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UnboundLocalError in SDXLInpaint.prepare_latents() #5648

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merged 7 commits into from
Nov 20, 2023
Merged

UnboundLocalError in SDXLInpaint.prepare_latents() #5648

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6a993f4
fix: UnboundLocalError with image_latents
a-r-r-o-w Nov 5, 2023
c47fe1b
chore: run make style, quality, fix-copies
a-r-r-o-w Nov 5, 2023
7451056
Merge branch 'main' into sdxl_bug
a-r-r-o-w Nov 17, 2023
f50aae2
revert changes from make fix-copies
a-r-r-o-w Nov 17, 2023
2a019ad
revert changes from make fix-copies
a-r-r-o-w Nov 17, 2023
8c15940
Merge branch 'main' into sdxl_bug
a-r-r-o-w Nov 17, 2023
b9f3cbe
Merge branch 'main' into sdxl_bug
yiyixuxu Nov 17, 2023
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1 change: 0 additions & 1 deletion src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py
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Expand Up @@ -106,7 +106,6 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""

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model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"]
_exclude_from_cpu_offload = ["safety_checker"]
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1 change: 0 additions & 1 deletion src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
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Expand Up @@ -144,7 +144,6 @@ class AltDiffusionImg2ImgPipeline(
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""

model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"]
_exclude_from_cpu_offload = ["safety_checker"]
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3 changes: 2 additions & 1 deletion src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
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Expand Up @@ -741,10 +741,11 @@ def prepare_latents(

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):
image = image.to(device=device, dtype=dtype)
image_latents = self._encode_vae_image(image=image, generator=generator)
image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)

if latents is None and add_noise:
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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