Merge pull request #8064 from laksjdjf/master

Add cond and uncond hidden states to CFGDenoiserParams

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"""
         
@@ -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):

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: