v0.29.2: fix deprecation and LoRA bugs 🐞

All commits

• [SD3] Fix mis-matched shape when num_images_per_prompt > 1 using without T5 (text_encoder_3=None) by @Dalanke in #8558
• [LoRA] refactor lora conversion utility. by @sayakpaul in #8295
• [LoRA] fix conversion utility so that lora dora loads correctly by @sayakpaul in #8688
• [Chore] remove deprecation from transformer2d regarding the output class. by @sayakpaul in #8698
• [LoRA] fix vanilla fine-tuned lora loading. by @sayakpaul in #8691
• Release: v0.29.2 by @sayakpaul (direct commit on v0.29.2-patch)

v0.29.1: SD3 ControlNet, Expanded SD3 `from_single_file` support, Using long Prompts with T5 Text Encoder & Bug fixes

SD3 CntrolNet

import torch
from diffusers import StableDiffusion3ControlNetPipeline
from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
from diffusers.utils import load_image

controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny", torch_dtype=torch.float16)

pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.to("cuda")
control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg")
prompt = "A girl holding a sign that says InstantX"
image = pipe(prompt, control_image=control_image, controlnet_conditioning_scale=0.7).images[0]
image.save("sd3.png")

📜 Refer to the official docs here to learn more about it.

Thanks to @haofanwang @wangqixun from the @ResearcherXman team for contributing this pipeline!

Expanded single file support

We now support all available single-file checkpoints for sd3 in diffusers! To load the single file checkpoint with t5

import torch
from diffusers import StableDiffusion3Pipeline

pipe = StableDiffusion3Pipeline.from_single_file(
    "https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips_t5xxlfp8.safetensors",
    torch_dtype=torch.float16,
)
pipe.enable_model_cpu_offload()

image = pipe("a picture of a cat holding a sign that says hello world").images[0]
image.save('sd3-single-file-t5-fp8.png')

Using Long Prompts with the T5 Text Encoder

We increased the default sequence length for the T5 Text Encoder from a maximum of 77 to 256! It can be adjusted to accept fewer or more tokens by setting the max_sequence_length to a maximum of 512. Keep in mind that longer sequences require additional resources and will result in longer generation times. This effect is particularly noticeable during batch inference.

prompt = "A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus. This imaginative creature features the distinctive, bulky body of a hippo, but with a texture and appearance resembling a golden-brown, crispy waffle. The creature might have elements like waffle squares across its skin and a syrup-like sheen. It’s set in a surreal environment that playfully combines a natural water habitat of a hippo with elements of a breakfast table setting, possibly including oversized utensils or plates in the background. The image should evoke a sense of playful absurdity and culinary fantasy."

image = pipe(
    prompt=prompt,
    negative_prompt="",
    num_inference_steps=28,
    guidance_scale=4.5,
    max_sequence_length=512,
).images[0]

Before	max_sequence_length=256	max_sequence_length=512

All commits

• Release: v0.29.0 by @sayakpaul (direct commit on v0.29.1-patch)
• prepare for patch release by @yiyixuxu (direct commit on v0.29.1-patch)
• fix warning log for Transformer SD3 by @sayakpaul in #8496
• Add SD3 AutoPipeline mappings by @Beinsezii in #8489
• Add Hunyuan AutoPipe mapping by @Beinsezii in #8505
• Expand Single File support in SD3 Pipeline by @DN6 in #8517
• [Single File Loading] Handle unexpected keys in CLIP models when accelerate isn't installed. by @DN6 in #8462
• Fix sharding when no device_map is passed by @SunMarc in #8531
• [SD3 Inference] T5 Token limit by @asomoza in #8506
• Fix gradient checkpointing issue for Stable Diffusion 3 by @Carolinabanana in #8542
• Support SD3 ControlNet and Multi-ControlNet. by @wangqixun in #8566
• fix from_single_file for checkpoints with t5 by @yiyixuxu in #8631
• [SD3] Fix mis-matched shape when num_images_per_prompt > 1 using without T5 (text_encoder_3=None) by @Dalanke in #8558

Significant community contributions

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

• @wangqixun
  • Support SD3 ControlNet and Multi-ControlNet. (#8566)

v0.29.0: Stable Diffusion 3

This release emphasizes Stable Diffusion 3, Stability AI’s latest iteration of the Stable Diffusion family of models. It was introduced in Scaling Rectified Flow Transformers for High-Resolution Image Synthesis by Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach.

