Merge pull request #8064 from laksjdjf/master
Add cond and uncond hidden states to CFGDenoiserParams
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@ -29,7 +29,7 @@ class ImageSaveParams:
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class CFGDenoiserParams:
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def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
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def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, tensor, uncond):
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self.x = x
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"""Latent image representation in the process of being denoised"""
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@ -44,6 +44,12 @@ class CFGDenoiserParams:
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self.total_sampling_steps = total_sampling_steps
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"""Total number of sampling steps planned"""
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self.tensor = tensor
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""" Encoder hidden states of conditioning"""
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self.uncond = uncond
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""" Encoder hidden states of unconditioning"""
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class CFGDenoisedParams:
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@ -101,11 +101,13 @@ class CFGDenoiser(torch.nn.Module):
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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] + [torch.zeros_like(self.init_latent)])
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denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
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denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
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cfg_denoiser_callback(denoiser_params)
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x_in = denoiser_params.x
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image_cond_in = denoiser_params.image_cond
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sigma_in = denoiser_params.sigma
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tensor = denoiser_params.tensor
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uncond = denoiser_params.uncond
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if tensor.shape[1] == uncond.shape[1]:
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if not is_edit_model:
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