.

utils.py

@@ -37,14 +37,14 @@ def sample_frame(model: LatentDiffusion, prompt: str, image_sequence: torch.Tens
         #uc = model.get_learned_conditioning(u_dict)
         #uc = model.enc_concat_seq(uc, u_dict, 'c_concat')
         
-        c_dict = {'c_crossattn': prompt, 'c_concat': image_sequence}
+        #c_dict = {'c_crossattn': prompt, 'c_concat': image_sequence}
         
         model.eval()
         #c = model.get_learned_conditioning(c_dict)
         #print (c['c_crossattn'].shape)
         #print (c['c_crossattn'][0])
         print (prompt)
-        c = {}
+        c = {'c_concat': image_sequence}
         #c = model.enc_concat_seq(c, c_dict, 'c_concat')
         # Zero out the corresponding subtensors in c_concat for padding images
         #padding_mask = torch.isclose(image_sequence, torch.tensor(-1.0), rtol=1e-5, atol=1e-5).all(dim=(1, 2, 3)).unsqueeze(0)