michael/stable-diffusion-webui
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

Merge pull request #8064 from laksjdjf/master

Browse source 
Add cond and uncond hidden states to CFGDenoiserParams
This commit is contained in:
AUTOMATIC1111 2023-03-11 14:48:55 +03:00 committed by GitHub
commit af416a2dbd
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
2 changed files with 10 additions and 2 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

8
modules/script_callbacks.py
View file

@ -29,7 +29,7 @@ class ImageSaveParams:
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"""
@ -45,6 +45,12 @@ class CFGDenoiserParams:
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:
def __init__(self, x, sampling_step, total_sampling_steps):

4
modules/sd_samplers_kdiffusion.py
View file

@ -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])
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: