Update app.py

app.py

@@ -162,11 +162,11 @@ app.prepare(ctx_id=0, det_size=(640, 640))
 
 
 # download checkpoints
-hf_hub_download(repo_id="briaai/ID_preservation_2.3", filename="controlnet/config.json", local_dir="./checkpoints")
-hf_hub_download(repo_id="briaai/ID_preservation_2.3", filename="controlnet/diffusion_pytorch_model.safetensors", local_dir="./checkpoints")
-hf_hub_download(repo_id="briaai/ID_preservation_2.3", filename="ip-adapter.bin", local_dir="./checkpoints")
-hf_hub_download(repo_id="briaai/ID_preservation_2.3", filename="image_encoder/pytorch_model.bin", local_dir="./checkpoints")
-hf_hub_download(repo_id="briaai/ID_preservation_2.3", filename="image_encoder/config.json", local_dir="./checkpoints")
+hf_hub_download(repo_id="briaai/ID_preservation_2.3_auraFaceEnc", filename="checkpoint_105000/controlnet/config.json", local_dir="./checkpoints")
+hf_hub_download(repo_id="briaai/ID_preservation_2.3_auraFaceEnc", filename="checkpoint_105000/controlnet/diffusion_pytorch_model.safetensors", local_dir="./checkpoints")
+hf_hub_download(repo_id="briaai/ID_preservation_2.3_auraFaceEnc", filename="checkpoint_105000/ip-adapter.bin", local_dir="./checkpoints")
+hf_hub_download(repo_id="briaai/ID_preservation_2.3_auraFaceEnc", filename="image_encoder/pytorch_model.bin", local_dir="./checkpoints")
+hf_hub_download(repo_id="briaai/ID_preservation_2.3_auraFaceEnc", filename="image_encoder/config.json", local_dir="./checkpoints")
 
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -265,30 +265,6 @@ def generate_image(image_path, prompt, num_steps, guidance_scale, seed, num_imag
                     
     generator = torch.Generator(device=device).manual_seed(seed)
     
-    # if lora_name != CURRENT_LORA_NAME:  # Check if LoRA needs to be changed
-    #     if CURRENT_LORA_NAME is not None:  # If a LoRA is already loaded, unload it
-    #         pipe.disable_lora()
-    #         pipe.unfuse_lora()
-    #         pipe.unload_lora_weights()
-    #         print(f"Unloaded LoRA: {CURRENT_LORA_NAME}")
-
-    #     if lora_name != "":  # Load the new LoRA if specified
-    #         # pipe.enable_model_cpu_offload()
-    #         lora_path = os.path.join(lora_base_path, lora_name, "pytorch_lora_weights.safetensors")
-    #         pipe.load_lora_weights(lora_path)
-    #         pipe.fuse_lora(lora_scale)  
-    #         pipe.enable_lora()
-
-    #         # lora_prefix = Loras_dict[lora_name]
-
-    #         print(f"Loaded new LoRA: {lora_name}")
-
-    #     # Update the current LoRA name
-    #     CURRENT_LORA_NAME = lora_name
-
-    # if lora_name != "":
-    #     full_prompt = f"{Loras_dict[lora_name]} + " " + {prompt}"
-    # else:
     full_prompt = prompt
 
     print("Start inference...")    
@@ -353,8 +329,6 @@ with gr.Blocks(css=css) as demo:
             info="Describe what you want to generate or modify in the image."
             )
             
-            # lora_name = gr.Dropdown(choices=lora_names, label="LoRA", value="",  info="Select a LoRA name from the list, not selecting any will disable LoRA.")
-
             submit = gr.Button("Submit", variant="primary")
  
             with gr.Accordion(open=False, label="Advanced Options"):
@@ -400,13 +374,7 @@ with gr.Blocks(css=css) as demo:
                     step=0.01,
                     value=0.4,
                 )
-                # lora_scale = gr.Slider(
-                #     label="lora_scale",
-                #     minimum=0.0,
-                #     maximum=1.0,
-                #     step=0.01,
-                #     value=0.7,
-                # )
+
                 seed = gr.Slider(
                     label="Seed",
                     minimum=0,
@@ -427,7 +395,6 @@ with gr.Blocks(css=css) as demo:
             api_name=False,
         ).then(
             fn=generate_image,
-            # inputs=[img_file, prompt, num_steps, guidance_scale, seed, num_images, ip_adapter_scale, kps_scale, canny_scale, lora_name, lora_scale],
             inputs=[img_file, prompt, num_steps, guidance_scale, seed, num_images, ip_adapter_scale, kps_scale, canny_scale],
             outputs=[gallery]
         )