[Bugfix] Fix diffusion pipeline CFG (guidance_scale parsing failure bug) (#922)

Signed-off-by: samithuang <285365963@qq.com>
Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

examples/offline_inference/text_to_video/text_to_video.md

@@ -9,10 +9,11 @@ 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 \
+  --width 832 \
   --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
@@ -21,9 +22,9 @@ python text_to_video.py \
 Key arguments:
 
 - `--prompt`: text description (string).
-- `--height/--width`: output resolution (defaults 720x1280). Dimensions should align with Wan VAE downsampling (multiples of 8).
+- `--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)..
+- `--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).

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py

@@ -672,6 +672,8 @@ class QwenImagePipeline(
         num_inference_steps = req.num_inference_steps or num_inference_steps
         generator = req.generator or generator
         true_cfg_scale = req.true_cfg_scale or true_cfg_scale
+        if req.guidance_scale_provided:
+            guidance_scale = req.guidance_scale
         req_num_outputs = getattr(req, "num_outputs_per_prompt", None)
         if req_num_outputs and req_num_outputs > 0:
             num_images_per_prompt = req_num_outputs

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit.py

@@ -748,6 +748,8 @@ class QwenImageEditPipeline(
         num_inference_steps = req.num_inference_steps or num_inference_steps
         generator = req.generator or generator
         true_cfg_scale = req.true_cfg_scale or true_cfg_scale
+        if req.guidance_scale_provided:
+            guidance_scale = req.guidance_scale
         req_num_outputs = getattr(req, "num_outputs_per_prompt", None)
         if req_num_outputs and req_num_outputs > 0:
             num_images_per_prompt = req_num_outputs

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit_plus.py

@@ -704,6 +704,8 @@ class QwenImageEditPlusPipeline(nn.Module, SupportImageInput):
         num_inference_steps = req.num_inference_steps or num_inference_steps
         generator = req.generator or generator
         true_cfg_scale = req.true_cfg_scale or true_cfg_scale
+        if req.guidance_scale_provided:
+            guidance_scale = req.guidance_scale
         req_num_outputs = getattr(req, "num_outputs_per_prompt", None)
         if req_num_outputs and req_num_outputs > 0:
             num_images_per_prompt = req_num_outputs

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_layered.py

@@ -722,6 +722,8 @@ the image\n<|vision_start|><|image_pad|><|vision_end|><|im_end|>\n<|im_start|>as
         num_inference_steps = req.num_inference_steps or num_inference_steps
         generator = req.generator or generator
         true_cfg_scale = req.true_cfg_scale or true_cfg_scale
+        if req.guidance_scale_provided:
+            guidance_scale = req.guidance_scale
         req_num_outputs = getattr(req, "num_outputs_per_prompt", None)
         if req_num_outputs and req_num_outputs > 0:
             num_images_per_prompt = req_num_outputs

vllm_omni/diffusion/models/wan2_2/pipeline_wan2_2.py

@@ -284,11 +284,11 @@ class Wan22Pipeline(nn.Module):
         req: OmniDiffusionRequest,
         prompt: str | None = None,
         negative_prompt: str | None = None,
-        height: int | None = None,
-        width: int | None = None,
-        num_inference_steps: int | None = None,
+        height: int = 480,
+        width: int = 832,
+        num_inference_steps: int = 40,
         guidance_scale: float | tuple[float, float] = 4.0,
-        frame_num: int | None = None,
+        frame_num: int = 81,
         output_type: str | None = "np",
         generator: torch.Generator | None = None,
         prompt_embeds: torch.Tensor | None = None,
@@ -301,9 +301,9 @@ class Wan22Pipeline(nn.Module):
         if prompt is None and prompt_embeds is None:
             raise ValueError("Prompt or prompt_embeds is required for Wan2.2 generation.")
 
