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/config.json from hnpinq/Quantized_CL: direct link, hf CLI and curl.
- Browser
- Download file 469 Bytes
-
https://huggingface.co/hnpinq/Quantized_CL/resolve/main/backbone_quantized/config.json
- Command line
-
hf download hf://hnpinq/Quantized_CL/backbone_quantized/config.json
-
curl -L -o config.json https://huggingface.co/hnpinq/Quantized_CL/resolve/main/backbone_quantized/config.json
469 Bytes
| { | |
| "_class_name": "FluxTransformer2DModel", | |
| "_diffusers_version": "0.33.1", | |
| "_name_or_path": "/content/drive/MyDrive/Duy/Clothe/flux_models/transformer", | |
| "attention_head_dim": 128, | |
| "axes_dims_rope": [ | |
| 16, | |
| 56, | |
| 56 | |
| ], | |
| "guidance_embeds": true, | |
| "in_channels": 64, | |
| "joint_attention_dim": 4096, | |
| "num_attention_heads": 24, | |
| "num_layers": 19, | |
| "num_single_layers": 38, | |
| "out_channels": null, | |
| "patch_size": 1, | |
| "pooled_projection_dim": 768 | |
| } | |