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Repository: LambdaLabsML/lambda-diffusers
Branch: main
Commit: 8571d8d492c6
Files: 20
Total size: 1.8 MB
Directory structure:
gitextract_xdc54des/
├── .gitignore
├── Dockerfile
├── LICENSE
├── README.md
├── benchmarks/
│ ├── benchmark.csv
│ ├── benchmark_H100_MIG.csv
│ └── benchmark_no_xformers.csv
├── docs/
│ ├── benchmark-update.md
│ └── benchmark.md
├── lambda_diffusers/
│ ├── __init__.py
│ └── pipelines/
│ ├── __init__.py
│ └── pipeline_stable_diffusion_im_embed.py
├── notebooks/
│ └── pokemon_demo.ipynb
├── requirements.txt
├── scripts/
│ ├── Dockerfile
│ ├── Makefile
│ ├── benchmark.py
│ ├── benchmark_quality.py
│ └── convert_sd_image_to_diffusers.py
└── setup.py
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FILE CONTENTS
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FILE: .gitignore
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model_zoo/
outputs/
*benchmark_tmp.csv
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*.egg-info/
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
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# Installer logs
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cover/
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*.log
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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FILE: Dockerfile
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FROM nvidia/cuda:11.2.1-base-ubuntu20.04
RUN apt-get update && \
apt-get install --no-install-recommends --no-install-suggests -y \
curl python3 python3-pip
WORKDIR /lambda_diffusers
COPY . .
RUN pip3 install --no-cache-dir -r requirements.txt
CMD ["python3", "-u", "scripts/benchmark.py", "--samples", "1,2,4,8,16"]
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2022 Lambda, Inc
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# Lambda Diffusers
_Additional models and pipelines for 🤗 Diffusers created by [Lambda Labs](https://lambdalabs.com/)_
- [Stable Diffusion Image Variations](#stable-diffusion-image-variations)
- [Pokemon text to image](#pokemon-text-to-image)
<p align="center">
🦄 Other exciting ML projects at Lambda: <a href="https://news.lambdalabs.com/news/today">ML Times</a>, <a href="https://github.com/LambdaLabsML/distributed-training-guide/tree/main">Distributed Training Guide</a>, <a href="https://lambdalabsml.github.io/Open-Sora/introduction/">Text2Video</a>, <a href="https://lambdalabs.com/gpu-benchmarks">GPU Benchmark</a>.
</p>
## Installation
```bash
git clone https://github.com/LambdaLabsML/lambda-diffusers.git
cd lambda-diffusers
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```
## Stable Diffusion Image Variations
A fine-tuned version of Stable Diffusion conditioned on CLIP image embeddings to enabel Image Variations.
[](https://colab.research.google.com/drive/1JqNbI_kDq_Gth2MIYdsphgNgyGIJxBgB?usp=sharing)
[](https://huggingface.co/spaces/lambdalabs/stable-diffusion-image-variations)
[](https://replicate.com/lambdal/stable-diffusion-image-variation)
- Download the weights ported to 🤗 Diffusers [here](https://huggingface.co/lambdalabs/sd-image-variations-diffusers).
- See the original training repo [here](https://github.com/justinpinkney/stable-diffusion).
### Usage
```python
from diffusers import StableDiffusionImageVariationPipeline
from PIL import Image
device = "cuda:0"
sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(
"lambdalabs/sd-image-variations-diffusers",
revision="v2.0",
)
sd_pipe = sd_pipe.to(device)
im = Image.open("path/to/image.jpg")
tform = transforms.Compose([
transforms.ToTensor(),
transforms.Resize(
(224, 224),
interpolation=transforms.InterpolationMode.BICUBIC,
antialias=False,
),
transforms.Normalize(
[0.48145466, 0.4578275, 0.40821073],
[0.26862954, 0.26130258, 0.27577711]),
])
inp = tform(im).to(device)
out = sd_pipe(inp, guidance_scale=3)
out["images"][0].save("result.jpg")
```
## Pokemon text to image
__Stable Diffusion fine tuned on Pokémon by [Lambda Labs](https://lambdalabs.com/).__
[](https://replicate.com/lambdal/text-to-pokemon)
[](https://colab.research.google.com/github/LambdaLabsML/lambda-diffusers/blob/main/notebooks/pokemon_demo.ipynb)
[](https://huggingface.co/spaces/lambdalabs/text-to-pokemon)
Put in a text prompt and generate your own Pokémon character, no "prompt engineering" required!
If you want to find out how to train your own Stable Diffusion variants, see this [example](https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning) from Lambda Labs.
> Girl with a pearl earring, Cute Obama creature, Donald Trump, Boris Johnson, Totoro, Hello Kitty
## Model description
Trained on [BLIP captioned Pokémon images](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) using 2xA6000 GPUs on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud) for around 15,000 step (about 6 hours, at a cost of about $10).
## Usage
```python
import torch
from diffusers import StableDiffusionPipeline
from torch import autocast
pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/sd-pokemon-diffusers", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "Yoda"
scale = 10
n_samples = 4
# Sometimes the nsfw checker is confused by the Pokémon images, you can disable
# it at your own risk here
disable_safety = False
if disable_safety:
def null_safety(images, **kwargs):
return images, False
pipe.safety_checker = null_safety
with autocast("cuda"):
images = pipe(n_samples*[prompt], guidance_scale=scale).images
for idx, im in enumerate(images):
im.save(f"{idx:06}.png")
```
## Benchmarking inference
We have updated the original benchmark using xformers and a newer version of Diffusers, see the [new results here](./docs/benchmark-update.md) (original results can still be found [here](./docs/benchmark.md)).
### Usage
Ensure that [NVIDIA container toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) is installed on your system and then run the following:
```bash
git clone https://github.com/LambdaLabsML/lambda-diffusers.git
cd lambda-diffusers/scripts
make bench
```
Currently `xformers` does not support H100. The "without xformers" results below are generated by running the benchmark with `--xformers no` (can be set in `scripts/Makefile`)
### Results
With [xformers](https://github.com/facebookresearch/xformers), raw data can be found [here](./benchmarks/benchmark.csv).
Without [xformers](https://github.com/facebookresearch/xformers), raw data can be found [here](./benchmarks/benchmark_no_xformers.csv).
H100 MIG performance, raw data can be found [here](./benchmarks/benchmark_H100_MIG.csv).
Cost analysis
## Links
- [Captioned Pokémon dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions)
- [Model weights in Diffusers format](https://huggingface.co/lambdalabs/sd-pokemon-diffusers)
- [Original model weights](https://huggingface.co/justinpinkney/pokemon-stable-diffusion)
- [Training code](https://github.com/justinpinkney/stable-diffusion)
Trained by [Justin Pinkney](justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda Labs](https://lambdalabs.com/).
================================================
FILE: benchmarks/benchmark.csv
================================================
device,precision,autocast,xformers,runtime,n_samples,latency,memory
NVIDIA A10,half,FALSE,TRUE,pytorch,1,2.01,3.13
NVIDIA A10,single,FALSE,TRUE,pytorch,1,4.69,6.29
NVIDIA A10,half,FALSE,TRUE,pytorch,2,3.65,4.3
NVIDIA A10,single,FALSE,TRUE,pytorch,2,7.75,8.57
NVIDIA A10,half,FALSE,TRUE,pytorch,4,6.68,6.63
NVIDIA A10,single,FALSE,TRUE,pytorch,4,14.35,11.24
NVIDIA A10,half,FALSE,TRUE,pytorch,8,12.93,11.05
NVIDIA A10,single,FALSE,TRUE,pytorch,8,28.28,17.91
NVIDIA A10,half,FALSE,TRUE,pytorch,16,24.65,19.86
NVIDIA A10,single,FALSE,TRUE,pytorch,16,57.5,21.21
NVIDIA A10,half,FALSE,TRUE,pytorch,32,48.79,7.37
NVIDIA A10,single,FALSE,TRUE,pytorch,32,108.78,15.88
NVIDIA A10,half,FALSE,TRUE,pytorch,64,108.26,17.54
NVIDIA A10,single,FALSE,TRUE,pytorch,64,-1,-1
NVIDIA A10,half,FALSE,TRUE,pytorch,128,212.94,22.18
NVIDIA A10,single,FALSE,TRUE,pytorch,128,-1,-1
NVIDIA A100 80GB PCIe,single,FALSE,TRUE,pytorch,1,1.78,6.1
NVIDIA A100 80GB PCIe,half,FALSE,TRUE,pytorch,1,1.17,3.19
NVIDIA A100 80GB PCIe,single,FALSE,TRUE,pytorch,2,3.68,8.03
NVIDIA A100 80GB PCIe,half,FALSE,TRUE,pytorch,2,1.73,4.33
NVIDIA A100 80GB PCIe,single,FALSE,TRUE,pytorch,4,5.56,11.53
NVIDIA A100 80GB PCIe,half,FALSE,TRUE,pytorch,4,3.73,6.62
NVIDIA A100 80GB PCIe,single,FALSE,TRUE,pytorch,8,10.95,18.12
NVIDIA A100 80GB PCIe,half,FALSE,TRUE,pytorch,8,5.25,11.12
NVIDIA A100 80GB PCIe,single,FALSE,TRUE,pytorch,16,21.05,33.04
NVIDIA A100 80GB PCIe,half,FALSE,TRUE,pytorch,16,9.93,19.81
NVIDIA A100 80GB PCIe,single,FALSE,TRUE,pytorch,32,41.02,14.41
NVIDIA A100 80GB PCIe,half,FALSE,TRUE,pytorch,32,18.75,7.34
NVIDIA A100 80GB PCIe,single,FALSE,TRUE,pytorch,64,80.45,26.17
NVIDIA A100 80GB PCIe,half,FALSE,TRUE,pytorch,64,36.89,12.46
NVIDIA A100 80GB PCIe,single,FALSE,TRUE,pytorch,128,161.52,48.01
NVIDIA A100 80GB PCIe,half,FALSE,TRUE,pytorch,128,73.72,22.68
NVIDIA A100-SXM4-40GB,single,FALSE,TRUE,pytorch,1,1.79,6.11
NVIDIA A100-SXM4-40GB,half,FALSE,TRUE,pytorch,1,1.18,3.18
NVIDIA A100-SXM4-40GB,single,FALSE,TRUE,pytorch,2,2.97,8.03
NVIDIA A100-SXM4-40GB,half,FALSE,TRUE,pytorch,2,1.66,4.32
NVIDIA A100-SXM4-40GB,single,FALSE,TRUE,pytorch,4,5.35,11.54
NVIDIA A100-SXM4-40GB,half,FALSE,TRUE,pytorch,4,2.68,6.61
NVIDIA A100-SXM4-40GB,single,FALSE,TRUE,pytorch,8,10.16,18.11
NVIDIA A100-SXM4-40GB,half,FALSE,TRUE,pytorch,8,4.85,11.12
NVIDIA A100-SXM4-40GB,half,FALSE,TRUE,pytorch,16,9.13,19.8
NVIDIA A100-SXM4-40GB,single,FALSE,TRUE,pytorch,16,19.71,33.25
NVIDIA A100-SXM4-40GB,half,FALSE,TRUE,pytorch,32,17.72,7.33
NVIDIA A100-SXM4-40GB,single,FALSE,TRUE,pytorch,32,39.03,14.39
NVIDIA A100-SXM4-40GB,half,FALSE,TRUE,pytorch,64,34.92,13.79
NVIDIA A100-SXM4-40GB,single,FALSE,TRUE,pytorch,64,77.05,26.34
NVIDIA A100-SXM4-40GB,half,FALSE,TRUE,pytorch,128,69.31,22.68
NVIDIA A100-SXM4-40GB,single,FALSE,TRUE,pytorch,128,-1,-1
NVIDIA RTX A6000,single,FALSE,TRUE,pytorch,1,3.61,6.35
NVIDIA RTX A6000,half,FALSE,TRUE,pytorch,1,1.93,3.15
NVIDIA RTX A6000,single,FALSE,TRUE,pytorch,2,5.57,7.73
NVIDIA RTX A6000,half,FALSE,TRUE,pytorch,2,2.84,4.37
NVIDIA RTX A6000,single,FALSE,TRUE,pytorch,4,9.67,10.7
NVIDIA RTX A6000,half,FALSE,TRUE,pytorch,4,4.56,6.64
NVIDIA RTX A6000,single,FALSE,TRUE,pytorch,8,18.96,16.87
NVIDIA RTX A6000,half,FALSE,TRUE,pytorch,8,8.39,11.19
NVIDIA RTX A6000,single,FALSE,TRUE,pytorch,16,37.89,28.82
NVIDIA RTX A6000,half,FALSE,TRUE,pytorch,16,15.62,20.01
NVIDIA RTX A6000,single,FALSE,TRUE,pytorch,32,71.57,14.26
NVIDIA RTX A6000,half,FALSE,TRUE,pytorch,32,31.19,7.65
NVIDIA RTX A6000,single,FALSE,TRUE,pytorch,64,143.26,26.42
NVIDIA RTX A6000,half,FALSE,TRUE,pytorch,64,65.72,23.84
NVIDIA RTX A6000,single,FALSE,TRUE,pytorch,128,287.96,47.92
NVIDIA RTX A6000,half,FALSE,TRUE,pytorch,128,130.38,34.36
Tesla V100-SXM2-16GB,single,FALSE,TRUE,pytorch,1,4.42,5.7
Tesla V100-SXM2-16GB,half,FALSE,TRUE,pytorch,1,1.84,3.24
Tesla V100-SXM2-16GB,single,FALSE,TRUE,pytorch,2,8.33,8.6
Tesla V100-SXM2-16GB,half,FALSE,TRUE,pytorch,2,3.08,4.17
Tesla V100-SXM2-16GB,single,FALSE,TRUE,pytorch,4,16.56,11.86
Tesla V100-SXM2-16GB,half,FALSE,TRUE,pytorch,4,5.62,6.42
Tesla V100-SXM2-16GB,single,FALSE,TRUE,pytorch,8,28.71,15.88
Tesla V100-SXM2-16GB,half,FALSE,TRUE,pytorch,8,10.64,10.45
Tesla V100-SXM2-16GB,half,FALSE,TRUE,pytorch,16,20.96,10.87
Tesla V100-SXM2-16GB,single,FALSE,TRUE,pytorch,16,-1,-1
Tesla V100-SXM2-16GB,half,FALSE,TRUE,pytorch,32,40.13,7.73
Tesla V100-SXM2-16GB,single,FALSE,TRUE,pytorch,32,110.17,15.72
Tesla V100-SXM2-16GB,half,FALSE,TRUE,pytorch,64,79.82,13.51
Tesla V100-SXM2-16GB,single,FALSE,TRUE,pytorch,64,-1,-1
