Update README.md

README.md

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 ---
 license: creativeml-openrail-m
+base_model: CompVis/stable-diffusion-v1-4
+training_prompt: A man is surfing
+tags:
+- tune-a-video
+- text-to-video
+- diffusers
+inference: false
 ---
+
+# Tune-A-Video - Redshift
+
+## Model Description
+- Base model: [nitrosocke/redshift-diffusion](https://huggingface.co/nitrosocke/redshift-diffusion)
+- Training prompt: a man is skiing.
+![sample-train](samples/train.gif)
+
+## Samples
+
+![sample-500](samples/sample-500.gif)
+Test prompt: (redshift style) [spider man/black widow/batman/hulk] is skiing.
+
+## Usage
+Clone the [github repo](https://github.com/showlab/Tune-A-Video)
+```bash
+git clone https://github.com/showlab/Tune-A-Video.git
+```
+
+Run inference code
+
+```python
+from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
+from tuneavideo.models.unet import UNet3DConditionModel
+from tuneavideo.util import save_videos_grid
+import torch
+
+pretrained_model_path = "nitrosocke/redshift-diffusion"
+unet_model_path = "Tune-A-Video-library/redshift-man-skiing"
+unet = UNet3DConditionModel.from_pretrained(unet_model_path, subfolder='unet', torch_dtype=torch.float16).to('cuda')
+pipe = TuneAVideoPipeline.from_pretrained(pretrained_model_path, unet=unet, torch_dtype=torch.float16).to("cuda")
+pipe.enable_xformers_memory_efficient_attention()
+
+prompt = "(redshift style) spider man is skiing"
+video = pipe(prompt, video_length=8, height=512, width=512, num_inference_steps=50, guidance_scale=7.5).videos
+
+save_videos_grid(video, f"./{prompt}.gif")
+```
+
+## Related Papers:
+- [Tune-A-Video](https://arxiv.org/abs/2212.11565): One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
+- [Stable Diffusion](https://arxiv.org/abs/2112.10752): High-Resolution Image Synthesis with Latent Diffusion Models