Instructions to use AbstractPhil/sd15-flow-lune-flux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AbstractPhil/sd15-flow-lune-flux with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AbstractPhil/sd15-flow-lune-flux", 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
Ctrl+K
- checkpoint-00001000
- checkpoint-00001250
- checkpoint-00002000
- checkpoint-00002500
- checkpoint-00005000
- checkpoint-00006000
- checkpoint-00006250
- checkpoint-00007000
- checkpoint-00007500
- checkpoint-00008000
- checkpoint-00009000
- checkpoint-00011000
- checkpoint-00012000
- checkpoint-00012500
- ffhq_low_t_portraits
- flux_pose_t200_500
- flux_pose_t200_600
- flux_t2_6_pose_t4_6_port_t1_4
- flux_t2_6_pose_t4_6_port_t1_9
- pose_controlnet_t600_900
- pose_controlnet_t700_900
- pose_controlnet_t700_900_nomask
- 7.56 kB
- 871 Bytes
- 24.6 MB xet
- 10.3 GB xet
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- 10.3 GB xet
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- 10.3 GB xet