app.py

import gradio as gr
import requests
import time
import json
import base64
import os
from io import BytesIO
import html
import re
from PIL import Image  # Убедитесь, что PIL импортируется для работы с изображениями


class Prodia:
    def __init__(self, api_key, base=None):
        self.base = base or "https://api.prodia.com/v1"
        self.headers = {
            "X-Prodia-Key": api_key
        }

    def generate(self, params):
        return self._post(f"{self.base}/sd/generate", params).json()

    def transform(self, params):
        return self._post(f"{self.base}/sd/transform", params).json()

    def controlnet(self, params):
        return self._post(f"{self.base}/sd/controlnet", params).json()

    def get_job(self, job_id):
        return self._get(f"{self.base}/job/{job_id}").json()

    def wait(self, job):
        job_result = job
        while job_result['status'] not in ['succeeded', 'failed']:
            time.sleep(0.25)
            job_result = self.get_job(job['job'])
        return job_result

    def list_models(self):
        return self._get(f"{self.base}/sd/models").json()

    def list_samplers(self):
        return self._get(f"{self.base}/sd/samplers").json()

    # Изменение: добавлены повторные попытки при сбоях сети
    def _post(self, url, params):
        headers = {**self.headers, "Content-Type": "application/json"}
        for _ in range(3):
            try:
                response = requests.post(url, headers=headers, data=json.dumps(params))
                response.raise_for_status()
                return response
            except requests.exceptions.RequestException as e:
                print(f"Request failed: {e}, retrying...")
                time.sleep(1)
        raise Exception("Failed after 3 attempts")

    # Изменение: добавлены повторные попытки при сбоях сети
    def _get(self, url):
        for _ in range(3):
            try:
                response = requests.get(url, headers=self.headers)
                response.raise_for_status()
                return response
            except requests.exceptions.RequestException as e:
                print(f"Request failed: {e}, retrying...")
                time.sleep(1)
        raise Exception("Failed after 3 attempts")


def image_to_base64(image, format="PNG"):
    buffered = BytesIO()
    image.save(buffered, format=format)
    img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return img_str


def remove_id_and_ext(text):
    text = re.sub(r'\[.*\]$', '', text)
    extension = text[-12:].strip()
    if extension == "safetensors":
        text = text[:-13]
    elif extension == "ckpt":
        text = text[:-4]
    return text


# Изменение: оптимизация функции get_data
def get_data(text):
    patterns = {
        'prompt': r'(.*)',
        'negative_prompt': r'Negative prompt: (.*)',
        'steps': r'Steps: (\d+),',
        'seed': r'Seed: (\d+),',
        'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)',
        'model': r'Model:\s*([^\s,]+)',
        'cfg_scale': r'CFG scale:\s*([\d\.]+)',
        'size': r'Size:\s*([0-9]+x[0-9]+)'
    }
    results = {key: re.search(pattern, text).group(1) if re.search(pattern, text) else None for key, pattern in patterns.items()}
    if results['size']:
        results['w'], results['h'] = map(int, results['size'].split("x"))
    else:
        results['w'], results['h'] = None, None
    return results


# Изменение: оптимизация функции send_to_txt2img
def send_to_txt2img(image):
    result = {tabs: gr.update(selected="t2i")}
    try:
        text = image.info['parameters']
        data = get_data(text)
        fields = ['prompt', 'negative_prompt', 'steps', 'seed', 'cfg_scale', 'w', 'h', 'sampler', 'model']
        for field in fields:
            result[field] = gr.update(value=data[field]) if data[field] is not None else gr.update()
        return result
    except Exception as e:
        print(e)
        return result


prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
model_list = prodia_client.list_models()
model_names = {remove_id_and_ext(model_name): model_name for model_name in model_list}


def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
    result = prodia_client.generate({
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "model": model,
        "steps": steps,
        "sampler": sampler,
        "cfg_scale": cfg_scale,
        "width": width,
        "height": height,
        "seed": seed
    })
    job = prodia_client.wait(result)
    return job["imageUrl"]


