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Molecule conformation generation |
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Overview Generating high-quality outputs is computationally intensive, especially during each iterative step where you go from a noisy output to a less noisy output. One of 🤗 Diffuser’s goals is to make this technology widely accessible to everyone, which includes enabling fast inference on consumer and specialized ha... |
Token merging Token merging (ToMe) merges redundant tokens/patches progressively in the forward pass of a Transformer-based network which can speed-up the inference latency of StableDiffusionPipeline. Install ToMe from pip: Copied pip install tomesd You can use ToMe from the tomesd library with the apply_patch functi... |
import torch |
import tomesd |
pipeline = StableDiffusionPipeline.from_pretrained( |
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True, |
).to("cuda") |
+ tomesd.apply_patch(pipeline, ratio=0.5) |
image = pipeline("a photo of an astronaut riding a horse on mars").images[0] The apply_patch function exposes a number of arguments to help strike a balance between pipeline inference speed and the quality of the generated tokens. The most important argument is ratio which controls the number of tokens that are merge... |
- Python version: 3.8.16 |
- PyTorch version (GPU?): 1.13.1+cu116 (True) |
- Huggingface_hub version: 0.13.2 |
- Transformers version: 4.27.2 |
- Accelerate version: 0.18.0 |
- xFormers version: 0.0.16 |
- tomesd version: 0.1.2 To reproduce this benchmark, feel free to use this script. The results are reported in seconds, and where applicable we report the speed-up percentage over the vanilla pipeline when using ToMe and ToMe + xFormers. GPU Resolution Batch size Vanilla ToMe ToMe + xFormers A100 512 10 6.88 5.26 (+23.... |
Overview Generating high-quality outputs is computationally intensive, especially during each iterative step where you go from a noisy output to a less noisy output. One of 🤗 Diffuser’s goals is to make this technology widely accessible to everyone, which includes enabling fast inference on consumer and specialized ha... |
Installation 🤗 Diffusers is tested on Python 3.8+, PyTorch 1.7.0+, and Flax. Follow the installation instructions below for the deep learning library you are using: PyTorch installation instructions Flax installation instructions Install with pip You should install 🤗 Diffusers in a virtual environment. |
If you’re unfamiliar with Python virtual environments, take a look at this guide. |
A virtual environment makes it easier to manage different projects and avoid compatibility issues between dependencies. Start by creating a virtual environment in your project directory: Copied python -m venv .env Activate the virtual environment: Copied source .env/bin/activate You should also install 🤗 Transform... |
The main version is useful for staying up-to-date with the latest developments. |
For instance, if a bug has been fixed since the last official release but a new release hasn’t been rolled out yet. |
However, this means the main version may not always be stable. |
We strive to keep the main version operational, and most issues are usually resolved within a few hours or a day. |
If you run into a problem, please open an Issue so we can fix it even sooner! Editable install You will need an editable install if you’d like to: Use the main version of the source code. Contribute to 🤗 Diffusers and need to test changes in the code. Clone the repository and install 🤗 Diffusers with the following c... |
cd diffusers Pytorch Hide Pytorch content Copied pip install -e ".[torch]" JAX Hide JAX content Copied pip install -e ".[flax]" These commands will link the folder you cloned the repository to and your Python library paths. |
Python will now look inside the folder you cloned to in addition to the normal library paths. |
For example, if your Python packages are typically installed in ~/anaconda3/envs/main/lib/python3.8/site-packages/, Python will also search the ~/diffusers/ folder you cloned to. You must keep the diffusers folder if you want to keep using the library. Now you can easily update your clone to the latest version of 🤗 Di... |
git pull Your Python environment will find the main version of 🤗 Diffusers on the next run. Cache Model weights and files are downloaded from the Hub to a cache which is usually your home directory. You can change the cache location by specifying the HF_HOME or HUGGINFACE_HUB_CACHE environment variables or configurin... |
The data gathered includes the version of 🤗 Diffusers and PyTorch/Flax, the requested model or pipeline class, |
and the path to a pretrained checkpoint if it is hosted on the Hugging Face Hub. |
This usage data helps us debug issues and prioritize new features. |
Telemetry is only sent when loading models and pipelines from the Hub, |
and it is not collected if you’re loading local files. We understand that not everyone wants to share additional information,and we respect your privacy. |
You can disable telemetry collection by setting the DISABLE_TELEMETRY environment variable from your terminal: On Linux/MacOS: Copied export DISABLE_TELEMETRY=YES On Windows: Copied set DISABLE_TELEMETRY=YES |
Tiny AutoEncoder Tiny AutoEncoder for Stable Diffusion (TAESD) was introduced in madebyollin/taesd by Ollin Boer Bohan. It is a tiny distilled version of Stable Diffusion’s VAE that can quickly decode the latents in a StableDiffusionPipeline or StableDiffusionXLPipeline almost instantly. To use with Stable Diffusion v-... |
from diffusers import DiffusionPipeline, AutoencoderTiny |
pipe = DiffusionPipeline.from_pretrained( |
"stabilityai/stable-diffusion-2-1-base", torch_dtype=torch.float16 |
) |
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd", torch_dtype=torch.float16) |
pipe = pipe.to("cuda") |
prompt = "slice of delicious New York-style berry cheesecake" |
image = pipe(prompt, num_inference_steps=25).images[0] |
image To use with Stable Diffusion XL 1.0 Copied import torch |
from diffusers import DiffusionPipeline, AutoencoderTiny |
pipe = DiffusionPipeline.from_pretrained( |
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 |
) |
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16) |
pipe = pipe.to("cuda") |
prompt = "slice of delicious New York-style berry cheesecake" |
image = pipe(prompt, num_inference_steps=25).images[0] |
image AutoencoderTiny class diffusers.AutoencoderTiny < source > ( in_channels: int = 3 out_channels: int = 3 encoder_block_out_channels: Tuple = (64, 64, 64, 64) decoder_block_out_channels: Tuple = (64, 64, 64, 64) act_fn: str = 'relu' latent_channels: int = 4 upsampling_scaling_factor: int = 2 num_encoder_blocks: ... |
Tuple of integers representing the number of output channels for each encoder block. The length of the |
tuple should be equal to the number of encoder blocks. decoder_block_out_channels (Tuple[int], optional, defaults to (64, 64, 64, 64)) — |
Tuple of integers representing the number of output channels for each decoder block. The length of the |
tuple should be equal to the number of decoder blocks. act_fn (str, optional, defaults to "relu") — |
Activation function to be used throughout the model. latent_channels (int, optional, defaults to 4) — |
Number of channels in the latent representation. The latent space acts as a compressed representation of |
the input image. upsampling_scaling_factor (int, optional, defaults to 2) — |
Scaling factor for upsampling in the decoder. It determines the size of the output image during the |
upsampling process. num_encoder_blocks (Tuple[int], optional, defaults to (1, 3, 3, 3)) — |
Tuple of integers representing the number of encoder blocks at each stage of the encoding process. The |
length of the tuple should be equal to the number of stages in the encoder. Each stage has a different |
number of encoder blocks. num_decoder_blocks (Tuple[int], optional, defaults to (3, 3, 3, 1)) — |
Tuple of integers representing the number of decoder blocks at each stage of the decoding process. The |
length of the tuple should be equal to the number of stages in the decoder. Each stage has a different |
number of decoder blocks. latent_magnitude (float, optional, defaults to 3.0) — |
Magnitude of the latent representation. This parameter scales the latent representation values to control |
the extent of information preservation. latent_shift (float, optional, defaults to 0.5) — |
Shift applied to the latent representation. This parameter controls the center of the latent space. scaling_factor (float, optional, defaults to 1.0) — |
The component-wise standard deviation of the trained latent space computed using the first batch of the |
training set. This is used to scale the latent space to have unit variance when training the diffusion |
model. The latents are scaled with the formula z = z * scaling_factor before being passed to the |
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: z = 1 / scaling_factor * z. For more details, refer to sections 4.3.2 and D.1 of the High-Resolution Image |
Synthesis with Latent Diffusion Models paper. For this Autoencoder, |
however, no such scaling factor was used, hence the value of 1.0 as the default. force_upcast (bool, optional, default to False) — |
If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE |
can be fine-tuned / trained to a lower range without losing too much precision, in which case |
force_upcast can be set to False (see this fp16-friendly |
AutoEncoder). A tiny distilled VAE model for encoding images into latents and decoding latent representations into images. AutoencoderTiny is a wrapper around the original implementation of TAESD. This model inherits from ModelMixin. Check the superclass documentation for its generic methods implemented for |
all models (such as downloading or saving). disable_slicing < source > ( ) Disable sliced VAE decoding. If enable_slicing was previously enabled, this method will go back to computing |
decoding in one step. disable_tiling < source > ( ) Disable tiled VAE decoding. If enable_tiling was previously enabled, this method will go back to computing |
decoding in one step. enable_slicing < source > ( ) Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. enable_tiling < source > ( use_tiling: bool = True ) Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow |
processing larger images. forward < source > ( sample: FloatTensor return_dict: bool = True ) Parameters sample (torch.FloatTensor) — Input sample. return_dict (bool, optional, defaults to True) — |
Whether or not to return a DecoderOutput instead of a plain tuple. scale_latents < source > ( x: FloatTensor ) raw latents -> [0, 1] unscale_latents < source > ( x: FloatTensor ) [0, 1] -> raw latents AutoencoderTinyOutput class diffusers.models.autoencoders.autoencoder_tiny.AutoencoderTinyOutput < so... |
Installing xFormers |
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