VAE encode is used for training, image-to-image and image-to-video - turning into images or videos into latent representations.
These tables demonstrate the VRAM requirements for VAE encode with SD v1 and SD XL on different GPUs.
For the majority of these GPUs the memory usage % dictates other models (text encoders, UNet/Transformer) must be offloaded, or tiled encoding has to be used which increases time taken and impacts quality.
GPU | Resolution | Time (seconds) | Memory (%) | Tiled Time (secs) | Tiled Memory (%) |
---|---|---|---|---|---|
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 |
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 | OOM | 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 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 |
Model support can be requested here.
Install diffusers
from main
to run the code: pip install git+https://github.com/huggingface/diffusers@main
A helper method simplifies interacting with Hybrid Inference.
from diffusers.utils.remote_utils import remote_encode
Let’s encode an image, then decode it to demonstrate.
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,
)
Now let’s look at a generation example, we’ll encode the image, generate then remotely decode too!
import torch
from diffusers import StableDiffusionImg2ImgPipeline
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")