Instructions to use xgemstarx/subset_1024_step_16384_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xgemstarx/subset_1024_step_16384_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("xgemstarx/subset_1024_step_16384_model") prompt = "a photo of xjiminx" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-2000/optimizer.bin from xgemstarx/subset_1024_step_16384_model: direct link, hf CLI and curl.
- Browser
- Download file 79.2 MB
-
https://huggingface.co/xgemstarx/subset_1024_step_16384_model/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://xgemstarx/subset_1024_step_16384_model/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/xgemstarx/subset_1024_step_16384_model/resolve/main/checkpoint-2000/optimizer.bin
79.2 MB
- Xet hash:
- cb64c012cc52bc3cabe5196ca0e249a974673fc1106a4f43fc51c9215580a591
- Size of remote file:
- 79.2 MB
- SHA256:
- 6075f08f3605f899bccaf44461d4dbc39348a688c7e2c2d370201b27f5255e58
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