import os import gradio as gr import json from gradio_client import Client, handle_file from gradio_imageslider import ImageSlider with open('loras.json', 'r') as f: loras = json.load(f) job = None # Verificar las URLs de los modelos custom_model_url = "https://fffiloni-sd-xl-custom-model.hf.space" tile_upscaler_url = "https://gokaygokay-tileupscalerv2.hf.space" client_custom_model = None client_tile_upscaler = None # try: # client_custom_model = Client(custom_model_url) # print(f"Loaded custom model from {custom_model_url}") # except ValueError as e: # print(f"Failed to load custom model: {e}") # try: # client_tile_upscaler = Client(tile_upscaler_url) # print(f"Loaded custom model from {tile_upscaler_url}") # except ValueError as e: # print(f"Failed to load custom model: {e}") def infer(selected_index, prompt, style_prompt, inf_steps, guidance_scale, width, height, seed, lora_weight, progress=gr.Progress(track_tqdm=True)): try: global job if selected_index is None: raise gr.Error("You must select a LoRA before proceeding.") selected_lora = loras[selected_index] custom_model = selected_lora["repo"] trigger_word = selected_lora["trigger_word"] global client_custom_model if client_custom_model is None: try: client_custom_model = Client(custom_model_url) print(f"Loaded custom model from {custom_model_url}") except ValueError as e: print(f"Failed to load custom model: {e}") client_custom_model = None raise gr.Error("Failed to load client for " + custom_model_url) try: result = client_custom_model.submit( custom_model=custom_model, api_name="/load_model" ) except ValueError as e: raise gr.Error(e) weight_name = result.result()[2]['value'] if trigger_word and prompt.startswith(trigger_word): prompt = prompt[len(trigger_word+'. '):].lstrip() if style_prompt and prompt.endswith(style_prompt): prompt = prompt[:-len('. '+style_prompt)].rstrip() prompt_arr = [trigger_word, prompt, style_prompt] prompt = '. '.join([element.strip() for element in prompt_arr if element.strip() != '']) try: job = client_custom_model.submit( custom_model=custom_model, weight_name=weight_name, prompt=prompt, inf_steps=inf_steps, guidance_scale=guidance_scale, width=width, height=height, seed=seed, lora_weight=lora_weight, api_name="/infer" ) result = job.result() except ValueError as e: raise gr.Error(e) new_result = result + (prompt, ) return new_result except Exception as e: gr.Warning("Error: " + str(e)) def cancel_infer(): global job if job: job.cancel() return "Job has been cancelled" return "No job to cancel" def update_selection(evt: gr.SelectData): selected_lora = loras[evt.index] new_placeholder = f"Type a prompt for {selected_lora['title']}" lora_repo = selected_lora["repo"] updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨" return ( gr.update(placeholder=new_placeholder), updated_text, evt.index ) def upscale_image(image, resolution, num_inference_steps, strength, hdr, guidance_scale, controlnet_strength, scheduler_name): global client_tile_upscaler if client_tile_upscaler is None: try: client_tile_upscaler = Client(tile_upscaler_url) print(f"Loaded custom model from {tile_upscaler_url}") except ValueError as e: print(f"Failed to load custom model: {e}") client_custom_model = None raise gr.Error("Failed to load client for " + tile_upscaler_url) try: result = client_tile_upscaler.predict( param_0=handle_file(image), param_1=resolution, param_2=num_inference_steps, param_3=strength, param_4=hdr, param_5=guidance_scale, param_6=controlnet_strength, param_7=scheduler_name, api_name="/wrapper" ) except ValueError as e: raise gr.Error(e) return result css=""" """ with gr.Blocks(css=css) as demo: gr.Markdown("# lichorosario LoRA Portfolio") gr.Markdown( "### This is my portfolio.