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@@ -49,7 +49,7 @@
     <a href="#" target="_blank">[Code (coming soon!)]</a>
   </div>
   <div class="teaser">
-    <img src="assets/teaser.jpg" width="85%">
+    <img src="assets/ctrl-x.jpg" width="85%">
   </div>
 </div>
 <!-- === Home Section Ends === -->
@@ -60,22 +60,6 @@
   <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.
-    <!-- Recent controllable generation approaches such as FreeControl and Diffusion
-    Self-guidance bring fine-grained spatial and appearance control to text-to-
-    image (T2I) diffusion models without training auxiliary modules. However, these
-    methods optimize the latent embedding for each type of score function with longer
-    diffusion steps, making the generation process time-consuming and limiting their
-    flexibility and use. This work presents Ctrl-X, a simple framework for T2I diffusion
-    controlling structure and appearance without additional training or guidance. Ctrl-X
-    designs feed-forward structure control to enable the structure alignment with a
-    structure image and semantic-aware appearance transfer to facilitate the appearance
-    transfer from a user-input image. Extensive qualitative and quantitative experiments
-    illustrate the superior performance of Ctrl-X on various condition inputs and model
-    checkpoints. In particular, Ctrl-X supports novel structure and appearance control
-    with arbitrary condition images of any modality, exhibits superior image quality and
-    appearance transfer compared to existing works, 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>