Update inference_manager.py

inference_manager.py

@@ -304,14 +304,6 @@ class InferenceManager:
         if not sampler:
             sampler = self.cfg.get("sampler", "Euler a")
         # Define samplers
-        samplers = {
-            "Euler a": EulerAncestralDiscreteScheduler.from_config(temp_pipeline.scheduler.config),
-            "DPM++ SDE Karras": DPMSolverSDEScheduler.from_config(temp_pipeline.scheduler.config, use_karras_sigmas=True),
-            "DPM2 a": DPMSolverMultistepScheduler.from_config(temp_pipeline.scheduler.config),
-            "DPM++ SDE": DPMSolverSDEScheduler.from_config(temp_pipeline.scheduler.config),
-            "DPM++ 2M SDE": DPMSolverSDEScheduler.from_config(temp_pipeline.scheduler.config, use_2m=True),
-            "DPM++ 2S a": DPMSolverMultistepScheduler.from_config(temp_pipeline.scheduler.config, use_2s=True)
-        }
         
         # Set the scheduler based on the selected sampler
         temp_pipeline.scheduler = samplers[sampler]
@@ -380,8 +372,7 @@ class ModelManager:
         """
         print("downloading models")
         print("loading face analysis...")
-        self.app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
-        self.app.prepare(ctx_id=0, det_size=(512, 512))
+        self.app = None
         #download_from_hf()
         self.ext_model_pathes = {
             "ip-adapter-faceid-sdxl": hf_hub_download(repo_id="h94/IP-Adapter-FaceID", filename="ip-adapter-faceid_sdxl.bin", repo_type="model")
@@ -509,10 +500,14 @@ class ModelManager:
             raise Exception(f"face images not provided")
         start = time.time()
         model.base_model_pipeline.to("cuda")
+        if not self.app:
+            self.app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+            self.app.prepare(ctx_id=0, det_size=(512, 512))
         print("extracting face...")
         faceid_all_embeds = []
         for image in images:
             face = image#cv2.imread(image)
+            print(image)
             faces = self.app.get(face)
             faceid_embed = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
             faceid_all_embeds.append(faceid_embed)
@@ -539,7 +534,7 @@ class ModelManager:
             num_inference_steps=steps,
             generator=generator,
             num_images_per_prompt=1,
-            output_type="pil",
+            #output_type="pil",
             #callback_on_step_end=callback_dynamic_cfg,
             #callback_on_step_end_tensor_inputs=['prompt_embeds', 'add_text_embeds', 'add_time_ids'],
         ).images