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入门：使用混合推理进行 VAE 编码

VAE 编码用于训练、图像到图像和图像到视频——将图像或视频转换为潜在表示。

##
内存

这些表格展示了在不同 GPU 上使用 SD v1 和 SD XL 进行 VAE 编码的 VRAM 需求。

对于这些 GPU 中的大多数，内存使用百分比决定了其他模型（文本编码器、UNet/Transformer）必须被卸载，或者必须使用分块编码，这会增加时间并影响质量。

SD v1.5

GPU | 分辨率 | 时间（秒） | 内存（%） | 分块时间（秒） | 分块内存（%）
--- | --- | --- | --- | --- | ---
NVIDIA GeForce RTX 4090 | 512x512 | 0.015 | 3.51901 | 0.015 | 3.51901
NVIDIA GeForce RTX 4090 | 256x256 | 0.004 | 1.3154 | 0.005 | 1.3154
NVIDIA GeForce RTX 4090 | 2048x2048 | 0.402 | 47.1852 | 0.496 | 3.51901
NVIDIA GeForce RTX 4090 | 1024x1024 | 0.078 | 12.2658 | 0.094 | 3.51901
NVIDIA GeForce RTX 4080 SUPER | 512x512 | 0.023 | 5.30105 | 0.023 | 5.30105
NVIDIA GeForce RTX 4080 SUPER | 256x256 | 0.006 | 1.98152 | 0.006 | 1.98152
NVIDIA GeForce RTX 4080 SUPER | 2048x2048 | 0.574 | 71.08 | 0.656 | 5.30105
NVIDIA GeForce RTX 4080 SUPER | 1024x1024 | 0.111 | 18.4772 | 0.14 | 5.30105
NVIDIA GeForce RTX 3090 | 512x512 | 0.032 | 3.52782 | 0.032 | 3.52782
NVIDIA GeForce RTX 3090 | 256x256 | 0.01 | 1.31869 | 0.009 | 1.31869
NVIDIA GeForce RTX 3090 | 2048x2048 | 0.742 | 47.3033 | 0.954 | 3.52782
NVIDIA GeForce RTX 3090 | 1024x1024 | 0.136 | 12.2965 | 0.207 | 3.52782
NVIDIA GeForce RTX 3080 | 512x512 | 0.036 | 8.51761 | 0.036 | 8.51761
NVIDIA GeForce RTX 3080 | 256x256 | 0.01 | 3.18387 | 0.01 | 3.18387
NVIDIA GeForce RTX 3080 | 2048x2048 | 0.863 | 86.7424 | 1.191 | 8.51761
NVIDIA GeForce RTX 3080 | 1024x1024 | 0.157 | 29.6888 | 0.227 | 8.51761
NVIDIA GeForce RTX 3070 | 512x512 | 0.051 | 10.6941 | 0.051 | 10.6941
NVIDIA GeForce RTX 3070 | 256x256 | 0.015 |  |  |
3.99743 | 0.015 | 3.99743 |  |  |
NVIDIA GeForce RTX 3070 | 2048x2048 | 1.217 | 96.054 | 1.482 | 10.6941
NVIDIA GeForce RTX 3070 | 1024x1024 | 0.223 | 37.2751 | 0.327 | 10.6941