def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
    result = prodia_client.transform({
        "imageData": image_to_base64(input_image),
        "denoising_strength": denoising,
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "model": model,
        "steps": steps,
        "sampler": sampler,
        "cfg_scale": cfg_scale,
        "width": width,
        "height": height,
        "seed": seed
    })
    job = prodia_client.wait(result)
    return job["imageUrl"]


css = """
#generate {
    height: 100%;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column(scale=6):
            model = gr.Dropdown(interactive=True, value="childrensStories_v1ToonAnime.safetensors [2ec7b88b]", show_label=True, label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())
  
        with gr.Column(scale=1):
            gr.Markdown(elem_id="powered-by-prodia", value="AUTOMATIC1111 Stable Diffusion Web UI переделано masteroko.<br>Powered by [Prodia](https://prodia.com).<br>For more features and faster generation times check out our [API Docs](https://docs.prodia.com/reference/getting-started-guide).")

    with gr.Tabs() as tabs:
        with gr.Tab("txt2img", id='t2i'):
            with gr.Row():
                with gr.Column(scale=6, min_width=600):
                    prompt = gr.Textbox("nsfw", placeholder="Prompt", show_label=False, lines=3)
                    negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
                with gr.Column():
                    text_button = gr.Button("Сгенерировать", variant='primary', elem_id="generate")
                    
            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Tab("Генерация"):
                        with gr.Row():
                            with gr.Column(scale=1):
                                sampler = gr.Dropdown(value="DPM++ 2M SDE Exponential", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
                                
                            with gr.Column(scale=1):
                                steps = gr.Slider(label="количество обработок", minimum=1, maximum=100, value=20, step=1)
    
                        with gr.Row():
                            with gr.Column(scale=1):
                                width = gr.Slider(label="Ширина", maximum=1024, value=512, step=8)
                                height = gr.Slider(label="Высота", maximum=1024, value=512, step=8)
                            
                            with gr.Column(scale=1):
                                batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
                                batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
    
                        cfg_scale = gr.Slider(label="CFG Scale(степень фантазии ии)", minimum=1, maximum=20, value=7, step=1)
                        seed = gr.Number(label="Семя рандома", value=-1)

                with gr.Column(scale=2):
                    image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
    
            # Изменение: добавлен limit на одновременные запросы
            text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output, concurrency_limit=1024)
        
        with gr.Tab("img2img", id='i2i'):
            with gr.Row():
                with gr.Column(scale=6, min_width=600):
                    image = gr.Image(show_label=False)
                    prompt = gr.Textbox(placeholder="Prompt", show_label=False, lines=3)
                    negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3)
                with gr.Column():
                    text_button = gr.Button("Трансформировать", variant='primary', elem_id="generate")
                    
            with gr.Row():
                with gr.Column(scale=3):
                    with gr.Tab("Генерация"):
                        with gr.Row():
                            with gr.Column(scale=1):
                                sampler = gr.Dropdown(value="DPM++ 2M SDE Exponential", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
                                
                            with gr.Column(scale=1):
                                steps = gr.Slider(label="количество обработок", minimum=1, maximum=100, value=20, step=1)
    
                        with gr.Row():
                            with gr.Column(scale=1):
                                width = gr.Slider(label="Ширина", maximum=1024, value=512, step=8)
                                height = gr.Slider(label="Высота", maximum=1024, value=512, step=8)
                            
                            with gr.Column(scale=1):
                                batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
                                batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
    
                        cfg_scale = gr.Slider(label="CFG Scale(степень фантазии ии)", minimum=1, maximum=20, value=7, step=1)
                        seed = gr.Number(label="Семя рандома", value=-1)
                        denoising = gr.Slider(label="Denoising", value=0.5)

                with gr.Column(scale=2):
                    image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
    
            # Изменение: добавлен limit на одновременные запросы
            text_button.click(img2img, inputs=[image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output, concurrency_limit=1024)
            
# Запуск Gradio интерфейса
demo.launch()