Tesla V100-SXM2-16GB,single,FALSE,TRUE,pytorch,128,-1,-1
Tesla V100-SXM2-16GB,half,FALSE,TRUE,pytorch,128,-1,-1
================================================
FILE: benchmarks/benchmark_H100_MIG.csv
================================================
device,precision,autocast,xformers,runtime,n_samples,latency,memory,
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,1,1.73,7.7
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,1,1.06,3.46
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,2,2.66,9.79
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,2,1.73,4.57
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,4,4.47,18.49
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,4,2.63,8.91
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,8,8.16,23.86
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,8,4.97,12.57
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,16,15.98,42.38
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,16,9.61,29.01
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,32,32.04,80.51
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,32,19.07,55.57
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA H100 PCIe MIG 4g.40gb,single,FALSE,FALSE,pytorch,1,2.3,7.74
NVIDIA H100 PCIe MIG 4g.40gb,half,FALSE,FALSE,pytorch,1,1.52,3.45
NVIDIA H100 PCIe MIG 4g.40gb,single,FALSE,FALSE,pytorch,2,3.95,9.48
NVIDIA H100 PCIe MIG 4g.40gb,half,FALSE,FALSE,pytorch,2,2.42,4.57
NVIDIA H100 PCIe MIG 4g.40gb,single,FALSE,FALSE,pytorch,4,7.12,18.2
NVIDIA H100 PCIe MIG 4g.40gb,half,FALSE,FALSE,pytorch,4,4.17,8.9
NVIDIA H100 PCIe MIG 4g.40gb,single,FALSE,FALSE,pytorch,8,13.91,23.75
NVIDIA H100 PCIe MIG 4g.40gb,half,FALSE,FALSE,pytorch,8,7.91,12.49
NVIDIA H100 PCIe MIG 4g.40gb,single,FALSE,FALSE,pytorch,16,-1,-1
NVIDIA H100 PCIe MIG 4g.40gb,half,FALSE,FALSE,pytorch,16,15.73,29.01
NVIDIA H100 PCIe MIG 4g.40gb,single,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA H100 PCIe MIG 4g.40gb,half,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA H100 PCIe MIG 4g.40gb,single,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA H100 PCIe MIG 4g.40gb,half,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA H100 PCIe MIG 4g.40gb,single,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA H100 PCIe MIG 4g.40gb,half,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA H100 PCIe MIG 2g.20gb,single,FALSE,FALSE,pytorch,1,4.2,7.76
NVIDIA H100 PCIe MIG 2g.20gb,half,FALSE,FALSE,pytorch,1,2.58,3.41
NVIDIA H100 PCIe MIG 2g.20gb,single,FALSE,FALSE,pytorch,2,7.61,11.09
NVIDIA H100 PCIe MIG 2g.20gb,half,FALSE,FALSE,pytorch,2,4.56,4.59
NVIDIA H100 PCIe MIG 2g.20gb,single,FALSE,FALSE,pytorch,4,14.45,17.65
NVIDIA H100 PCIe MIG 2g.20gb,half,FALSE,FALSE,pytorch,4,8.24,6.78
NVIDIA H100 PCIe MIG 2g.20gb,single,FALSE,FALSE,pytorch,8,-1,-1
NVIDIA H100 PCIe MIG 2g.20gb,half,FALSE,FALSE,pytorch,8,15.81,15.65
NVIDIA H100 PCIe MIG 2g.20gb,single,FALSE,FALSE,pytorch,16,-1,-1
NVIDIA H100 PCIe MIG 2g.20gb,half,FALSE,FALSE,pytorch,16,-1,-1
NVIDIA H100 PCIe MIG 2g.20gb,single,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA H100 PCIe MIG 2g.20gb,half,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA H100 PCIe MIG 2g.20gb,single,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA H100 PCIe MIG 2g.20gb,half,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA H100 PCIe MIG 2g.20gb,single,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA H100 PCIe MIG 2g.20gb,half,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,single,FALSE,FALSE,pytorch,1,9.17,7.76
NVIDIA H100 PCIe MIG 1g.10gb,half,FALSE,FALSE,pytorch,1,5.39,3.47
NVIDIA H100 PCIe MIG 1g.10gb,single,FALSE,FALSE,pytorch,2,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,half,FALSE,FALSE,pytorch,2,9.29,4.63
NVIDIA H100 PCIe MIG 1g.10gb,single,FALSE,FALSE,pytorch,4,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,half,FALSE,FALSE,pytorch,4,17.4,6.8
NVIDIA H100 PCIe MIG 1g.10gb,single,FALSE,FALSE,pytorch,8,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,half,FALSE,FALSE,pytorch,8,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,single,FALSE,FALSE,pytorch,16,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,half,FALSE,FALSE,pytorch,16,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,single,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,half,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,single,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,half,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,single,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA H100 PCIe MIG 1g.10gb,half,FALSE,FALSE,pytorch,128,-1,-1
================================================
FILE: benchmarks/benchmark_no_xformers.csv
================================================
device,precision,autocast,xformers,runtime,n_samples,latency,memory,
NVIDIA A10,single,FALSE,FALSE,pytorch,1,4.75,6.73
NVIDIA A10,half,FALSE,FALSE,pytorch,1,2.71,3.43
NVIDIA A10,single,FALSE,FALSE,pytorch,2,8.75,9
NVIDIA A10,half,FALSE,FALSE,pytorch,2,4.99,5.53
NVIDIA A10,single,FALSE,FALSE,pytorch,4,17.18,18.14
NVIDIA A10,half,FALSE,FALSE,pytorch,4,9.65,6.84
NVIDIA A10,single,FALSE,FALSE,pytorch,8,-1,-1
NVIDIA A10,half,FALSE,FALSE,pytorch,8,18.58,12.66
