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  1. app.py +99 -0
  2. utils.py +23 -0
app.py ADDED
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+ import gradio as gr
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+ import numpy as np
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+ import random
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+ import torch
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+
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+ from diffusers import StableDiffusionXLPipeline, AutoencoderKL
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+ from utils import randomize_seed_fn
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+
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+ MAX_SEED = np.iinfo(np.int32).max
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+
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+ def model_load():
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+ vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
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+ pipe = StableDiffusionXLPipeline.from_pretrained(
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+ "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16
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+ )
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+ # load lora weight
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+ pipe.load_lora_weights("jjuun/vivid_color_style")
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+
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+ return pipe.to('cuda')
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+
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+
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+ def sdxl_process(seed, prompt, additional_prompt, negative_prompt, num_steps, guidance_scale):
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+ pipe = model_load()
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+ generator = torch.Generator("cuda")
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+ generator.manual_seed(seed)
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+
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+ special_prompt = 'jjj, scratch art style'
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+ prompt = f'{special_prompt}, {prompt}, with a black background'
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+ output = pipe(prompt, additional_prompt, negative_prompt=negative_prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale,
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+ generator=generator).images[0]
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+
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+ return output
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+
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+
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+ title = "🌈 Colorful illustration"
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+ description_en = "πŸš€ How to use: please make sure to include 'a colorful' in prompt and click Run button!"
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+
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+
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+ def create_demo():
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+
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+ with gr.Blocks() as demo:
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+ gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
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+ gr.Markdown(f"<h3 style='text-align: center'>{description_en}</h3>")
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+
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+ with gr.Row():
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+ with gr.Column():
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+ prompt = gr.Textbox(label="Prompt")
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+ run_button = gr.Button("Run")
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+ with gr.Accordion("Advanced options", open=False):
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+
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+ num_steps = gr.Slider(label="Number of steps", minimum=1, maximum=100, value=20, step=1)
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+ guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
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+ seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
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+ randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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+ a_prompt = gr.Textbox(label="Additional prompt", value="")
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+ n_prompt = gr.Textbox(
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+ label="Negative prompt",
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+ value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
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+ )
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+ with gr.Column():
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+ result = gr.Image(label="Output")
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+ result_seed = gr.Textbox(label="Used seed")
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+
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+ gr.Examples(
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+
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+ examples= [["a colorful lion", "20", "9", "0", "", "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", "examples/lion.png"],
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+ ["a colorful messi", "20", "9", "0", "", "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", "examples/messi.png"]],
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+ inputs = [prompt, num_steps, guidance_scale, seed, a_prompt, n_prompt, result]
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+ )
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+
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+ inputs = [
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+ seed,
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+ prompt,
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+ a_prompt,
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+ n_prompt,
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+ num_steps,
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+ guidance_scale,
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+ ]
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+
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+ run_button.click(
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+ fn=randomize_seed_fn,
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+ inputs=[seed, randomize_seed],
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+ outputs=result_seed,
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+ queue=False,
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+ api_name=False,
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+ ).then(
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+ fn=sdxl_process,
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+ inputs=inputs,
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+ outputs=result,
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+ api_name=False,
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+ )
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+
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+
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+ return demo
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+
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+
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+ if __name__ == "__main__":
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+ demo = create_demo()
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+ demo.queue().launch()
utils.py ADDED
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+ import numpy as np
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+ import random
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+ import torch
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+ import os
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+
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+
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+ def seed_everything(seed: int = 42):
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+ random.seed(seed)
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+ np.random.seed(seed)
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+ os.environ["PYTHONHASHSEED"] = str(seed)
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+ torch.manual_seed(seed)
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+ torch.cuda.manual_seed(seed) # type: ignore
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+ torch.backends.cudnn.deterministic = True # type: ignore
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+ torch.backends.cudnn.benchmark = True # type: ignore
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+
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+
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+ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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+ MAX_SEED = np.iinfo(np.int32).max
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+
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+ if randomize_seed:
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+ seed = random.randint(0, MAX_SEED)
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+ seed_everything(seed)
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+ return seed