Instructions to use kwagh20ite/FFHQ_3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kwagh20ite/FFHQ_3000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kwagh20ite/FFHQ_3000", 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 learned_embeds-step-2500.bin from kwagh20ite/FFHQ_3000: direct link, hf CLI and curl.
- Browser
- Download file 5.02 kB
-
https://huggingface.co/kwagh20ite/FFHQ_3000/resolve/main/learned_embeds-step-2500.bin
- Command line
-
hf download hf://kwagh20ite/FFHQ_3000/learned_embeds-step-2500.bin
-
curl -L -o learned_embeds-step-2500.bin https://huggingface.co/kwagh20ite/FFHQ_3000/resolve/main/learned_embeds-step-2500.bin
5.02 kB
- Xet hash:
- 4033a954f826cfa587332570d036442422e143cd45f9c027e76d7a58ddf72ade
- Size of remote file:
- 5.02 kB
- SHA256:
- b03ccaea366ca508e180633e4f61135b7e83b31bfea38e5ba6e6b279c841d81f
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