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# MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation

## Overview

[MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://arxiv.org/abs/2302.08113) by Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel.

The abstract of the paper is the following:

*Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes.

Resources:

* [Project Page](https://multidiffusion.github.io/).
* [Paper](https://arxiv.org/abs/2302.08113).
* [Original Code](https://github.com/omerbt/MultiDiffusion).
* [Demo](https://huggingface.co/spaces/weizmannscience/MultiDiffusion).

## Available Pipelines:

| Pipeline | Tasks | Demo
|---|---|:---:|
| [StableDiffusionPanoramaPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py) | *Text-Guided Panorama View Generation* | [🤗 Space](https://huggingface.co/spaces/weizmannscience/MultiDiffusion)) |

<!-- TODO: add Colab -->

## Usage example

```python
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")
```

<Tip>

While calling this pipeline, it's possible to specify the `view_batch_size` to have a >1 value. 
For some GPUs with high performance, higher a `view_batch_size`, can speedup the generation
and increase the VRAM usage.

</Tip>

## StableDiffusionPanoramaPipeline
[[autodoc]] StableDiffusionPanoramaPipeline
	- __call__
	- all
