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prepare_latents_inpaint always return noise and image_latents

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yiyixuxu
2025-07-16 11:57:29 +02:00
parent d8fa2de36f
commit 4b7a9e9fa9
1 changed files with 3 additions and 13 deletions
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16
src/diffusers/modular_pipelines/stable_diffusion_xl/before_denoise.py
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@@ -744,8 +744,6 @@ class StableDiffusionXLInpaintPrepareLatentsStep(PipelineBlock):
timestep=None,
is_strength_max=True,
add_noise=True,
return_noise=False,
return_image_latents=False,
):
shape = (
batch_size,
@@ -768,7 +766,7 @@ class StableDiffusionXLInpaintPrepareLatentsStep(PipelineBlock):
if image.shape[1] == 4:
image_latents = image.to(device=device, dtype=dtype)
image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
elif return_image_latents or (latents is None and not is_strength_max):
elif latents is None and not is_strength_max:
image = image.to(device=device, dtype=dtype)
image_latents = self._encode_vae_image(components, image=image, generator=generator)
image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
@@ -786,13 +784,7 @@ class StableDiffusionXLInpaintPrepareLatentsStep(PipelineBlock):
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
latents = image_latents.to(device)
outputs = (latents,)
if return_noise:
outputs += (noise,)
if return_image_latents:
outputs += (image_latents,)
outputs = (latents, noise, image_latents)
return outputs
@@ -864,7 +856,7 @@ class StableDiffusionXLInpaintPrepareLatentsStep(PipelineBlock):
block_state.height = block_state.image_latents.shape[-2] * components.vae_scale_factor
block_state.width = block_state.image_latents.shape[-1] * components.vae_scale_factor
block_state.latents, block_state.noise = self.prepare_latents_inpaint(
block_state.latents, block_state.noise, block_state.image_latents = self.prepare_latents_inpaint(
components,
block_state.batch_size * block_state.num_images_per_prompt,
components.num_channels_latents,
@@ -878,8 +870,6 @@ class StableDiffusionXLInpaintPrepareLatentsStep(PipelineBlock):
timestep=block_state.latent_timestep,
is_strength_max=block_state.is_strength_max,
add_noise=block_state.add_noise,
return_noise=True,
return_image_latents=False,
)
# 7. Prepare mask latent variables