pypi diffusers 0.19.0
v0.19.0: SD-XL 1.0 (permissive license), AutoPipelines, Improved Kanidnsky & Asymmetric VQGAN, T2I Adapter
on Python PyPI

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

SDXL 1.0

Stable Diffusion XL (SDXL) 1.0 with permissive CreativeML Open RAIL++-M License was released today. We provide full compatibility with SDXL in diffusers. 

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipe.to("cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt=prompt).images[0]
image

download (6)

Many additional cool features are released:

Refer to the documentation to know more.

New training scripts for SDXL

When there’s a new pipeline, there ought to be new training scripts. We added support for the following training scripts that build on top of SDXL:

Shoutout to @harutatsuakiyama for contributing the training script for InstructPix2Pix in #4079.

New pipelines for SDXL

The ControlNet and InstructPix2Pix training scripts also needed their respective pipelines. So, we added support for the following pipelines as well:

  • StableDiffusionXLControlNetPipeline
  • StableDiffusionXLInstructPix2PixPipeline

The ControlNet and InstructPix2Pix pipelines don’t have interesting checkpoints yet. We hope that the community will be able to leverage the training scripts from this release to help produce some.

Shoutout to @harutatsuakiyama for contributing the StableDiffusionXLInstructPix2PixPipeline in #4079.

The AutoPipeline API

We now support Auto APIs for the following tasks: text-to-image, image-to-image, and inpainting:

Here is how to use one: 

from diffusers import AutoPipelineForTextToImage
import torch

pipe_t2i = AutoPipelineForText2Image.from_pretrained(
    "runwayml/stable-diffusion-v1-5", requires_safety_checker=False, torch_dtype=torch.float16
).to("cuda")

prompt = "photo a majestic sunrise in the mountains, best quality, 4k"
image = pipe_t2i(prompt).images[0]
image.save("image.png")

Without any extra memory, you can then switch to Image-to-Image 

from diffusers import AutoPipelineForImageToImage

pipe_i2i = AutoPipelineForImageToImage.from_pipe(pipe_t2i)

image = pipe_t2i("sunrise in snowy mountains", image=image, strength=0.75).images[0]
image.save("image.png")

Supported Pipelines: SDv1, SDv2, SDXL, Kandinksy, ControlNet, IF ... with more to come.

Refer to the documentation to know more.

A new “combined pipeline” for the Kandinsky series

We introduced a new “combined pipeline” for the Kandinsky series to make it easier to use the Kandinsky prior and decoder together. This eliminates the need to initialize and use multiple pipelines for Kandinsky to generate images. Here is an example: 

from diffusers import AutoPipelineForTextToImage
import torch

pipe = AutoPipelineForTextToImage.from_pretrained(
    "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()

prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"
image = pipe(prompt=prompt, num_inference_steps=25).images[0] 
image.save("image.png")

The following pipelines, which can be accessed via the "Auto" pipelines were added:

To know more, check out the following pages:

🚨🚨🚨 Breaking change for Kandinsky Mask Inpainting 🚨🚨🚨

NOW: mask_image repaints white pixels and preserves black pixels.

Kandinksy was using an incorrect mask format. Instead of using white pixels as a mask (like SD & IF do), Kandinsky models were using black pixels. This needs to be corrected and so that the diffusers API is aligned. We cannot have different mask formats for different pipelines.

Important => This means that everyone that already used Kandinsky Inpaint in production / pipeline now needs to change the mask to: 

# For PIL input
import PIL.ImageOps
mask = PIL.ImageOps.invert(mask)

# For PyTorch and Numpy input
mask = 1 - mask

Asymmetric VQGAN

Designing a Better Asymmetric VQGAN for StableDiffusion introduced a VQGAN that is particularly well-suited for inpainting tasks. This release brings the support of this new VQGAN. Here is how it can be used: 

from io import BytesIO
from PIL import Image
import requests
from diffusers import AsymmetricAutoencoderKL, StableDiffusionInpaintPipeline

def download_image(url: str) -> Image.Image:
    response = requests.get(url)
    return Image.open(BytesIO(response.content)).convert("RGB")

prompt = "a photo of a person"
img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"
mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"

image = download_image(img_url).resize((256, 256))
mask_image = download_image(mask_url).resize((256, 256))

pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
pipe.vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe.to("cuda")

image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0]
image.save("image.jpeg")

Refer to the documentation to know more.

Thanks to @cross-attention for contributing this model in #3956.

Improved support for loading Kohya-style LoRA checkpoints

We are committed to providing seamless interoperability support of Kohya-trained checkpoints from diffusers. To that end, we improved the existing support for loading Kohya-trained checkpoints in diffusers. Users can expect further improvements in the upcoming releases.

Thanks to @takuma104 and @isidentical for contributing the improvements in #4147.

T2I Adapter

pip install matplotlib

from PIL import Image
import torch
import numpy as np
import matplotlib
from diffusers import T2IAdapter, StableDiffusionAdapterPipeline

def colorize(value, vmin=None, vmax=None, cmap='gray_r', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None):
    """Converts a depth map to a color image.

    Args:
        value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed
        vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None.
        vmax (float, optional):  vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None.
        cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'.
        invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99.
        invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None.
        background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255).
        gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False.
        value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None.

    Returns:
        numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4)
    """
    if isinstance(value, torch.Tensor):
        value = value.detach().cpu().numpy()

    value = value.squeeze()
    if invalid_mask is None:
        invalid_mask = value == invalid_val
    mask = np.logical_not(invalid_mask)

    # normalize
    vmin = np.percentile(value[mask],2) if vmin is None else vmin
    vmax = np.percentile(value[mask],85) if vmax is None else vmax
    if vmin != vmax:
        value = (value - vmin) / (vmax - vmin)  # vmin..vmax
    else:
        # Avoid 0-division
        value = value * 0.

    # squeeze last dim if it exists
    # grey out the invalid values

    value[invalid_mask] = np.nan
    cmapper = matplotlib.cm.get_cmap(cmap)
    if value_transform:
        value = value_transform(value)
        # value = value / value.max()
    value = cmapper(value, bytes=True)  # (nxmx4)

    img = value[...]
    img[invalid_mask] = background_color

    if gamma_corrected:
        img = img / 255
        img = np.power(img, 2.2)
        img = img * 255
        img = img.astype(np.uint8)
    return img

model = torch.hub.load("isl-org/ZoeDepth", "ZoeD_N", pretrained=True)

img = Image.open('./images/zoedepth_in.png')

out = model.infer_pil(img)

zoedepth_image = Image.fromarray(colorize(out)).convert('RGB')

zoedepth_image.save('images/zoedepth.png')

adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_zoedepth_sd15v1", torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", adapter=adapter, safety_checker=None, torch_dtype=torch.float16, variant="fp16"
)

pipe.to('cuda')
zoedepth_image_out = pipe(prompt="motorcycle", image=zoedepth_image).images[0]

zoedepth_image_out.save('images/zoedepth_out.png')

zoedepth_in
zoedepth
zoedepth_out

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Significant community contributions

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

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