Update pipeline.py
Browse files- pipeline.py +8 -6
pipeline.py
CHANGED
@@ -915,7 +915,7 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
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# image = self.decode_latents(upscale_latents)
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if return_latents:
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return latents
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else:
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image = self.decode_latents(latents)
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@@ -1011,7 +1011,7 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
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(nsfw) content, according to the `safety_checker`.
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"""
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-
latents = self.__call__(
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prompt=prompt,
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negative_prompt=negative_prompt,
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height=height,
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@@ -1032,19 +1032,21 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
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)
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latents = torch.nn.functional.interpolate(latents, size=(int(height*resize_scale)//8, int(width*resize_scale)//8))
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-
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return self.__call__(
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prompt=prompt,
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negative_prompt=negative_prompt,
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height=height
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width=width
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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strength=strength,
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num_images_per_prompt=num_images_per_prompt,
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eta=eta,
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generator=generator,
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-
latents=
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return_latents=False,
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max_embeddings_multiples=max_embeddings_multiples,
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output_type=output_type,
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# image = self.decode_latents(upscale_latents)
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if return_latents:
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+
return latents,noise,init_latents_orig
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else:
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image = self.decode_latents(latents)
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(nsfw) content, according to the `safety_checker`.
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"""
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+
latents,noise,init_latents_orig = self.__call__(
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prompt=prompt,
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negative_prompt=negative_prompt,
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height=height,
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)
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latents = torch.nn.functional.interpolate(latents, size=(int(height*resize_scale)//8, int(width*resize_scale)//8))
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print("noise")
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print(noise)
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+
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return self.__call__(
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prompt=prompt,
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negative_prompt=negative_prompt,
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+
height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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strength=strength,
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num_images_per_prompt=num_images_per_prompt,
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eta=eta,
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generator=generator,
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latents=None,
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return_latents=False,
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max_embeddings_multiples=max_embeddings_multiples,
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output_type=output_type,
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