<!doctype html> <html lang="en"> <!-- === Header Starts === --> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <title>Ctrl-X</title> <link href="./assets/bootstrap.min.css" rel="stylesheet"> <link href="./assets/font.css" rel="stylesheet" type="text/css"> <link href="./assets/style.css" rel="stylesheet" type="text/css"> </head> <!-- === Header Ends === --> <body> <!-- === Home Section Starts === --> <div class="section"> <!-- === Title Starts === --> <div class="header"> <div class="logo"> <a href="https://genforce.github.io/" target="_blank"><img src="./assets/genforce.png"></a> </div> <div class="title", style="padding-top: 25pt;"> <!-- Set padding as 10 if title is with two lines. --> Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance </div> </div> <!-- === Title Ends === --> <div class="author"> <a href="https://kuanhenglin.github.io" target="_blank">Kuan Heng Lin</a><sup>1</sup>* <a href="https://sichengmo.github.io/" target="_blank">Sicheng Mo</a><sup>1</sup>* <a href="https://bklingher.github.io" target="_blank">Ben Klingher</a><sup>1</sup> <a href="https://pages.cs.wisc.edu/~fmu/" target="_blank">Fangzhou Mu</a><sup>2</sup> <a href="https://boleizhou.github.io/" target="_blank">Bolei Zhou</a><sup>1</sup> </div> <div class="institution"> <sup>1</sup>UCLA <sup>2</sup>NVIDIA </div> <div class="note"> *Equal contribution </div> <div class="title" style="font-size: 18pt;margin: 15pt 0 15pt 0"> NeurIPS 2024 </div> <div class="link"> [<a href="https://arxiv.org/abs/2406.07540" target="_blank">Paper</a>] [<a href="https://github.com/genforce/ctrl-x" target="_blank">Code</a>] </div> <div class="teaser"> <img src="assets/ctrl-x.jpg" width="85%"> </div> </div> <!-- === Home Section Ends === --> <!-- === Overview Section Starts === --> <div class="section"> <div class="title">Overview</div> <div class="body"> We present <b>Ctrl-X</b>, a simple <i>training-free</i> and <i>guidance-free</i> framework for text-to-image (T2I) generation with structure and appearance control. Given user-provided structure and appearance images, Ctrl-X designs feedforward structure control to enable structure alignment with the structure image and semantic-aware appearance transfer to facilitate the appearance transfer from the appearance image. Ctrl-X supports novel structure control with arbitrary condition images of any modality, is significantly faster than prior training-free appearance transfer methods, and provides instant plug-and-play to any T2I and text-to-video (T2V) diffusion model. <table width="100%" style="margin: 20pt 0; text-align: center;"> <tr> <td><img src="assets/pipeline.jpg" width="85%"></td> </tr> </table> <b>How does it work?</b> Given clean structure and appearance latents, we first obtain noised structure and appearance latents via the diffusion forward process, then extracting their U-Net features from a pretrained T2I diffusion model. When denoising the output latent, we inject convolution and self-attention features from the structure latent and leverage self-attention correspondence to transfer spatially-aware appearance statistics from the appearance latent to achieve structure and appearance control. We name our method "Ctrl-X" because we reformulate the controllable generation problem by 'cutting' (and 'pasting') structure preservation and semantic-aware stylization together. </div> </div> <!-- === Overview Section Ends === --> <!-- === Result Section Starts === --> <div class="section"> <div class="title">Results: Structure and appearance control</div> <div class="body"> Results of training-free and guidance-free T2I diffusion with structure and appearance control, where Ctrl-X supports a diverse variety of structure images, including natural images, ControlNet-supported conditions (e.g., canny maps, normal maps), and in-the-wild conditions (e.g., wireframes, 3D meshes). The base model here is <a href="https://arxiv.org/abs/2307.01952" target="_blank">Stable Diffusion XL v1.0</a>. <!