Update README.md

README.md

@@ -7,13 +7,13 @@ license: openrail++
 inference: false
 ---
 
-# Latent Consistency Model (LCM): SDXL
+# Latent Consistency Model (LCM): SSD-1B
 
 Latent Consistency Model (LCM) was proposed in [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) 
 by *Simian Luo, Yiqin Tan et al.* and [Simian Luo](https://huggingface.co/SimianLuo), [Suraj Patil](https://huggingface.co/valhalla), and [Daniel Gu](https://huggingface.co/dg845)
 succesfully applied the same approach to create LCM for SDXL.
 
-This checkpoint is a LCM distilled version of [`stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) that allows
+This checkpoint is a LCM distilled version of [`segmind/SSD-1B`](https://huggingface.co/segmind/SSD-1B) that allows
 to reduce the number of inference steps to only between **2 - 8 steps**.
 
 
@@ -30,16 +30,15 @@ pip install --upgrade diffusers transformers accelerate peft
 
 ### Text-to-Image
 
-The model can be loaded with it's base pipeline `stabilityai/stable-diffusion-xl-base-1.0`. Next, the scheduler needs to be changed to [`LCMScheduler`](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler) and we can reduce the number of inference steps to just 2 to 8 steps.
-Please make sure to either disable `guidance_scale` or use values between 1.0 and 2.0.
+The model can be loaded with it's base pipeline `segmind/SSD-1B`. Next, the scheduler needs to be changed to [`LCMScheduler`](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler) and we can reduce the number of inference steps to just 2 to 8 steps.
 
 ```python
 from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler
 
-unet = UNet2DConditionModel.from_pretrained("latent-consistency/lcm-sdxl", torch_dtype=torch.float16, variant="fp16")
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16")
+unet = UNet2DConditionModel.from_pretrained("latent-consistency/lcm-ssd-1b", torch_dtype=torch.float16, variant="fp16")
+pipe = DiffusionPipeline.from_pretrained("segmind/SSD-1B", unet=unet, torch_dtype=torch.float16, variant="fp16")
 
-pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 pipe.to("cuda")
 
 prompt = "a red car standing on the side of the street"