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 _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} - {response.text}") 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} - {response.text}") return response def image_to_base64(image_path): with Image.open(image_path) as image: buffered = BytesIO() image.save(buffered, format="PNG") img_str = base64.b64encode(buffered.getvalue()) return img_str.decode('utf-8') # Ensure that the API key is available api_key = os.getenv("PRODIA_API_KEY") if not api_key: raise ValueError("Prodia API key not found in environment variables") prodia_client = Prodia(api_key=api_key) def flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed): try: 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) if job['status'] == 'succeeded': return job["imageUrl"] else: return "Generation failed, please try again." except Exception as e: return f"An error occurred: {e}" css = """ /* Overall Styling */ body { font-family: 'Arial', sans-serif; } .container { display: flex; flex-direction: column; gap: 20px; } /* Image Output Area */ #image-output-container { border: 2px solid #ccc; border-radius: 8px; overflow: hidden; } #image-output { max-width: 100%; height: auto; } /* Settings Section */ #settings { display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 20px; } .setting-group { border: 1px solid #ccc; padding: 20px; border-radius: 8px; } /* Button Styling */ #generate { background-color: #007bff; color: white; padding: 15px 25px; border: none; border-radius: 5px; cursor: pointer; } #generate:hover { background-color: #0056b3; } /* Responsive Design */ @media screen and (max-width: 768px) { #settings { grid-template-columns: 1fr; } } """ with gr.Blocks(css=css) as demo: state = gr.State(value="Welcome Screen") with gr.Tabs() as tabs: with gr.TabItem("Welcome Screen"): with gr.Row(): logo = gr.Image( value="http://disneypixaraigenerator.com/wp-content/uploads/2023/12/cropped-android-chrome-512x512-1.png", elem_id="logo", height=200, width=300 ) with gr.Row(): title = gr.Markdown("

Disney Pixar AI Generator

", elem_id="title") with gr.Row(): start_button = gr.Button("Get Started", variant='primary', elem_id="start-button") with gr.TabItem("Main Generation Screen"): with gr.Row(): gr.Markdown("

Create Your Disney Pixar AI Poster

", elem_id="title") with gr.Row(elem_id="image-output-container"): image_output = gr.Image( value="https://cdn-uploads.huggingface.co/production/uploads/noauth/XWJyh9DhMGXrzyRJk7SfP.png", label="Generated Image", elem_id="image-output" ) with gr.Row(elem_id="settings"): with gr.Column(scale=1, min_width=300, elem_classes="setting-group"): prompt = gr.Textbox( "space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Enter your prompt here...", show_label=False, lines=3, elem_id="prompt-input" ) negative_prompt = gr.Textbox( placeholder="Enter negative prompts (optional)...", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly" ) text_button = gr.Button("Generate", variant='primary', elem_id="generate") with gr.Column(scale=1, min_width=300, elem_classes="setting-group"): model = gr.Dropdown( interactive=True, value="sd_xl_base_1.0.safetensors [be9edd61]", show_label=True, label="Model", choices=prodia_client.list_models() ) sampler = gr.Dropdown( value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers() ) steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1) with gr.Column(scale=1, min_width=300, elem_classes="setting-group"): width = gr.Slider(label="Width", minimum=512, maximum=1536, value=1024, step=8) height = gr.Slider(label="Height", minimum=512, maximum=1536, value=1024, step=8) cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) seed = gr.Number(label="Seed", value=-1) text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output) start_button.click(fn=lambda: "Main Generation Screen", inputs=None, outputs=state) state.change(fn=lambda x: gr.update(visible=(x == "Main Generation Screen")), inputs=state, outputs=tabs) # Launch the Gradio app demo.launch(max_threads=128)