update

README.md

@@ -22,7 +22,7 @@ tags:
 
 
 
-# KOALA-Lightning-1B Model Card
+# KOALA-Lightning-700M Model Card
 
 ### Summary 
 - Trained using a **self-attention-based knowledge distillation** method
@@ -44,10 +44,10 @@ As text-to-image (T2I) synthesis models increase in size, they demand higher inf
 <br>
 
 
-These 1024x1024 samples are generated by KOALA-700M with 25 denoising steps.
+These 1024x1024 samples are generated by KOALA-Lightning-700M with 10 denoising steps.
 
 <div align="center">
-  <img src="https://dl.dropboxusercontent.com/scl/fi/rjsqqgfney7be069y2yr7/teaser.png?rlkey=7lq0m90xpjcoqclzl4tieajpo&dl=1" width="1024px" />
+  <img src="https://dl.dropbox.com/scl/fi/fjpw93dbrl8xc8pwljclb/teaser_final.png?rlkey=6kf216quj6am8y20nduhenva2&dl=1" width="1024px" />
 </div>
 
 
@@ -112,7 +112,7 @@ The inference code with denoising step 25
 import torch
 from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
 
-pipe = StableDiffusionXLPipeline.from_pretrained("etri-vilab/koala-lightning-1b", torch_dtype=torch.float16)
+pipe = StableDiffusionXLPipeline.from_pretrained("etri-vilab/koala-lightning-700m", torch_dtype=torch.float16)
 pipe = pipe.to("cuda")
 
 # Ensure sampler uses "trailing" timesteps and "sample" prediction type.