import numpy as np import gradio as gr import requests import time import json import base64 import os from io import BytesIO import PIL from PIL.ExifTags import TAGS 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}/sd/generate", params) return response.json() def transform(self, params): response = self._post(f"{self.base}/sd/transform", params) return response.json() def controlnet(self, params): response = self._post(f"{self.base}/sd/controlnet", 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}/models/list") return response.json() def _post(self, url, params): headers = { **self.headers, "Content-Type": "application/json" } 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, 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 get_exif_data(image): print(image) exif = image.getexif() print(exif) return image.info 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="absolutereality_v181.safetensors [3d9d4d2b]", 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.
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="Euler a", show_label=True, label="Sampling Method", choices=[ "Euler", "Euler a", "LMS", "Heun", "DPM2", "DPM2 a", "DPM++ 2S a", "DPM++ 2M", "DPM++ SDE", "DPM fast", "DPM adaptive", "LMS Karras", "DPM2 Karras", "DPM2 a Karras", "DPM++ 2S a Karras", "DPM++ 2M Karras", "DPM++ SDE Karras", "DDIM", "PLMS", ]) with gr.Column(scale=1): steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1) with gr.Row(): with gr.Column(scale=1): width = gr.Slider(label="Width", maximum=1024, value=512, step=8) height = gr.Slider(label="Height", 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", 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://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png") text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output) with gr.Tab("PNG Info"): with gr.Row(): with gr.Column(): image_input = gr.Image(type="pil") with gr.Column(): exif_button = gr.Button("Get input data") exif_output = gr.Textbox(label="EXIF Data") exif_button.click(get_exif_data, inputs=[image_input], outputs=exif_output) demo.queue(concurrency_count=24) demo.launch()