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Fix Wan I2V Quality #11087

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Merged
merged 5 commits into from
Mar 17, 2025
Merged

Fix Wan I2V Quality #11087

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566a915
fix_wan_i2v_quality
chengzeyi Mar 17, 2025
482415c
Update src/diffusers/pipelines/wan/pipeline_wan_i2v.py
chengzeyi Mar 17, 2025
c01d2bf
Update src/diffusers/pipelines/wan/pipeline_wan_i2v.py
chengzeyi Mar 17, 2025
ac15382
Update src/diffusers/pipelines/wan/pipeline_wan_i2v.py
chengzeyi Mar 17, 2025
378e412
Update pipeline_wan_i2v.py
hlky Mar 17, 2025
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27 changes: 7 additions & 20 deletions src/diffusers/pipelines/wan/pipeline_wan_i2v.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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")

Expand Down Expand Up @@ -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]
Expand Down
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