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Add cond and uncond hidden states to CFGDenoiserParams #8064

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merged 3 commits into from
Mar 11, 2023
Merged

Add cond and uncond hidden states to CFGDenoiserParams #8064

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327186b
Update script_callbacks.py
laksjdjf Feb 24, 2023
9a14359
Update sd_samplers_kdiffusion.py
laksjdjf Feb 24, 2023
534cf60
Update script_callbacks.py
laksjdjf Feb 24, 2023
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8 changes: 7 additions & 1 deletion modules/script_callbacks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ def __init__(self, image, p, filename, pnginfo):


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
"""Latent image representation in the process of being denoised"""

Expand All @@ -44,6 +44,12 @@ def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):

self.total_sampling_steps = total_sampling_steps
"""Total number of sampling steps planned"""

self.tensor = tensor
""" Encoder hidden states of conditioning"""

self.uncond = uncond
""" Encoder hidden states of unconditioning"""


class CFGDenoisedParams:
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4 changes: 3 additions & 1 deletion modules/sd_samplers_kdiffusion.py
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Expand Up @@ -101,11 +101,13 @@ def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
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)])

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)
x_in = denoiser_params.x
image_cond_in = denoiser_params.image_cond
sigma_in = denoiser_params.sigma
tensor = denoiser_params.tensor
uncond = denoiser_params.uncond

if tensor.shape[1] == uncond.shape[1]:
if not is_edit_model:
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