Instructions to use viraj9837/general_prompts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use viraj9837/general_prompts 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("viraj9837/general_prompts") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
control-lora-viraj9837/general_prompts
These are Control LoRA weights trained on black-forest-labs/FLUX.1-dev with new type of conditioning. You can find some example images below.
prompt: Mark plant placements on a 2D floorplan using colored circles. Use red, blue, and orange in triangular groups of three. Avoid red-blue and orange-blue pairings, straight-line patterns, or sequentially escalating sizes in rows.

License
Please adhere to the licensing terms as described here
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]
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Model tree for viraj9837/general_prompts
Base model
black-forest-labs/FLUX.1-dev