Instructions to use fal/Emu3.5-Image-FlashPack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/Emu3.5-Image-FlashPack with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fal/Emu3.5-Image-FlashPack", 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
Download vision_tokenizer/model.flashpack from fal/Emu3.5-Image-FlashPack: direct link, hf CLI and curl.
- Browser
- Download file 911 MB
-
https://huggingface.co/fal/Emu3.5-Image-FlashPack/resolve/main/vision_tokenizer/model.flashpack
- Command line
-
hf download hf://fal/Emu3.5-Image-FlashPack/vision_tokenizer/model.flashpack
-
curl -L -o model.flashpack https://huggingface.co/fal/Emu3.5-Image-FlashPack/resolve/main/vision_tokenizer/model.flashpack
911 MB
- Xet hash:
- 1e335ebb3c7d1bbd4d698ae670bef54f74e450af2ceb429bd5ea5cece0e2e4a9
- Size of remote file:
- 911 MB
- SHA256:
- 0f977308d18d077f7d6c381319ea9975cd02bc470643a57b24074f566c97a0f7
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