Spaces:
Running
on
Zero
Running
on
Zero
Update stablenormal/pipeline_yoso_normal.py
Browse files
stablenormal/pipeline_yoso_normal.py
CHANGED
@@ -181,7 +181,6 @@ class YOSONormalsPipeline(StableDiffusionControlNetPipeline):
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self.prompt_embeds = None
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self.empty_text_embedding = empty_text_embedding
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self.t_start= t_start # target_out latents
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self.gauss_latent = None
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def check_inputs(
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self,
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@@ -522,7 +521,6 @@ class YOSONormalsPipeline(StableDiffusionControlNetPipeline):
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return_dict=False,
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)
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-
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# 7. YOSO sampling
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latent_x_t = self.unet(
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gauss_latent,
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@@ -587,11 +585,7 @@ class YOSONormalsPipeline(StableDiffusionControlNetPipeline):
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) # [N,4,h,w]
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image_latent = image_latent * self.vae.config.scaling_factor
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image_latent = image_latent.repeat_interleave(ensemble_size, dim=0) # [N*E,4,h,w]
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pred_latent = self.gauss_latent
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if pred_latent is None:
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self.gauss_latent = torch.randn_like(image_latent)
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pred_latent = self.gauss_latent
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return image_latent, pred_latent
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self.prompt_embeds = None
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self.empty_text_embedding = empty_text_embedding
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self.t_start= t_start # target_out latents
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def check_inputs(
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self,
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return_dict=False,
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)
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# 7. YOSO sampling
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latent_x_t = self.unet(
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gauss_latent,
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) # [N,4,h,w]
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image_latent = image_latent * self.vae.config.scaling_factor
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image_latent = image_latent.repeat_interleave(ensemble_size, dim=0) # [N*E,4,h,w]
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+
pred_latent = torch.randn_like(image_latent)
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return image_latent, pred_latent
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