Instructions to use rdcoder/del_flt3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rdcoder/del_flt3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rdcoder/del_flt3", 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
Download text_encoder/pytorch_model.bin from rdcoder/del_flt3: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/rdcoder/del_flt3/resolve/ebcd3b926bcac814dd3a60d10a78c5c2eb2fdc8a/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://rdcoder/del_flt3@ebcd3b926bcac814dd3a60d10a78c5c2eb2fdc8a/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rdcoder/del_flt3/resolve/ebcd3b926bcac814dd3a60d10a78c5c2eb2fdc8a/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- ce05171e279222a7360c262e1286251c8076f856e3f9d4227e9964370c1d45da
- Size of remote file:
- 492 MB
- SHA256:
- f9d798d2304eb716357edba020591f2e264096b08d038a4535df19d020bf28ce
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