Feature Extraction
Transformers
Safetensors
Diffusers
English
qwen3
flux
text-encoder
pruning
distillation
Instructions to use SearchingMan/FLUX.2-klein-9B-Text-Encoder-Pruned-5.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SearchingMan/FLUX.2-klein-9B-Text-Encoder-Pruned-5.1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SearchingMan/FLUX.2-klein-9B-Text-Encoder-Pruned-5.1B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SearchingMan/FLUX.2-klein-9B-Text-Encoder-Pruned-5.1B") model = AutoModel.from_pretrained("SearchingMan/FLUX.2-klein-9B-Text-Encoder-Pruned-5.1B", device_map="auto") - Diffusers
How to use SearchingMan/FLUX.2-klein-9B-Text-Encoder-Pruned-5.1B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SearchingMan/FLUX.2-klein-9B-Text-Encoder-Pruned-5.1B", torch_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
Add support section to model card
Browse files
README.md
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@@ -236,3 +236,10 @@ This is a **modified version (Derivative) of the FLUX.2-klein-9B text encoder**
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Forest Labs, distributed under the FLUX Non-Commercial License (see `LICENSE.md` and
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`NOTICE`). This project is not affiliated with, endorsed, approved, or validated by Black
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Forest Labs. For commercial licensing of FLUX models see https://bfl.ai/licensing.
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Forest Labs, distributed under the FLUX Non-Commercial License (see `LICENSE.md` and
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`NOTICE`). This project is not affiliated with, endorsed, approved, or validated by Black
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Forest Labs. For commercial licensing of FLUX models see https://bfl.ai/licensing.
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## Support
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Models like this one are trained on my own hardware and my own cloud budget. If this work
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saves you VRAM, time or money, you can [buy me a coffee](https://ko-fi.com/michelangelofussion)
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or simply follow the next runs on [X](https://x.com/kgonia7) and like this model. Every bit
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funds the next experiment. Full write up: [kgonia.github.io](https://kgonia.github.io/projects/flux2-klein-text-encoder-pruned/)
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