Update README.md

README.md

@@ -2,61 +2,19 @@
 library_name: diffusers
 ---
 
-# 👋 HyVideo
+This is a development model meant to help test the HunyuanVideoPipeline integration to diffusers. Please help out if you can on the [PR](https://github.com/huggingface/diffusers/pull/10136).
 
-This project is a first step in integrating [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) into [Diffusers](https://github.com/huggingface/diffusers).
-
-**All credit go to [Tencent](https://github.com/Tencent) for the original [HunyuanVideo](https://github.com/Tencent/HunyuanVideo) project.**
-
-**Thank you to Huggingface for the [Diffusers](https://github.com/huggingface/diffusers) library.** Special shout-out to [@a-r-r-o-w](https://github.com/a-r-r-o-w) for his work on integrating HunyuanVideo.
-
-The License is inherted from [HunyuanVideo](https://github.com/Tencent/HunyuanVideo).
-
-This library is provided as-is and will be superseded by the official release of HunyuanVideo via [Diffusers](https://github.com/huggingface/diffusers). Please help out if you can on the [PR](https://github.com/huggingface/diffusers/pull/10136).
-
-
-## Installation
 
 ```bash
-pip install git+https://github.com/ollanoinc/hyvideo.git
+pip install -qq git+https://github.com/huggingface/diffusers.git@hunyuan-video
 ```
 
-You will also need to install [flash-attn](https://github.com/Dao-AILab/flash-attention) for now.
-
-## Usage
-
-Please note that you need at least 80GB VRAM to run this pipeline. CPU offloading is having issues at the moment (PRs welcome!).
-
 ```python
 import torch
-from hyvideo.diffusion.pipelines.pipeline_hunyuan_video import HunyuanVideoPipeline
-from hyvideo.modules.models import HYVideoDiffusionTransformer
-from hyvideo.vae.autoencoder_kl_causal_3d import AutoencoderKLCausal3D
-
 pipe = HunyuanVideoPipeline.from_pretrained(
-    'magespace/hyvideo-diffusers',
-    transformer=HYVideoDiffusionTransformer.from_pretrained(
-        'magespace/hyvideo-diffusers',
-        torch_dtype=torch.bfloat16,
-        subfolder='transformer'
-    ),
-    vae=AutoencoderKLCausal3D.from_pretrained(
-        'magespace/hyvideo-diffusers',
-        torch_dtype=torch.bfloat16,
-        subfolder='vae'
-    ),
+    "magespace/hyvideo-diffusers-dev",
     torch_dtype=torch.bfloat16,
 )
-pipe = pipe.to('cuda')
-pipe.vae.enable_tiling()
-```
-
-Then running:
-
-```python
-prompt = "Close-up, A little girl wearing a red hoodie in winter strikes a match. The sky is dark, there is a layer of snow on the ground, and it is still snowing lightly. The flame of the match flickers, illuminating the girl's face intermittently."
-
-result = pipe(prompt)
 ```
 
 Post-processing:
@@ -65,11 +23,7 @@ Post-processing:
 import PIL.Image
 from diffusers.utils import export_to_video
 
-output = result.videos[0].permute(1, 2, 3, 0).detach().cpu().numpy()
-output = (output * 255).clip(0, 255).astype("uint8")
-output = [PIL.Image.fromarray(x) for x in output]
-
-export_to_video(output, "output.mp4", fps=24)
+export_to_video(result.frames[0], "output.mp4", fps=24)
 ```
 
 For faster generation, you can optimize the `transformer` with `torch.compile`. Additionally, increasing `shift` in the scheduler can allow for lower step values as shown in the original paper.