--- license: mit tags: - generation - text-to-image - image-variation - image-to-text - image-editing - vision datasets: - Laion2B-en widget: - text: "A high tech solarpunk utopia in the Amazon rainforest" example_title: Amazon rainforest --- # Versatile Diffusion V1.0 Model Card We built **Versatile Diffusion (VD), the first unified multi-flow multimodal diffusion framework**, as a step towards **Universal Generative AI**. Versatile Diffusion can natively support image-to-text, image-variation, text-to-image, and text-variation, and can be further extended to other applications such as semantic-style disentanglement, image-text dual-guided generation, latent image-to-text-to-image editing, and more. Future versions will support more modalities such as speech, music, video and 3D. Resources for more information: [GitHub](https://github.com/SHI-Labs/Versatile-Diffusion), [arXiv](https://arxiv.org/abs/2211.08332). # Model Details One single flow of Versatile Diffusion contains a VAE, a diffuser, and a context encoder, and thus handles one task (e.g., text-to-image) under one data type (e.g., image) and one context type (e.g., text). The multi-flow structure of Versatile Diffusion shows in the following diagram:

- **Developed by:** Xingqian Xu, Atlas Wang, Eric Zhang, Kai Wang, and Humphrey Shi - **Model type:** Diffusion-based multimodal generation model - **Language(s):** English - **License:** MIT - **Resources for more information:** [GitHub Repository](https://github.com/SHI-Labs/Versatile-Diffusion), [Paper](https://arxiv.org/abs/2211.08332). - **Cite as:** ``` @article{xu2022versatile, title = {Versatile Diffusion: Text, Images and Variations All in One Diffusion Model}, author = {Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi}, year = 2022, url = {https://arxiv.org/abs/2211.08332}, eprint = {2211.08332}, archiveprefix = {arXiv}, primaryclass = {cs.CV} } ``` # Usage You can use the model both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [SHI-Labs Versatile Diffusion codebase](https://github.com/SHI-Labs/Versatile-Diffusion). ## 🧨 Diffusers Diffusers let's you both use a unified and more memory-efficient, task-specific pipelines. **Make sure to install `transformers` from `"main"` in order to use this model.**: ``` pip install git+https://github.com/huggingface/transformers ``` ## VersatileDiffusionPipeline To use Versatile Diffusion for all tasks, it is recommend to use the [`VersatileDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#diffusers.VersatileDiffusionPipeline) ```py #! pip install git+https://github.com/huggingface/transformers diffusers torch 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") # prompt prompt = "a red car" # 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") # 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] ``` ### Task Specific The task specific pipelines load only the weights that are needed onto GPU. You can find all task specific pipelines [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion#versatilediffusion). You can use them as follows: ### Text to Image ```py from diffusers import VersatileDiffusionTextToImagePipeline import torch pipe = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe.remove_unused_weights() pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) image = pipe("an astronaut riding on a horse on mars", generator=generator).images[0] image.save("./astronaut.png") ``` #### Image variations ```py from diffusers import VersatileDiffusionImageVariationPipeline import torch import requests from io import BytesIO from PIL import Image # download an 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") pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) image = pipe(image, generator=generator).images[0] image.save("./car_variation.png") ``` #### Dual-guided generation ```py from diffusers import VersatileDiffusionDualGuidedPipeline import torch import requests from io import BytesIO from PIL import Image # download an 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") text = "a red car in the sun" pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe.remove_unused_weights() pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) text_to_image_strength = 0.75 image = pipe(prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator).images[0] image.save("./red_car.png") ``` ### Original GitHub Repository Follow the instructions [here](https://github.com/SHI-Labs/Versatile-Diffusion/#evaluation). # Cautions, Biases, and Content Acknowledgment We would like the raise the awareness of users of this demo of its potential issues and concerns. Like previous large foundation models, Versatile Diffusion could be problematic in some cases, partially due to the imperfect training data and pretrained network (VAEs / context encoders) with limited scope. In its future research phase, VD may do better on tasks such as text-to-image, image-to-text, etc., with the help of more powerful VAEs, more sophisticated network designs, and more cleaned data. So far, we have kept all features available for research testing both to show the great potential of the VD framework and to collect important feedback to improve the model in the future. We welcome researchers and users to report issues with the HuggingFace community discussion feature or email the authors. Beware that VD may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography, and violence. VD was trained on the LAION-2B dataset, which scraped non-curated online images and text, and may contain unintended exceptions as we removed illegal content. VD in this demo is meant only for research purposes.