Merge branch 'main' into dev

nodes.py

@@ -3104,6 +3104,8 @@ class WanVideoSampler:
                         frame_num = clip_length = image_embeds.get("num_frames", 81)
                         vae = image_embeds.get("vae", None)
                         clip_embeds = image_embeds.get("clip_context", None)
+                        if clip_embeds is not None:
+                            clip_embeds = clip_embeds.to(dtype)
                         colormatch = image_embeds.get("colormatch", "disabled")
                         motion_frame = image_embeds.get("motion_frame", 25)
                         target_w = image_embeds.get("target_w", None)
@@ -3295,7 +3297,7 @@ class WanVideoSampler:
                                     cfg[idx], 
                                     text_embeds["prompt_embeds"], 
                                     text_embeds["negative_prompt_embeds"], 
-                                    timestep, idx, y.squeeze(0), clip_embeds.to(dtype), control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond,
+                                    timestep, idx, y.squeeze(0), clip_embeds, control_latents, vace_data, unianim_data, audio_proj, control_camera_latents, add_cond,
                                     cache_state=self.cache_state, multitalk_audio_embeds=audio_embs)
 
                                 if callback is not None:

utils.py

@@ -208,6 +208,17 @@ def apply_lora(model, device_to, transformer_load_device, params_to_keep=None, d
                         continue
             m.comfy_patched_weights = True
             #pbar.update(1)
+        
+        # After LoRA patching, scale weights that have scale_weight but are NOT LoRA patched
+        if len(scale_weights) > 0:
+            for name, param in model.model.diffusion_model.named_parameters():
+                scale_key = name.replace("weight", "scale_weight").replace("diffusion_model.", "") if "weight" in name else None
+                full_param_name = f"diffusion_model.{name}"
+                if scale_key and scale_key in scale_weights and full_param_name not in model.patches:
+                    scale = scale_weights[scale_key]
+                    param_fp32 = param.to(torch.float32)
+                    param_fp32.mul_(scale.to(param.device, torch.float32))
+                    param.copy_(param_fp32.to(param.dtype))
       
         model.current_weight_patches_uuid = model.patches_uuid
         if low_mem_load: