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| import gradio as gr | |
| from fetch import get_values | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| import prodia | |
| import requests | |
| import random | |
| from datetime import datetime | |
| import os | |
| prodia_key = os.getenv('PRODIA_X_KEY', None) | |
| if prodia_key is None: | |
| print("Please set PRODIA_X_KEY in .env, closing...") | |
| exit() | |
| client = prodia.Client(api_key=prodia_key) | |
| def process_input_text2img(prompt, negative_prompt, steps, cfg_scale, number, seed, model, sampler, aspect_ratio, upscale, save): | |
| images = [] | |
| for image in range(number): | |
| result = client.txt2img(prompt=prompt, negative_prompt=negative_prompt, model=model, sampler=sampler, | |
| steps=steps, cfg_scale=cfg_scale, seed=seed, aspect_ratio=aspect_ratio, upscale=upscale) | |
| images.append(result.url) | |
| if save: | |
| date = datetime.now() | |
| if not os.path.isdir(f'./outputs/{date.year}-{date.month}-{date.day}'): | |
| os.mkdir(f'./outputs/{date.year}-{date.month}-{date.day}') | |
| img_data = requests.get(result.url).content | |
| with open(f"./outputs/{date.year}-{date.month}-{date.day}/{random.randint(1, 10000000000000)}_{result.seed}.png", "wb") as f: | |
| f.write(img_data) | |
| return images | |
| def process_input_img2img(init, prompt, negative_prompt, steps, cfg_scale, number, seed, model, sampler, ds, upscale, save): | |
| images = [] | |
| for image in range(number): | |
| result = client.img2img(imageUrl=init, prompt=prompt, negative_prompt=negative_prompt, model=model, sampler=sampler, | |
| steps=steps, cfg_scale=cfg_scale, seed=seed, denoising_strength=ds, upscale=upscale) | |
| images.append(result.url) | |
| if save: | |
| date = datetime.now() | |
| if not os.path.isdir(f'./outputs/{date.year}-{date.month}-{date.day}'): | |
| os.mkdir(f'./outputs/{date.year}-{date.month}-{date.day}') | |
| img_data = requests.get(result.url).content | |
| with open(f"./outputs/{date.year}-{date.month}-{date.day}/{random.randint(1, 10000000000000)}_{result.seed}.png", "wb") as f: | |
| f.write(img_data) | |
| return images | |
| """ | |
| def process_input_control(init, prompt, negative_prompt, steps, cfg_scale, number, seed, model, control_model, sampler): | |
| images = [] | |
| for image in range(number): | |
| result = client.controlnet(imageUrl=init, prompt=prompt, negative_prompt=negative_prompt, model=model, sampler=sampler, | |
| steps=steps, cfg_scale=cfg_scale, seed=seed, controlnet_model=control_model) | |
| images.append(result.url) | |
| return images | |
| """ | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| # Prodia API web-ui by @zenafey | |
| This is simple web-gui for using Prodia API easily, build on Python, gradio, prodiapy | |
| """) | |
| with gr.Tab(label="text2img"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox(label="Prompt", lines=2) | |
| negative = gr.Textbox(label="Negative Prompt", lines=3, placeholder="badly drawn") | |
| with gr.Row(): | |
| steps = gr.Slider(label="Steps", value=30, step=1, maximum=50, minimum=1, interactive=True) | |
| cfg = gr.Slider(label="CFG Scale", maximum=20, minimum=1, value=7, interactive=True) | |
| with gr.Row(): | |
| num = gr.Slider(label="Number of images", value=1, step=1, minimum=1, interactive=True) | |
| seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967295, interactive=True) | |
| with gr.Row(): | |
| model = gr.Dropdown(label="Model", choices=get_values()[0], value="v1-5-pruned-emaonly.ckpt [81761151]", interactive=True) | |
| sampler = gr.Dropdown(label="Sampler", choices=get_values()[1], value="DDIM", interactive=True) | |
| with gr.Row(): | |
| ar = gr.Radio(label="Aspect Ratio", choices=["square", "portrait", "landscape"], value="square", interactive=True) | |
| with gr.Column(): | |
| upscale = gr.Checkbox(label="upscale", interactive=True) | |
| save = gr.Checkbox(label="auto save", interactive=True) | |
| with gr.Row(): | |
| run_btn = gr.Button("Run", variant="primary") | |
| with gr.Column(): | |
| result_image = gr.Gallery(label="Result Image(s)") | |
| run_btn.click( | |
| process_input_text2img, | |
| inputs=[ | |
| prompt, | |
| negative, | |
| steps, | |
| cfg, | |
| num, | |
| seed, | |
| model, | |
| sampler, | |
| ar, | |
| upscale, | |
| save | |
| ], | |
| outputs=[result_image], | |
| ) | |
| with gr.Tab(label="img2img"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox(label="Prompt", lines=2) | |
| with gr.Row(): | |
| negative = gr.Textbox(label="Negative Prompt", lines=3, placeholder="badly drawn") | |
| init_image = gr.Textbox(label="Init Image Url", lines=2, placeholder="https://cdn.openai.com/API/images/guides/image_generation_simple.webp") | |
| with gr.Row(): | |
| steps = gr.Slider(label="Steps", value=30, step=1, maximum=50, minimum=1, interactive=True) | |
| cfg = gr.Slider(label="CFG Scale", maximum=20, minimum=1, value=7, interactive=True) | |
| with gr.Row(): | |
| num = gr.Slider(label="Number of images", value=1, step=1, minimum=1, interactive=True) | |
| seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967295, interactive=True) | |
| with gr.Row(): | |
| model = gr.Dropdown(label="Model", choices=get_values()[0], value="v1-5-pruned-emaonly.ckpt [81761151]", interactive=True) | |
| sampler = gr.Dropdown(label="Sampler", choices=get_values()[1], value="DDIM", interactive=True) | |
| with gr.Row(): | |
| ds = gr.Slider(label="Denoising strength", maximum=0.9, minimum=0.1, value=0.5, interactive=True) | |
| with gr.Column(): | |
| upscale = gr.Checkbox(label="upscale", interactive=True) | |
| save = gr.Checkbox(label="auto save", interactive=True) | |
| with gr.Row(): | |
| run_btn = gr.Button("Run", variant="primary") | |
| with gr.Column(): | |
| result_image = gr.Gallery(label="Result Image(s)") | |
| run_btn.click( | |
| process_input_img2img, | |
| inputs=[ | |
| init_image, | |
| prompt, | |
| negative, | |
| steps, | |
| cfg, | |
| num, | |
| seed, | |
| model, | |
| sampler, | |
| ds, | |
| upscale, | |
| save | |
| ], | |
| outputs=[result_image], | |
| ) | |
| with gr.Tab(label="controlnet(coming soon)"): | |
| gr.Button(label="lol") | |
| if __name__ == "__main__": | |
| demo.launch(show_api=True) | |