Instructions to use Fer14/flux-coffee-machines with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Fer14/flux-coffee-machines 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("Fer14/flux-coffee-machines") prompt = "Black espresso coffee machine, rectangle shaped, with LED screen, built-in water filter, and 3 red buttons." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Upload folder using huggingface_hub
Browse files- README.md +3 -0
- config.yaml +3 -3
- lora.safetensors +1 -1
README.md
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@@ -21,6 +21,9 @@ Trained on Replicate using:
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https://replicate.com/ostris/flux-dev-lora-trainer/train
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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https://replicate.com/ostris/flux-dev-lora-trainer/train
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## Trigger words
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You should use `TOK` to trigger the image generation.
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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config.yaml
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linear_alpha: 16
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save:
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dtype: float16
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save_every:
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max_step_saves_to_keep: 1
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datasets:
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- folder_path: input_images
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- 1024
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train:
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batch_size: 1
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steps:
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gradient_accumulation_steps: 1
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train_unet: true
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train_text_encoder: false
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quantize: true
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sample:
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sampler: flowmatch
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sample_every:
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width: 1024
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height: 1024
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prompts: []
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linear_alpha: 16
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save:
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dtype: float16
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save_every: 4001
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max_step_saves_to_keep: 1
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datasets:
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- folder_path: input_images
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- 1024
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train:
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batch_size: 1
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steps: 4000
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gradient_accumulation_steps: 1
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train_unet: true
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train_text_encoder: false
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quantize: true
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sample:
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sampler: flowmatch
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sample_every: 4001
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width: 1024
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height: 1024
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prompts: []
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lora.safetensors
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version https://git-lfs.github.com/spec/v1
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size 171969408
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version https://git-lfs.github.com/spec/v1
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size 171969408
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