Update class name and assign back to vars
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@ -11,7 +11,7 @@ from modules import prompt_parser, devices, processing, images
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from modules.shared import opts, cmd_opts, state
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from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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import modules.shared as shared
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from modules.script_callbacks import CGFDenoiserParams, cfg_denoiser_callback
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from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
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@ -279,7 +279,11 @@ class CFGDenoiser(torch.nn.Module):
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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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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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sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
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cfg_denoiser_callback(CGFDenoiserParams(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)
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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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if tensor.shape[1] == uncond.shape[1]:
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if tensor.shape[1] == uncond.shape[1]:
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cond_in = torch.cat([tensor, uncond])
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cond_in = torch.cat([tensor, uncond])
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