| import gradio as gr |
| import numpy as np |
| import random |
| import torch |
|
|
| from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler |
| from utils import randomize_seed_fn |
|
|
| MAX_SEED = np.iinfo(np.int32).max |
|
|
| def model_load(): |
| vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) |
| pipe = StableDiffusionXLPipeline.from_pretrained( |
| "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16 |
| ) |
| pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) |
| |
| |
| pipe.load_lora_weights("jjuun/vivid_color_style") |
|
|
| return pipe.to('cuda') |
|
|
|
|
| def sdxl_process(seed, prompt, additional_prompt, negative_prompt, num_steps, guidance_scale): |
| pipe = model_load() |
| generator = torch.Generator("cuda") |
| generator.manual_seed(int(seed)) |
| |
| special_prompt = 'jjj, scratch art style' |
| prompt = f'{special_prompt}, {prompt}, with a black background' |
| output = pipe(prompt, additional_prompt, negative_prompt=negative_prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale, |
| generator=generator).images[0] |
| |
| return output |
|
|
|
|
| title = "🌈 Colorful illustration" |
| description_en = "🚀 How to use: please make sure to include 'a colorful' in prompt and click Run button!" |
|
|
|
|
| def create_demo(): |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>") |
| gr.Markdown(f"<h3 style='text-align: center'>{description_en}</h3>") |
| gr.Markdown(f"<a href='https://github.com/jjuun0'><img src='https://img.shields.io/badge/GitHub-181717?style=flat-square&logo=GitHub&logoColor=white'/></a>") |
|
|
| with gr.Row(): |
| with gr.Column(): |
| prompt = gr.Textbox(label="Prompt") |
| run_button = gr.Button("Run") |
| with gr.Accordion("Advanced options", open=False): |
| |
| num_steps = gr.Slider(label="Number of steps", minimum=1, maximum=100, value=20, step=1) |
| guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) |
| seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0) |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
| a_prompt = gr.Textbox(label="Additional prompt", value="") |
| n_prompt = gr.Textbox( |
| label="Negative prompt", |
| value="", |
| ) |
| with gr.Column(): |
| result = gr.Image(label="Output") |
| result_seed = gr.Textbox(label="Used seed") |
| |
| gr.Examples( |
| |
| examples= [["a colorful fox", "20", "9", "0", "", "", "examples/fox.png"], |
| ["a colorful messi", "20", "9", "191251724", "", "", "examples/messi.png"], |
| ["a colorful pyramid", "20", "9", "0", "", "", "examples/pyramid.png"], |
| ["a colorful octopus playing violin", "20", "9", "0", "", "", "examples/octopus.png"]], |
| inputs = [prompt, num_steps, guidance_scale, seed, a_prompt, n_prompt, result] |
| ) |
|
|
| inputs = [ |
| seed, |
| prompt, |
| a_prompt, |
| n_prompt, |
| num_steps, |
| guidance_scale, |
| ] |
|
|
| run_button.click( |
| fn=randomize_seed_fn, |
| inputs=[seed, randomize_seed], |
| outputs=result_seed, |
| queue=False, |
| api_name=False, |
| ).then( |
| fn=sdxl_process, |
| inputs=inputs, |
| outputs=result, |
| api_name=False, |
| ) |
| |
|
|
| return demo |
|
|
|
|
| if __name__ == "__main__": |
| demo = create_demo() |
| demo.queue().launch() |
|
|
