mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers

Instalación y uso con Diffusers:

pip install -U diffusers transformers accelerate

import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained(
    "mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers",
    dtype=torch.bfloat16,
    device_map="cuda"
)

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

Ejemplo de uso del autoencoder DC-AE:

# build DC-AE models
# full DC-AE model list: https://huggingface.co/collections/mit-han-lab/dc-ae-670085b9400ad7197bb1009b
from efficientvit.ae_model_zoo import DCAE_HF

dc_ae = DCAE_HF.from_pretrained(f"mit-han-lab/dc-ae-f64c128-in-1.0")

# encode
from PIL import Image
import torch
import torchvision.transforms as transforms
from torchvision.utils import save_image
from efficientvit.apps.utils.image import DMCrop

device = torch.device("cuda")
dc_ae = dc_ae.to(device).eval()
transform = transforms.Compose([
    DMCrop(512), # resolution
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
image = Image.open("assets/fig/girl.png")
x = transform(image)[None].to(device)
latent = dc_ae.encode(x)
print(latent.shape)

# decode
y = dc_ae.decode(latent)
save_image(y * 0.5 + 0.5, "demo_dc_ae.png")

Ejemplo de difusión eficiente con DC-AE:

# build DC-AE-Diffusion models
# full DC-AE-Diffusion model list: https://huggingface.co/collections/mit-han-lab/dc-ae-diffusion-670dbb8d6b6914cf24c1a49d
from efficientvit.diffusion_model_zoo import DCAE_Diffusion_HF

dc_ae_diffusion = DCAE_Diffusion_HF.from_pretrained(
    f"mit-han-lab/dc-ae-f64c128-in-1.0-uvit-h-in-512px-train2000k"
)

# denoising on the latent space
import torch
import numpy as np
from torchvision.utils import save_image

torch.set_grad_enabled(False)
device = torch.device("cuda")
dc_ae_diffusion = dc_ae_diffusion.to(device).eval()

seed = 0
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
eval_generator = torch.Generator(device=device)
eval_generator.manual_seed(seed)

prompts = torch.tensor(
    [279, 333, 979, 936, 933, 145, 497, 1, 248, 360, 793, 12, 387, 437, 938, 978],
    dtype=torch.int,
    device=device
)
num_samples = prompts.shape[0]
prompts_null = 1000 * torch.ones((num_samples,), dtype=torch.int, device=device)
latent_samples = dc_ae_diffusion.diffusion_model.generate(prompts, prompts_null, 6.0, eval_generator)
latent_samples = latent_samples / dc_ae_diffusion.scaling_factor

# decode
image_samples = dc_ae_diffusion.autoencoder.decode(latent_samples)
save_image(image_samples * 0.5 + 0.5, "demo_dc_ae_diffusion.png", nrow=int(np.sqrt(num_samples)))