As the model is gated, before using it with diffusers, you first need to go to the Stable Diffusion 3 Medium Hugging Face page, fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate.

huggingface-cli login

The code below shows how to perform text-to-image generation with SD3:

import torch
from diffusers import StableDiffusion3Pipeline

pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

image = pipe(
    "A cat holding a sign that says hello world",
    negative_prompt="",
    num_inference_steps=28,
    guidance_scale=7.0,
).images[0]
image

Refer to our documentation for learning all the optimizations you can apply to SD3 as well as the image-to-image pipeline.

Additionally, we support DreamBooth + LoRA fine-tuning of Stable Diffusion 3 through rectified flow. Check out this directory for more details.

v0.28.2: fix `from_single_file` clip model checkpoint key error 🐞

• Change checkpoint key used to identify CLIP models in single file checkpoints by @DN6 in #8319

v0.28.1: HunyuanDiT and Transformer2D model class variants

This patch release primarily introduces the Hunyuan DiT pipeline from the Tencent team.

Hunyuan DiT

Hunyuan DiT is a transformer-based diffusion pipeline, introduced in the Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding paper by the Tencent Hunyuan.

import torch
from diffusers import HunyuanDiTPipeline

pipe = HunyuanDiTPipeline.from_pretrained(
    "Tencent-Hunyuan/HunyuanDiT-Diffusers", torch_dtype=torch.float16
)
pipe.to("cuda")

# You may also use English prompt as HunyuanDiT supports both English and Chinese
# prompt = "An astronaut riding a horse"
prompt = "一个宇航员在骑马"
image = pipe(prompt).images[0]

🧠 This pipeline has support for multi-linguality.

📜 Refer to the official docs here to learn more about it.

Thanks to @gnobitab, for contributing Hunyuan DiT in #8240.

All commits

• Release: v0.28.0 by @sayakpaul (direct commit on v0.28.1-patch)
• [Core] Introduce class variants for Transformer2DModel by @sayakpaul in #7647
• resolve comflicts by @toshas (direct commit on v0.28.1-patch)
• Tencent Hunyuan Team: add HunyuanDiT related updates by @gnobitab in #8240
• Tencent Hunyuan Team - Updated Doc for HunyuanDiT by @gnobitab in #8383
• [Transformer2DModel] Handle norm_type safely while remapping by @sayakpaul in #8370
• Release: v0.28.1 by @sayakpaul (direct commit on v0.28.1-patch)

Significant community contributions

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

• @gnobitab
  • Tencent Hunyuan Team: add HunyuanDiT related updates (#8240)
  • Tencent Hunyuan Team - Updated Doc for HunyuanDiT (#8383)

v0.28.0: Marigold, PixArt Sigma, AnimateDiff SDXL, InstantStyle, VQGAN Training Script, and more

Diffusion models are known for their abilities in the space of generative modeling. This release of diffusers introduces the first official pipeline (Marigold) for discriminative tasks such as depth estimation and surface normals’ estimation!

Starting this release, we will also highlight the changes and features from the library that make it easy to integrate community checkpoints, features, and so on. Read on!

Marigold

Proposed in Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation, Marigold introduces a diffusion model and associated fine-tuning protocol for monocular depth estimation. It can also be extended to perform surface normals’ estimation.

(Image taken from the official repository)

The code snippet below shows how to use this pipeline for depth estimation:

import diffusers
import torch

pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
    "prs-eth/marigold-depth-lcm-v1-0", variant="fp16", torch_dtype=torch.float16
).to("cuda")

image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
depth = pipe(image)

vis = pipe.image_processor.visualize_depth(depth.prediction)
vis[0].save("einstein_depth.png")

depth_16bit = pipe.image_processor.export_depth_to_16bit_png(depth.prediction)
depth_16bit[0].save("einstein_depth_16bit.png")

Check out the API documentation here. We also have a detailed guide about the pipeline here.

Thanks to @toshas, one of the authors of Marigold, who contributed this in #7847.

🌀 Massive Refactor of from_single_file 🌀

We have further refactored from_single_file to align its logic more closely to the from_pretrained method. The biggest benefit of doing this is that it allows us to expand single file loading support beyond Stable Diffusion-like pipelines and models. It also makes it easier to load models that are saved and shared in their original format.

Some of the changes introduced in this refactor:

1. When loading a single file checkpoint, we will attempt to use the keys present in the checkpoint to infer a model repository on the Hugging Face Hub that we can use to configure the pipeline. For example, if you are using a single file checkpoint based on SD 1.5, we would use the configuration files in the runwayml/stable-diffusion-v1-5 repository to configure the model components and pipeline.
2. Suppose this inferred configuration isn’t appropriate for your checkpoint. In that case, you can override it using the config argument and pass in either a path to a local model repo or a repo id on the Hugging Face Hub.

pipe = StableDiffusionPipeline.from_single_file("...", config=<model repo id or local repo path>) 

3. Deprecation of model configuration arguments for the from_single_file method in Pipelines such as num_in_channels, scheduler_type , image_size and upcast_attention . This is an anti-pattern that we have supported in previous versions of the library when we assumed that it would only be relevant to Stable Diffusion based models. However, given that there is a demand to support other model types, we feel it is necessary for single-file loading behavior to adhere to the conventions set in our other loading methods. Configuring individual model components through a pipeline loading method is not something we support in from_pretrained, and therefore, we will be deprecating support for this behavior in from_single_file as well.

PixArt Sigma

PixArt Simga is the successor to PixArt Alpha. PixArt Sigma is capable of directly generating images at 4K resolution. It can also produce images of markedly higher fidelity and improved alignment with text prompts. It comes with a massive sequence length of 300 (for reference, PixArt Alpha has a maximum sequence length of 120)!

(Taken from the project website.)

import torch
from diffusers import PixArtSigmaPipeline

# You can replace the checkpoint id with "PixArt-alpha/PixArt-Sigma-XL-2-512-MS" too.
pipe = PixArtSigmaPipeline.from_pretrained(
    "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", torch_dtype=torch.float16
)
# Enable memory optimizations.
pipe.enable_model_cpu_offload()

prompt = "A small cactus with a happy face in the Sahara desert."
image = pipe(prompt).images[0]

📃 Refer to the documentation here to learn more about PixArt Sigma.

Thanks to @lawrence-cj, one of the authors of PixArt Sigma, who contributed this in #7857.

AnimateDiff SDXL

@a-r-r-o-w contributed the Stable Diffusion XL (SDXL) version of AnimateDiff in #6721. However, note that this is currently an experimental feature, as only a beta release of the motion adapter checkpoint is available.

import torch
from diffusers.models import MotionAdapter
from diffusers import AnimateDiffSDXLPipeline, DDIMScheduler
from diffusers.utils import export_to_gif

adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-sdxl-beta", torch_dtype=torch.float16)

model_id = "stabilityai/stable-diffusion-xl-base-1.0"
scheduler = DDIMScheduler.from_pretrained(
    model_id,
    subfolder="scheduler",
    clip_sample=False,
    beta_schedule="linear",
    steps_offset=1,
)
pipe = AnimateDiffSDXLPipeline.from_pretrained(
    model_id,
    motion_adapter=adapter,
    scheduler=scheduler,
    torch_dtype=torch.float16,
    variant="fp16",
).enable_model_cpu_offload()

# enable memory savings
pipe.enable_vae_slicing()
pipe.enable_vae_tiling()

output = pipe(
    prompt="a panda surfing in the ocean, realistic, high quality",
    negative_prompt="low quality, worst quality",
    num_inference_steps=20,
    guidance_scale=8,
    width=1024,
    height=1024,
    num_frames=16,
)

frames = output.frames[0]
export_to_gif(frames, "animation.gif")

📜 Refer to the documentation to learn more.

Block-wise LoRA

@UmerHA contributed the support to control the scales of different LoRA blocks in a granular manner in #7352. Depending on the LoRA checkpoint one is using, this granular control can significantly impact the quality of the generated outputs. Following code block shows how this feature can be used while performing inference:

...

adapter_weight_scales = { "unet": { "down": 0, "mid": 1, "up": 0} }
pipe.set_adapters("pixel", adapter_weight_scales)
image = pipe(
		prompt, num_inference_steps=30, generator=torch.manual_seed(0)
).images[0]

✍️ Refer to our documentation for more details and a full-fledged example.

InstantStyle

More granular control of scale could be extended to IP-Adapters too. @DannHuang contributed to the support of InstantStyle, aka granular control of IP-Adapter scales, in #7668. The following code block shows how this feature could be used when performing inference with IP-Adapters:

...

scale = {
    "down": {"block_2": [0.0, 1.0]},
    "up": {"block_0": [0.0, 1.0, 0.0]},
}
pipeline.set_ip_adapter_scale(scale)

This way, one can generate images following only the style or layout from the image prompt, with significantly improved diversity. This is achieved by only activating IP-Adapters to specific parts of the model.

Check out the documentation here.

ControlNetXS

ControlNet-XS was introduced in ControlNet-XS by Denis Zavadski and Carsten Rother. Based on the observation, the control model in the original ControlNet can be made much smaller and still produce good results. ControlNet-XS generates images comparable to a regular ControlNet, but it is 20-25% faster (see benchmark with StableDiffusion-XL) and uses ~45% less memory.

ControlNet-XS is supported for both Stable Diffusion and Stable Diffusion XL.

Thanks to @UmerHA for contributing ControlNet-XS in #5827 and #6772.

Custom Timesteps

We introduced custom timesteps support for some of our pipelines and schedulers. You can now set your scheduler with a list of arbitrary timesteps. For example, you can use the AYS timesteps schedule to achieve very nice results with only 10 denoising steps.

from diffusers.schedulers import AysSchedules
sampling_schedule = AysSchedules["StableDiffusionXLTimesteps"]
pipe = StableDiffusionXLPipeline.from_pretrained(
    "SG16...

v0.27.2: Fix scheduler `add_noise` 🐞, embeddings in StableCascade, `scale` when using LoRA

All commits

• [scheduler] fix a bug in add_noise by @yiyixuxu in #7386
• [LoRA] fix cross_attention_kwargs problems and tighten tests by @sayakpaul in #7388
• Fix issue with prompt embeds and latents in SD Cascade Decoder with multiple image embeddings for a single prompt. by @DN6 in #7381

v0.27.1: Clear `scale` argument confusion for LoRA

All commits

• Release: v0.27.0 by @DN6 (direct commit on v0.27.1-patch)
• [LoRA] pop the LoRA scale so that it doesn't get propagated to the weeds by @sayakpaul in #7338
• Release: 0.27.1-patch by @sayakpaul (direct commit on v0.27.1-patch)

v0.27.0: Stable Cascade, Playground v2.5, EDM-style training, IP-Adapter image embeds, and more

Stable Cascade

We are adding support for a new text-to-image model building on Würstchen called Stable Cascade, which comes with a non-commercial license. The Stable Cascade line of pipelines differs from Stable Diffusion in that they are built upon three distinct models and allow for hierarchical compression of image patients, achieving remarkable outputs.

from diffusers import StableCascadePriorPipeline, StableCascadeDecoderPipeline
import torch

prior = StableCascadePriorPipeline.from_pretrained(
    "stabilityai/stable-cascade-prior",
    torch_dtype=torch.bfloat16,
).to("cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image_emb = prior(prompt=prompt).image_embeddings[0]

decoder = StableCascadeDecoderPipeline.from_pretrained(
    "stabilityai/stable-cascade",
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe(image_embeddings=image_emb, prompt=prompt).images[0]
image

📜 Check out the docs here to know more about the model.

Note: You will need a torch>=2.2.0 to use the torch.bfloat16 data type with the Stable Cascade pipeline.

Playground v2.5

PlaygroundAI released a new v2.5 model (playgroundai/playground-v2.5-1024px-aesthetic), which particularly excels at aesthetics. The model closely follows the architecture of Stable Diffusion XL, except for a few tweaks. This release comes with support for this model:

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained(
    "playgroundai/playground-v2.5-1024px-aesthetic",
    torch_dtype=torch.float16,
    variant="fp16",
).to("cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt=prompt, num_inference_steps=50, guidance_scale=3).images[0]
image

Loading from the original single-file checkpoint is also supported:

from diffusers import StableDiffusionXLPipeline, EDMDPMSolverMultistepScheduler
import torch

url = "https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic/blob/main/playground-v2.5-1024px-aesthetic.safetensors"
pipeline = StableDiffusionXLPipeline.from_single_file(url)
pipeline.to(device="cuda", dtype=torch.float16)

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image  = pipeline(prompt=prompt, guidance_scale=3.0).images[0]
image.save("playground_test_image.png")

You can also perform LoRA DreamBooth training with the playgroundai/playground-v2.5-1024px-aesthetic checkpoint:

accelerate launch train_dreambooth_lora_sdxl.py \
  --pretrained_model_name_or_path="playgroundai/playground-v2.5-1024px-aesthetic"  \
  --instance_data_dir="dog" \
  --output_dir="dog-playground-lora" \
  --mixed_precision="fp16" \
  --instance_prompt="a photo of sks dog" \
  --resolution=1024 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=4 \
  --learning_rate=1e-4 \
  --use_8bit_adam \
  --report_to="wandb" \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --max_train_steps=500 \
  --validation_prompt="A photo of sks dog in a bucket" \
  --validation_epochs=25 \
  --seed="0" \
  --push_to_hub

To know more, follow the instructions here.

EDM-style training support

EDM refers to the training and sampling techniques introduced in the following paper: Elucidating the Design Space of Diffusion-Based Generative Models. We have introduced support for training using the EDM formulation in our train_dreambooth_lora_sdxl.py script.

