add model link

gradio_app.py

@@ -27,6 +27,36 @@ CLIP_L_PATH="/tiamat-NAS/hailong/storage_backup/models/stabilityai/stable-diffus
 T5XXL_PATH="/tiamat-NAS/hailong/storage_backup/models/stabilityai/stable-diffusion-3-medium/text_encoders/t5xxl_fp16.safetensors"
 AE_PATH="/tiamat-vePFS/share_data/storage/huggingface/models/black-forest-labs/FLUX.1-dev/ae.safetensors"
 
+from huggingface_hub import hf_hub_download
+repo_id = "Kijai/flux-fp8"
+file_name = "flux1-dev-fp8.safetensors"
+BASE_FLUX_CHECKPOINT = hf_hub_download(repo_id=repo_id, filename=file_name)
+print(f"Downloaded BASE_FLUX_CHECKPOINT saved at: {file_path}")
+
+from huggingface_hub import hf_hub_download
+repo_id = "comfyanonymous/flux_text_encoders"
+file_name = "clip_l.safetensors"
+CLIP_L_PATH = hf_hub_download(repo_id=repo_id, filename=file_name)
+print(f"Downloaded CLIP_L_PATH saved at: {file_path}")
+
+from huggingface_hub import hf_hub_download
+repo_id = "comfyanonymous/flux_text_encoders"
+file_name = "t5xxl_fp8_e4m3fn.safetensors"
+T5XXL_PATH = hf_hub_download(repo_id=repo_id, filename=file_name)
+print(f"Downloaded T5XXL_PATH saved at: {file_path}")
+
+from huggingface_hub import hf_hub_download
+repo_id = "black-forest-labs/FLUX.1-dev"
+file_name = "ae.safetensors"
+AE_PATH = hf_hub_download(repo_id=repo_id, filename=file_name)
+print(f"Downloaded AE_PATH saved at: {file_path}")
+
+from huggingface_hub import hf_hub_download
+repo_id = "showlab/makeanything"
+file_name = "recraft_9f_lego.safetensors"
+LORA_WEIGHTS_PATH = hf_hub_download(repo_id=repo_id, filename=file_name)
+print(f"Downloaded LORA_WEIGHTS_PATH saved at: {file_path}")
+
 # Load model function
 def load_target_model():
     logger.info("Loading models...")
@@ -201,7 +231,7 @@ with gr.Blocks() as demo:
         sample_image = gr.Image(label="Upload a Conditional Image", type="pil")
         
         # Frame number selection
-        frame_num = gr.Radio([4, 9], label="Select Frame Number", value=4)
+        frame_num = gr.Radio([4, 9], label="Select Frame Number", value=9)
         
         # Seed and randomize seed options
         seed = gr.Slider(0, np.iinfo(np.int32).max, step=1, label="Seed", value=0)