pypi diffusers 0.13.0
Controllable Generation: Pix2Pix0, Attend and Excite, SEGA, SAG, ...
on Python PyPI

latest releases: 0.31.0, 0.30.3, 0.30.2...
21 months ago

🎯 Controlling Generation

There has been much recent work on fine-grained control of diffusion networks!

Diffusers now supports:

  1. Instruct Pix2Pix
  2. Pix2Pix 0, more details in docs
  3. Attend and excite, more details in docs
  4. Semantic guidance, more details in docs
  5. Self-attention guidance, more details in docs
  6. Depth2image
  7. MultiDiffusion panorama, more details in docs

See our doc on controlling image generation and the individual pipeline docs for more details on the individual methods.

🆙 Latent Upscaler

Latent Upscaler is a diffusion model that is designed explicitly for Stable Diffusion. You can take the generated latent from Stable Diffusion and pass it into the upscaler before decoding with your standard VAE. Or you can take any image, encode it into the latent space, use the upscaler, and decode it. It is incredibly flexible and can work with any SD checkpoints.

Original output image 2x upscaled output image

The model was developed by Katherine Crowson in collaboration with Stability AI 

from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline
import torch

pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipeline.to("cuda")

upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16)
upscaler.to("cuda")

prompt = "a photo of an astronaut high resolution, unreal engine, ultra realistic"
generator = torch.manual_seed(33)

# we stay in latent space! Let's make sure that Stable Diffusion returns the image
# in latent space
low_res_latents = pipeline(prompt, generator=generator, output_type="latent").images

upscaled_image = upscaler(
    prompt=prompt,
    image=low_res_latents,
    num_inference_steps=20,
    guidance_scale=0,
    generator=generator,
).images[0]

# Let's save the upscaled image under "upscaled_astronaut.png"
upscaled_image.save("astronaut_1024.png")

# as a comparison: Let's also save the low-res image
with torch.no_grad():
    image = pipeline.decode_latents(low_res_latents)
image = pipeline.numpy_to_pil(image)[0]

image.save("astronaut_512.png")

⚡ Optimization

In addition to new features and an increasing number of pipelines, diffusers cares a lot about performance. This release brings a number of optimizations that you can turn on easily.

xFormers

Memory efficient attention, as implemented by xFormers, has been available in diffusers for some time. The problem was that installing xFormers could be complicated because there were no official pip wheels (or they were outdated), and you had to resort to installing from source.

From xFormers 0.0.16, official pip wheels are now published with every release, so installing and using xFormers is now as simple as these two steps:

  1. pip install xformers in your terminal.
  2. pipe.enable_xformers_memory_efficient_attention() in your code to opt-in in your pipelines.

These actions will unlock dramatic memory savings, and usually faster inference too!

See more details in the documentation.

Torch 2.0

Speaking of memory-efficient attention, Accelerated PyTorch 2.0 Transformers now comes with built-in native support for it! When PyTorch 2.0 is released you'll no longer have to install xFormers or any third-party package to take advantage of it. In diffusers we are already preparing for that, and it works out of the box. So, if you happen to be using the latest "nightlies" of PyTorch 2.0 beta, then you're all set – diffusers will use Accelerated PyTorch 2.0 Transformers by default.

In our tests, the built-in PyTorch 2.0 implementation is usually as fast as xFormers', and sometimes even faster. Performance depends on the card you are using and whether you run your code in float16 or float32, so check our documentation for details.

Coarse-grained CPU offload

Community member @keturn, with whom we have enjoyed thoughtful software design conversations, called our attention to the fact that enabling sequential cpu offloading via enable_sequential_cpu_offload worked great to save a lot of memory, but made inference much slower.

This is because enable_sequential_cpu_offload() is optimized for memory, and it recursively works across all the submodules contained in a model, moving them to GPU when they are needed and back to CPU when another submodule needs to run. These cpu-to-gpu-to-cpu transfers happen hundreds of times during the stable diffusion denoising loops, because the UNet runs multiple times and it consists of several PyTorch modules.

This release of diffusers introduces a coarser enable_model_cpu_offload() pipeline API, which copies whole models (not modules) to GPU and makes sure they stay there until another model needs to run. The consequences are:

  • Less memory savings than enable_sequential_cpu_offload, but:
  • Almost as fast inference as when the pipeline is used without any type of offloading.

Pix2Pix Zero

Remember the CycleGAN days where one would turn a horse into a zebra in an image while keeping the rest of the content almost untouched? Well, that day has arrived but in the context of Diffusion. Pix2Pix Zero allows users to edit a particular image (be it real or generated), targeting a source concept (horse, for example) and replacing it with a target concept (zebra, for example).

Input image Edited image
original edited

Pix2Pix Zero was proposed in Zero-shot Image-to-Image Translation. The StableDiffusionPix2PixZeroPipeline allows you to

  1. Edit an image generated from an input prompt
  2. Provide an input image and edit it

For the latter, it uses the newly introduced DDIMInverseScheduler to first obtain the inverted noise from the input image and use that in the subsequent generation process.

