1 files changed, 6 insertions, 1 deletions
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index e9e41818..61f23ad7 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -8,6 +8,7 @@ from modules.shared import opts, state
 import modules.shared as shared
 from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
 
 samplers_k_diffusion = [
     ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
@@ -160,7 +161,7 @@ class CFGDenoiser(torch.nn.Module):
             fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
             x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
 
-        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
+        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
         cfg_denoised_callback(denoised_params)
 
         devices.test_for_nans(x_out, "unet")
@@ -180,6 +181,10 @@ class CFGDenoiser(torch.nn.Module):
         if self.mask is not None:
             denoised = self.init_latent * self.mask + self.nmask * denoised
 
+        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
+        cfg_after_cfg_callback(after_cfg_callback_params)
+        denoised = after_cfg_callback_params.x
+
         self.step += 1
         return denoised