diff options
| -rw-r--r-- | modules/script_callbacks.py | 8 | ||||
| -rw-r--r-- | modules/sd_samplers_kdiffusion.py | 4 |
2 files changed, 10 insertions, 2 deletions
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index edd0e2a7..d1703135 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -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"""
@@ -44,6 +44,12 @@ 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:
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 528f513f..ea974be0 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -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:
|