Text-to-Image
Diffusers
Safetensors
FluxPipeline
FLUX
FLUX-diffusers
diffusers-training
textual_inversion
Instructions to use rangwani-harsh/logs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rangwani-harsh/logs 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_textual_inversion("rangwani-harsh/logs") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Textual inversion text2image fine-tuning - rangwani-harsh/logs
These are textual inversion adaption weights for black-forest-labs/FLUX.1-dev. You can find some example images in the following.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
- Downloads last month
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Model tree for rangwani-harsh/logs
Base model
black-forest-labs/FLUX.1-dev