To train stabilityai/stable-diffusion-xl-base-1.0 using the EDM formulation, you just have to specify the --do_edm_style_training flag in your training command, and voila 🤗

If you’re interested in extending this formulation to other training scripts, we refer you to this PR.

New schedulers with the EDM formulation

To better support the Playground v2.5 model and EDM-style training in general, we are bringing support for EDMDPMSolverMultistepScheduler and EDMEulerScheduler. These support the EDM formulations of the DPMSolverMultistepScheduler and EulerDiscreteScheduler, respectively.

Trajectory Consistency Distillation

Trajectory Consistency Distillation (TCD) enables a model to generate higher quality and more detailed images with fewer steps. Moreover, owing to the effective error mitigation during the distillation process, TCD demonstrates superior performance even under conditions of large inference steps. It was proposed in Trajectory Consistency Distillation.

This release comes with the support of a TCDScheduler that enables this kind of fast sampling. Much like LCM-LoRA, TCD requires an additional adapter for the acceleration. The code snippet below shows a usage:

import torch
from diffusers import StableDiffusionXLPipeline, TCDScheduler

device = "cuda"
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
tcd_lora_id = "h1t/TCD-SDXL-LoRA"

pipe = StableDiffusionXLPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device)
pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)

pipe.load_lora_weights(tcd_lora_id)
pipe.fuse_lora()

prompt = "Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna."

image = pipe(
    prompt=prompt,
    num_inference_steps=4,
    guidance_scale=0,
    eta=0.3, 
    generator=torch.Generator(device=device).manual_seed(0),
).images[0]

📜 Check out the docs here to know more about TCD.

Many thanks to @mhh0318 for contributing the TCDScheduler in #7174 and the guide in #7259.

IP-Adapter image embeddings and masking

All the pipelines supporting IP-Adapter accept a ip_adapter_image_embeds argument. If you need to run the IP-Adapter multiple times with the same image, you can encode the image once and save the embedding to the disk. This saves computation time and is especially useful when building UIs. Additionally, ComfyUI image embeddings for IP-Adapters are fully compatible in Diffusers and should work out-of-box.

We have also introduced support for providing binary masks to specify which portion of the output image should be assigned to an IP-Adapter. For each input IP-Adapter image, a binary mask and an IP-Adapter must be provided. Thanks to @fabiorigano for contributing this feature through #6847.

📜 To know about the exact usage of both of the above, refer to our official guide.

We thank our community members, @fabiorigano, @asomoza, and @cubiq, for their guidance and input on these features.

Guide on merging LoRAs

Merging LoRAs can be a fun and creative way to create new and unique images. Diffusers provides merging support with the set_adapters method which concatenates the weights of the LoRAs to merge.

Now, Diffusers also supports the add_weighted_adapter method from the PEFT library, unlocking more efficient merging method like TIES, DARE, linear, and even combinations of these merging methods like dare_ties.

📜 Take a look at the Merge LoRAs guide to learn more about merging in Diffusers.

LEDITS++

We are adding support to the real image editing technique called LEDITS++: Limitless Image Editing using Text-to-Image Models, a parameter-free method, requiring no fine-tuning nor any optimization.
To edit real images, the LEDITS++ pipelines first invert the image DPM-solver++ scheduler that facilitates editing with as little as 20 total diffusion steps for inversion and inference combined. LEDITS++ guidance is defined such that it both reflects the direction of the edit (if we want to push away from/towards the edit concept) and the strength of the effect. The guidance also includes a masking term focused on relevant image regions which, for multiple edits especially, ensures that the corresponding guidance terms for each concept remain mostly isolated, limiting interference.

The code snippet below shows a usage:

import torch
import PIL
import requests
from io import BytesIO
from diffusers import LEditsPPPipelineStableDiffusionXL, AutoencoderKL

device = "cuda"
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

pipe = LEditsPPPipelineStableDiffusionXL.from_pretrained(
    base_model_id, 
    vae=vae, 
    torch_dtype=torch.float16
).to(device)

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

img_url = "https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/tennis.jpg"
image = download_image(img_url)

_ = pipe.invert(
    image = image,
    num_inversion_steps=50,
    skip=0.2
)

edited_image = pipe(
    editing_prompt...

v0.26.3: Patch release to fix DPMSolverSinglestepScheduler and configuring VAE from single file mixin

All commits

• Fix configuring VAE from single file mixin by @DN6 in #6950
• [DPMSolverSinglestepScheduler] correct get_order_list for solver_order=2and lower_order_final=True by @yiyixuxu in #6953