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https://github.com/invoke-ai/InvokeAI
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Merge branch 'main' into feat/solid-infill
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commit
56e2d22b6e
@ -544,6 +544,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
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init_image = init_image.to(device=device, dtype=latents_dtype)
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mask = mask.to(device=device, dtype=latents_dtype)
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if init_image.dim() == 3:
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init_image = init_image.unsqueeze(0)
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@ -562,17 +563,22 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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if mask.dim() == 3:
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mask = mask.unsqueeze(0)
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mask = tv_resize(mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR) \
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latent_mask = tv_resize(mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR) \
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.to(device=device, dtype=latents_dtype)
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guidance: List[Callable] = []
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if is_inpainting_model(self.unet):
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# You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint
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# (that's why there's a mask!) but it seems to really want that blanked out.
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masked_init_image = init_image * torch.where(mask < 0.5, 1, 0)
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masked_latents = self.non_noised_latents_from_image(masked_init_image, device=device, dtype=latents_dtype)
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# TODO: we should probably pass this in so we don't have to try/finally around setting it.
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self.invokeai_diffuser.model_forward_callback = \
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AddsMaskLatents(self._unet_forward, mask, init_image_latents)
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AddsMaskLatents(self._unet_forward, latent_mask, masked_latents)
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else:
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guidance.append(AddsMaskGuidance(mask, init_image_latents, self.scheduler, noise))
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guidance.append(AddsMaskGuidance(latent_mask, init_image_latents, self.scheduler, noise))
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try:
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result_latents, result_attention_maps = self.latents_from_embeddings(
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