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Merge pull request #15806 from huchenlei/inpaint_fix

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[Performance 4/6] Precompute is_sdxl_inpaint flag
This commit is contained in:
AUTOMATIC1111
2024-06-08 11:06:39 +03:00
committed by GitHub
parent 64bf57b5ea 3e20b36e8f
commit 6450d24afe
3 changed files with 22 additions and 22 deletions
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28
modules/processing.py
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@@ -115,20 +115,17 @@ def txt2img_image_conditioning(sd_model, x, width, height):
return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
else:
sd = sd_model.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
# The "masked-image" in this case will just be all 0.5 since the entire image is masked.
image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
image_conditioning = images_tensor_to_samples(image_conditioning,
approximation_indexes.get(opts.sd_vae_encode_method))
if getattr(sd_model.model, "is_sdxl_inpaint", False):
# The "masked-image" in this case will just be all 0.5 since the entire image is masked.
image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
image_conditioning = images_tensor_to_samples(image_conditioning,
approximation_indexes.get(opts.sd_vae_encode_method))
# Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
image_conditioning = image_conditioning.to(x.dtype)
# Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
image_conditioning = image_conditioning.to(x.dtype)
return image_conditioning
return image_conditioning
# Dummy zero conditioning if we're not using inpainting or unclip models.
# Still takes up a bit of memory, but no encoder call.
@@ -392,11 +389,8 @@ class StableDiffusionProcessing:
if self.sampler.conditioning_key == "crossattn-adm":
return self.unclip_image_conditioning(source_image)
sd = self.sampler.model_wrap.inner_model.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
if getattr(self.sampler.model_wrap.inner_model.model, "is_sdxl_inpaint", False):
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
# Dummy zero conditioning if we're not using inpainting or depth model.
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)