app.py

import numpy as np
import gradio as gr
import requests
import time
import json
import base64
import os
from PIL import Image
from io import BytesIO

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):
        response = self._post(f"{self.base}/sdxl/generate", params)
        return response.json()

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

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


    def generate_v2(self, config):
        response = self._post("https://inference.prodia.com/v2/job", {"type": "v2.job.sdxl.txt2img", "config": config}, v2=True)
        return Image.open(BytesIO(response.content)).convert("RGBA")
        

    def _post(self, url, params, v2=False):
        headers = {
            **self.headers,
            "Content-Type": "application/json"
        }
        if v2:
            headers['Authorization'] = f"Bearer {os.getenv('API_KEY')}"
            
        response = requests.post(url, headers=headers, data=json.dumps(params))

        if response.status_code != 200:
            raise Exception(f"Bad Prodia Response: {response.status_code}")

        return response

    def _get(self, url):
        response = requests.get(url, headers=self.headers)

        if response.status_code != 200:
            raise Exception(f"Bad Prodia Response: {response.status_code}")

        return response

    


def image_to_base64(image_path):
    # Open the image with PIL
    with Image.open(image_path) as image:
        # Convert the image to bytes
        buffered = BytesIO()
        image.save(buffered, format="PNG")  # You can change format to PNG if needed

        # Encode the bytes to base64
        img_str = base64.b64encode(buffered.getvalue())

    return img_str.decode('utf-8')  # Convert bytes to string



prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))

def flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale, resolution, seed):

    width, height = resolution.split("x")

    config_without_model_and_sampler = {
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "width": int(width),
        "height": int(height),
        "seed": seed
    }

# 本条注释替换成下面那条，避免sd_xl_base_1.0.safetensors [be9edd61]使用API_KEY导致的无法使用
#    if model == "sd_xl_base_1.0.safetensors [be9edd61]":

    if model == "xxxxx":
        return prodia_client.generate_v2(config_without_model_and_sampler)
    
    result = prodia_client.generate({
        **config_without_model_and_sampler,
        "model": model,
        "sampler": sampler
    })

    job = prodia_client.wait(result)

    return job["imageUrl"]

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

list_resolutions = [
    "512x512",
    "640x960",
    "800x1200",
    "1280x720",
    "1368x768",
    "1024x1024",
    "1216x832",
    "1344x768",
    "1536x640",
    "640x1536",
    "768x1344",
    "832x1216"
]

with gr.Blocks(css=css) as demo:


    with gr.Row():
        with gr.Column(scale=6):
            model = gr.Dropdown(interactive=True,value="animagineXLV3_v30.safetensors [75f2f05b]", 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 for SDXL V1.0.<br>Powered by [Prodia](https://prodia.com).")

    with gr.Tab("txt2img"):
        with gr.Row():
            with gr.Column(scale=6, min_width=600):
                prompt = gr.Textbox("(masterpiece,highres,best quality,8k),1girl,solo,space warrior, ultrarealistic, soft lighting", placeholder="Prompt", show_label=False, lines=3)
                negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="(nsfw:1.2),lowres,[bad anatomy,bad hands,missing fingers,long neck],text,error")
            with gr.Column():
                text_button = gr.Button("Generate", variant='primary', elem_id="generate")

        with gr.Row():
            with gr.Column(scale=3):
                with gr.Tab("Generation"):
                    with gr.Row():
                        with gr.Column(scale=1):
                            sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())

                        with gr.Column(scale=1):
                            steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=50, value=25, step=1)

                    with gr.Row():
                        with gr.Column(scale=1):
                            resolution = gr.Dropdown(value="800x1200", show_label=True, label="Resolution", choices=list_resolutions)

                        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", minimum=1, maximum=20, value=10, step=1)
                    seed = gr.Number(label="Seed", value=-1)

            with gr.Column(scale=2):
#                image_output = gr.Image(value="https://cdn-uploads.huggingface.co/production/uploads/noauth/XWJyh9DhMGXrzyRJk7SfP.png")
                image_output = gr.Image(value="./image.png")

        text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, resolution, seed], outputs=image_output)

demo.queue(default_concurrency_limit=1, max_size=32, api_open=False).launch(max_threads=128, auth=(os.getenv("USERNAME"), os.getenv("PASSWORD")))