Instructions to use fofr/flux-jwst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fofr/flux-jwst with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("fofr/flux-jwst") prompt = "a beautiful JWST landscape astrophotography photo of snowy mountains, distant village, with a beautiful nebula" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Add generated example
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README.md
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distant village, with a beautiful nebula
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output:
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url: images/example_sg9jpkstq.png
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---
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distant village, with a beautiful nebula
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output:
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url: images/example_sg9jpkstq.png
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- text: >-
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a dynamic and beautiful JWST landscape astrophotography photo of snowy
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mountains, distant village, with a beautiful nebula
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output:
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url: images/example_714d369g1.png
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images/example_714d369g1.png
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