NVIDIA A10,single,FALSE,FALSE,pytorch,16,-1,-1
NVIDIA A10,half,FALSE,FALSE,pytorch,16,36.32,20.64
NVIDIA A10,single,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA A10,half,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA A10,single,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA A10,half,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA A10,single,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA A10,half,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA A100-SXM4-40GB,single,FALSE,FALSE,pytorch,1,1.72,7.76
NVIDIA A100-SXM4-40GB,half,FALSE,FALSE,pytorch,1,1.18,3.41
NVIDIA A100-SXM4-40GB,single,FALSE,FALSE,pytorch,2,3.03,9.04
NVIDIA A100-SXM4-40GB,half,FALSE,FALSE,pytorch,2,1.88,5.53
NVIDIA A100-SXM4-40GB,single,FALSE,FALSE,pytorch,4,5.53,18.04
NVIDIA A100-SXM4-40GB,half,FALSE,FALSE,pytorch,4,3.35,6.74
NVIDIA A100-SXM4-40GB,single,FALSE,FALSE,pytorch,8,10.95,23.85
NVIDIA A100-SXM4-40GB,half,FALSE,FALSE,pytorch,8,6.28,12.6
NVIDIA A100-SXM4-40GB,single,FALSE,FALSE,pytorch,16,-1,-1
NVIDIA A100-SXM4-40GB,half,FALSE,FALSE,pytorch,16,12.57,20.58
NVIDIA A100-SXM4-40GB,single,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA A100-SXM4-40GB,half,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA A100-SXM4-40GB,single,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA A100-SXM4-40GB,half,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA A100-SXM4-40GB,single,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA A100-SXM4-40GB,half,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA A100-PCIE-40GB,single,FALSE,FALSE,pytorch,1,1.99,7.76
NVIDIA A100-PCIE-40GB,half,FALSE,FALSE,pytorch,1,1.5,3.45
NVIDIA A100-PCIE-40GB,single,FALSE,FALSE,pytorch,2,3.52,11.11
NVIDIA A100-PCIE-40GB,half,FALSE,FALSE,pytorch,2,2.3,4.53
NVIDIA A100-PCIE-40GB,single,FALSE,FALSE,pytorch,4,6.31,13.98
NVIDIA A100-PCIE-40GB,half,FALSE,FALSE,pytorch,4,4.04,8.91
NVIDIA A100-PCIE-40GB,single,FALSE,FALSE,pytorch,8,12.21,23.91
NVIDIA A100-PCIE-40GB,half,FALSE,FALSE,pytorch,8,7.59,12.75
NVIDIA A100-PCIE-40GB,single,FALSE,FALSE,pytorch,16,-1,-1
NVIDIA A100-PCIE-40GB,half,FALSE,FALSE,pytorch,16,14.54,21.24
NVIDIA A100-PCIE-40GB,single,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA A100-PCIE-40GB,half,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA A100-PCIE-40GB,single,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA A100-PCIE-40GB,half,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA A100-PCIE-40GB,single,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA A100-PCIE-40GB,half,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA A100 80GB PCIe,single,False,False,pytorch,1,2.05,7.76
NVIDIA A100 80GB PCIe,half,False,False,pytorch,1,1.53,3.41
NVIDIA A100 80GB PCIe,single,False,False,pytorch,2,3.09,9.04
NVIDIA A100 80GB PCIe,half,False,False,pytorch,2,3.06,5.53
NVIDIA A100 80GB PCIe,single,False,False,pytorch,4,6.34,18.04
NVIDIA A100 80GB PCIe,half,False,False,pytorch,4,4.57,6.74
NVIDIA A100 80GB PCIe,single,False,False,pytorch,8,11.16,23.85
NVIDIA A100 80GB PCIe,half,False,False,pytorch,8,7.91,12.6
NVIDIA A100 80GB PCIe,single,False,False,pytorch,16,22.59,42.63
NVIDIA A100 80GB PCIe,half,False,False,pytorch,16,14.22,20.58
NVIDIA A100 80GB PCIe,single,False,False,pytorch,32,44.02,79.6
NVIDIA A100 80GB PCIe,half,False,False,pytorch,32,27.73,45.19
NVIDIA A100 80GB PCIe,single,False,False,pytorch,64,-1.0,-1.0
NVIDIA A100 80GB PCIe,half,False,False,pytorch,64,55.55,79.54
NVIDIA A100 80GB PCIe,single,False,False,pytorch,128,-1.0,-1.0
NVIDIA A100 80GB PCIe,half,False,False,pytorch,128,-1.0,-1.0
NVIDIA RTX A6000,single,FALSE,FALSE,pytorch,1,4.15,6.76
NVIDIA RTX A6000,half,FALSE,FALSE,pytorch,1,2.43,3.42
NVIDIA RTX A6000,single,FALSE,FALSE,pytorch,2,6,11.1
NVIDIA RTX A6000,half,FALSE,FALSE,pytorch,2,3.88,4.5
NVIDIA RTX A6000,single,FALSE,FALSE,pytorch,4,12.85,13.97
NVIDIA RTX A6000,half,FALSE,FALSE,pytorch,4,7.77,8.88
NVIDIA RTX A6000,single,FALSE,FALSE,pytorch,8,32.69,23.88
NVIDIA RTX A6000,half,FALSE,FALSE,pytorch,8,21.21,12.74
NVIDIA RTX A6000,single,FALSE,FALSE,pytorch,16,81.14,42.77
NVIDIA RTX A6000,half,FALSE,FALSE,pytorch,16,48.49,21.23
NVIDIA RTX A6000,single,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA RTX A6000,half,FALSE,FALSE,pytorch,32,-1,-1
NVIDIA RTX A6000,single,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA RTX A6000,half,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA RTX A6000,single,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA RTX A6000,half,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,1,1.73,7.7
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,1,1.06,3.46
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,2,2.66,9.79
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,2,1.73,4.57
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,4,4.47,18.49
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,4,2.63,8.91
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,8,8.16,23.86
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,8,4.97,12.57
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,16,15.98,42.38
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,16,9.61,29.01
