add dowloader

app.py

@@ -1,20 +1,20 @@
-import torch.multiprocessing as mp
-import multiprocessing as mp2
-from huggingface_hub import login
-
-mp.set_start_method('spawn', force=True)
-mp2.set_start_method('spawn', force=True)
 
+from huggingface_hub import hf_hub_download, login
+import os
 import spaces
 import gradio as gr
 from PIL import Image
 import os
 
 import random
-login()
+login(os.getenv("HF_TOKEN"))
+hf_hub_download("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors")
+hf_hub_download("XLabs-AI/flux-controlnet-collections", "flux-depth-controlnet.safetensor")
+hf_hub_download("XLabs-AI/flux-ip-adapter", "flux-ip-adapter.safetensors")
+hf_hub_download("XLabs-AI/flux-controlnet-canny", "controlnet.safetensors")
 
 @spaces.GPU(duration=300)
-def process_image(image, prompt, steps, use_lora, use_controlnet, use_depth, use_hed, use_ip, lora_name, lora_path, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg):
+def process_image(image, prompt, steps, use_lora, use_controlnet, use_depth, use_hed, use_ip lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg):
     from src.flux.xflux_pipeline import XFluxPipeline
     def run_xflux_pipeline(
         prompt, image, repo_id, name, device,
@@ -111,9 +111,7 @@ def process_image(image, prompt, steps, use_lora, use_controlnet, use_depth, use
           neg_prompt=neg_prompt,
           image=image,
           negative_image=negative_image,
-          lora_name=lora_name,
           lora_weight=lora_weight,
-          lora_repo_id=lora_path,
           control_type="depth" if use_depth else "hed" if use_hed else "canny",
           repo_id="XLabs-AI/flux-controlnet-collections",
           name="flux-depth-controlnet.safetensors",
@@ -209,8 +207,6 @@ with gr.Blocks(css=custom_css) as demo:
                 use_depth = gr.Checkbox(label="Use depth")
                 use_hed = gr.Checkbox(label="Use hed")
                 use_lora = gr.Checkbox(label="Use LORA", value=True)
-                lora_path = gr.Textbox(label="LoraPath", value="XLabs-AI/flux-lora-collection")
-                lora_name = gr.Textbox(label="LoraName", value="realism_lora.safetensors")
                 lora_weight = gr.Slider(step=0.1, minimum=0, maximum=1, value=0.7, label="Lora Weight")
                 
                 true_gs = gr.Slider(step=0.1, minimum=0, maximum=10, value=3.5, label="TrueGs")
@@ -223,7 +219,7 @@ with gr.Blocks(css=custom_css) as demo:
         with gr.Column(scale=2, elem_classes="app"):
             output = gr.Gallery(label="Galery output", elem_classes="galery", selected_index=0)
 
-    submit_btn.click(process_image, inputs=[input_image, prompt, steps, use_lora, controlnet, use_depth, use_hed, use_ip, lora_name, lora_path, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg], outputs=output)
+    submit_btn.click(process_image, inputs=[input_image, prompt, steps, use_lora, controlnet, use_depth, use_hed, use_ip, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg], outputs=output)
 
 if __name__ == '__main__':
     demo.launch(share=True, debug=True)