Instructions to use Chunte/huggy-style-v5-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Chunte/huggy-style-v5-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("Chunte/huggy-style-v5-lora") prompt = "hggycrcl" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
End of training
Browse files- README.md +72 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-1250/optimizer.bin +3 -0
- checkpoint-1250/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1250/random_states_0.pkl +3 -0
- checkpoint-1250/scheduler.bin +3 -0
- checkpoint-1500/optimizer.bin +3 -0
- checkpoint-1500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1500/random_states_0.pkl +3 -0
- checkpoint-1500/scheduler.bin +3 -0
- checkpoint-1750/optimizer.bin +3 -0
- checkpoint-1750/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1750/random_states_0.pkl +3 -0
- checkpoint-1750/scheduler.bin +3 -0
- checkpoint-2000/optimizer.bin +3 -0
- checkpoint-2000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-2000/random_states_0.pkl +3 -0
- checkpoint-2000/scheduler.bin +3 -0
- checkpoint-2250/optimizer.bin +3 -0
- checkpoint-2250/pytorch_lora_weights.safetensors +3 -0
- checkpoint-2250/random_states_0.pkl +3 -0
- checkpoint-2250/scheduler.bin +3 -0
- checkpoint-250/optimizer.bin +3 -0
- checkpoint-250/pytorch_lora_weights.safetensors +3 -0
- checkpoint-250/random_states_0.pkl +3 -0
- checkpoint-250/scheduler.bin +3 -0
- checkpoint-2500/optimizer.bin +3 -0
- checkpoint-2500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-2500/random_states_0.pkl +3 -0
- checkpoint-2500/scheduler.bin +3 -0
- checkpoint-2750/optimizer.bin +3 -0
- checkpoint-2750/pytorch_lora_weights.safetensors +3 -0
- checkpoint-2750/random_states_0.pkl +3 -0
- checkpoint-2750/scheduler.bin +3 -0
- checkpoint-500/optimizer.bin +3 -0
- checkpoint-500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scheduler.bin +3 -0
- checkpoint-750/optimizer.bin +3 -0
- checkpoint-750/pytorch_lora_weights.safetensors +3 -0
- checkpoint-750/random_states_0.pkl +3 -0
- checkpoint-750/scheduler.bin +3 -0
- pytorch_lora_weights.safetensors +3 -0
README.md
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---
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base_model: black-forest-labs/FLUX.1-dev
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library_name: diffusers
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license: other
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instance_prompt: hggycrcl
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widget: []
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tags:
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- text-to-image
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- diffusers-training
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- diffusers
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- lora
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- flux
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- flux-diffusers
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- template:sd-lora
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Flux DreamBooth LoRA - Chunte/huggy-style-v5-lora
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<Gallery />
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## Model description
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These are Chunte/huggy-style-v5-lora DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
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The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
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Was LoRA for the text encoder enabled? False.
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## Trigger words
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You should use `hggycrcl` to trigger the image generation.
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## Download model
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[Download the *.safetensors LoRA](Chunte/huggy-style-v5-lora/tree/main) in the Files & versions tab.
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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```py
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from diffusers import AutoPipelineForText2Image
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import torch
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pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
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pipeline.load_lora_weights('Chunte/huggy-style-v5-lora', weight_name='pytorch_lora_weights.safetensors')
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image = pipeline('hggycrcl').images[0]
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```
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For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
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## License
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Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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checkpoint-2500/scheduler.bin
ADDED
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ADDED
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checkpoint-2750/random_states_0.pkl
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checkpoint-2750/scheduler.bin
ADDED
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checkpoint-500/optimizer.bin
ADDED
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checkpoint-500/pytorch_lora_weights.safetensors
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checkpoint-500/random_states_0.pkl
ADDED
|
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checkpoint-500/scheduler.bin
ADDED
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checkpoint-750/optimizer.bin
ADDED
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checkpoint-750/pytorch_lora_weights.safetensors
ADDED
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checkpoint-750/random_states_0.pkl
ADDED
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checkpoint-750/scheduler.bin
ADDED
|
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pytorch_lora_weights.safetensors
ADDED
|
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|
|
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