app.py

import spaces
import gradio as gr
import numpy as np
import PIL.Image
from PIL import Image
import random
from diffusers import ControlNetModel, StableDiffusionXLPipeline, AutoencoderKL
import cv2
import torch

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerDiscreteScheduler,
    EulerAncestralDiscreteScheduler,
    HeunDiscreteScheduler,
    KDPM2DiscreteScheduler,
    KDPM2AncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    UniPCMultistepScheduler,
)


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

#vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

#pipe = StableDiffusionXLPipeline.from_pretrained(
#    #"yodayo-ai/clandestine-xl-1.0", 
#    torch_dtype=torch.float16, 
#    use_safetensors=True,
#    custom_pipeline="lpw_stable_diffusion_xl",
#    add_watermarker=False #,
#    #variant="fp16"
#)
pipe = StableDiffusionXLPipeline.from_single_file(
    #"https://huggingface.co/Laxhar/noob_sdxl_beta/noob_hercules4/fp16/checkpoint-e0_s10000.safetensors/checkpoint-e0_s10000.safetensors",
    "https://huggingface.co/bluepen5805/illustrious_pencil-XL/illustrious_pencil-XL-v1.2.1.safetensors",
    use_safetensors=True,
    torch_dtype=torch.float16,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1216

    
@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, sampler_name):

    # サンプラーの設定
    if sampler_name == "DDIM":
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "DPMSolverMultistep":
        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "Euler":
        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "EulerAncestral":
        pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "Heun":
        pipe.scheduler = HeunDiscreteScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "KDPM2":
        pipe.scheduler = KDPM2DiscreteScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "KDPM2Ancestral":
        pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "LMS":
        pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
    elif sampler_name == "UniPC":
        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
    else:
        pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    output_image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator
    ).images[0]

    return output_image


css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:

    with gr.Column(elem_id="col-container"):
        gr.Markdown("""
         Text-to-Image Demo
        using [illustrious_pencil-XL](https://huggingface.co/bluepen5805/illustrious_pencil-XL)
        """)
        #yodayo-ai/clandestine-xl-1.0　
        #yodayo-ai/holodayo-xl-2.1
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0)

        result = gr.Image(label="Result", show_label=False)
        
        with gr.Accordion("Advanced Settings", open=False):

            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                value="nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            sampler_name = gr.Dropdown(
                label="Sampler",
                choices=["DDIM", "DPMSolverMultistep", "Euler", "EulerAncestral", "Heun", "KDPM2", "KDPM2Ancestral", "LMS", "UniPC"],
                value="EulerAncestral",
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,#832,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,#1216,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=20.0,
                    step=0.1,
                    value=4,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=28,
                    step=1,
                    value=28,
                )

                

    run_button.click(#lambda x: None, inputs=None, outputs=result).then(
        fn=infer,
        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, sampler_name],
        outputs=[result]
    )

demo.queue().launch()