Update README.md

README.md

@@ -4,6 +4,7 @@ language:
 - en
 library_name: diffusers
 pipeline_tag: text-to-image
+base_model: stabilityai/stable-diffusion-xl-base-1.0
 ---
 
 # Target-Driven Distillation
@@ -15,12 +16,12 @@ pipeline_tag: text-to-image
 
 </div>
 
-## Introduction
-
 Target-Driven Distillation: Consistency Distillation with Target Timestep Selection and Decoupled Guidance
 
-<div  align="center">
-<img src='teaser.jpg'>
+<div align="center">
+  <img src="assets/teaser.jpg" alt="teaser" style="zoom:80%;" />
+
+  Samples generated by TDD-distilled SDXL, with only 4--8 steps.
 </div>
 
 ## Update
@@ -67,4 +68,34 @@ image = pipe(
 ).images[0]
 
 image.save("tdd.png")
-```
+```
+
+## Introduction
+
+Target-Driven Distillation (TDD) features three key designs, that differ from previous consistency distillation methods.
+1. **TDD adopts a delicate selection strategy of target timesteps, increasing the training efficiency.** Specifically, it first chooses from a predefined set of equidistant denoising schedules (*e.g.* 4--8 steps), then adds a stochatic offset to accomodate non-deterministic sampling (*e.g.* $\gamma$-sampling).
+2. **TDD utilizes decoupled guidances during training, making itself open to post-tuning on guidance scale during inference periods.** Specifically, it replaces a portion of the text conditions with unconditional (*i.e.* empty) prompts, in order to align with the standard training process using CFG.
+3. **TDD can be optionally equipped with non-equidistant sampling and x0 clipping, enabling a more flexible and accurate way for image sampling.**
+
+<div align="center">
+  <img src="assets/tdd_overview.jpg" alt="overview"/>
+
+  An overview of TDD. (a) The training process features target timestep selection and decoupled guidance. (b) The inference process can optionally adopt non-equidistant denoising schedules.
+</div>
+
+<div align="center">
+  <img src="assets/compare.png" alt="comparison" style="zoom:80%;" />
+
+  Samples generated by SDXL models distilled by mainstream consistency distillation methods LCM, PCM, TCD, and our TDD, from the same seeds. Our method demonstrates advantages in both image complexity and clarity.
+</div>
+
+<div align="center">
+  <img src="assets/other_1.jpg" alt="other"/>
+
+  Samples generated by TDD-distilled different base models, and by SDXL with different LoRA adapters or ControlNets.  
+</div>
+
+
+<div align="center">
+  Video samples generated by AnimateLCM-distilled (top) and TDD-distilled (bottom) SVD-xt 1.1, also with 4--8 steps.
+</div>