app.py

import torch
from tqdm import tqdm
from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionXLPipeline
import gradio as gr
import requests
import spaces

models_list = []
loras_list = ["None"]
models = {}

def download_file(url, filename, progress=gr.Progress(track_tqdm=True)):
    response = requests.get(url, stream=True)
    total_size_in_bytes = int(response.headers.get('content-length', 0))
    block_size = 1024  # 1 Kibibyte
    progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)

    with open(filename, 'wb') as file:
        for data in response.iter_content(block_size):
            progress_bar.update(len(data))
            file.write(data)
    progress_bar.close()
    if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
        print("ERROR, something went wrong")

def get_civitai_model_info(model_id):
    url = f"https://civitai.com/api/v1/models/{model_id}"
    response = requests.get(url)
    if response.status_code != 200:
        return None
    return response.json()

def find_download_url(data, file_extension):
    for file in data.get('modelVersions', [{}])[0].get('files', []):
        if file['name'].endswith(file_extension):
            return file['downloadUrl']
    return None

def download_civitai_model(model_id, lora_id=""):
    try:
        model_data = get_civitai_model_info(model_id)
        if model_data is None:
            return f"Error: Model with ID {model_id} not found."

        model_name = model_data['name']
        model_ckpt_url = find_download_url(model_data, '.ckpt')
        model_safetensors_url = find_download_url(model_data, '.safetensors')
        model_url = model_ckpt_url or model_safetensors_url

        if not model_url:
            return f"Error: No suitable file found for model {model_name}."

        file_extension = '.ckpt' if model_ckpt_url else '.safetensors'
        download_file(model_url, f"{model_name}{file_extension}")

        if lora_id:
            lora_data = get_civitai_model_info(lora_id)
            if lora_data is None:
                return f"Error: LoRA with ID {lora_id} not found."

            lora_name = lora_data['name']
            lora_safetensors_url = find_download_url(lora_data, '.safetensors')
            if not lora_safetensors_url:
                return f"Error: No suitable file found for LoRA {lora_name}."

            download_file(lora_safetensors_url, f"{lora_name}.safetensors")
            loras_list.append(lora_name)
        else:
            lora_name = "None"

        models_list.append(model_name)
        return "Model/LoRA Downloaded!"
    except Exception as e:
        return f"Error downloading model or LoRA: {e}"

def load_model(model, lora="", use_lora=False):
    try:
        print(f"\n\nLoading {model}...")
        vae = AutoencoderKL.from_pretrained(
            "madebyollin/sdxl-vae-fp16-fix",
            torch_dtype=torch.float16,
        )

        pipeline = StableDiffusionXLPipeline.from_pretrained(
            model,
            vae=vae,
            torch_dtype=torch.float16,
        )

        if use_lora and lora != "":
            pipeline.load_lora_weights(lora)

        pipeline.to("cuda")
        models[model] = pipeline
        return "Model/LoRA loaded successfully!"
    except Exception as e:
        return f"Error loading model {model}: {e}"

@spaces.GPU
def generate_images(
    model_name,
    lora_name,
    prompt,
    negative_prompt,
    num_inference_steps,
    guidance_scale,
    height,
    width,
    num_images=4,
    progress=gr.Progress(track_tqdm=True)
):
    if prompt is not None and prompt.strip() != "":
        if lora_name == "None":
            load_model(model_name, "", False)
        elif lora_name in loras_list and lora_name != "None":
            load_model(model_name, lora_name, True)

        pipe = models.get(model_name)
        if pipe is None:
            return []

        outputs = []

        for _ in range(num_images):
            output = pipe(
                prompt,
                negative_prompt=negative_prompt,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
                height=height,
                width=width
            )["images"][0]
            outputs.append(output)

        return outputs
    else:
        return gr.Warning("Prompt empty!")

# Create the Gradio blocks
with gr.Blocks(theme='ParityError/Interstellar') as demo:
    with gr.Row(equal_height=False):
        with gr.Tab("Generate"):
            with gr.Column(elem_id="input_column"):
                with gr.Group(elem_id="input_group"):
                    model_dropdown = gr.Dropdown(choices=models_list, value=models_list[0] if models_list else None, label="Model", elem_id="model_dropdown")
                    lora_dropdown = gr.Dropdown(choices=loras_list, value=loras_list[0], label="LoRA")
                    prompt = gr.Textbox(label="Prompt", elem_id="prompt_textbox")
                    generate_btn = gr.Button("Generate Image", elem_id="generate_button")
                with gr.Accordion("Advanced", open=False, elem_id="advanced_accordion"):
                    negative_prompt = gr.Textbox(label="Negative Prompt", value="lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]", elem_id="negative_prompt_textbox")
                    num_inference_steps = gr.Slider(minimum=10, maximum=50, step=1, value=25, label="Number of Inference Steps", elem_id="num_inference_steps_slider")
                    guidance_scale = gr.Slider(minimum=1, maximum=20, step=0.5, value=7.5, label="Guidance Scale", elem_id="guidance_scale_slider")
                    height = gr.Slider(minimum=1024, maximum=2048, step=256, value=1024, label="Height", elem_id="height_slider")
                    width = gr.Slider(minimum=1024, maximum=2048, step=256, value=1024, label="Width", elem_id="width_slider")
                    num_images = gr.Slider(minimum=1, maximum=4, step=1, value=4, label="Number of Images", elem_id="num_images_slider")
            with gr.Column(elem_id="output_column"):
                output_gallery = gr.Gallery(label="Generated Images", height=480, scale=1, elem_id="output_gallery")

            generate_btn.click(generate_images, inputs=[model_dropdown, lora_dropdown, prompt, negative_prompt, num_inference_steps, guidance_scale, height, width, num_images], outputs=output_gallery)

        with gr.Tab("Download Custom Model"):
            with gr.Group():
                model_id = gr.Textbox(label="CivitAI Model ID")
                lora_id = gr.Textbox(label="CivitAI LoRA ID (Optional)")
                download_button = gr.Button("Download Model")

            download_output = gr.Textbox(label="Download Output")

            download_button.click(download_civitai_model, inputs=[model_id, lora_id], outputs=download_output)

demo.launch()