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https://github.com/invoke-ai/InvokeAI
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Added resizing of controlnet image based on noise latent. Fixes a tensor mismatch issue.
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@ -366,6 +366,10 @@ class TextToLatentsInvocation(BaseInvocation):
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)
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)
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latents_shape = noise.shape
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control_height_resize = latents_shape[2] * 8
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control_width_resize = latents_shape[3] * 8
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# copied from old backend/txt2img.py
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# FIXME: still need to test with different widths, heights, devices, dtypes
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# and add in batch_size, num_images_per_prompt?
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@ -375,10 +379,8 @@ class TextToLatentsInvocation(BaseInvocation):
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image=control_image,
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# do_classifier_free_guidance=do_classifier_free_guidance,
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do_classifier_free_guidance=True,
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# width=width,
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# height=height,
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width=512,
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height=512,
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width=control_width_resize,
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height=control_height_resize,
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# batch_size=batch_size * num_images_per_prompt,
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# num_images_per_prompt=num_images_per_prompt,
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device=control_model.device,
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@ -391,10 +393,8 @@ class TextToLatentsInvocation(BaseInvocation):
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image=image_,
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# do_classifier_free_guidance=do_classifier_free_guidance,
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do_classifier_free_guidance=True,
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# width=width,
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# height=height,
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width=512,
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height=512,
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width=control_width_resize,
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height=control_height_resize,
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# batch_size=batch_size * num_images_per_prompt,
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# num_images_per_prompt=num_images_per_prompt,
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device=control_model.device,
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@ -403,8 +403,6 @@ class TextToLatentsInvocation(BaseInvocation):
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images.append(image_)
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control_image = images
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# TODO: Verify the noise is the right size
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result_latents, result_attention_map_saver = model.latents_from_embeddings(
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latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)),
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@ -1030,6 +1030,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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dtype=torch.float16,
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do_classifier_free_guidance=True,
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):
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if not isinstance(image, torch.Tensor):
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if isinstance(image, PIL.Image.Image):
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image = [image]
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