import gradio as gr from huggingface_hub import login, HfFileSystem, HfApi, ModelCard import os import spaces import random import torch is_shared_ui = True if "fffiloni/sdxl-control-loras" in os.environ['SPACE_ID'] else False hf_token = os.environ.get("HF_TOKEN") login(token=hf_token) fs = HfFileSystem(token=hf_token) api = HfApi() device="cuda" if torch.cuda.is_available() else "cpu" from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL from diffusers.utils import load_image from PIL import Image import torch import numpy as np import cv2 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) controlnet = ControlNetModel.from_pretrained( "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16 ) def check_use_custom_or_no(value): if value is True: return gr.update(visible=True) else: return gr.update(visible=False) def get_files(file_paths): last_files = {} # Dictionary to store the last file for each path for file_path in file_paths: # Split the file path into directory and file components directory, file_name = file_path.rsplit('/', 1) # Update the last file for the current path last_files[directory] = file_name # Extract the last files from the dictionary result = list(last_files.values()) return result def load_model(model_name): if model_name == "": gr.Warning("If you want to use a private model, you need to duplicate this space on your personal account.") raise gr.Error("You forgot to define Model ID.") # Get instance_prompt a.k.a trigger word card = ModelCard.load(model_name) repo_data = card.data.to_dict() instance_prompt = repo_data.get("instance_prompt") if instance_prompt is not None: print(f"Trigger word: {instance_prompt}") else: instance_prompt = "no trigger word needed" print(f"Trigger word: no trigger word needed") # List all ".safetensors" files in repo sfts_available_files = fs.glob(f"{model_name}/*safetensors") sfts_available_files = get_files(sfts_available_files) if sfts_available_files == []: sfts_available_files = ["NO SAFETENSORS FILE"] print(f"Safetensors available: {sfts_available_files}") return model_name, "Model Ready", gr.update(choices=sfts_available_files, value=sfts_available_files[0], visible=True), gr.update(value=instance_prompt, visible=True) def custom_model_changed(model_name, previous_model): if model_name == "" and previous_model == "" : status_message = "" elif model_name != previous_model: status_message = "model changed, please reload before any new run" else: status_message = "model ready" return status_message def resize_image(input_path, output_path, target_height): # Open the input image img = Image.open(input_path) # Calculate the aspect ratio of the original image original_width, original_height = img.size original_aspect_ratio = original_width / original_height # Calculate the new width while maintaining the aspect ratio and the target height new_width = int(target_height * original_aspect_ratio) # Resize the image while maintaining the aspect ratio and fixing the height img = img.resize((new_width, target_height), Image.LANCZOS) # Save the resized image img.save(output_path) return output_path @spaces.GPU def infer(use_custom_model, model_name, weight_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, inf_steps, seed, progress=gr.Progress(track_tqdm=True)): pipe = StableDiffusionXLControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) pipe.to(device) prompt = prompt negative_prompt = negative_prompt if seed < 0 : seed = random.randint(0, 423538377342) generator = torch.Generator(device=device).manual_seed(seed) if image_in == None: raise gr.Error("You forgot to upload a source image.") image_in = resize_image(image_in, "resized_input.jpg", 1024) if preprocessor == "canny": image = load_image(image_in) image = np.array(image) image = cv2.Canny(image, 100, 200) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) image = Image.fromarray(image) if use_custom_model: if model_name == "": raise gr.Error("you forgot to set a custom model name.") custom_model = model_name # This is where you load your trained weights if weight_name == "NO SAFETENSORS FILE": pipe.load_lora_weights( custom_model, low_cpu_mem_usage = True, use_auth_token = True ) else: pipe.load_lora_weights( custom_model, weight_name = weight_name, low_cpu_mem_usage = True, use_auth_token = True ) lora_scale=custom_lora_weight images = pipe( prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=float(controlnet_conditioning_scale), guidance_scale = float(guidance_scale), num_inference_steps=inf_steps, generator=generator, cross_attention_kwargs={"scale": lora_scale} ).images else: images = pipe( prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=float(controlnet_conditioning_scale), guidance_scale = float(guidance_scale), num_inference_steps=inf_steps, generator=generator, ).images images[0].save(f"result.png") return f"result.png", seed css=""" #col-container{ margin: 0 auto; max-width: 720px; text-align: left; } div#warning-duplicate { background-color: #ebf5ff; padding: 0 16px 16px; margin: 20px 0; } div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p { color: #0f4592!important; } div#warning-duplicate strong { color: #0f4592; } p.actions { display: flex; align-items: center; margin: 20px 0; } div#warning-duplicate .actions a { display: inline-block; margin-right: 10px; } button#load_model_btn{ height: 46px; } #status_info{ font-size: 0.9em; } .custom-color { color: #030303 !important; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): if is_shared_ui: top_description = gr.HTML(f'''

