Create README.md

README.md

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+---
+library_name: diffusers
+base_model: segmind/SSD-1B
+tags:
+- lora
+- text-to-image
+license: openrail++
+inference: false
+---
+
+# Latent Consistency Model (LCM) LoRA: SSD-Tiny
+
+Latent Consistency Model (LCM) LoRA was proposed in [LCM-LoRA: A universal Stable-Diffusion Acceleration Module](https://arxiv.org/abs/2311.05556) 
+by *Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al.*
+
+It is a distilled consistency adapter for [`segmind/SSD-Tiny`]("https://huggingface.co/segmind/SSD-1B") that allows
+to reduce the number of inference steps to only between **2 - 8 steps**.
+
+| Model                                                                      | Params / M | 
+|----------------------------------------------------------------------------|------------|
+| [lcm-lora-sdv1-5](https://huggingface.co/latent-consistency/lcm-lora-sdv1-5)   | 67.5       |
+| [**lcm-lora-ssd-tiny**](https://huggingface.co/segmind/lcm-lora-ssd-tiny)   | **62.7**        |
+| [lcm-lora-sdxl](https://huggingface.co/latent-consistency/lcm-lora-sdxl) | 197  |
+
+## Usage
+
+LCM-LoRA is supported in 🤗 Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first 
+install the latest version of the Diffusers library as well as `peft`, `accelerate` and `transformers`.
+audio dataset from the Hugging Face Hub:
+
+```bash
+pip install --upgrade pip
+pip install --upgrade diffusers transformers accelerate peft
+```
+
+### Text-to-Image
+
+Let's load the base model `segmind/SSD-Tiny` first. 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.
+
+```python
+import torch
+from diffusers import LCMScheduler, AutoPipelineForText2Image
+
+model_id = "segmind/SSD-Tiny"
+adapter_id = "segmind/lcm-lora-ssd-tiny"
+
+pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+pipe.to("cuda")
+
+# load and fuse lcm lora
+pipe.load_lora_weights(adapter_id)
+pipe.fuse_lora()
+
+
+prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+
+# disable guidance_scale by passing 0
+image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0]
+```
+
+![SSD-Tiny LCM LoRA Image]()