Fix issue with wan i2v scaling. Adjust aggressive loader to be compatable with updated diffusers.

toolkit/models/wan21/wan21_i2v.py

@@ -99,7 +99,8 @@ class AggressiveWanI2VUnloadPipeline(WanImageToVideoPipeline):
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
             
         # unload vae and transformer
-        device = self.transformer.device
+        # device = self.transformer.device
+        device = self._exec_device
         
         self.text_encoder.to(device)
         
@@ -116,7 +117,8 @@ class AggressiveWanI2VUnloadPipeline(WanImageToVideoPipeline):
             width,
             prompt_embeds,
             negative_prompt_embeds,
-            callback_on_step_end_tensor_inputs,
+            image_embeds=None,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
         )
 
         self._guidance_scale = guidance_scale
@@ -145,7 +147,6 @@ class AggressiveWanI2VUnloadPipeline(WanImageToVideoPipeline):
         )
         
         # unload text encoder
-        print("Unloading text encoder")
         self.text_encoder.to("cpu")
         self.transformer.to(device)
         flush()
@@ -456,13 +457,20 @@ class Wan21I2V(Wan21):
         # Resize to 224x224 (using bilinear interpolation, which is resample=3 in PIL)
         if tensor.shape[2] != 224 or tensor.shape[3] != 224:
             tensor = F.interpolate(tensor, size=(224, 224), mode='bilinear', align_corners=False)
+            
+        tensors_0_1 = tensor.clamp(0, 1)  # Ensure values are in [0, 1] range
         
-        # Normalize with mean and std
-        mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).view(1, 3, 1, 1).to(tensor.device)
-        std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).view(1, 3, 1, 1).to(tensor.device)
-        tensor = (tensor - mean) / std
+        mean = torch.tensor(self.image_processor.image_mean).to(
+            tensors_0_1.device, dtype=tensors_0_1.dtype
+        ).view([1, 3, 1, 1]).detach()
+        std = torch.tensor(self.image_processor.image_std).to(
+            tensors_0_1.device, dtype=tensors_0_1.dtype
+        ).view([1, 3, 1, 1]).detach()
         
-        return tensor
+        # tensors_0_1 = torch.clip((255. * tensors_0_1), 0, 255).round() / 255.0
+        clip_image = (tensors_0_1 - mean) / std
+
+        return clip_image.detach()
 
     def get_noise_prediction(
         self,

toolkit/models/wan21/wan_utils.py

@@ -65,6 +65,17 @@ def add_first_frame_conditioning(
         video_condition.to(device, dtype)
     ).latent_dist.sample()
     latent_condition = latent_condition.to(device, dtype)
+    
+    latents_mean = (
+        torch.tensor(vae.config.latents_mean)
+        .view(1, vae.config.z_dim, 1, 1, 1)
+        .to(device, dtype)
+    )
+    latents_std = 1.0 / torch.tensor(vae.config.latents_std).view(1, vae.config.z_dim, 1, 1, 1).to(
+        device, dtype
+    )
+    latent_condition = (latent_condition - latents_mean) * latents_std
+    
 
     # Create mask: 1 for conditioning frames, 0 for frames to generate
     batch_size = first_frame.shape[0]