Create app.py
Browse files
app.py
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import numpy as np
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import gradio as gr
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import requests
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import time
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import json
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import base64
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import os
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from PIL import Image
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from io import BytesIO
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class Prodia:
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def __init__(self, api_key, base=None):
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self.base = base or "https://api.prodia.com/v1"
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self.headers = {
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"X-Prodia-Key": api_key
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}
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def generate(self, params):
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response = self._post(f"{self.base}/sdxl/generate", params)
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return response.json()
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def get_job(self, job_id):
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response = self._get(f"{self.base}/job/{job_id}")
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return response.json()
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def wait(self, job):
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job_result = job
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while job_result['status'] not in ['succeeded', 'failed']:
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time.sleep(0.25)
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job_result = self.get_job(job['job'])
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return job_result
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def list_models(self):
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response = self._get(f"{self.base}/sdxl/models")
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return response.json()
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def list_samplers(self):
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response = self._get(f"{self.base}/sdxl/samplers")
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return response.json()
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def generate_v2(self, config):
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response = self._post("https://inference.prodia.com/v2/job", {"type": "v2.job.sdxl.txt2img", "config": config}, v2=True)
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return Image.open(BytesIO(response.content)).convert("RGBA")
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def _post(self, url, params, v2=False):
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headers = {
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**self.headers,
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"Content-Type": "application/json"
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}
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if v2:
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headers['Authorization'] = f"Bearer {os.getenv('API_KEY')}"
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response = requests.post(url, headers=headers, data=json.dumps(params))
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if response.status_code != 200:
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raise Exception(f"Bad Prodia Response: {response.status_code}")
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return response
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def _get(self, url):
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response = requests.get(url, headers=self.headers)
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if response.status_code != 200:
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raise Exception(f"Bad Prodia Response: {response.status_code}")
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return response
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def image_to_base64(image_path):
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# Open the image with PIL
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with Image.open(image_path) as image:
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# Convert the image to bytes
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buffered = BytesIO()
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image.save(buffered, format="PNG") # You can change format to PNG if needed
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# Encode the bytes to base64
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img_str = base64.b64encode(buffered.getvalue())
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return img_str.decode('utf-8') # Convert bytes to string
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prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
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def flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale, resolution, seed):
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width, height = resolution.split("x")
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config_without_model_and_sampler = {
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"steps": steps,
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"cfg_scale": cfg_scale,
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"width": int(width),
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"height": int(height),
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"seed": seed
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}
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# 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
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# if model == "sd_xl_base_1.0.safetensors [be9edd61]":
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if model == "xxxxx":
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return prodia_client.generate_v2(config_without_model_and_sampler)
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result = prodia_client.generate({
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**config_without_model_and_sampler,
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"model": model,
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"sampler": sampler
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})
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job = prodia_client.wait(result)
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return job["imageUrl"]
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css = """
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#generate {
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height: 100%;
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}
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"""
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list_resolutions = [
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"512x512",
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"640x960",
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"800x1200",
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"1280x720",
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"1368x768",
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"1024x1024",
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"1216x832",
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"1344x768",
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"1536x640",
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"640x1536",
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"768x1344",
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"832x1216"
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]
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with gr.Blocks(css=css) as demo:
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with gr.Row():
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with gr.Column(scale=6):
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model = gr.Dropdown(interactive=True,value="animagineXLV3_v30.safetensors [75f2f05b]", show_label=True, label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())
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with gr.Column(scale=1):
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gr.Markdown(elem_id="powered-by-prodia", value="AUTOMATIC1111 Stable Diffusion Web UI for SDXL V1.0.<br>Powered by [Prodia](https://prodia.com).")
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with gr.Tab("txt2img"):
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with gr.Row():
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with gr.Column(scale=6, min_width=600):
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prompt = gr.Textbox("(masterpiece,highres,best quality,8k),1girl,solo,space warrior, ultrarealistic, soft lighting", placeholder="Prompt", show_label=False, lines=3)
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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")
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with gr.Column():
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text_button = gr.Button("Generate", variant='primary', elem_id="generate")
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with gr.Row():
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with gr.Column(scale=3):
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with gr.Tab("Generation"):
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with gr.Row():
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with gr.Column(scale=1):
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sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
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with gr.Column(scale=1):
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steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=50, value=25, step=1)
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with gr.Row():
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with gr.Column(scale=1):
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resolution = gr.Dropdown(value="800x1200", show_label=True, label="Resolution", choices=list_resolutions)
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with gr.Column(scale=1):
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batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
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batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
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cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=10, step=1)
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seed = gr.Number(label="Seed", value=-1)
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with gr.Column(scale=2):
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# image_output = gr.Image(value="https://cdn-uploads.huggingface.co/production/uploads/noauth/XWJyh9DhMGXrzyRJk7SfP.png")
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image_output = gr.Image(value="./image.png")
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text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, resolution, seed], outputs=image_output)
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demo.queue(default_concurrency_limit=1, max_size=32, api_open=False).launch(max_threads=128, auth=(os.getenv("USERNAME"), os.getenv("PASSWORD")))
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