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| import os | |
| import uuid | |
| import gradio as gr | |
| import json | |
| from gradio_client import Client, handle_file | |
| from gradio_imageslider import ImageSlider | |
| from PIL import Image | |
| from huggingface_hub import InferenceClient | |
| from loadimg import load_img | |
| 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) | |
| generated_image_path = result[0] # Esto puede necesitar ser ajustado basado en la estructura real de result | |
| used_seed = result[1] # Esto puede necesitar ser ajustado basado en la estructura real de result | |
| used_prompt = prompt # El prompt usado es simplemente el prompt procesado | |
| generated_image_path = load_img(generated_image_path, output_type="str") | |
| return [generated_image_path, generated_image_path], used_seed, used_prompt | |
| 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 resize_image(image_path, reduction_factor): | |
| image = Image.open(image_path) | |
| width, height = image.size | |
| new_size = (width // reduction_factor, height // reduction_factor) | |
| resized_image = image.resize(new_size) | |
| return resized_image | |
| def save_image(image): | |
| unique_filename = f"resized_image_{uuid.uuid4().hex}.png" | |
| image.save(unique_filename) | |
| return unique_filename | |
| def upscale_image(image, resolution, num_inference_steps, strength, hdr, guidance_scale, controlnet_strength, scheduler_name, reduce_factor): | |
| global client_tile_upscaler | |
| image = image[1] | |
| 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_tile_upscaler = None | |
| raise gr.Error("Failed to load client for " + tile_upscaler_url) | |
| if (reduce_factor > 1): | |
| image = resize_image(image, reduce_factor) | |
| image = save_image(image) | |
| try: | |
| job = client_tile_upscaler.submit( | |
| 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" | |
| ) | |
| result = job.result() | |
| except ValueError as e: | |
| raise gr.Error(e) | |
| return result | |
| def refine_image(apply_refiner, image, model ,prompt, negative_prompt, num_inference_steps, guidance_scale, seed, strength): | |
| if (not apply_refiner): | |
| return image | |
| client = InferenceClient() | |
| refined_image = client.image_to_image( | |
| image[0], | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| num_inference_steps=num_inference_steps, | |
| guidance_scale=guidance_scale, | |
| seed=seed, | |
| model=model, | |
| strength=strength | |
| ) | |
| return [image[0], refined_image] | |
| 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.Group(): | |
| apply_refiner = gr.Checkbox(label="Apply refiner", value=False) | |
| with gr.Accordion("Refiner params", open=False) as refiner_params: | |
| refiner_prompt = gr.Textbox(lines=3, label="Prompt") | |
| refiner_negative_prompt = gr.Textbox(lines=3, label="Negative Prompt") | |
| refiner_strength = gr.Slider( | |
| label="Strength", | |
| minimum=0, | |
| maximum=300, | |
| step=0.01, | |
| value=1 | |
| ) | |
| refiner_num_inference_steps = gr.Slider( | |
| label="Inference steps", | |
| minimum=3, | |
| maximum=300, | |
| step=1, | |
| value=25 | |
| ) | |
| refiner_guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.0, | |
| maximum=50.0, | |
| step=0.1, | |
| value=12 | |
| ) | |
| refiner_seed = gr.Slider( | |
| label="Seed", | |
| info="-1 denotes a random seed", | |
| minimum=-1, | |
| maximum=423538377342, | |
| step=1, | |
| value=-1 | |
| ) | |
| refiner_model = gr.Textbox(label="Model", value="stabilityai/stable-diffusion-xl-refiner-1.0") | |
| apply_refiner.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=apply_refiner, | |
| outputs=refiner_params, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| 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(): | |
| with gr.Column(): | |
| generated_image = ImageSlider(label="Image / Refined Image", type="filepath") | |
| enhace_button = gr.Button("Enhance Image") | |
| with gr.Column(): | |
| output_slider = ImageSlider(label="Before / After", type="filepath", show_download_button=False) | |
| with gr.Accordion("Enhacer params", open=False): | |
| upscale_reduce_factor = gr.Slider(minimum=1, maximum=10, step=1, label="Reduce Factor", info="1/n") | |
| upscale_resolution = gr.Slider(minimum=128, maximum=2048, value=1024, step=128, label="Resolution", info="Image width") | |
| upscale_num_inference_steps = gr.Slider(minimum=1, maximum=150, value=50, step=1, label="Number of Inference Steps") | |
| upscale_strength = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label="Strength", info="Higher values give more detail") | |
| 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=12, 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] | |
| ).then(refine_image, | |
| [apply_refiner, generated_image, refiner_model, refiner_prompt, refiner_negative_prompt, refiner_num_inference_steps, refiner_guidance_scale, refiner_seed, refiner_strength], | |
| generated_image | |
| ) | |
| cancel_btn.click( | |
| fn=cancel_infer, | |
| outputs=[] | |
| ) | |
| def clear_output(image_slider): | |
| return None | |
| 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, upscale_reduce_factor], | |
| output_slider | |
| ) | |
| gallery.select(update_selection, outputs=[prompt_in, selected_info, selected_index]) | |
| demo.launch(show_error=True) | |