viteGradio/README.md

import gradio as gr from huggingface_hub import login, HfFileSystem, HfApi, ModelCard import os import spaces import random import torch

is_shared_ui = False

hf_token = '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' 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 10px 5px; 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; } """

theme = gr.themes.Soft( primary_hue="teal", secondary_hue="gray", ).set( body_text_color_dark='*neutral_800', background_fill_primary_dark='*neutral_50', background_fill_secondary_dark='*neutral_50', border_color_accent_dark='*primary_300', border_color_primary_dark='*neutral_200', color_accent_soft_dark='*neutral_50', link_text_color_dark='*secondary_600', link_text_color_active_dark='*secondary_600', link_text_color_hover_dark='*secondary_700', link_text_color_visited_dark='*secondary_500', code_background_fill_dark='*neutral_100', shadow_spread_dark='6px', block_background_fill_dark='white', block_label_background_fill_dark='*primary_100', block_label_text_color_dark='*primary_500', block_title_text_color_dark='*primary_500', checkbox_background_color_dark='*background_fill_primary', checkbox_background_color_selected_dark='*primary_600', checkbox_border_color_dark='*neutral_100', checkbox_border_color_focus_dark='*primary_500', checkbox_border_color_hover_dark='*neutral_300', checkbox_border_color_selected_dark='*primary_600', checkbox_label_background_fill_selected_dark='*primary_500', checkbox_label_text_color_selected_dark='white', error_background_fill_dark='#fef2f2', error_border_color_dark='#b91c1c', error_text_color_dark='#b91c1c', error_icon_color_dark='#b91c1c', input_background_fill_dark='white', input_background_fill_focus_dark='*secondary_500', input_border_color_dark='*neutral_50', input_border_color_focus_dark='*secondary_300', input_placeholder_color_dark='*neutral_400', slider_color_dark='*primary_500', stat_background_fill_dark='*primary_300', table_border_color_dark='*neutral_300', table_even_background_fill_dark='white', table_odd_background_fill_dark='*neutral_50', button_primary_background_fill_dark='*primary_500', button_primary_background_fill_hover_dark='*primary_400', button_primary_border_color_dark='*primary_00', button_secondary_background_fill_dark='whiite', button_secondary_background_fill_hover_dark='*neutral_100', button_secondary_border_color_dark='*neutral_200', button_secondary_text_color_dark='*neutral_800' )

with gr.Blocks(theme=theme, css=css) as demo: with gr.Column(elem_id="col-container"):

    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)", visible = False, value=False, info="To use a private model, you'll need to duplicate the space with your own access token.")
    
    with gr.Blocks(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, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured")
            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=0.9, 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=0.9, 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]
)

return demo

demo.queue(max_size=12).launch(share=True)