Instructions to use Keelima/PSA_ZB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Keelima/PSA_ZB 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("Keelima/PSA_ZB") prompt = "ZssBecker" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from Keelima/PSA_ZB: direct link, hf CLI and curl.
- Browser
- Download file 711 Bytes
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https://huggingface.co/Keelima/PSA_ZB/resolve/main/README.md
- Command line
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hf download hf://Keelima/PSA_ZB/README.md
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curl -L -o README.md https://huggingface.co/Keelima/PSA_ZB/resolve/main/README.md
711 Bytes
metadata
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: ZssBecker
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
PSA_ZB
Model description
Trigger words
You should use ZssBecker to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Training at fal.ai
Training was done using fal.ai/models/fal-ai/flux-lora-fast-training.