Fix Wan I2V Quality (#11087)

* fix_wan_i2v_quality

* Update src/diffusers/pipelines/wan/pipeline_wan_i2v.py

Co-authored-by: YiYi Xu <[email protected]>

* Update src/diffusers/pipelines/wan/pipeline_wan_i2v.py

Co-authored-by: YiYi Xu <[email protected]>

* Update src/diffusers/pipelines/wan/pipeline_wan_i2v.py

Co-authored-by: YiYi Xu <[email protected]>

* Update pipeline_wan_i2v.py

---------

Co-authored-by: YiYi Xu <[email protected]>
Co-authored-by: hlky <[email protected]>

Author: 3 people

src/diffusers/pipelines/wan/pipeline_wan_i2v.py

@@ -108,31 +108,16 @@ def prompt_clean(text):
     return text
 
 
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
 def retrieve_latents(
-    encoder_output: torch.Tensor,
-    latents_mean: torch.Tensor,
-    latents_std: torch.Tensor,
-    generator: Optional[torch.Generator] = None,
-    sample_mode: str = "sample",
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
 ):
     if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
-        encoder_output.latent_dist.mean = (encoder_output.latent_dist.mean - latents_mean) * latents_std
-        encoder_output.latent_dist.logvar = torch.clamp(
-            (encoder_output.latent_dist.logvar - latents_mean) * latents_std, -30.0, 20.0
-        )
-        encoder_output.latent_dist.std = torch.exp(0.5 * encoder_output.latent_dist.logvar)
-        encoder_output.latent_dist.var = torch.exp(encoder_output.latent_dist.logvar)
         return encoder_output.latent_dist.sample(generator)
     elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
-        encoder_output.latent_dist.mean = (encoder_output.latent_dist.mean - latents_mean) * latents_std
-        encoder_output.latent_dist.logvar = torch.clamp(
-            (encoder_output.latent_dist.logvar - latents_mean) * latents_std, -30.0, 20.0
-        )
-        encoder_output.latent_dist.std = torch.exp(0.5 * encoder_output.latent_dist.logvar)
-        encoder_output.latent_dist.var = torch.exp(encoder_output.latent_dist.logvar)
         return encoder_output.latent_dist.mode()
     elif hasattr(encoder_output, "latents"):
-        return (encoder_output.latents - latents_mean) * latents_std
+        return encoder_output.latents
     else:
         raise AttributeError("Could not access latents of provided encoder_output")
 
@@ -412,13 +397,15 @@ def prepare_latents(
 
         if isinstance(generator, list):
             latent_condition = [
-                retrieve_latents(self.vae.encode(video_condition), latents_mean, latents_std, g) for g in generator
+                retrieve_latents(self.vae.encode(video_condition), sample_mode="argmax") for _ in generator
             ]
             latent_condition = torch.cat(latent_condition)
         else:
-            latent_condition = retrieve_latents(self.vae.encode(video_condition), latents_mean, latents_std, generator)
+            latent_condition = retrieve_latents(self.vae.encode(video_condition), sample_mode="argmax")
             latent_condition = latent_condition.repeat(batch_size, 1, 1, 1, 1)
 
+        latent_condition = (latent_condition - latents_mean) * latents_std
+
         mask_lat_size = torch.ones(batch_size, 1, num_frames, latent_height, latent_width)
         mask_lat_size[:, :, list(range(1, num_frames))] = 0
         first_frame_mask = mask_lat_size[:, :, 0:1]