app.py

import torch
from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import gradio as gr
import subprocess
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 download_civitai_model(model_id, lora_id = ""):      
    if model_id.startswith("http"):    
        headers = {
            "Content-Type": "application/json"
        }

        response = requests.get(model_id, headers=headers)

        # The response is a requests.Response object, and you can get the JSON content like this:
        data = response.json()

        # The model name should be accessible as:
        model_name = data['name']
        download_file(model_id, model_name)
        
    else:
        model_url = "https://civitai.com/api/download/models/{model_id}"
        
        headers = {
            "Content-Type": "application/json"
        }

        response = requests.get(model_url, headers=headers)

        # The response is a requests.Response object, and you can get the JSON content like this:
        data = response.json()

        # The model name should be accessible as:
        model_name = data['name']
        download_file(model_url, model_name)
    
    if lora_id.startswith("http"):            
        headers = {
            "Content-Type": "application/json"
        }

        response = requests.get(model_id, headers=headers)

        # The response is a requests.Response object, and you can get the JSON content like this:
        data = response.json()

        # The model name should be accessible as:
        model_name = data['name']
        download_file(lora_id, lora_name)
        
    elif lora_id != None or "":
        lora_url = "https://civitai.com/api/download/models/{lora_id}"
        
        headers = {
            "Content-Type": "application/json"
        }

        response = requests.get(lora_url, headers=headers)

        # The response is a requests.Response object, and you can get the JSON content like this:
        data = response.json()

        # The model name should be accessible as:
        lora_name = data['name']
        download_file(lora_id, lora_name)
        
    models_list.append(model_name)
    loras_list.append(lora_name)
        
    return "Model/LoRA Downloaded!"  

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
        )
        
        models[model] = pipeline(
            model,
            vae=vae,
            torch_dtype=torch.float16,
            custom_pipeline="lpw_stable_diffusion_xl",
            add_watermarker=False,
        )
        
        if use_lora and lora != "":
            models[model].load_lora_weights(lora)

        models[model].to("cuda")
        
        return "Model/LoRA downloaded successfully!"
    except Exception as e:
        gr.Error(f"Error loading model {model}: {e}")
        print(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:
        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():
                modelId = gr.Textbox(label="CivitAI Model ID")
                loraId = 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=[modelId, loraId], outputs=download_output)

demo.launch()