ComfyUI 基础工作原理

ComfyUI 整个流程如下所示

下面包含了基本的 ComfyUI 执行流程

import random
import torch
import sys

sys.path.append("../")
from nodes import (
    VAEDecode,
    KSamplerAdvanced,
    EmptyLatentImage,
    SaveImage,
    CheckpointLoaderSimple,
    CLIPTextEncode,
)

def main():
    with torch.inference_mode():

        # 首先加载扩散模型，扩散模型用来去除潜在的噪声。该节点还将提供适当的 VAE 和 CLIP 模型。
        checkpointloadersimple = CheckpointLoaderSimple()
        checkpointloadersimple_4 = checkpointloadersimple.load_checkpoint(
            ckpt_name="sd_xl_base_1.0.safetensors"
        )

        # 创建一个空的隐空间（txt2img）
        emptylatentimage = EmptyLatentImage()
        emptylatentimage_5 = emptylatentimage.generate(
            width=1024, height=1024, batch_size=1
        )

        # 创建一个 CLIP 用来加载引导词（下同）
        cliptextencode = CLIPTextEncode()
        cliptextencode_6 = cliptextencode.encode(
            text="evening sunset scenery blue sky nature, glass bottle with a galaxy in it",
            clip=checkpointloadersimple_4[1],
        )

        cliptextencode_7 = cliptextencode.encode(
            text="text, watermark", clip=checkpointloadersimple_4[1]
        )

        ksampleradvanced = KSamplerAdvanced()
        vaedecode = VAEDecode()
        saveimage = SaveImage()

        # 采样器开始轮询采样，这个 KSampler 使用了 euler 采样器
        ksampleradvanced_10 = ksampleradvanced.sample(
            add_noise="enable",
            noise_seed=random.randint(1, 2**64),  # 随机生成噪声
            steps=25,                             # 25 步采样
            cfg=8,                                # 配置与关键词的引导强度
            sampler_name="euler",
            scheduler="normal",
            # 这个 start_at_step 和 end_at_step 主要用于多模型的场景，例如通过模型 A 完成 0-10 step，再通过 B 模型完成 10-20 step
            start_at_step=0,       
            end_at_step=20,
            return_with_leftover_noise="enable",
            model=checkpointloadersimple_4[0],
            positive=cliptextencode_6[0],
            negative=cliptextencode_7[0],
            latent_image=emptylatentimage_5[0],
        )

        # 隐空间经过 VAEDecode 节点解码后还原到像素空间
        vaedecode_17 = vaedecode.decode(
            samples=ksampleradvanced_10[0], vae=checkpointloadersimple_4[2]
        )

        # 这里就是图片了
        saveimage_19 = saveimage.save_images(
            filename_prefix="ComfyUI", images=vaedecode_17[0]
        )


if __name__ == "__main__":
    main()