parent
269833067d
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cf0cfefe91
@ -186,7 +186,7 @@ class StableDiffusionProcessing:
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return conditioning
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def edit_image_conditioning(self, source_image):
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conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
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return conditioning_image
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@ -77,9 +77,9 @@ class CFGDenoiser(torch.nn.Module):
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batch_size = len(conds_list)
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repeats = [len(conds_list[i]) for i in range(batch_size)]
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x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
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sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
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image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [image_cond])
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x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
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image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
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sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
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denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
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cfg_denoiser_callback(denoiser_params)
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@ -88,7 +88,7 @@ class CFGDenoiser(torch.nn.Module):
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sigma_in = denoiser_params.sigma
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if tensor.shape[1] == uncond.shape[1]:
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cond_in = torch.cat([tensor, uncond, uncond])
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cond_in = torch.cat([tensor, uncond])
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if shared.batch_cond_uncond:
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x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
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