• Apple SiliconのGPU（mps）で実行します

• 動作状況確認の動画は以下のとおりです

from diffusers import DiffusionPipeline
import torch
import torchvision.transforms as T
from PIL import Image


# モデルをロードし、MPS（Apple Silicon用のGPU）を使用してfloat32に設定
pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", revision="fb9c5d167af11fd84454ae6493878b10bb63b067", safety_checker=None, custom_pipeline="latent_consistency_img2img")
pipe.to(torch_device="mps", torch_dtype=torch.float32)  # float32に設定
pipe.set_progress_bar_config(disable=True)

import gradio as gr

# 画像をTensorに変換するための変換関数
transform = T.Compose([
	T.Resize((224, 224)),  # 必要に応じてサイズを調整
	T.ToTensor(),
	T.ConvertImageDtype(torch.float32)  # PIL.ImageからTensorへ変換し、dtypeをfloat32に設定
])


def generate(prompt, input_image):
	input_tensor = transform(input_image)  # PIL.ImageをTensorに変換
	image = pipe(prompt, image=input_tensor, num_inference_steps=8, guidance_scale=8.0, lcm_origin_steps=50, strength=0.8).images[0]
	if not isinstance(image, Image.Image):
		image = T.ToPILImage()(image)  # TensorをPIL.Imageに変換
	return image.resize((768, 768))

# Gradioインターフェース
with gr.Blocks(title=f"Realtime Latent Consistency Model") as demo:
	with gr.Row():
		with gr.Column(scale=23):
			textbox = gr.Textbox(show_label=False, value="a close-up picture of a fluffy cat")

	with gr.Row(variant="default"):
		input_image = gr.Image(
			show_label=False,
			type="pil",
			tool="color-sketch",
			source="canvas",
			height=742,
			width=742,
			brush_radius=10.0,
		)
		output_image = gr.Image(
			show_label=False,
			type="pil",
			interactive=False,
			height=742,
			width=742,
			elem_id="output_image",
		)

	textbox.change(fn=generate, inputs=[textbox, input_image], outputs=[output_image], show_progress=False)
	input_image.change(fn=generate, inputs=[textbox, input_image], outputs=[output_image], show_progress=False)

demo.launch(inline=False, share=True)

コード参考