SDXL

GPU | Resolution | Time (seconds) | Memory Consumed (%) | Tiled Time (seconds) | Tiled Memory (%)
--- | --- | --- | --- | --- | ---
NVIDIA GeForce RTX 4090 | 512x512 | 0.029 | 4.95707 | 0.029 | 4.95707
NVIDIA GeForce RTX 4090 | 256x256 | 0.007 | 2.29666 | 0.007 | 2.29666
NVIDIA GeForce RTX 4090 | 2048x2048 | 0.873 | 66.3452 | 0.863 | 15.5649
NVIDIA GeForce RTX 4090 | 1024x1024 | 0.142 | 15.5479 | 0.143 | 15.5479
NVIDIA GeForce RTX 4080 SUPER | 512x512 | 0.044 | 7.46735 | 0.044 | 7.46735
NVIDIA GeForce RTX 4080 SUPER | 256x256 | 0.01 | 3.4597 | 0.01 | 3.4597
NVIDIA GeForce RTX 4080 SUPER | 2048x2048 | 1.317 | 87.1615 | 1.291 | 23.447
NVIDIA GeForce RTX 4080 SUPER | 1024x1024 | 0.213 | 23.4215 | 0.214 | 23.4215
NVIDIA GeForce RTX 3090 | 512x512 | 0.058 | 5.65638 | 0.058 | 5.65638
NVIDIA GeForce RTX 3090 | 256x256 | 0.016 | 2.45081 | 0.016 | 2.45081
NVIDIA GeForce RTX 3090 | 2048x2048 | 1.755 | 77.8239 | 1.614 | 18.4193
NVIDIA GeForce RTX 3090 | 1024x1024 | 0.265 | 18.4023 | 0.265 | 18.4023
NVIDIA GeForce RTX 3080 | 512x512 | 0.064 | 13.6568 | 0.064 | 13.6568
NVIDIA GeForce RTX 3080 | 256x256 | 0.018 | 5.91728 | 0.018 | 5.91728
NVIDIA GeForce RTX 3080 | 2048x2048 | 内存不足 (OOM) | 内存不足 (OOM) | 1.866 | 44.4717
NVIDIA GeForce RTX 3080 | 1024x1024 | 0.302 | 44.4308 | 0.302 | 44.4308
NVIDIA GeForce RTX 3070 | 512x512 | 0.093 | 17.1465 | 0.093 | 17.1465

| NVIDIA GeForce R | NVIDIA GeForce RTX 3070 | 256x256 | 0.025 | 7.42931 | 0.026 | 7.42931 | | NVIDIA GeForce RTX 3070 | 2048x2048 | OOM | OOM | 2.674 | 55.8355 | | NVIDIA GeForce RTX 3070 | 1024x1024 | 0.443 | 55.7841 | 0.443 | 55.7841 |

##
可用 VAE

| 端点 | 模型
--- | --- | ---
Stable Diffusion v1 | https://qc6479g0aac6qwy9.us-east-1.aws.endpoints.huggingface.cloud | `stabilityai/sd-vae-ft-mse`
Stable Diffusion XL | https://xjqqhmyn62rog84g.us-east-1.aws.endpoints.huggingface.cloud | `madebyollin/sdxl-vae-fp16-fix`
Flux | https://ptccx55jz97f9zgo.us-east-1.aws.endpoints.huggingface.cloud | `black-forest-labs/FLUX.1-schnell`

> 模型支持可以在此处请求：这里。

##
代码

> 从 `main` 安装 `diffusers` 以运行代码：`pip install git+https://github.com/huggingface/diffusers@main`

一个辅助方法简化了与混合推理的交互。

    from diffusers.utils.remote_utils import remote_encode

###
基本示例

让我们编码一张图像，然后解码以演示。

代码

    from diffusers.utils import load_image
    from diffusers.utils.remote_utils import remote_decode

    image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg?download=true")

    latent = remote_encode(
        endpoint="https://ptccx55jz97f9zgo.us-east-1.aws.endpoints.huggingface.cloud/",
        scaling_factor=0.3611,
        shift_factor=0.1159,
    )

    decoded = remote_decode(
        endpoint="https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/",
        tensor=latent,
        scaling_factor=0.3611,
        shift_factor=0.1159,
    )

###
生成

现在让我们看一个生成示例，我们将编码图像，生成，然后远程解码！

代码

    import torch
    from diffusers import StableDiffusionImg2ImgPip
    from diffusers.utils import load_image
    from diffusers.utils.remote_utils import remote_decode, remote_encode

    pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
        "stable-diffusion-v1-5/stable-diffusion-v1-5",
        torch_dtype=torch.float16,
        variant="fp16",
        vae=None,
    ).to("cuda")

    init_image = load_image(
        "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
    )
    init_image = init_image.resize((768, 512))

    init_latent = remote_encode(
        endpoint="https://qc6479g0aac6qwy9.us-east-1.aws.endpoints.huggingface.cloud/",
        image=init_image,
        scaling_factor=0.18215,
    )

    prompt = "A fantasy landscape, trending on artstation"
    latent = pipe(
        prompt=prompt,
        image=init_latent,
        strength=0.75,
        output_type="latent",
    ).images

    image = remote_decode(
        endpoint="https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud/",
        tensor=latent,
        scaling_factor=0.18215,
    )
    image.save("fantasy_landscape.jpg")

##
集成

- SD.Next: 具有直接支持混合推理功能的一体化用户界面。
- ComfyUI-HFRemoteVae: 用于混合推理的 ComfyUI 节点。

Update on GitHub