fix(texture): avoid dual pipelines for IP generation

common.py

@@ -667,12 +667,17 @@ def generate_texture_mvimages(
     sub_idxs: tuple[tuple[int]] = ((0, 1, 2), (3, 4, 5)),
     req: gr.Request = None,
 ) -> list[str]:
-    global PIPELINE_IP
+    global PIPELINE, PIPELINE_IP
 
     output_root = os.path.join(TMP_DIR, str(req.session_hash))
     use_ip_adapter = True if ip_img_path and ip_adapt_scale > 0 else False
-    pipeline = PIPELINE
+
     if use_ip_adapter:
+        if PIPELINE is not None:
+            logger.info("Release default texture pipeline before IP generation")
+            PIPELINE = None
+            gc.collect()
+            torch.cuda.empty_cache()
         if PIPELINE_IP is None:
             PIPELINE_IP = build_texture_gen_pipe(
                 base_ckpt_dir="./weights",
@@ -681,25 +686,37 @@ def generate_texture_mvimages(
             )
         PIPELINE_IP.set_ip_adapter_scale([ip_adapt_scale])
         pipeline = PIPELINE_IP
-
-    img_save_paths = infer_pipe(
-        index_file=f"{output_root}/condition/index.json",
-        controlnet_cond_scale=controlnet_cond_scale,
-        guidance_scale=guidance_scale,
-        strength=strength,
-        num_inference_steps=num_inference_steps,
-        ip_adapt_scale=ip_adapt_scale,
-        ip_img_path=ip_img_path,
-        uid=uid,
-        prompt=prompt,
-        save_dir=f"{output_root}/multi_view",
-        sub_idxs=sub_idxs,
-        pipeline=pipeline,
-        seed=seed,
-    )
-
-    gc.collect()
-    torch.cuda.empty_cache()
+    else:
+        if PIPELINE is None:
+            PIPELINE = build_texture_gen_pipe(
+                base_ckpt_dir="./weights",
+                ip_adapt_scale=0,
+                device="cuda",
+            )
+        pipeline = PIPELINE
+
+    try:
+        img_save_paths = infer_pipe(
+            index_file=f"{output_root}/condition/index.json",
+            controlnet_cond_scale=controlnet_cond_scale,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            num_inference_steps=num_inference_steps,
+            ip_adapt_scale=ip_adapt_scale,
+            ip_img_path=ip_img_path,
+            uid=uid,
+            prompt=prompt,
+            save_dir=f"{output_root}/multi_view",
+            sub_idxs=sub_idxs,
+            pipeline=pipeline,
+            seed=seed,
+        )
+    finally:
+        if use_ip_adapter:
+            pipeline = None
+            PIPELINE_IP = None
+        gc.collect()
+        torch.cuda.empty_cache()
 
     return img_save_paths + img_save_paths