Create README.md

README.md

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+---
+license: mit
+language:
+- en
+pipeline_tag: text-to-image
+tags:
+- text-to-image
+---
+
+# Latent Consistency Models
+
+Official Repository of the paper: *[Latent Consistency Models](https://arxiv.org/abs/2310.04378)*.
+
+Project Page: https://latent-consistency-models.github.io
+
+## Model Descriptions:
+Copied from [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) to experiment with quantization.
+Originally distilled from [Dreamshaper v7](https://huggingface.co/Lykon/dreamshaper-7) fine-tune of [Stable-Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with only 4,000 training iterations (~32 A100 GPU Hours). 
+
+## Usage
+
+To run the model yourself, you can leverage the 🧨 Diffusers library:
+1. Install the library:
+```
+pip install --upgrade diffusers  # make sure to use at least diffusers >= 0.22
+pip install transformers accelerate
+```
+
+2. Run the model:
+```py
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained("TobDeBer/lcm_dream7")
+
+# To save GPU memory, torch.float16 can be used, but it may compromise image quality.
+pipe.to(torch_device="cuda", torch_dtype=torch.float32)
+
+prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+
+# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
+num_inference_steps = 4 
+
+images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type="pil").images
+```
+
+For more information, please have a look at the official docs:
+👉 https://huggingface.co/docs/diffusers/api/pipelines/latent_consistency_models#latent-consistency-models