Update pipeline.py

pipeline.py

@@ -1064,14 +1064,26 @@ class StableDiffusionPipeline(
                 # perform guidance
                 if self.do_classifier_free_guidance:
                     if lora_composite:
-                        noise_preds = torch.stack(noise_preds, dim=0)
-                        print(noise_preds.size())
-                        noise_pred_uncond, noise_pred_text = noise_preds.chunk(2, dim=-1)
-                        print(noise_pred_uncond.size())
-                        print(noise_pred_text.size())
-                        noise_pred_uncond = noise_pred_uncond.mean(dim=0)
-                        noise_pred_text = noise_pred_text.mean(dim=0)
-                        noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+                        # noise_preds = torch.stack(noise_preds, dim=0)
+                        # print(noise_preds.size())
+                        # noise_pred_uncond, noise_pred_text = noise_preds.chunk(2, dim=-1)
+                        # print(noise_pred_uncond.size())
+                        # print(noise_pred_text.size())
+                        # noise_pred_uncond = noise_pred_uncond.mean(dim=0)
+                        # noise_pred_text = noise_pred_text.mean(dim=0)
+                        # noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                        noise_pred_uncond, noise_pred_text = 0, 0
+                        for lora_i in range(len(noise_preds)):
+                            cur_noise_pred_uncond, cur_noise_pred_text = noise_preds[lora_i].chunk(2)
+                            print(noise_preds[lora_i].size())
+                            print(cur_noise_pred_uncond.size())
+                            prnt(cur_noise_pred_text.size())
+                            noise_pred_uncond += cur_noise_pred_uncond
+                            noise_pred_text += self.guidance_scale * (cur_noise_pred_text - cur_noise_pred_uncond)
+                        noise_pred_uncond /= len(noise_preds)
+                        noise_pred_text /= len(noise_preds)
+                        noise_pred = noise_pred_uncond + noise_pred_text
                     else:
                         noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                         noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)