Instructions to use pennypacker/cinface-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pennypacker/cinface-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("pennypacker/cinface-lora") prompt = "CINFACE" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Rename config-2.yaml to config.yaml
Browse files
config-2.yaml → config.yaml
RENAMED
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@@ -5,7 +5,7 @@ config:
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- type: custom_sd_trainer
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training_folder: output
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device: cuda:0
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-
trigger_word:
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network:
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type: lora
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linear: 16
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- type: custom_sd_trainer
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training_folder: output
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device: cuda:0
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trigger_word: CINFACE
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network:
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type: lora
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linear: 16
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