Instructions to use Srinivaskolla/Geospatial-Lidar-Flux-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Srinivaskolla/Geospatial-Lidar-Flux-V1 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("Srinivaskolla/Geospatial-Lidar-Flux-V1") prompt = "in the style of GS-LIDAR, a top-down nadir view of a busy city intersection at night. Discrete glowing cyan and orange particles define the cars and buildings on a pure black background. High spatial resolution point cloud." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
GS-LIDAR: Geospatial Lidar Point Cloud

- Prompt
- in the style of GS-LIDAR, a top-down nadir view of a busy city intersection at night. Discrete glowing cyan and orange particles define the cars and buildings on a pure black background. High spatial resolution point cloud.

- Prompt
- in the style of GS-LIDAR, a side profile portrait of a person's face. The features are rendered as millions of tiny, disconnected white dots. Geometric laser returns, digital twin aesthetic, dark void background.

- Prompt
- in the style of GS-LIDAR, a classic vintage car visualized as a 3D Lidar scan. The metallic body is a dense yellow point cluster, while the glass windows are sparse and translucent. Intensity-based color mapping, sharp XYZ coordinate visualization.
Model description
Trigger words
You should use in the style of GS-LIDAR to trigger the image generation.
Download model
Download them in the Files & versions tab.
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Model tree for Srinivaskolla/Geospatial-Lidar-Flux-V1
Base model
black-forest-labs/FLUX.1-dev