Instructions to use SeanScripts/pyramid-flow-sd3-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SeanScripts/pyramid-flow-sd3-bf16 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SeanScripts/pyramid-flow-sd3-bf16", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 754 Bytes
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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.
I highly recommend using `cpu_offloading=True` when generating, unless you have more than 24 GB VRAM. |