-        height = req.height or height or 720
-        width = req.width or width or 1280
-        num_frames = req.num_frames if req.num_frames else frame_num or 81
+        height = req.height or height
+        width = req.width or width
+        num_frames = req.num_frames if req.num_frames else frame_num
 
         # Ensure dimensions are compatible with VAE and patch size
         # For expand_timesteps mode, we need latent dims to be even (divisible by patch_size)
@@ -311,7 +311,11 @@ class Wan22Pipeline(nn.Module):
         mod_value = self.vae_scale_factor_spatial * patch_size[1]  # 16*2=32 for TI2V, 8*2=16 for I2V
         height = (height // mod_value) * mod_value
         width = (width // mod_value) * mod_value
-        num_steps = req.num_inference_steps or num_inference_steps or 40
+        num_steps = req.num_inference_steps or num_inference_steps
+
+        # Respect per-request guidance_scale when explicitly provided.
+        if req.guidance_scale_provided:
+            guidance_scale = req.guidance_scale
 
         guidance_low = guidance_scale if isinstance(guidance_scale, (int, float)) else guidance_scale[0]
         guidance_high = (

vllm_omni/diffusion/models/wan2_2/pipeline_wan2_2_i2v.py

@@ -261,11 +261,11 @@ class Wan22I2VPipeline(nn.Module, SupportImageInput):
         prompt: str | None = None,
         negative_prompt: str | None = None,
         image: PIL.Image.Image | torch.Tensor | None = None,
-        height: int | None = None,
-        width: int | None = None,
-        num_inference_steps: int | None = None,
+        height: int = 480,
+        width: int = 832,
+        num_inference_steps: int = 40,
         guidance_scale: float | tuple[float, float] = 5.0,
-        frame_num: int | None = None,
+        frame_num: int = 81,
         output_type: str | None = "np",
         generator: torch.Generator | None = None,
         prompt_embeds: torch.Tensor | None = None,
@@ -287,10 +287,14 @@ class Wan22I2VPipeline(nn.Module, SupportImageInput):
         if image is None:
             raise ValueError("Image is required for I2V generation.")
 
-        height = req.height or height or 480
-        width = req.width or width or 832
-        num_frames = req.num_frames if req.num_frames else frame_num or 81
-        num_steps = req.num_inference_steps or num_inference_steps or 50
+        height = req.height or height
+        width = req.width or width
+        num_frames = req.num_frames if req.num_frames else frame_num
+        num_steps = req.num_inference_steps or num_inference_steps
+
+        # Respect per-request guidance_scale when explicitly provided.
+        if req.guidance_scale_provided:
+            guidance_scale = req.guidance_scale
 
         # Handle guidance scales
         guidance_low = guidance_scale if isinstance(guidance_scale, (int, float)) else guidance_scale[0]

vllm_omni/diffusion/models/wan2_2/pipeline_wan2_2_ti2v.py

@@ -200,11 +200,11 @@ class Wan22TI2VPipeline(nn.Module, SupportImageInput):
         prompt: str | None = None,
         negative_prompt: str | None = None,
         image: PIL.Image.Image | torch.Tensor | None = None,
-        height: int | None = None,
-        width: int | None = None,
-        num_inference_steps: int | None = None,
+        height: int = 704,
+        width: int = 1280,
+        num_inference_steps: int = 40,
         guidance_scale: float = 5.0,
-        frame_num: int | None = None,
+        frame_num: int = 81,
         output_type: str | None = "np",
         generator: torch.Generator | None = None,
         prompt_embeds: torch.Tensor | None = None,
@@ -223,10 +223,14 @@ class Wan22TI2VPipeline(nn.Module, SupportImageInput):
             image = req.pil_image
 
         # Default dimensions for TI2V-5B (720P)
-        height = req.height or height or 704
-        width = req.width or width or 1280
-        num_frames = req.num_frames if req.num_frames else frame_num or 121
-        num_steps = req.num_inference_steps or num_inference_steps or 50
+        height = req.height or height
+        width = req.width or width
+        num_frames = req.num_frames if req.num_frames else frame_num
+        num_steps = req.num_inference_steps or num_inference_steps
+
+        # Respect per-request guidance_scale when explicitly provided.
+        if req.guidance_scale_provided:
+            guidance_scale = req.guidance_scale
 
         self._guidance_scale = guidance_scale
 

vllm_omni/entrypoints/async_omni_diffusion.py

@@ -138,6 +138,9 @@ class AsyncOmniDiffusion:
             if key in field_names:
                 init_kwargs[key] = value
 
+        if "guidance_scale" in kwargs:
+            init_kwargs["guidance_scale_provided"] = True
+
         return OmniDiffusionRequest(**init_kwargs)
 
     async def generate(