Instructions to use rootlocalghost/LongCat-Image-Edit-Turbo-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rootlocalghost/LongCat-Image-Edit-Turbo-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="rootlocalghost/LongCat-Image-Edit-Turbo-FP8")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rootlocalghost/LongCat-Image-Edit-Turbo-FP8", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 508 Bytes
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"_class_name": "LongCatImageEditPipeline",
"_diffusers_version": "0.35.1",
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"Qwen2_5_VLForConditionalGeneration"
],
"tokenizer": [
"transformers",
"Qwen2Tokenizer"
],
"text_processor": [
"transformers",
"Qwen2VLProcessor"
],
"transformer": [
"diffusers",
"LongCatImageTransformer2DModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
} |