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,32,32.04,80.51
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,32,19.07,55.57
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,64,-1,-1
NVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,128,-1,-1
NVIDIA H100 PCIe,half,FALSE,FALSE,pytorch,128,-1,-1
================================================
FILE: docs/benchmark-update.md
================================================
# Benchmark update
We are currently running benchmarks to update our Stable Diffusion numbers using a more recent version of Diffusers and to take advantage of xformers. THe interim results on a limited set of GPUs are presented here.
## Running the benchmark
Ensure that [NVIDIA container toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) is installed on your system and then run the following:
```bash
git clone https://github.com/LambdaLabsML/lambda-diffusers.git
cd lambda-diffusers/scripts
make bench
```
Results will be written to `results.csv`, the benchmark will take different amounts of time depending on the GPU present but expect it to take at least several minutes.
## Results
The current results for the benchmark are available in [`benchmark.csv`](../benchmarks/benchmark.csv). These results were run with Diffusers 0.11.0 and xformers using Ubuntu 20.04, Python 3.8, PyTorch 1.13, CUDA 11.8 ([NGC PyTorch container 22.11](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-22-11.html)).
xformers provides a significant boost in performance and memory consumption allowing large batch sizes to maximise utilisation of GPUs. Our best performance comes using NVIDIA A100-SXM4-40GB on [Lambda GPU cloud](https://cloud.lambdalabs.com), at the maximum batch size tested (128) at half precision we observe a throughput of 1.85 images/second when using DDIM 30 steps for sampling.
================================================
FILE: docs/benchmark.md
================================================
# Benchmarking Diffuser Models
__We are currently in the process of updating our Stable Diffusion benchmark using more recent version of Diffusers and taking advantage of xformers. See the summary of interim result [here](./benchmark-update.md)__
We present a benchmark of [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) model inference. This text2image model uses a text prompt as input and outputs an image of resolution `512x512`.
Our experiments analyze inference performance in terms of speed, memory consumption, throughput, and quality of the output images. We look at how different choices in hardware (GPU model, GPU vs CPU) and software (single vs half precision, pytorch vs onnxruntime) affect inference performance.
For reference, we will be providing benchmark results for the following GPU devices: A100 80GB PCIe, RTX3090, RTXA5500, RTXA6000, RTX3080, RTX8000. Please refer to the ["Reproducing the experiments"](#reproducing-the-experiments) section for details on running these experiments in your own environment.
## Inference speed
The figure below shows the latency at inference when using different hardware and precision for generating a single image using the (arbitrary) text prompt: *"a photo of an astronaut riding a horse on mars"*.
<img src="./pictures/pretty_benchmark_sd_txt2img_latency.png" alt="Stable Diffusion Text2Image Latency (seconds)" width="800"/>
We find that:
* The inference latencies range between `3.74` to `5.56` seconds across our tested Ampere GPUs, including the consumer 3080 card to the flagship A100 80GB card.
* Half-precision reduces the latency by about `40%` for Ampere GPUs, and by `52%` for the previous generation `RTX8000` GPU.
We believe Ampere GPUs enjoy a relatively "smaller" speedup from half-precision due to their use of `TF32`. For readers who are not familiar with `TF32`, it is a [`19-bit` format](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) that has been used as the default single-precision data type on Ampere GPUs for major deep learning frameworks such as PyTorch and TensorFlow. One can expect half-precision's speedup over `FP32` to be bigger since it is a true `32-bit` format.
We run these same inference jobs CPU devices to put in perspective the inference speed performance observed on GPU.
<img src="./pictures/pretty_benchmark_sd_txt2img_gpu_vs_cpu.png" alt="Stable Diffusion Text2Image GPU v CPU" width="700"/>
We note that:
* GPUs are significantly faster -- by one or two orders of magnitudes depending on the precisions.
* `onnxruntime` can reduce the latency for CPU by about `40%` to `50%`, depending on the type of CPUs.
ONNX currently does not have [stable support](https://github.com/huggingface/diffusers/issues/489) for Huggingface diffusers.
We will investigate `onnxruntime-gpu` in future benchmarks.
## Memory
We also measure the memory consumption of running stable diffusion inference.
<img src="./pictures/pretty_benchmark_sd_txt2img_mem.png" alt="Stable Diffusion Text2Image Memory (GB)" width="640"/>
Memory usage is observed to be consistent across all tested GPUs:
* It takes about `7.7 GB` GPU memory to run single-precision inference with batch size one.
* It takes about `4.5 GB` GPU memory to run half-precision inference with batch size one.