-- Adjust the number of rows and columns (EVERY project differs). --> <table width="100%" style="margin: 20pt 0; text-align: center;"> <tr> <td><img src="assets/results_struct+app.jpg" width="100%"></td> </tr> </table> <table width="100%" style="margin: 20pt 0; text-align: center;"> <tr> <td><img src="assets/results_struct+app_2.jpg" width="85%"></td> </tr> </table> </div> </div> <div class="section"> <div class="title">Results: Multi-subject structure and appearance control</div> <div class="body"> Ctrl-X is capable of multi-subject generation with semantic correspondence between appearance and structure images across both subjects and backgrounds. In comparison, <a href="https://arxiv.org/abs/2302.05543" target="_blank">ControlNet</a> + <a href="https://arxiv.org/abs/2308.06721" target="_blank">IP-Adapter</a> often fails at transferring all subject and background appearances. <!-- Adjust the number of rows and columns (EVERY project differs). --> <table width="100%" style="margin: 20pt 0; text-align: center;"> <tr> <td><img src="assets/results_multi_subject.jpg" width="90%"></td> </tr> </table> </div> </div> <div class="section"> <div class="title">Results: Prompt-driven conditional generation</div> <div class="body"> Ctrl-X also supports prompt-driven conditional generation, where it generates an output image complying with the given text prompt while aligning with the structure of the structure image. Ctrl-X continues to support any structure image/condition type here as well. The base model here is <a href="https://arxiv.org/abs/2307.01952" target="_blank">Stable Diffusion XL v1.0</a>. <!-- Adjust the number of rows and columns (EVERY project differs). --> <table width="100%" style="margin: 20pt 0; text-align: center;"> <tr> <td><img src="assets/results_struct+prompt.jpg" width="100%"></td> </tr> </table> </div> </div> <div class="section"> <div class="title">Results: Extension to video generation</div> <div class="body"> We can directly apply Ctrl-X to text-to-video (T2V) models. We show results of <a href="https://animatediff.github.io/" target="_blank">AnimateDiff v1.5.3</a> (with base model <a href="https://huggingface.co/SG161222/Realistic_Vision_V5.1_noVAE" target="_blank">Realistic Vision v5.1</a>) here. <!-- Demo video here. Adjust the frame size based on the demo (EVERY project differs). --> <div style="position: relative; padding-top: 50%; margin: 20pt 0; text-align: center;"> <iframe src="assets/results_animatediff.mp4" frameborder=0 style="position: absolute; top: 2.5%; left: 0%; width: 100%; height: 100%;" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> </div> </div> </div> <!-- === Result Section Ends === --> <!-- === Reference Section Starts === --> <div class="section"> <div class="bibtex">BibTeX</div> <pre> @inproceedings{lin2024ctrlx, author = {Lin, {Kuan Heng} and Mo, Sicheng and Klingher, Ben and Mu, Fangzhou and Zhou, Bolei}, booktitle = {Advances in Neural Information Processing Systems}, title = {Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance}, year = {2024} } </pre> <!-- BZ: we should give other related work enough credits, --> <!-- so please include some most relevant work and leave some comment to summarize work and the difference. --> <div class="ref">Related Work</div> <div class="citation"> <div class="image"><img src="assets/freecontrol.jpg"></div> <div class="comment"> <a href="https://genforce.github.io/freecontrol/" target="_blank"> Sicheng Mo, Fangzhou Mu, Kuan Heng Lin, Yanli Liu, Bochen Guan, Yin Li, Bolei Zhou. FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition. CVPR 2024.</a><br> <b>Comment:</b> Training-free conditional generation by guidance in diffusion U-Net subspaces for structure control and appearance regularization. </div> </div> <div class="citation"> <div class="image"><img src="assets/cross_image_attention.jpg"></div> <div class="comment"> <a href="https://garibida.github.io/cross-image-attention/" target="_blank"> Yuval Alaluf, Daniel Garibi, Or Patashnik, Hadar Averbuch-Elor, Daniel Cohen-Or. Cross-Image Attention for Zero-Shot Appearance Transfer. SIGGRAPH 2024.</a><br> <b>Comment:</b> Guidance-free appearance transfer to natural images with self-attention key + value swaps via cross-image correspondence. </div> </div> </div> <!-- === Reference Section Ends === --> </body> </html>