bk-sdm-small

Examples with 🤗Diffusers library.

An inference code with the default PNDM scheduler and 50 denoising steps is as follows.

import torch
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained("nota-ai/bk-sdm-small", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "a tropical bird sitting on a branch of a tree"
image = pipe(prompt).images[0]

image.save("example.png")

The following code is also runnable, because we compressed the U-Net of Stable Diffusion v1.4 while keeping the other parts (i.e., Text Encoder and Image Decoder) unchanged:

import torch
from diffusers import StableDiffusionPipeline, UNet2DConditionModel

pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe.unet = UNet2DConditionModel.from_pretrained("nota-ai/bk-sdm-small", subfolder="unet", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "a tropical bird sitting on a branch of a tree"
image = pipe(prompt).images[0]

image.save("example.png")

Compression Method

U-Net Architecture

Certain residual and attention blocks were eliminated from the U-Net of SDM-v1.4:

• 1.04B-param SDM-v1.4 (0.86B-param U-Net): the original source model.
• 0.76B-param BK-SDM-Base (0.58B-param U-Net): obtained with ① fewer blocks in outer stages.
• 0.66B-param BK-SDM-Small (0.49B-param U-Net): obtained with ① and ② mid-stage removal.
• 0.50B-param BK-SDM-Tiny (0.33B-param U-Net): obtained with ①, ②, and ③ further inner-stage removal.

Distillation Pretraining

The compact U-Net was trained to mimic the behavior of the original U-Net. We leveraged feature-level and output-level distillation, along with the denoising task loss.

• Training Data: 212,776 image-text pairs (i.e., 0.22M pairs) from LAION-Aesthetics V2 6.5+.
• Hardware: A single NVIDIA A100 80GB GPU
• Gradient Accumulations: 4
• Batch: 256 (=4×64)
• Optimizer: AdamW
• Learning Rate: a constant learning rate of 5e-5 for 50K-iteration pretraining

Experimental Results

The following table shows the zero-shot results on 30K samples from the MS-COCO validation split. After generating 512×512 images with the PNDM scheduler and 25 denoising steps, we downsampled them to 256×256 for evaluating generation scores. Our models were drawn at the 50K-th training iteration.

The following figure depicts synthesized images with some MS-COCO captions.

Uses

Note: This section is taken from the Stable Diffusion v1 model card (which was based on the DALLE-MINI model card) and applies in the same way to BK-SDMs.

Direct Use

The model is intended for research purposes only. Possible research areas and tasks include

• Safe deployment of models which have the potential to generate harmful content.
• Probing and understanding the limitations and biases of generative models.
• Generation of artworks and use in design and other artistic processes.
• Applications in educational or creative tools.
• Research on generative models.

Excluded uses are described below.

Misuse, Malicious Use, and Out-of-Scope Use

The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

Out-of-Scope Use

The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

Misuse and Malicious Use

Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

• Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
• Intentionally promoting or propagating discriminatory content or harmful stereotypes.
• Impersonating individuals without their consent.
• Sexual content without consent of the people who might see it.
• Mis- and disinformation