from huggingface_hub import hf_hub_download, login import os import spaces import gradio as gr from PIL import Image import os import random token = os.environ.get("HF_TOKEN") login(token=token) print("downloading models - 1/4") hf_hub_download("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors") print("downloading models - 2/4") hf_hub_download("XLabs-AI/flux-controlnet-collections", "flux-depth-controlnet.safetensors") print("downloading models - 3/4") hf_hub_download("XLabs-AI/flux-ip-adapter", "flux-ip-adapter.safetensors") print("downloading models - 4/4") hf_hub_download("XLabs-AI/flux-controlnet-canny", "controlnet.safetensors") print("downloaded!") @spaces.GPU(duration=200) def process_image(lora_path, lora_name, 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, model_type, width, height, timestep_to_start_cfg, num_steps, true_gs, guidance, neg_prompt="", negative_image=None, save_path='results', control_type='depth', use_controlnet=False, seed=None, num_images_per_prompt=1, use_lora=False, lora_weight=0.7, lora_repo_id="XLabs-AI/flux-lora-collection", lora_name="realism_lora.safetensors", use_ip=False ): # Montando os argumentos simulando a linha de comando class Args: def __init__(self): self.prompt = prompt self.image = image self.control_type = control_type self.repo_id = repo_id self.name = name self.device = device self.use_controlnet = use_controlnet self.model_type = model_type self.width = width self.height = height self.timestep_to_start_cfg = timestep_to_start_cfg self.num_steps = num_steps self.true_gs = true_gs self.guidance = guidance self.num_images_per_prompt = num_images_per_prompt self.seed = seed if seed else 123456789 self.neg_prompt = neg_prompt self.img_prompt = Image.open(image) if use_ip else None self.neg_img_prompt = Image.open(negative_image) if negative_image and use_ip else None self.ip_scale = 1.0 self.neg_ip_scale = 1.0 self.local_path = None self.ip_repo_id = "XLabs-AI/flux-ip-adapter" self.ip_name = "flux-ip-adapter.safetensors" self.ip_local_path = None self.lora_repo_id = lora_repo_id self.lora_name = lora_name self.lora_local_path = None self.offload = False self.use_ip = use_ip self.use_lora = use_lora self.lora_weight = lora_weight self.save_path = save_path args = Args() # Carregar a imagem se fornecida if args.image: image = Image.open(args.image) else: image = None # Inicializar o pipeline com os parâmetros necessários xflux_pipeline = XFluxPipeline(args.model_type, args.device, args.offload) # Configurar ControlNet se necessário if args.use_controlnet: print('Loading ControlNet:', args.local_path, args.repo_id, args.name) xflux_pipeline.set_controlnet(args.control_type, args.local_path, args.repo_id, args.name) if args.use_ip: print('load ip-adapter:', args.ip_local_path, args.ip_repo_id, args.ip_name) xflux_pipeline.set_ip(args.ip_local_path, args.ip_repo_id, args.ip_name) if args.use_lora: print('load lora:', args.lora_local_path, args.lora_repo_id, args.lora_name) xflux_pipeline.set_lora(args.lora_local_path, args.lora_repo_id, args.lora_name, args.lora_weight) # Laço para gerar imagens images = [] for _ in range(1): seed = random.randint(0, 2147483647) result = xflux_pipeline( prompt=args.prompt, controlnet_image=image, width=args.width, height=args.height, guidance=args.guidance, num_steps=args.num_steps, seed=seed, true_gs=args.true_gs, neg_prompt=args.neg_prompt, timestep_to_start_cfg=args.timestep_to_start_cfg, image_prompt=args.img_prompt, neg_image_prompt=args.neg_img_prompt, ip_scale=args.ip_scale, neg_ip_scale=args.neg_ip_scale, ) images.append(result) return images return run_xflux_pipeline( prompt=prompt, neg_prompt=neg_prompt, image=image, negative_image=negative_image, lora_weight=lora_weight, 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", device="cuda", use_controlnet=use_controlnet, model_type="flux-dev", width=1024, height=1024, timestep_to_start_cfg=cfg, num_steps=steps, num_images_per_prompt=1, use_lora=use_lora, lora_repo_id=lora_path, lora_name=lora_name, true_gs=true_gs, use_ip=use_ip, guidance=guidance ) with gr.Blocks() as demo: with gr.Row(elem_classes="app-container"): with gr.Column(scale=1, min_width=500, elem_classes="sidebar"): with gr.Column(elem_classes="side_items"): input_image = gr.Image(label="Image", type="filepath") prompt = gr.Textbox(label="Prompt") submit_btn = gr.Button("Submit") neg_prompt = gr.Textbox(label="Neg Prompt") steps = gr.Slider(step=1, minimum=1, maximum=64, value=28, label="Num Steps") use_ip = gr.Checkbox(label="Use IP Adapter") controlnet = gr.Checkbox(label="Use Controlnet(by default uses canny)", value=True) 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="Lora Path", value="XLabs-AI/flux-lora-collection") lora_name = gr.Textbox(label="Lora Name", 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") guidance = gr.Slider(minimum=1, maximum=10, value=4, label="Guidance") cfg = gr.Slider(minimum=1, maximum=10, value=1, label="CFG") negative_image = gr.Image(label="Negative_image", type="filepath") gr.HTML("""

Space By: EuFountai

""") with gr.Column(scale=2, elem_classes="app"): output = gr.Gallery(label="Galery output", elem_classes="galery") submit_btn.click(process_image, inputs=[lora_path, lora_name, 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)