[Bugfix] Fix generation artifacts of Qwen-Image-Edit-2511 and update pipeline DiT param parsing

examples/offline_inference/image_to_image/image_edit.py

@@ -55,7 +55,15 @@
         --prompt "Edit description" \
         --cfg_parallel_size 2 \
         --num_inference_steps 50 \
-        --cfg_scale 4.0 \
+        --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
 
 For more options, run:
     python image_edit.py --help
@@ -260,6 +268,11 @@ def parse_args() -> argparse.Namespace:
         choices=[1, 2],
         help="Number of GPUs used for classifier free guidance parallel size.",
     )
+    parser.add_argument(
+        "--enforce_eager",
+        action="store_true",
+        help="Disable torch.compile and force eager execution.",
+    )
     return parser.parse_args()
 
 
@@ -321,6 +334,7 @@ def main():
         cache_backend=args.cache_backend,
         cache_config=cache_config,
         parallel_config=parallel_config,
+        enforce_eager=args.enforce_eager,
     )
     print("Pipeline loaded")
 

examples/online_serving/image_to_image/openai_chat_client.py

@@ -127,7 +127,7 @@ def main():
     parser.add_argument("--width", type=int, default=1024, help="Output image width")
     parser.add_argument("--steps", type=int, default=50, help="Inference steps")
     parser.add_argument("--guidance", type=float, default=7.5, help="Guidance scale")
-    parser.add_argument("--seed", type=int, help="Random seed")
+    parser.add_argument("--seed", type=int, default=0, help="Random seed")
     parser.add_argument("--negative", help="Negative prompt")
 
     args = parser.parse_args()

examples/online_serving/text_to_image/openai_chat_client.py

@@ -100,7 +100,7 @@ def main():
     parser.add_argument("--width", type=int, default=1024, help="Image width")
     parser.add_argument("--steps", type=int, default=50, help="Inference steps")
     parser.add_argument("--cfg-scale", type=float, default=4.0, help="True CFG scale")
-    parser.add_argument("--seed", type=int, default=42, help="Random seed")
+    parser.add_argument("--seed", type=int, default=0, help="Random seed")
     parser.add_argument("--negative", help="Negative prompt")
 
     args = parser.parse_args()

vllm_omni/diffusion/diffusion_engine.py

@@ -293,6 +293,7 @@ def add_req_and_wait_for_response(self, requests: list[OmniDiffusionRequest]):
     def _dummy_run(self):
         """A dummy run to warm up the model."""
         prompt = "dummy run"
+        # note that num_inference_steps=1 will cause timestep and temb None in the pipeline
         num_inference_steps = 1
         height = 1024
         width = 1024

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py

@@ -36,6 +36,7 @@
     QwenImageTransformer2DModel,
 )
 from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.diffusion.utils.tf_utils import get_transformer_config_kwargs
 from vllm_omni.model_executor.model_loader.weight_utils import (
     download_weights_from_hf_specific,
 )
@@ -274,7 +275,8 @@ def __init__(
         self.vae = AutoencoderKLQwenImage.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to(
             self.device
         )
-        self.transformer = QwenImageTransformer2DModel(od_config=od_config)
+        transformer_kwargs = get_transformer_config_kwargs(od_config.tf_model_config, QwenImageTransformer2DModel)
+        self.transformer = QwenImageTransformer2DModel(od_config=od_config, **transformer_kwargs)
 
         self.tokenizer = Qwen2Tokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
 

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit.py

@@ -38,6 +38,7 @@
     QwenImageTransformer2DModel,
 )
 from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.diffusion.utils.tf_utils import get_transformer_config_kwargs
 from vllm_omni.model_executor.model_loader.weight_utils import (
     download_weights_from_hf_specific,
 )
@@ -231,7 +232,8 @@ def __init__(
         self.vae = AutoencoderKLQwenImage.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to(
             self.device
         )
-        self.transformer = QwenImageTransformer2DModel(od_config=od_config)
+        transformer_kwargs = get_transformer_config_kwargs(od_config.tf_model_config, QwenImageTransformer2DModel)
+        self.transformer = QwenImageTransformer2DModel(od_config=od_config, **transformer_kwargs)
         self.tokenizer = Qwen2Tokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
         self.processor = Qwen2VLProcessor.from_pretrained(
             model, subfolder="processor", local_files_only=local_files_only

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit_plus.py

@@ -41,6 +41,7 @@
     QwenImageTransformer2DModel,
 )
 from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.diffusion.utils.tf_utils import get_transformer_config_kwargs
 from vllm_omni.model_executor.model_loader.weight_utils import (
     download_weights_from_hf_specific,
 )
@@ -191,7 +192,9 @@ def __init__(
         self.vae = AutoencoderKLQwenImage.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to(
             self.device
         )
-        self.transformer = QwenImageTransformer2DModel(od_config=od_config)
+
+        transformer_kwargs = get_transformer_config_kwargs(od_config.tf_model_config, QwenImageTransformer2DModel)
+        self.transformer = QwenImageTransformer2DModel(od_config=od_config, **transformer_kwargs)
         self.tokenizer = Qwen2Tokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
         self.processor = Qwen2VLProcessor.from_pretrained(
             model, subfolder="processor", local_files_only=local_files_only

