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v0.8.0: Versatile Diffusion - Text, Images and Variations All in One Diffusion Model
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@patil-suraj patil-suraj released this 23 Nov 18:23
· 3584 commits to main since this release
v0.8.0
16a32c9

🙆‍♀️ New Models

VersatileDiffusion

VersatileDiffusion, released by SHI-Labs, is a unified multi-flow multimodal diffusion model that is capable of doing multiple tasks such as text2image, image variations, dual-guided(text+image) image generation, image2text.

pip install git+https://github.com/huggingface/transformers

Then you can run:

from diffusers import VersatileDiffusionPipeline
import torch
import requests
from io import BytesIO
from PIL import Image

pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

# initial image
url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# prompt
prompt = "a red car"

# text to image
image = pipe.text_to_image(prompt).images[0]

# image variation
image = pipe.image_variation(image).images[0]

# image variation
image = pipe.dual_guided(prompt, image).images[0]

More in-depth details can be found on:

AltDiffusion

AltDiffusion is a multilingual latent diffusion model that supports text-to-image generation for 9 different languages: English, Chinese, Spanish, French, Japanese, Korean, Arabic, Russian and Italian.

Stable Diffusion Image Variations

StableDiffusionImageVariationPipeline by @justinpinkney is a stable diffusion model that takes an image as an input and generates variations of that image. It is conditioned on CLIP image embeddings instead of text.

Safe Latent Diffusion

Safe Latent Diffusion (SLD), released by ml-research@TUDarmstadt group, is a new practical and sophisticated approach to prevent unsolicited content from being generated by diffusion models. One of the authors of the research contributed their implementation to diffusers.

VQ-Diffusion with classifier-free sampling

vq diffusion classifier free sampling by @williamberman #1294

LDM super resolution

LDM super resolution is a latent 4x super-resolution diffusion model released by CompVis.

CycleDiffusion

CycleDiffusion is a method that uses Text-to-Image Diffusion Models for Image-to-Image Editing. It is capable of

  1. Zero-shot image-to-image translation with text-to-image diffusion models such as Stable Diffusion.
    Traditional unpaired image-to-image translation with diffusion models trained on two related domains.
  2. Zero-shot image-to-image translation with text-to-image diffusion models such as Stable Diffusion.
    Traditional unpaired image-to-image translation with diffusion models trained on two related domains.
  • Add CycleDiffusion pipeline using Stable Diffusion by @ChenWu98 #888

CLIPSeg + StableDiffusionInpainting.

Uses CLIPSeg to automatically generate a mask using segmentation, and then applies Stable Diffusion in-painting.

K-Diffusion wrapper

K-Diffusion Pipeline is community pipeline that allows to use any sampler from K-diffusion with diffusers models.

🌀New SOTA Scheduler

DPMSolverMultistepScheduler is the 🧨 diffusers implementation of DPM-Solver++, a state-of-the-art scheduler that was contributed by one of the authors of the paper. This scheduler is able to achieve great quality in as few as 20 steps. It's a drop-in replacement for the default Stable Diffusion scheduler, so you can use it to essentially half generation times. It works so well that we adopted it for the Stable Diffusion demo Spaces: https://huggingface.co/spaces/stabilityai/stable-diffusion, https://huggingface.co/spaces/runwayml/stable-diffusion-v1-5.

You can use it like this:

from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler

repo_id = "runwayml/stable-diffusion-v1-5"
scheduler = DPMSolverMultistepScheduler.from_pretrained(repo_id, subfolder="scheduler")
stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, scheduler=scheduler)

🌐 Better scheduler API

The example above also demonstrates how to load schedulers using a new API that is coherent with model loading and therefore more natural and intuitive.

You can load a scheduler using from_pretrained, as demonstrated above, or you can instantiate one from an existing scheduler configuration. This is a way to replace the scheduler of a pipeline that was previously loaded:

from diffusers import DiffusionPipeline, EulerDiscreteScheduler

pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)

Read more about these changes in the documentation. See also the community pipeline that allows using any of the K-diffusion samplers with diffusers, as mentioned above!

🎉 Performance

We work relentlessly to incorporate performance optimizations and memory reduction techniques to 🧨 diffusers. These are two of the most noteworthy incorporations in this release:

  • Enable memory-efficient attention by default if xFormers is installed.
  • Use batched-matmuls when possible.

🎁 Quality of Life improvements

  • Fix/Enable all schedulers for in-painting
  • Easier loading of local pipelines
  • cpu offloading: mutli GPU support

📝 Changelog

Contributors

ctsims, justinpinkney, and 39 other contributors
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