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": "inference.sdxl.txt2img.v1", "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 } if model == "sd_xl_base_1.0.safetensors [be9edd61]": 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 = [ "1024x1024", "1152x896", "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="sd_xl_base_1.0.safetensors [be9edd61]", 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.
Powered by [Prodia](https://prodia.com).") with gr.Tab("txt2img"): with gr.Row(): with gr.Column(scale=6, min_width=600): prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", 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("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=25, value=20, step=1) with gr.Row(): with gr.Column(scale=1): resolution = gr.Dropdown(value="1024x1024", 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=7, 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") text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, resolution, seed], outputs=image_output) demo.queue(default_concurrency_limit=10, max_size=32, api_open=False).launch(max_threads=128)