asset/docs/model_zoo.md

🔥 1. We provide all the links of Sana pth and diffusers safetensor below

Model	Reso	pth link	diffusers	Precision	Description
Sana-0.6B	512px	Sana_600M_512px	Efficient-Large-Model/Sana_600M_512px_diffusers	fp16/fp32	Multi-Language
Sana-0.6B	1024px	Sana_600M_1024px	Efficient-Large-Model/Sana_600M_1024px_diffusers	fp16/fp32	Multi-Language
Sana-1.6B	512px	Sana_1600M_512px	Efficient-Large-Model/Sana_1600M_512px_diffusers	fp16/fp32	-
Sana-1.6B	512px	Sana_1600M_512px_MultiLing	Efficient-Large-Model/Sana_1600M_512px_MultiLing_diffusers	fp16/fp32	Multi-Language
Sana-1.6B	1024px	Sana_1600M_1024px	Efficient-Large-Model/Sana_1600M_1024px_diffusers	fp16/fp32	-
Sana-1.6B	1024px	Sana_1600M_1024px_MultiLing	Efficient-Large-Model/Sana_1600M_1024px_MultiLing_diffusers	fp16/fp32	Multi-Language
Sana-1.6B	1024px	Sana_1600M_1024px_BF16	Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers	bf16/fp32	Multi-Language
Sana-1.6B	1024px	-	mit-han-lab/svdq-int4-sana-1600m	int4	Multi-Language
Sana-1.6B	2Kpx	Sana_1600M_2Kpx_BF16	Efficient-Large-Model/Sana_1600M_2Kpx_BF16_diffusers	bf16/fp32	Multi-Language
Sana-1.6B	4Kpx	Sana_1600M_4Kpx_BF16	Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers	bf16/fp32	Multi-Language
Sana-1.6B	4Kpx	Sana_1600M_4Kpx_BF16	Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers	bf16/fp32	Multi-Language
ControlNet
Sana-1.6B-ControlNet	1Kpx	Sana_1600M_1024px_BF16_ControlNet_HED	Coming soon	bf16/fp32	Multi-Language
Sana-0.6B-ControlNet	1Kpx	Sana_600M_1024px_ControlNet_HED	Coming soon	fp16/fp32	-

❗ 2. Make sure to use correct precision(fp16/bf16/fp32) for training and inference.

We provide two samples to use fp16 and bf16 weights, respectively.

❗️Make sure to set variant and torch_dtype in diffusers pipelines to the desired precision.

1). For fp16 models

# run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers
import torch
from diffusers import SanaPipeline

pipe = SanaPipeline.from_pretrained(
    "Efficient-Large-Model/Sana_1600M_1024px_diffusers",
    variant="fp16",
    torch_dtype=torch.float16,
)
pipe.to("cuda")

pipe.vae.to(torch.bfloat16)
pipe.text_encoder.to(torch.bfloat16)

prompt = 'a cyberpunk cat with a neon sign that says "Sana"'
image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    guidance_scale=5.0,
    num_inference_steps=20,
    generator=torch.Generator(device="cuda").manual_seed(42),
)[0]

image[0].save("sana.png")

2). For bf16 models

# run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers
import torch
from diffusers import SanaPAGPipeline

pipe = SanaPAGPipeline.from_pretrained(
  "Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers",
  variant="bf16",
  torch_dtype=torch.bfloat16,
  pag_applied_layers="transformer_blocks.8",
)
pipe.to("cuda")

pipe.text_encoder.to(torch.bfloat16)
pipe.vae.to(torch.bfloat16)

prompt = 'a cyberpunk cat with a neon sign that says "Sana"'
image = pipe(
    prompt=prompt,
    guidance_scale=5.0,
    pag_scale=2.0,
    num_inference_steps=20,
    generator=torch.Generator(device="cuda").manual_seed(42),
)[0]
image[0].save('sana.png')

❗ 3. 4K models

4K models need VAE tiling to avoid OOM issue.(16 GPU is recommended)

# run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers
import torch
from diffusers import SanaPipeline

pipe = SanaPipeline.from_pretrained(
    "Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers",
    variant="bf16",
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

pipe.vae.to(torch.bfloat16)
pipe.text_encoder.to(torch.bfloat16)

# for 4096x4096 image generation OOM issue, feel free adjust the tile size
if pipe.transformer.config.sample_size == 128:
    pipe.vae.enable_tiling(
        tile_sample_min_height=1024,
        tile_sample_min_width=1024,
        tile_sample_stride_height=896,
        tile_sample_stride_width=896,
    )
prompt = 'a cyberpunk cat with a neon sign that says "Sana"'
image = pipe(
    prompt=prompt,
    height=4096,
    width=4096,
    guidance_scale=5.0,
    num_inference_steps=20,
    generator=torch.Generator(device="cuda").manual_seed(42),
)[0]

image[0].save("sana_4K.png")

❗ 4. int4 inference

This int4 model is quantized with SVDQuant-Nunchaku. You need first follow the guidance of installation of nunchaku engine, then you can use the following code snippet to perform inference with int4 Sana model.

Here we show the code snippet for SanaPipeline. For SanaPAGPipeline, please refer to the SanaPAGPipeline section.

import torch
from diffusers import SanaPipeline

from nunchaku.models.transformer_sana import NunchakuSanaTransformer2DModel

transformer = NunchakuSanaTransformer2DModel.from_pretrained("mit-han-lab/svdq-int4-sana-1600m")
pipe = SanaPipeline.from_pretrained(
    "Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers",
    transformer=transformer,
    variant="bf16",
    torch_dtype=torch.bfloat16,
).to("cuda")

pipe.text_encoder.to(torch.bfloat16)
pipe.vae.to(torch.bfloat16)

image = pipe(
    prompt="A cute 🐼 eating 🎋, ink drawing style",
    height=1024,
    width=1024,
    guidance_scale=4.5,
    num_inference_steps=20,
    generator=torch.Generator().manual_seed(42),
).images[0]
image.save("sana_1600m.png")