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---
base_model:
- rain1011/pyramid-flow-sd3
pipeline_tag: text-to-video
library_name: diffusers
---
Converted to bfloat16 from [rain1011/pyramid-flow-sd3](https://huggingface.co/rain1011/pyramid-flow-sd3). Use the text encoders and tokenizers from that repo (or from SD3), no point reuploading them over and over unchanged.
Inference code is available here: [github.com/jy0205/Pyramid-Flow](https://github.com/jy0205/Pyramid-Flow/tree/main).
Both 384p and 768p work on 24 GB VRAM. For 16 steps (5 second video), 384p takes a little over a minute on a 3090, and 768p takes about 7 minutes. For 31 steps (10 second video), 384p took about 10 minutes.
In `diffusion_schedulers/scheduling_flow_matching.py`, in the function `init_sigmas_for_each_stage`, one small change needs to be made:
Change this line:
```
self.timesteps_per_stage[i_s] = torch.from_numpy(timesteps[:-1])
```
To this:
```
self.timesteps_per_stage[i_s] = timesteps[:-1]
```
This will allow the model to be compatible with newer versions of pytorch and other libraries than is shown in the requirements.
Working with torch2.4.1+cu124. |