Add support for the Variations models (unclip-h and unclip-l)

1 files changed, 13 insertions, 6 deletions

diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 93f0e55a..e9f08518 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -92,14 +92,21 @@ class CFGDenoiser(torch.nn.Module):
         batch_size = len(conds_list)

         repeats = [len(conds_list[i]) for i in range(batch_size)]

 

+        if shared.sd_model.model.conditioning_key == "crossattn-adm":

+            image_uncond = torch.zeros_like(image_cond)

+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} 

+        else:

+            image_uncond = image_cond

+            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} 

+

         if not is_edit_model:

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

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

-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])

+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])

         else:

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

             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)])

+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])

 

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

         cfg_denoiser_callback(denoiser_params)

@@ -116,13 +123,13 @@ class CFGDenoiser(torch.nn.Module):
                 cond_in = torch.cat([tensor, uncond, uncond])

 

             if shared.batch_cond_uncond:

-                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})

+                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))

             else:

                 x_out = torch.zeros_like(x_in)

                 for batch_offset in range(0, x_out.shape[0], batch_size):

                     a = batch_offset

                     b = a + batch_size

-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})

+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))

         else:

             x_out = torch.zeros_like(x_in)

             batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size

@@ -135,9 +142,9 @@ class CFGDenoiser(torch.nn.Module):
                 else:

                     c_crossattn = torch.cat([tensor[a:b]], uncond)

 

-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})

+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))

 

-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})

+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))

 

         denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)

         cfg_denoised_callback(denoised_params)