## Throughput
Latency measures how quickly a _single_ input can be processed, which is critical to online applications that don't tolerate even the slightest delay. However, some (offline) applications may focus on "throughput", which measures the total volume of data processed in a fixed amount of time.
Our throughput benchmark pushes the batch size to the maximum for each GPU, and measures the number of images they can process per minute. The reason for maximizing the batch size is to keep tensor cores busy so that computation can dominate the workload, avoiding any non-computational bottlenecks.
We run a series of throughput experiment in pytorch with half-precision and using the maximum batch size that can be used for each GPU:
<img src="./pictures/pretty_benchmark_sd_txt2img_throughput.png" alt="Stable Diffusion Text2Image Throughput (images/minute)" width="390"/>
We note:
* Once again, A100 80GB is the top performer and has the highest throughput.
* The gap between A100 80GB and other cards in terms of throughput can be explained by the larger maximum batch size that can be used on this card.
As a concrete example, the chart below shows how A100 80GB's throughput increases by `64%` when we changed the batch size from 1 to 28 (the largest without causing an out of memory error). It is also interesting to see that the increase is not linear and flattens out when batch size reaches a certain value, at which point the tensor cores on the GPU are saturated and any new data in the GPU memory will have to be queued up before getting their own computing resources.
<img src="./pictures/pretty_benchmark_sd_txt2img_batchsize_vs_throughput.png" alt="Stable Diffusion Text2Image Batch size vs Throughput (images/minute)" width="380"/>
## Precision
We are curious about whether half-precision introduces degradations to the quality of the output images. To test this out, we fixed the text prompt as well as the "latent" input vector and fed them to the single-precision model and the half-precision model. We ran the inference for 100 steps and saved both models' outputs at each step, as well as the difference map:
Our observation is that there are indeed visible differences between the single-precision output and the half-precision output, especially in the early steps. The differences often decrease with the number of steps, but might not always vanish.
Interestingly, such a difference may not imply artifacts in half-precision's outputs. For example, in step 70, the picture below shows half-precision didn't produce the artifact in the single-precision output (an extra front leg):
---
## Reproducing the experiments
You can use this [Lambda Diffusers](https://github.com/LambdaLabsML/lambda-diffusers) repository to reproduce the results presented in this article.
### From your local machine
#### Setup
Before running the benchmark, make sure you have completed the repository [installation steps](../README.md#installation).
You will then need to set the huggingface access token:
1. Create a user account on HuggingFace and generate an access token.
2. Set your huggingface access token as the `ACCESS_TOKEN` environment variable:
```
export ACCESS_TOKEN=<hf_...>
```
#### Usage
Launch the `benchmark.py` script to append benchmark results to the existing [benchmark.csv](../benchmark.csv) results file:
```
python ./scripts/benchmark.py
```
Lauch the `benchmark_quality.py` script to compare the output of single-precision and half-precision models:
```
python ./scripts/benchmark_quality.py
```
### From a docker container
The following instructions show how to run the benchmarking program from a docker container on Ubuntu.
#### Prerequisites
#### Get a huggingface access token
Create a huggingface account.
Get a [huggingface access token](https://huggingface.co/docs/hub/security-tokens).
#### Install NVIDIA docker
This section can be skipped if the environment already uses [Lambda Stack](https://lambdalabs.com/lambda-stack-deep-learning-software) or if the experiments are running on a [Lambda cloud](https://lambdalabs.com/service/gpu-cloud) instance as docker and `nvidia-container-toolkit` comes pre-installed in these cases.
We first install docker:
```
# Install
sudo apt-get update
sudo apt-get remove docker docker-engine docker.io -y
sudo apt install containerd -y
sudo apt install docker.io -y
sudo systemctl start docker
sudo systemctl enable docker
# Test install
docker --version
# Put the user in the docker group
sudo usermod -a -G docker $USER
newgrp docker
```
We install requirements to run docker containers leveraging NVIDIA GPUs:
```
# Install
sudo apt install curl
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
# Test install
sudo docker run --rm --gpus all nvidia/cuda:11.2.1-base-ubuntu20.04 nvidia-smi
```
3. Build the benchmark docker image
```
docker build -t benchmark -f ./benchmarking/Dockerfile .
```
#### Running the benchmark
Set the HuggingFace access token as environment variable:
```
export ACCESS_TOKEN=<your-hugging-face-access-token-here>
```
Run the benchmark program from the container and export the output `.csv` file to the host:
```
containerid=$(docker run --gpus all -e ACCESS_TOKEN=${ACCESS_TOKEN} -d --entrypoint "python3" benchmark:latest scripts/benchmark.py --samples=1,2,4,8,16) && \
docker wait ${containerid} && \
docker cp ${containerid}:/lambda_diffusers/benchmark_tmp.csv ./benchmark_tmp.csv
```
*Note that the arguments `scripts/benchmark.py --samples=1,2,4,8,16` can be changed to point to a different script or use different arguments.*
================================================
FILE: lambda_diffusers/__init__.py
================================================
from .pipelines import StableDiffusionImageEmbedPipeline
================================================
FILE: lambda_diffusers/pipelines/__init__.py
================================================
from .pipeline_stable_diffusion_im_embed import StableDiffusionImageEmbedPipeline
================================================
FILE: lambda_diffusers/pipelines/pipeline_stable_diffusion_im_embed.py
================================================
import inspect
import warnings
from typing import List, Optional, Union
import torch
import PIL
from transformers import CLIPFeatureExtractor, CLIPModel
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
class StableDiffusionImageEmbedPipeline(DiffusionPipeline):
def __init__(
self,
vae: AutoencoderKL,
image_encoder: CLIPModel,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
self.register_modules(
vae=vae,
image_encoder=image_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.image_processor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-large-patch14")
@torch.no_grad()
def __call__(
self,
input_image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]],
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
**kwargs,
):
if "torch_device" in kwargs:
device = kwargs.pop("torch_device")
warnings.warn(
"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
" Consider using `pipe.to(torch_device)` instead."
)
# Set device as before (to be removed in 0.3.0)
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.to(device)
if isinstance(input_image, PIL.Image.Image):
batch_size = 1
elif isinstance(input_image, list):
batch_size = len(input_image)
else:
raise ValueError(f"`input_image` has to be of type `str` or `list` but is {type(input_image)}")
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if not isinstance(input_image, torch.FloatTensor):
input_image = self.image_processor(images=input_image, return_tensors="pt").to(self.device)
image_embeddings = self.image_encoder.get_image_features(**input_image)
image_embeddings = image_embeddings.unsqueeze(1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_embeddings = torch.zeros_like(image_embeddings)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
image_embeddings = torch.cat([uncond_embeddings, image_embeddings])
# get the initial random noise unless the user supplied it
latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
if latents is None:
latents = torch.randn(
latents_shape,
generator=generator,
device=self.device,
)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
latents = latents.to(self.device)
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
# if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = latents * self.scheduler.sigmas[0]
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
if isinstance(self.scheduler, LMSDiscreteScheduler):
sigma = self.scheduler.sigmas[i]
# the model input needs to be scaled to match the continuous ODE formulation in K-LMS
latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings)["sample"]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
if isinstance(self.scheduler, LMSDiscreteScheduler):
latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs)["prev_sample"]
else:
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)["prev_sample"]
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents)
image = (image["sample"] / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
# run safety checker
safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
if output_type == "pil":
image = self.numpy_to_pil(image)
return {"sample": image, "nsfw_content_detected": has_nsfw_concept}
================================================
FILE: notebooks/pokemon_demo.ipynb
================================================
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/LambdaLabsML/lambda-diffusers/blob/main/notebooks/pokemon_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Pokémon text to image\n",
"\n",
"This notebook demonstrates inference using Stable Diffusion fine tuned on Pokémon to generate new Pokémon form text prompts. The model has been ported to Huggingface Diffusers for easier inference.\n",
"\n",
"For more details about the fine tuning process and how to make your own specialised model see [this guide](https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning).\n",
"\n",
"Also see the following links for more info:\n",
"\n",
"- [Lambda Diffusers](https://github.com/LambdaLabsML/lambda-diffusers)\n",
"- [Captioned Pokémon dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions)\n",
"- [Model weights in Diffusers format](https://huggingface.co/lambdalabs/sd-pokemon-diffusers)\n",
"- [Original model weights](https://huggingface.co/justinpinkney/pokemon-stable-diffusion)\n",
"- [Training code](https://github.com/justinpinkney/stable-diffusion)\n",
"\n",
"Created by Justin Pinkney at [Lambda Labs](https://lambdalabs.com/)"
],
"metadata": {
"id": "IebtTUPpoORj"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "s8PvlrTpha7t",
"outputId": "581eaddc-96fd-4825-b745-924e280cab13"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Tue Sep 20 10:53:04 2022 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla V100-SXM2... Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 35C P0 23W / 300W | 0MiB / 16160MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
],
"source": [
"!nvidia-smi"
]
},
{
"cell_type": "code",
"source": [
"!pip install diffusers==0.3.0\n",
"!pip install transformers scipy ftfy"
],
"metadata": {
"id": "OfYmiqKBh4gU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import torch\n",
"from diffusers import StableDiffusionPipeline\n",
"from torch import autocast\n",
"\n",
"pipe = StableDiffusionPipeline.from_pretrained(\"lambdalabs/sd-pokemon-diffusers\", torch_dtype=torch.float16) \n",
"pipe = pipe.to(\"cuda\")"
],
"metadata": {
"id": "lFUOFx2oh8qU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from PIL import Image\n",
"\n",
"def image_grid(imgs, rows, cols):\n",
" assert len(imgs) == rows*cols\n",
"\n",
" w, h = imgs[0].size\n",
" grid = Image.new('RGB', size=(cols*w, rows*h))\n",
" grid_w, grid_h = grid.size\n",
" \n",
" for i, img in enumerate(imgs):\n",
" grid.paste(img, box=(i%cols*w, i//cols*h))\n",
" return grid"
],
"metadata": {
"id": "so1GmFN0q_M4"
},
"execution_count": 33,
"outputs": []
},
{
"cell_type": "code",
"source": [
"prompt = \"Abraham Lincoln\"\n",
"scale = 10\n",
"n_samples = 4\n",
"\n",
"# Sometimes the nsfw checker is confused by the Pokémon images, you can disable\n",
"# it at your own risk here\n",
"disable_safety = False\n",
"\n",
"if disable_safety:\n",
" def null_safety(images, **kwargs):\n",
" return images, False\n",
" pipe.safety_checker = null_safety\n",
"\n",
"with autocast(\"cuda\"):\n",
" images = pipe(n_samples*[prompt], guidance_scale=scale).images\n",
"\n",
"grid = image_grid(images, rows=2, cols=2)\n",
"grid"
],
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
gitextract_xdc54des/ ├── .gitignore ├── Dockerfile ├── LICENSE ├── README.md ├── benchmarks/ │ ├── benchmark.csv │ ├── benchmark_H100_MIG.csv │ └── benchmark_no_xformers.csv ├── docs/ │ ├── benchmark-update.md │ └── benchmark.md ├── lambda_diffusers/ │ ├── __init__.py │ └── pipelines/ │ ├── __init__.py │ └── pipeline_stable_diffusion_im_embed.py ├── notebooks/ │ └── pokemon_demo.ipynb ├── requirements.txt ├── scripts/ │ ├── Dockerfile │ ├── Makefile │ ├── benchmark.py │ ├── benchmark_quality.py │ └── convert_sd_image_to_diffusers.py └── setup.py
SYMBOL INDEX (25 symbols across 3 files)
FILE: lambda_diffusers/pipelines/pipeline_stable_diffusion_im_embed.py
class StableDiffusionImageEmbedPipeline (line 17) | class StableDiffusionImageEmbedPipeline(DiffusionPipeline):
method __init__ (line 18) | def __init__(
method __call__ (line 39) | def __call__(
FILE: scripts/benchmark.py
function make_bool (line 17) | def make_bool(yes_or_no):
function get_inference_pipeline (line 25) | def get_inference_pipeline(precision, backend):
function do_inference (line 63) | def do_inference(pipe, n_samples, use_autocast, num_inference_steps):
function get_inference_time (line 76) | def get_inference_time(
function get_inference_memory (line 98) | def get_inference_memory(pipe, n_samples, use_autocast, num_inference_st...
function run_benchmark (line 113) | def run_benchmark(
function get_device_description (line 149) | def get_device_description():
function run_benchmark_grid (line 164) | def run_benchmark_grid(grid, n_repeats, num_inference_steps, csv_fpath):
FILE: scripts/convert_sd_image_to_diffusers.py
function shave_segments (line 32) | def shave_segments(path, n_shave_prefix_segments=1):
function renew_resnet_paths (line 42) | def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
function renew_vae_resnet_paths (line 64) | def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
function renew_attention_paths (line 81) | def renew_attention_paths(old_list, n_shave_prefix_segments=0):
function renew_vae_attention_paths (line 102) | def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
function assign_to_checkpoint (line 132) | def assign_to_checkpoint(paths, checkpoint, old_checkpoint, attention_pa...
function conv_attn_to_linear (line 182) | def conv_attn_to_linear(checkpoint):
function create_unet_diffusers_config (line 194) | def create_unet_diffusers_config(original_config):
function create_vae_diffusers_config (line 231) | def create_vae_diffusers_config(original_config):
function create_diffusers_schedular (line 255) | def create_diffusers_schedular(original_config):
function create_ldm_bert_config (line 266) | def create_ldm_bert_config(original_config):
function convert_ldm_unet_checkpoint (line 276) | def convert_ldm_unet_checkpoint(checkpoint, config):
function convert_ldm_vae_checkpoint (line 401) | def convert_ldm_vae_checkpoint(checkpoint, config):
function convert_ldm_bert_checkpoint (line 496) | def convert_ldm_bert_checkpoint(checkpoint, config):
Condensed preview — 20 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,878K chars).
[
{
"path": ".gitignore",
"chars": 3135,
"preview": "model_zoo/\noutputs/\n*benchmark_tmp.csv\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C e"
},
{
"path": "Dockerfile",
"chars": 324,
"preview": "FROM nvidia/cuda:11.2.1-base-ubuntu20.04\nRUN apt-get update && \\\n apt-get install --no-install-recommends --no-instal"
},
{
"path": "LICENSE",
"chars": 1068,
"preview": "MIT License\n\nCopyright (c) 2022 Lambda, Inc\n\nPermission is hereby granted, free of charge, to any person obtaining a cop"
},
{
"path": "README.md",
"chars": 6510,
"preview": "# Lambda Diffusers\r\n\r\n_Additional models and pipelines for 🤗 Diffusers created by [Lambda Labs](https://lambdalabs.com/)"
},
{
"path": "benchmarks/benchmark.csv",
"chars": 4703,
"preview": "device,precision,autocast,xformers,runtime,n_samples,latency,memory\r\nNVIDIA A10,half,FALSE,TRUE,pytorch,1,2.01,3.13\r\nNVI"
},
{
"path": "benchmarks/benchmark_H100_MIG.csv",
"chars": 4155,
"preview": "device,precision,autocast,xformers,runtime,n_samples,latency,memory,\r\nNVIDIA H100 PCIe,single,FALSE,FALSE,pytorch,1,1.73"
},
{
"path": "benchmarks/benchmark_no_xformers.csv",
"chars": 5562,
"preview": "device,precision,autocast,xformers,runtime,n_samples,latency,memory,\r\nNVIDIA A10,single,FALSE,FALSE,pytorch,1,4.75,6.73\r"
},
{
"path": "docs/benchmark-update.md",
"chars": 1499,
"preview": "# Benchmark update\n\nWe are currently running benchmarks to update our Stable Diffusion numbers using a more recent versi"
},
{
"path": "docs/benchmark.md",
"chars": 9432,
"preview": "# Benchmarking Diffuser Models\n\n__We are currently in the process of updating our Stable Diffusion benchmark using more "
},
{
"path": "lambda_diffusers/__init__.py",
"chars": 56,
"preview": "from .pipelines import StableDiffusionImageEmbedPipeline"
},
{
"path": "lambda_diffusers/pipelines/__init__.py",
"chars": 81,
"preview": "from .pipeline_stable_diffusion_im_embed import StableDiffusionImageEmbedPipeline"
},
{
"path": "lambda_diffusers/pipelines/pipeline_stable_diffusion_im_embed.py",
"chars": 7315,
"preview": "import inspect\nimport warnings\nfrom typing import List, Optional, Union\n\nimport torch\nimport PIL\n\nfrom transformers impo"
},
{
"path": "notebooks/pokemon_demo.ipynb",
"chars": 1786846,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"provenance\": [],\n \"authorship_tag\":"
},
{
"path": "requirements.txt",
"chars": 174,
"preview": "--extra-index-url https://download.pytorch.org/whl/cu116 torch\n\ntransformers==4.22.1\nftfy==6.1.1\nPillow==9.2.0\ndiffusers"
},
{
"path": "scripts/Dockerfile",
"chars": 362,
"preview": "FROM nvcr.io/nvidia/pytorch:22.11-py3\n\nENV PYTHONDONTWRITEBYTECODE 1\nENV PYTHONUNBUFFERED 1\n\nRUN pip install --pre xform"
},
{
"path": "scripts/Makefile",
"chars": 315,
"preview": "bench:\n\tdocker build -t sd-bench .\n\tdocker run \\\n\t\t--rm -it \\\n\t\t--gpus all \\\n\t\t--shm-size=128g \\\n\t\t--net=host \\\n\t\t-v $(P"
},
{
"path": "scripts/benchmark.py",
"chars": 9090,
"preview": "import os\nimport subprocess\nimport multiprocessing\nimport argparse\nimport pathlib\nimport csv\nfrom contextlib import null"
},
{
"path": "scripts/benchmark_quality.py",
"chars": 3974,
"preview": "import os\nfrom platform import mac_ver\nimport numpy as np\nfrom PIL import Image, ImageDraw, ImageFont\nfrom skimage.metri"
},
{
"path": "scripts/convert_sd_image_to_diffusers.py",
"chars": 26575,
"preview": "# coding=utf-8\n# Copyright 2022 The HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"Lice"
},
{
"path": "setup.py",
"chars": 448,
"preview": "from setuptools import setup, find_packages\n\nsetup(\n name='lambda-diffusers',\n version='0.0.1',\n description='L"
}
]
About this extraction
This page contains the full source code of the LambdaLabsML/lambda-diffusers GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 20 files (1.8 MB), approximately 1.2M tokens, and a symbol index with 25 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.