Adds README.md

README.md

@@ -1,3 +1,69 @@
 ---
 license: creativeml-openrail-m
 ---
+
+---
+license: creativeml-openrail-m
+base_model: nitrosocke/mo-di-diffusion
+training_prompt: A bear is playing guitar.
+tags:
+- tune-a-video
+- text-to-video
+- diffusers
+inference: false
+---
+
+# Tune-A-Video - Modern Disney
+
+## Model Description
+This is a diffusers compatible checkpoint. When used with DiffusionPipeline, returns an instance of TuneAVideoPipeline
+
+>df-cpt is used to indicate that its a diffusers compatible equivalent of Tune-A-Video-library/mo-di-bear-guitar .
+
+- Base model: [nitrosocke/mo-di-diffusion](https://huggingface.co/nitrosocke/mo-di-diffusion)
+- Training prompt: a bear is playing guitar.
+![sample-train](samples/train.gif)
+
+## Samples
+
+![sample-500](samples/sample-500.gif)
+Test prompt: a [handsome prince/magical princess/rabbit/baby] is playing guitar, modern disney style.
+
+## Usage
+
+```python
+import torch
+from diffusers import DiffusionPipeline, DDIMScheduler
+from diffusers.utils import export_to_video
+from PIL import Image
+
+
+pretrained_model_path = "nitrosocke/mo-di-diffusion"
+
+pipe = TuneAVideoPipeline.from_pretrained(
+    "Tune-A-Video-library/df-cpt-mo-di-bear-guitar", torch_dtype=torch.float16
+).to("cuda")
+
+prompt = "A princess playing a guitar, modern disney style"
+generator = torch.Generator(device="cuda").manual_seed(42)
+
+video_frames = pipe(prompt, video_length=3, generator=generator, num_inference_steps=50, output_type="np").frames
+
+# Saving to gif.
+pil_frames = [Image.fromarray(frame) for frame in video_frames]
+duration = len(pil_frames) / 8
+pil_frames[0].save(
+    "animation.gif",
+    save_all=True,
+    append_images=pil_frames[1:],  # append rest of the images
+    duration=duration * 1000,  # in milliseconds
+    loop=0,
+)
+
+# Saving to video
+video_path = export_to_video(video_frames)
+```
+
+## 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