Update app.py

app.py

@@ -93,7 +93,7 @@ face_clip_model.to(device, dtype=dtype)
 face_helper.face_det.to(device)
 face_helper.face_parse.to(device)
 transformer.to(device, dtype=dtype)
-free_memory()
+##free_memory()
 
 pipe = ConsisIDPipeline.from_pretrained(model_path, transformer=transformer, scheduler=scheduler, torch_dtype=dtype)
 # If you're using with lora, add this code
@@ -140,49 +140,60 @@ def delete_old_files():
                         os.remove(file_path)
         time.sleep(600)
 
-
-##threading.Thread(target=delete_old_files, daemon=True).start()
-@spaces.GPU(duration=70)
-def generate(
-    prompt,
-    image_input,
-    seed_value,
-    scale_status,
-    rife_status,
-    progress=gr.Progress(track_tqdm=True)
+def infer(
+    prompt: str,
+    image_input: str,
+    num_inference_steps: int,
+    guidance_scale: float,
+    seed: int = 42,
+    progress=gr.Progress(track_tqdm=True),
 ):
-    def infer(prompt: str,image_input: str,num_inference_steps: int,guidance_scale: float,seed: int = 42,progress=gr.Progress(track_tqdm=True),):
-        if seed == -1:
-            seed = random.randint(0, 2**8 - 1)
+    if seed == -1:
+        seed = random.randint(0, 2**8 - 1)
 
-        id_image = np.array(ImageOps.exif_transpose(Image.fromarray(image_input)).convert("RGB"))
-        id_image = resize_numpy_image_long(id_image, 1024)
-        id_cond, id_vit_hidden, align_crop_face_image, face_kps = process_face_embeddings(face_helper, face_clip_model, handler_ante, 
+    id_image = np.array(ImageOps.exif_transpose(Image.fromarray(image_input)).convert("RGB"))
+    id_image = resize_numpy_image_long(id_image, 1024)
+    id_cond, id_vit_hidden, align_crop_face_image, face_kps = process_face_embeddings(face_helper, face_clip_model, handler_ante, 
                                                                             eva_transform_mean, eva_transform_std, 
                                                                             face_main_model, device, dtype, id_image, 
                                                                             original_id_image=id_image, is_align_face=True, 
                                                                             cal_uncond=False)
+    if is_kps:
+        kps_cond = face_kps
+    else:
+        kps_cond = None
+
+    tensor = align_crop_face_image.cpu().detach()
+    tensor = tensor.squeeze()
+    tensor = tensor.permute(1, 2, 0)
+    tensor = tensor.numpy() * 255
+    tensor = tensor.astype(np.uint8)
+    image  = ImageOps.exif_transpose(Image.fromarray(tensor))
+
+    prompt = prompt.strip('"')
     
-        if is_kps:
-            kps_cond = face_kps
-        else:
-            kps_cond = None
-
-        tensor = align_crop_face_image.cpu().detach()
-        tensor = tensor.squeeze()
-        tensor = tensor.permute(1, 2, 0)
-        tensor = tensor.numpy() * 255
-        tensor = tensor.astype(np.uint8)
-        image  = ImageOps.exif_transpose(Image.fromarray(tensor))
-
-        prompt = prompt.strip('"')
+    generator = torch.Generator(device).manual_seed(seed) if seed else None
     
-        generator = torch.Generator(device).manual_seed(seed) if seed else None
+    video_pt = pipe(
+        prompt=prompt,
+        image=image,
+        num_videos_per_prompt=1,
+        num_inference_steps=num_inference_steps,
+        num_frames=49,
+        use_dynamic_cfg=False,
+        guidance_scale=guidance_scale,
+        generator=generator,
+        id_vit_hidden=id_vit_hidden,
+        id_cond=id_cond,
+        kps_cond=kps_cond,
+        output_type="pt",
+    ).frames
     
-        video_pt = pipe(prompt=prompt,image=image,num_videos_per_prompt=1,num_inference_steps=num_inference_steps,num_frames=49,use_dynamic_cfg=False,guidance_scale=guidance_scale,generator=generator,id_vit_hidden=id_vit_hidden,id_cond=id_cond,kps_cond=kps_cond,output_type="pt",).frames
-    
-        ##free_memory()
-        return video_pt, seed
+    ##free_memory()
+    return video_pt, seed
+##threading.Thread(target=delete_old_files, daemon=True).start()
+@spaces.GPU(duration=70)
+def generate(prompt,image_input,seed_value,scale_status,rife_status,progress=gr.Progress(track_tqdm=True)):
 
     latents, seed = infer(prompt,image_input,num_inference_steps=4,guidance_scale=7.0,seed=seed_value,progress=progress,)
     if scale_status: