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# make sure you're logged in with `huggingface-cli login` |
from diffusers import StableDiffusionPipeline |
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") |
pipe = pipe.to("mps") |
# Recommended if your computer has < 64 GB of RAM |
pipe.enable_attention_slicing() |
prompt = "a photo of an astronaut riding a horse on mars" |
# First-time "warmup" pass (see explanation above) |
_ = pipe(prompt, num_inference_steps=1) |
# Results match those from the CPU device after the warmup pass. |
image = pipe(prompt).images[0] |
Performance Recommendations |
M1/M2 performance is very sensitive to memory pressure. The system will automatically swap if it needs to, but performance will degrade significantly when it does. |
We recommend you use attention slicing to reduce memory pressure during inference and prevent swapping, particularly if your computer has lass than 64 GB of system RAM, or if you generate images at non-standard resolutions larger than 512 × 512 pixels. Attention slicing performs the costly attention operation in multip... |
Copied |
pipeline.enable_attention_slicing() |
Known Issues |
As mentioned above, we are investigating a strange first-time inference issue. |
Generating multiple prompts in a batch crashes or doesn’t work reliably. We believe this is related to the mps backend in PyTorch. This is being resolved, but for now we recommend to iterate instead of batching. |
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... |
Load pipelines, models, and schedulers Having an easy way to use a diffusion system for inference is essential to 🧨 Diffusers. Diffusion systems often consist of multiple components like parameterized models, tokenizers, and schedulers that interact in complex ways. That is why we designed the DiffusionPipe... |
repo_id = "runwayml/stable-diffusion-v1-5" |
pipe = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True) You can also load a checkpoint with its specific pipeline class. The example above loaded a Stable Diffusion model; to get the same result, use the StableDiffusionPipeline class: Copied from diffusers import StableDiffusionPipeline |
repo_id = "runwayml/stable-diffusion-v1-5" |
pipe = StableDiffusionPipeline.from_pretrained(repo_id, use_safetensors=True) A checkpoint (such as CompVis/stable-diffusion-v1-4 or runwayml/stable-diffusion-v1-5) may also be used for more than one task, like text-to-image or image-to-image. To differentiate what task you want to use the checkpoint for, you have to l... |
repo_id = "runwayml/stable-diffusion-v1-5" |
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(repo_id) Local pipeline To load a diffusion pipeline locally, use git-lfs to manually download the checkpoint (in this case, runwayml/stable-diffusion-v1-5) to your local disk. This creates a local folder, ./stable-diffusion-v1-5, on your disk: Copied git-lfs ins... |
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 Then pass the local path to from_pretrained(): Copied from diffusers import DiffusionPipeline |
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