Instructions to use jaruce/output_lr1e4_accstep1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jaruce/output_lr1e4_accstep1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jaruce/output_lr1e4_accstep1", 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
Download checkpoint-2000/optimizer.bin from jaruce/output_lr1e4_accstep1: direct link, hf CLI and curl.
- Browser
- Download file 6.88 GB
-
https://huggingface.co/jaruce/output_lr1e4_accstep1/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://jaruce/output_lr1e4_accstep1/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/jaruce/output_lr1e4_accstep1/resolve/main/checkpoint-2000/optimizer.bin
6.88 GB
- Xet hash:
- 23a606c048ad0f944532a8c00cd59b39364ff75d348c756cce9a1e83b39590a7
- Size of remote file:
- 6.88 GB
- SHA256:
- b28d5e5f9f950dbd1ee50036bfc1aa992851a10b051c440d06c1fe5e8bad9d69
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