End of training
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- README.md +62 -0
- checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-10000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-10000/random_states_0.pkl +3 -0
- checkpoint-11000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-11000/random_states_0.pkl +3 -0
- checkpoint-12000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-12000/random_states_0.pkl +3 -0
- checkpoint-13000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-13000/random_states_0.pkl +3 -0
- checkpoint-14000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-14000/random_states_0.pkl +3 -0
- checkpoint-15000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-15000/random_states_0.pkl +3 -0
- checkpoint-16000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-16000/random_states_0.pkl +3 -0
- checkpoint-17000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-17000/random_states_0.pkl +3 -0
- checkpoint-18000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-18000/random_states_0.pkl +3 -0
- checkpoint-19000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-19000/random_states_0.pkl +3 -0
- checkpoint-2000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-2000/random_states_0.pkl +3 -0
- checkpoint-20000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-20000/random_states_0.pkl +3 -0
- checkpoint-3000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-3000/random_states_0.pkl +3 -0
- checkpoint-4000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-4000/random_states_0.pkl +3 -0
- checkpoint-5000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-5000/random_states_0.pkl +3 -0
- checkpoint-6000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-6000/random_states_0.pkl +3 -0
- checkpoint-7000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-7000/random_states_0.pkl +3 -0
- checkpoint-8000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-8000/random_states_0.pkl +3 -0
- checkpoint-9000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-9000/random_states_0.pkl +3 -0
- image_control.png +0 -0
- images_0.png +0 -0
- images_1.png +0 -0
- images_2.png +0 -0
- images_3.png +0 -0
- images_4.png +0 -0
- images_5.png +0 -0
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- images_7.png +0 -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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inference: true
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tags:
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- flux
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- flux-diffusers
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- text-to-image
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- diffusers
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- control-lora
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- diffusers-training
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- 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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# control-lora-IsraelBenDavid/orientation-control-lora-only-y
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These are Control LoRA weights trained on black-forest-labs/FLUX.1-dev with new type of conditioning.
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You can find some example images below.
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prompt: A car with a solid background
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prompt: A car with a solid background
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prompt: A car with a solid background
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prompt: A car with a solid background
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prompt: A credit card with a solid background
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prompt: A remote control with a solid background
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prompt: A bed with a solid background
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prompt: A television with a solid background
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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-1000/pytorch_lora_weights.safetensors
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