Instructions to use fal/Z-Image-Turbo-Control-FlashPack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/Z-Image-Turbo-Control-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/Z-Image-Turbo-Control-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
Upload ZImageControlPipeline
Browse files- .gitattributes +1 -0
- transformer/model.flashpack +3 -0
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text_encoder/model.flashpack filter=lfs diff=lfs merge=lfs -text
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tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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vae/model.flashpack filter=lfs diff=lfs merge=lfs -text
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text_encoder/model.flashpack filter=lfs diff=lfs merge=lfs -text
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tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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vae/model.flashpack filter=lfs diff=lfs merge=lfs -text
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transformer/model.flashpack filter=lfs diff=lfs merge=lfs -text
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transformer/model.flashpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:ce07e500baadb480bdc4319403fbe6589d7db3c972770d1ad46dec37d61b89a7
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size 15411439436
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