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 model_index.json from hnpinq/Quantized_CL: direct link, hf CLI and curl.
- Browser
- Download file 649 Bytes
-
https://huggingface.co/hnpinq/Quantized_CL/resolve/main/model_index.json
- Command line
-
hf download hf://hnpinq/Quantized_CL/model_index.json
-
curl -L -o model_index.json https://huggingface.co/hnpinq/Quantized_CL/resolve/main/model_index.json
649 Bytes
| { | |
| "_class_name": "FluxTryonPipeline", | |
| "_diffusers_version": "0.33.1", | |
| "_name_or_path": "/content/drive/MyDrive/Duy/Clothe/flux_models", | |
| "feature_extractor": [ | |
| null, | |
| null | |
| ], | |
| "image_encoder": [ | |
| null, | |
| null | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "text_encoder_2": [ | |
| null, | |
| null | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "tokenizer_2": [ | |
| "transformers", | |
| "T5TokenizerFast" | |
| ], | |
| "transformer": [ | |
| null, | |
| null | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |