Instructions to use rrw23/train8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rrw23/train8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("rrw23/train8") 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 checkpoint-3000/scheduler.bin from rrw23/train8: direct link, hf CLI and curl.
- Browser
- Download file 1 kB
-
https://huggingface.co/rrw23/train8/resolve/main/checkpoint-3000/scheduler.bin
- Command line
-
hf download hf://rrw23/train8/checkpoint-3000/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/rrw23/train8/resolve/main/checkpoint-3000/scheduler.bin
1 kB
- Xet hash:
- cfd7ef26486d120db7e90819ab5c1ff50bc1529866615cc59548a273d0860f58
- Size of remote file:
- 1 kB
- SHA256:
- 9e31a2548418a1ef5784752739e90341510cf150ab5e3aba4f3208c787d1d6bb
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