Update utils.py

utils.py

@@ -41,12 +41,12 @@ def sample_frame(model: LatentDiffusion, prompt: str, image_sequence: torch.Tens
         c = model.enc_concat_seq(c, c_dict, 'c_concat')
         if pos_map is not None:
             print (pos_map.shape, c['c_concat'].shape)
-            c['c_concat'] = torch.cat([c['c_concat'], pos_map.to(c['c_concat'].device).unsqueeze(0)], dim=1)
+            c['c_concat'] = torch.cat([c['c_concat'][:, :, :64, :64], pos_map.to(c['c_concat'].device).unsqueeze(0)], dim=1)
 
         print ('sleeping')
         #time.sleep(120)
         print ('finished sleeping')
-        #samples_ddim = model.p_sample_loop(cond=c, shape=[1, 3, 64, 64], return_intermediates=False, verbose=True)
+        samples_ddim = model.p_sample_loop(cond=c, shape=[1, 3, 64, 64], return_intermediates=False, verbose=True)
         #samples_ddim, _ = sampler.sample(S=999,
         #                                 conditioning=c,
         #                                 batch_size=1,
@@ -56,9 +56,9 @@ def sample_frame(model: LatentDiffusion, prompt: str, image_sequence: torch.Tens
         #                                 unconditional_conditioning=uc,
         #                                 eta=0)
         
-        #x_samples_ddim = model.decode_first_stage(samples_ddim)
-        x_samples_ddim = pos_map.to(c['c_concat'].device).unsqueeze(0).expand(-1, 3, -1, -1)
-        x_samples_ddim = model.decode_first_stage(x_samples_ddim)
+        x_samples_ddim = model.decode_first_stage(samples_ddim)
+        #x_samples_ddim = pos_map.to(c['c_concat'].device).unsqueeze(0).expand(-1, 3, -1, -1)
+        #x_samples_ddim = model.decode_first_stage(x_samples_ddim)
         #x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
         x_samples_ddim = torch.clamp(x_samples_ddim, min=-1.0, max=1.0)