Note: you might want to use a private custom LoRa model

To do so, duplicate the Space and run it on your own profile using your own access token and eventually a GPU (T4-small or A10G-small) for faster inference without waiting in the queue.

Duplicate this Space to start using private models and skip the queue

''', elem_id="warning-duplicate") gr.HTML("""

SD-XL Control LoRas

Use StableDiffusion XL with Diffusers' SDXL ControlNets

""") use_custom_model = gr.Checkbox(label="Use a custom pre-trained LoRa model ? (optional)", value=False, info="To use a private model, you'll need to duplicate the space with your own access token.") with gr.Group(visible=False) as custom_model_box: with gr.Row(): with gr.Column(): if not is_shared_ui: your_username = api.whoami()["name"] my_models = api.list_models(author=your_username, filter=["diffusers", "stable-diffusion-xl", 'lora']) model_names = [item.modelId for item in my_models] if not is_shared_ui: custom_model = gr.Dropdown( label = "Your custom model ID", info="You can pick one of your private models", choices = model_names, allow_custom_value = True #placeholder = "username/model_id" ) else: custom_model = gr.Textbox( label="Your custom model ID", placeholder="your_username/your_trained_model_name", info="Make sure your model is set to PUBLIC" ) weight_name = gr.Dropdown( label="Safetensors file", #value="pytorch_lora_weights.safetensors", info="specify which one if model has several .safetensors files", allow_custom_value=True, visible = False ) with gr.Column(): with gr.Group(): load_model_btn = gr.Button("Load my model", elem_id="load_model_btn") previous_model = gr.Textbox( visible = False ) model_status = gr.Textbox( label = "model status", show_label = False, elem_id = "status_info" ) trigger_word = gr.Textbox(label="Trigger word", interactive=False, visible=False) image_in = gr.Image(sources=["upload"], type="filepath") with gr.Row(): with gr.Column(): with gr.Group(): prompt = gr.Textbox(label="Prompt") negative_prompt = gr.Textbox(label="Negative prompt", value="extra digit, fewer digits, cropped") with gr.Group(): guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=7.5) inf_steps = gr.Slider(label="Inference Steps", minimum="25", maximum="50", step=1, value=25) custom_lora_weight = gr.Slider(label="Custom model weights", minimum=0.1, maximum=1.0, step=0.1, value=0.9) with gr.Column(): with gr.Group(): preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny", interactive=False, info="For the moment, only canny is available") controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=1.0, step=0.01, value=0.5) with gr.Group(): 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", ) submit_btn = gr.Button("Submit") result = gr.Image(label="Result") use_custom_model.change( fn = check_use_custom_or_no, inputs =[use_custom_model], outputs = [custom_model_box], queue = False ) custom_model.blur( fn=custom_model_changed, inputs = [custom_model, previous_model], outputs = [model_status], queue = False ) load_model_btn.click( fn = load_model, inputs=[custom_model], outputs = [previous_model, model_status, weight_name, trigger_word], queue = False ) submit_btn.click( fn = infer, inputs = [use_custom_model, custom_model, weight_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, inf_steps, seed], outputs = [result, last_used_seed] ) demo.queue(max_size=12).launch()