Merge pull request #4021 from AUTOMATIC1111/add-kdiff-cfgdenoiser-callback

Add mid-kdiffusion cfgdenoiser script callback - access latents, conditionings and sigmas mid-sampling

1 files changed, 7 insertions, 0 deletions

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 8772db56..44d4c189 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -12,6 +12,7 @@ from modules import prompt_parser, devices, processing, images
 

 from modules.shared import opts, cmd_opts, state

 import modules.shared as shared

+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback

 

 

 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])

@@ -280,6 +281,12 @@ class CFGDenoiser(torch.nn.Module):
         image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])

         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])

 

+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)

+        cfg_denoiser_callback(denoiser_params)

+        x_in = denoiser_params.x

+        image_cond_in = denoiser_params.image_cond

+        sigma_in = denoiser_params.sigma

+

         if tensor.shape[1] == uncond.shape[1]:

             cond_in = torch.cat([tensor, uncond])