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import torch |
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class TonemapNoiseWithRescaleCFG: |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": {"model": ("MODEL",), |
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"tonemap_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), |
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"rescale_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), |
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}} |
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RETURN_TYPES = ("MODEL",) |
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FUNCTION = "patch" |
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CATEGORY = "custom_node_experiments" |
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def patch(self, model, tonemap_multiplier, rescale_multiplier): |
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def tonemap_noise_rescale_cfg(args): |
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cond = args["cond"] |
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uncond = args["uncond"] |
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cond_scale = args["cond_scale"] |
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noise_pred = (cond - uncond) |
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noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred, dim=(1)) + 0.0000000001)[:, None] |
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noise_pred /= noise_pred_vector_magnitude |
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mean = torch.mean(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True) |
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std = torch.std(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True) |
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top = (std * 3 + mean) * tonemap_multiplier |
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noise_pred_vector_magnitude *= (1.0 / top) |
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new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0) |
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new_magnitude *= top |
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x_cfg = uncond + (noise_pred * new_magnitude * cond_scale) |
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ro_pos = torch.std(cond, dim=(1, 2, 3), keepdim=True) |
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ro_cfg = torch.std(x_cfg, dim=(1, 2, 3), keepdim=True) |
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x_rescaled = x_cfg * (ro_pos / ro_cfg) |
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x_final = rescale_multiplier * x_rescaled + (1.0 - rescale_multiplier) * x_cfg |
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return x_final |
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m = model.clone() |
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m.set_model_sampler_cfg_function(tonemap_noise_rescale_cfg) |
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return (m, ) |
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NODE_CLASS_MAPPINGS = { |
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"TonemapNoiseWithRescaleCFG": TonemapNoiseWithRescaleCFG, |
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} |
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