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sdxl_perturbed_attention_guidance_controlnet

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jyoung105 commited on May 21

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ea8b08c

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1 Parent(s): 2f83a8c

Update pipeline.py

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pipeline.py +5 -5

pipeline.py CHANGED Viewed

@@ -1661,7 +1661,7 @@ class StableDiffusionXLControlNetPipeline(
                 device=device,
                 dtype=controlnet.dtype,
                 do_classifier_free_guidance=self.do_classifier_free_guidance,
-                do_perturbed_attention_guidance=self.do_perturbed_attention_guidance,
                 guess_mode=guess_mode,
             )
             height, width = image.shape[-2:]
@@ -1677,7 +1677,7 @@ class StableDiffusionXLControlNetPipeline(
                     device=device,
                     dtype=controlnet.dtype,
                     do_classifier_free_guidance=self.do_classifier_free_guidance,
-                    do_perturbed_attention_guidance=self.do_perturbed_attention_guidance,
                     guess_mode=guess_mode,
                 )
                 images.append(image_)
@@ -1937,19 +1937,19 @@ class StableDiffusionXLControlNetPipeline(
                     return_dict=False,
                 )
-                if guess_mode and self.do_classifier_free_guidance and not self.do_perturbed_attention_guidance:
                     # Infered ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
                     mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
-                elif guess_mode and not self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:
                     # Infered ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
                     mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
-                elif guess_mode and self.do_classifier_free_guidance and self.do_perturbed_attention_guidance:
                     raise ValueError(
                         "guess mode with both guidance is not supported."
                     )

                 device=device,
                 dtype=controlnet.dtype,
                 do_classifier_free_guidance=self.do_classifier_free_guidance,
+                do_perturbed_attention_guidance=self.do_adversarial_guidance,
                 guess_mode=guess_mode,
             )
             height, width = image.shape[-2:]
                     device=device,
                     dtype=controlnet.dtype,
                     do_classifier_free_guidance=self.do_classifier_free_guidance,
+                    do_perturbed_attention_guidance=self.do_adversarial_guidance,
                     guess_mode=guess_mode,
                 )
                 images.append(image_)
                     return_dict=False,
                 )
+                if guess_mode and self.do_classifier_free_guidance and not self.do_adversarial_guidance:
                     # Infered ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
                     mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
+                elif guess_mode and not self.do_classifier_free_guidance and self.do_adversarial_guidance:
                     # Infered ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.
                     down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
                     mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
+                elif guess_mode and self.do_classifier_free_guidance and self.do_adversarial_guidance:
                     raise ValueError(
                         "guess mode with both guidance is not supported."
                     )