Both of the use cases leverage the idea of "edit directions", used for steering the generation toward the target concept gradually from the source concept. To know more, we recommend checking out the official documentation.

Attend and excite

Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models. Attend-and-Excite, guides the generative model to modify the cross-attention values during the image synthesis process to generate images that more faithfully depict the input text prompt. It allows creating images that are more semantically faithful with respect to the input text prompts. Thanks to community contributor @evinpinar for leading the charge to add this pipeline!

Semantic guidance

Semantic Guidance for Diffusion Models was proposed in SEGA: Instructing Diffusion using Semantic Dimensions and provides strong semantic control over image generation. Small changes to the text prompt usually result in entirely different output images. However, with SEGA, a variety of changes to the image are enabled that can be controlled easily and intuitively and stay true to the original image composition. Thanks to the lead author of SEFA, Manuel (@manuelbrack), who added the pipeline in #2223.

Here is a simple demo: 

import torch
from diffusers import SemanticStableDiffusionPipeline

pipe = SemanticStableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

out = pipe(
    prompt="a photo of the face of a woman",
    num_images_per_prompt=1,
    guidance_scale=7,
    editing_prompt=[
        "smiling, smile",  # Concepts to apply
        "glasses, wearing glasses",
        "curls, wavy hair, curly hair",
        "beard, full beard, mustache",
    ],
    reverse_editing_direction=[False, False, False, False],  # Direction of guidance i.e. increase all concepts
    edit_warmup_steps=[10, 10, 10, 10],  # Warmup period for each concept
    edit_guidance_scale=[4, 5, 5, 5.4],  # Guidance scale for each concept
    edit_threshold=[
        0.99,
        0.975,
        0.925,
        0.96,
    ],  # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
    edit_momentum_scale=0.3,  # Momentum scale that will be added to the latent guidance
    edit_mom_beta=0.6,  # Momentum beta
    edit_weights=[1, 1, 1, 1, 1],  # Weights of the individual concepts against each other
)

Self-attention guidance

SAG was proposed in Improving Sample Quality of Diffusion Models Using Self-Attention Guidance. SAG works by extracting the intermediate attention map from a diffusion model at every iteration and selects tokens above a certain attention score for masking and blurring to obtain a partially blurred input. Then, the dissimilarity is measured between the predicted noise outputs obtained from feeding the blurred and original input to the diffusion model and this is further leveraged as guidance. With this guidance, the authors observe apparent improvements in a wide range of diffusion models. 

import torch
from diffusers import StableDiffusionSAGPipeline
from accelerate.utils import set_seed

pipe = StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

seed = 8978
prompt = "."
guidance_scale = 7.5
num_images_per_prompt = 1

sag_scale = 1.0

set_seed(seed)
images = pipe(
    prompt, num_images_per_prompt=num_images_per_prompt, guidance_scale=guidance_scale, sag_scale=sag_scale
).images
images[0].save("example.png")

SAG was contributed by @SusungHong (lead author of SAG) in #2193.

MultiDiffusion panorama

Proposed in MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation, it presents a new generation process, "MultiDiffusion", based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. 

import torch
from diffusers import StableDiffusionPanoramaPipeline, DDIMScheduler

model_ckpt = "stabilityai/stable-diffusion-2-base"
scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
pipe = StableDiffusionPanoramaPipeline.from_pretrained(model_ckpt, scheduler=scheduler, torch_dtype=torch.float16)

pipe = pipe.to("cuda")

prompt = "a photo of the dolomites"
image = pipe(prompt).images[0]
image.save("dolomites.png")

The pipeline was contributed by @omerbt (lead author of MultiDiffusion Panorama) and @sayakpaul in #2393.

Ethical Guidelines

Diffusers is no stranger to the different opinions and perspectives about the challenges that generative technologies bring. Thanks to @giadilli, we have drafted our first Diffusers' Ethical Guidelines with which we hope to initiate a fruitful conversation with the community.

Keras Integration

Many practitioners find it easy to fine-tune the Stable Diffusion models shipped by KerasCV. At the same time, diffusers provides a lot of options for inference, deployment and optimization. We have made it possible to easily import and use KerasCV Stable Diffusion checkpoints in diffusers, read more about the process in our new guide.

🕒 UniPC scheduler

UniPC is a new fast scheduler in diffusion town! UniPC is a training-free framework designed for the fast sampling of diffusion models, which consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders.
The orginal codebase can be found here. Thanks to @wl-zhao for the great work and integrating UniPC into the diffusers!

🏃 Training: consistent EMA support

As part of 0.13.0 we improved the support for EMA in training. We added a common EMAModel in diffusers.training_utils which can be used by all scripts. The EMAModel is improved to support distributed training,
new methods to easily evaluate the EMA model during training and a consistent way to save and load the EMA model similar to other models in diffusers.

🐶 Ruff & black

We have replaced flake8 with ruff (much faster), and updated our version of black. These tools are now in sync with the ones used in transformers, so the contributing experience is now more consistent for people using both codebases :)

All commits

Significant community contributions

The following contributors have made significant changes to the library over the last release:

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releases around diffusers 0.13.0

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