Instructions to use hnpinq/Quantized_CL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Pruna AI
How to use hnpinq/Quantized_CL with Pruna AI:
from pruna import PrunaModel pip install -U diffusers transformers accelerate
from pruna import PrunaModel import torch # switch to "mps" for apple devices pipe = PrunaModel.from_pretrained("hnpinq/Quantized_CL", 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 backbone_quantized/qmodel.pt from hnpinq/Quantized_CL: direct link, hf CLI and curl.
- Browser
- Download file 13 GB
-
https://huggingface.co/hnpinq/Quantized_CL/resolve/main/backbone_quantized/qmodel.pt
- Command line
-
hf download hf://hnpinq/Quantized_CL/backbone_quantized/qmodel.pt
-
curl -L -o qmodel.pt https://huggingface.co/hnpinq/Quantized_CL/resolve/main/backbone_quantized/qmodel.pt
13 GB
- Xet hash:
- b991c6f9e703ea1e6609d263fabb94e7fa938623403e997b1c2bd0b882ddb2c4
- Size of remote file:
- 13 GB
- SHA256:
- de8818a0e1e73667a0d80036789d554239ec2eb43f33539f7a3e806b738ee201
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