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_layered.py

@@ -37,6 +37,7 @@
     QwenImageTransformer2DModel,
 )
 from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.diffusion.utils.tf_utils import get_transformer_config_kwargs
 from vllm_omni.model_executor.model_loader.weight_utils import (
     download_weights_from_hf_specific,
 )
@@ -211,18 +212,8 @@ def __init__(
             )
         ]
 
-        use_additional_t_cond = od_config.tf_model_config.use_additional_t_cond
-        zero_cond_t = od_config.tf_model_config.zero_cond_t
-        use_layer3d_rope = od_config.tf_model_config.use_layer3d_rope
-        guidance_embeds = od_config.tf_model_config.guidance_embeds
-
-        self.transformer = QwenImageTransformer2DModel(
-            od_config=od_config,
-            use_additional_t_cond=use_additional_t_cond,
-            zero_cond_t=zero_cond_t,
-            use_layer3d_rope=use_layer3d_rope,
-            guidance_embeds=guidance_embeds,
-        )
+        transformer_kwargs = get_transformer_config_kwargs(od_config.tf_model_config, QwenImageTransformer2DModel)
+        self.transformer = QwenImageTransformer2DModel(od_config=od_config, **transformer_kwargs)
 
         # Pipeline configuration & processing parameters
         self.vae_scale_factor = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8

vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py

@@ -604,7 +604,7 @@ def forward(
         txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1)  # Each [B, 3*dim]
 
         # Process image stream - norm1 + modulation
-        img_modulated, img_gate1 = self.img_norm1(hidden_states, img_mod1)
+        img_modulated, img_gate1 = self.img_norm1(hidden_states, img_mod1, modulate_index)
 
         # Process text stream - norm1 + modulation
         txt_modulated, txt_gate1 = self.txt_norm1(encoder_hidden_states, txt_mod1)
@@ -632,7 +632,8 @@ def forward(
         encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output
 
         # Process image stream - norm2 + MLP
-        img_modulated2, img_gate2 = self.img_norm2(hidden_states, img_mod2)
+        img_modulated2, img_gate2 = self.img_norm2(hidden_states, img_mod2, modulate_index)
+
         img_mlp_output = self.img_mlp(img_modulated2)
         hidden_states = hidden_states + img_gate2 * img_mlp_output
 
@@ -692,15 +693,13 @@ def __init__(
         attention_head_dim: int = 128,
         num_attention_heads: int = 24,
         joint_attention_dim: int = 3584,
-        guidance_embeds: bool = False,  # TODO: this should probably be removed
+        guidance_embeds: bool = False,
         axes_dims_rope: tuple[int, int, int] = (16, 56, 56),
         zero_cond_t: bool = False,
         use_additional_t_cond: bool = False,
         use_layer3d_rope: bool = False,
     ):
         super().__init__()
-        model_config = od_config.tf_model_config
-        num_layers = model_config.num_layers
         self.parallel_config = od_config.parallel_config
         self.in_channels = in_channels
         self.out_channels = out_channels or in_channels

vllm_omni/diffusion/utils/tf_utils.py

@@ -0,0 +1,54 @@
+import inspect
+from typing import Any
+
+from vllm_omni.diffusion.data import TransformerConfig
+
+
+def get_transformer_config_kwargs(
+    tf_model_config: TransformerConfig, model_class: type[Any] | None = None
+) -> dict[str, Any]:
+    """
+    This function extracts parameters from a TransformerConfig instance and filters out internal
+    diffusers metadata keys (those starting with '_') that should not be passed to model initialization.
+    Also filters out parameters that are not accepted by the model's __init__ method (e.g., pooled_projection_dim
+    for QwenImageTransformer2DModel).
+
+    This uses inspect.signature to dynamically detect accepted parameters, making it general for any model class.
+    Similar to how diffusers' @register_to_config decorator works.
+
+    Args:
+        tf_model_config: TransformerConfig instance containing model parameters
+        model_class: Optional model class to inspect for accepted __init__ parameters.
+                   If None, all non-internal parameters are returned (backward compatibility).
+
+    Returns:
+        dict: Filtered dictionary of parameters suitable for transformer model initialization
+    """
+    # Extract transformer config parameters, filtering out internal diffusers metadata
+    # TransformerConfig stores params in a 'params' dict, and we need to exclude
+    # internal keys like '_class_name' and '_diffusers_version'
+    tf_config_params = tf_model_config.to_dict()
+
+    # Filter out internal diffusers metadata keys that start with '_'
+    filtered_params = {k: v for k, v in tf_config_params.items() if not k.startswith("_")}
+
+    # If model_class is provided, use inspect.signature to get accepted parameters
+    if model_class is not None:
+        try:
+            # Get the signature of the model's __init__ method
+            sig = inspect.signature(model_class.__init__)
+            # Get all parameter names (excluding 'self' and special parameters)
+            accepted_params = {
+                name
+                for name, param in sig.parameters.items()
+                if name != "self" and param.kind != inspect.Parameter.VAR_KEYWORD  # Exclude **kwargs
+            }
+
+            # Filter to only include parameters that are in the model's signature
+            filtered_params = {k: v for k, v in filtered_params.items() if k in accepted_params}
+        except (TypeError, AttributeError):
+            # If inspection fails, fall back to returning all non-internal params
+            # This maintains backward compatibility
+            pass
+
+    return filtered_params