        "--flow-shift", type=float, default=5.0, help="Scheduler flow_shift (5.0 for 720p, 12.0 for 480p)."
     )
     parser.add_argument(
-        "--cache_backend",
+        "--cache-backend",
         type=str,
         default=None,
         choices=["cache_dit"],
@@ -60,17 +60,17 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--output", type=str, default="wan22_output.mp4", help="Path to save the video (mp4).")
     parser.add_argument("--fps", type=int, default=24, help="Frames per second for the output video.")
     parser.add_argument(
-        "--vae_use_slicing",
+        "--vae-use-slicing",
         action="store_true",
         help="Enable VAE slicing for memory optimization.",
     )
     parser.add_argument(
-        "--vae_use_tiling",
+        "--vae-use-tiling",
         action="store_true",
         help="Enable VAE tiling for memory optimization.",
     )
     parser.add_argument(
-        "--enforce_eager",
+        "--enforce-eager",
         action="store_true",
         help="Disable torch.compile and force eager execution.",
     )
@@ -91,26 +91,26 @@ def parse_args() -> argparse.Namespace:
         help="Number of ready layers (blocks) to keep on GPU during generation.",
     )
     parser.add_argument(
-        "--ulysses_degree",
+        "--ulysses-degree",
         type=int,
         default=1,
         help="Number of GPUs used for ulysses sequence parallelism.",
     )
     parser.add_argument(
-        "--ring_degree",
+        "--ring-degree",
         type=int,
         default=1,
         help="Number of GPUs used for ring sequence parallelism.",
     )
     parser.add_argument(
-        "--cfg_parallel_size",
+        "--cfg-parallel-size",
         type=int,
         default=1,
         choices=[1, 2],
         help="Number of GPUs used for classifier free guidance parallel size.",
     )
     parser.add_argument(
-        "--tensor_parallel_size",
+        "--tensor-parallel-size",
         type=int,
         default=1,
         help="Number of GPUs used for tensor parallelism (TP) inside the DiT.",