Instructions to use VHKE/intgra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VHKE/intgra 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("VHKE/intgra") prompt = "INTGRA on a stand --d 45" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/intgra_000500_01_20250305075757_45.png
text: INTGRA on a stand --d 45
- output:
url: sample/intgra_001000_01_20250305080753_45.png
text: INTGRA on a table --d 45
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: INTGRA
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
INTGRA
A Flux LoRA trained on a local computer with Fluxgym

- Prompt
- INTGRA on a stand --d 45

- Prompt
- INTGRA on a table --d 45
Trigger words
You should use INTGRA to trigger the image generation.
Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.