\n" "**Note**: Generation quality may vary. For best results, adjust the parameters.\n" "Special thanks to [@artificialguybr](https://huggingface.co/artificialguybr) and [@fffiloni](https://huggingface.co/fffiloni).\n" "Based on [https://huggingface.co/spaces/fffiloni/sd-xl-custom-model](https://huggingface.co/spaces/fffiloni/sd-xl-custom-model) and [https://huggingface.co/spaces/gokaygokay/TileUpscalerV2](https://huggingface.co/spaces/gokaygokay/TileUpscalerV2)" ) with gr.Row(): with gr.Column(scale=2): prompt_in = gr.Textbox( label="Your Prompt", info="Don't forget to include your trigger word if necessary" ) style_prompt_in = gr.Textbox( label="Your Style Prompt" ) selected_info = gr.Markdown("") used_prompt = gr.Textbox( label="Used prompt" ) with gr.Column(elem_id="col-container"): with gr.Accordion("Advanced Settings", open=False): with gr.Row(): inf_steps = gr.Slider( label="Inference steps", minimum=3, maximum=150, step=1, value=25 ) guidance_scale = gr.Slider( label="Guidance scale", minimum=0.0, maximum=50.0, step=0.1, value=12 ) with gr.Row(): width = gr.Slider( label="Width", minimum=256, maximum=3072, step=32, value=1024, ) height = gr.Slider( label="Height", minimum=256, maximum=3072, step=32, value=512, ) examples = [ [1024,512], [2048,512], [3072, 512] ] gr.Examples( label="Presets", examples=examples, inputs=[width, height], outputs=[] ) with gr.Row(): seed = gr.Slider( label="Seed", info="-1 denotes a random seed", minimum=-1, maximum=423538377342, step=1, value=-1 ) last_used_seed = gr.Number( label="Last used seed", info="the seed used in the last generation", ) lora_weight = gr.Slider( label="LoRa weight", minimum=0.0, maximum=1.0, step=0.01, value=1.0 ) with gr.Column(scale=1): gallery = gr.Gallery( [(item["image"], item["title"]) for item in loras], label="LoRA Gallery", allow_preview=False, columns=2, height="100%" ) submit_btn = gr.Button("Submit") cancel_btn = gr.Button("Cancel") with gr.Row(): def clear_output(image_slider): image_slider[0] = None image_slider[1] = None return image_slider with gr.Column(): generated_image = gr.Image(label="Input Image", type="filepath") enhace_button = gr.Button("Enhance Image") with gr.Column(): output_slider = ImageSlider(label="Before / After", type="numpy", show_download_button=False) with gr.Accordion("Advanced Options", open=False): upscale_resolution = gr.Slider(minimum=128, maximum=2048, value=1024, step=128, label="Resolution") upscale_num_inference_steps = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Number of Inference Steps") upscale_strength = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label="Strength") upscale_hdr = gr.Slider(minimum=0, maximum=1, value=0, step=0.1, label="HDR Effect") upscale_guidance_scale = gr.Slider(minimum=0, maximum=20, value=6, step=0.5, label="Guidance Scale") upscale_controlnet_strength = gr.Slider(minimum=0.0, maximum=2.0, value=0.75, step=0.05, label="ControlNet Strength") upscale_scheduler_name = gr.Dropdown( choices=["DDIM", "DPM++ 3M SDE Karras", "DPM++ 3M Karras"], value="DDIM", label="Scheduler" ) selected_index = gr.State(None) submit_btn.click( fn=infer, inputs=[selected_index, prompt_in, style_prompt_in, inf_steps, guidance_scale, width, height, seed, lora_weight], outputs=[generated_image, last_used_seed, used_prompt] ) cancel_btn.click( fn=cancel_infer, outputs=[] ) enhace_button.click( fn=clear_output, inputs=[output_slider], outputs=[output_slider] ).then( upscale_image, [generated_image, upscale_resolution, upscale_num_inference_steps, upscale_strength, upscale_hdr, upscale_guidance_scale, upscale_controlnet_strength, upscale_scheduler_name], output_slider ) gallery.select(update_selection, outputs=[prompt_in, selected_info, selected_index]) demo.launch()