Instructions to use amd/FLUX.1-schnell_io32_amdgpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use amd/FLUX.1-schnell_io32_amdgpu with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("amd/FLUX.1-schnell_io32_amdgpu", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
ozhang commited on
Commit ·
3517911
1
Parent(s): 05377e6
Update unet model data name to standard
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.gitattributes
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vae_decoder/model.onnx.data filter=lfs diff=lfs merge=lfs -text
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vae_encoder/model.onnx.data filter=lfs diff=lfs merge=lfs -text
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transformer/971cb45e-0f5d-11f0-bf9e-f1e337651b5a filter=lfs diff=lfs merge=lfs -text
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vae_decoder/model.onnx.data filter=lfs diff=lfs merge=lfs -text
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vae_encoder/model.onnx.data filter=lfs diff=lfs merge=lfs -text
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transformer/971cb45e-0f5d-11f0-bf9e-f1e337651b5a filter=lfs diff=lfs merge=lfs -text
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*.onnx.data filter=lfs diff=lfs merge=lfs -text
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transformer/model.onnx
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:b01b63d9441942254886745eccb2387bbaa79992e81ed086067745316f76300b
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size 3681787
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transformer/{971cb45e-0f5d-11f0-bf9e-f1e337651b5a → model.onnx.data}
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