Instructions to use hanlincs/FLUX_ControlNet_Outputs_May3_256res_drop_prob_00 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hanlincs/FLUX_ControlNet_Outputs_May3_256res_drop_prob_00 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hanlincs/FLUX_ControlNet_Outputs_May3_256res_drop_prob_00", 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 optimizer.bin from hanlincs/FLUX_ControlNet_Outputs_May3_256res_drop_prob_00: direct link, hf CLI and curl.
- Browser
- Download file 13.2 GB
-
https://huggingface.co/hanlincs/FLUX_ControlNet_Outputs_May3_256res_drop_prob_00/resolve/main/optimizer.bin
- Command line
-
hf download hf://hanlincs/FLUX_ControlNet_Outputs_May3_256res_drop_prob_00/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/hanlincs/FLUX_ControlNet_Outputs_May3_256res_drop_prob_00/resolve/main/optimizer.bin
13.2 GB
- Xet hash:
- f3f0e04599a24e55cf32bd92d7b64f9f8811e7ce1e46f1af159abef925cce409
- Size of remote file:
- 13.2 GB
- SHA256:
- abf75284d1bfa972d10241782db7b7b66d9b43f729655bbe42780c501bf80ef0
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