Instructions to use brianling16/logo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use brianling16/logo-lora 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("brianling16/logo-lora") prompt = "unconditional (blank prompt)" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Trained for 0 epochs and 2600 steps.
Browse filesTrained with datasets ['text-embed-cache', 'app_data']
Learning rate 8e-05, batch size 1, and 1 gradient accumulation steps.
Used DDPM noise scheduler for training with epsilon prediction type and rescaled_betas_zero_snr=False
Using 'trailing' timestep spacing.
Base model: black-forest-labs/FLUX.1-dev
VAE: None
pytorch_lora_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:4c0efe0e5bf3089e8328b7697f8cd5f77aca38ef3c5b26efb09ecdafaff99490
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size 209263048
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