Instructions to use xgemstarx/subset_128_step_2048_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xgemstarx/subset_128_step_2048_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_128_step_2048_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_128_step_2048_model: direct link, hf CLI and curl.
- Browser
- Download file 79.2 MB
-
https://huggingface.co/xgemstarx/subset_128_step_2048_model/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://xgemstarx/subset_128_step_2048_model/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/xgemstarx/subset_128_step_2048_model/resolve/main/checkpoint-2000/optimizer.bin
79.2 MB
- Xet hash:
- 3efce29d3440e4f0d24f74fcbd0ed58ed522973d557c335a474ba3d3bc5a1391
- Size of remote file:
- 79.2 MB
- SHA256:
- 1c66d65f89614869a14b53afa28b171e09785d84b86f5c3443a238b890f38638
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