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Merge pull request #8064 from laksjdjf/master

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Add cond and uncond hidden states to CFGDenoiserParams
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AUTOMATIC1111 2023-03-11 14:48:55 +03:00 committed by GitHub
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2 changed files with 10 additions and 2 deletions
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8
modules/script_callbacks.py
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@ -29,7 +29,7 @@ class ImageSaveParams:
class CFGDenoiserParams: class CFGDenoiserParams:
def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps): def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, tensor, uncond):
self.x = x self.x = x
"""Latent image representation in the process of being denoised""" """Latent image representation in the process of being denoised"""
@ -45,6 +45,12 @@ class CFGDenoiserParams:
self.total_sampling_steps = total_sampling_steps self.total_sampling_steps = total_sampling_steps
"""Total number of sampling steps planned""" """Total number of sampling steps planned"""
self.tensor = tensor
""" Encoder hidden states of conditioning"""
self.uncond = uncond
""" Encoder hidden states of unconditioning"""
class CFGDenoisedParams: class CFGDenoisedParams:
def __init__(self, x, sampling_step, total_sampling_steps): def __init__(self, x, sampling_step, total_sampling_steps):

4
modules/sd_samplers_kdiffusion.py
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@ -101,11 +101,13 @@ class CFGDenoiser(torch.nn.Module):
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma]) sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
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)]) 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)])
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps) denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
cfg_denoiser_callback(denoiser_params) cfg_denoiser_callback(denoiser_params)
x_in = denoiser_params.x x_in = denoiser_params.x
image_cond_in = denoiser_params.image_cond image_cond_in = denoiser_params.image_cond
sigma_in = denoiser_params.sigma sigma_in = denoiser_params.sigma
tensor = denoiser_params.tensor
uncond = denoiser_params.uncond
if tensor.shape[1] == uncond.shape[1]: if tensor.shape[1] == uncond.shape[1]:
if not is_edit_model: if not is_edit_model: