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IsraelBenDavid
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orientation-control-lora-only-x

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Diffusers
flux
flux-diffusers
control-lora
diffusers-training
lora
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End of training

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  1. README.md +62 -0
  2. checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
  3. checkpoint-1000/random_states_0.pkl +3 -0
  4. checkpoint-10000/pytorch_lora_weights.safetensors +3 -0
  5. checkpoint-10000/random_states_0.pkl +3 -0
  6. checkpoint-11000/pytorch_lora_weights.safetensors +3 -0
  7. checkpoint-11000/random_states_0.pkl +3 -0
  8. checkpoint-12000/pytorch_lora_weights.safetensors +3 -0
  9. checkpoint-12000/random_states_0.pkl +3 -0
  10. checkpoint-13000/pytorch_lora_weights.safetensors +3 -0
  11. checkpoint-13000/random_states_0.pkl +3 -0
  12. checkpoint-14000/pytorch_lora_weights.safetensors +3 -0
  13. checkpoint-14000/random_states_0.pkl +3 -0
  14. checkpoint-15000/pytorch_lora_weights.safetensors +3 -0
  15. checkpoint-15000/random_states_0.pkl +3 -0
  16. checkpoint-16000/pytorch_lora_weights.safetensors +3 -0
  17. checkpoint-16000/random_states_0.pkl +3 -0
  18. checkpoint-17000/pytorch_lora_weights.safetensors +3 -0
  19. checkpoint-17000/random_states_0.pkl +3 -0
  20. checkpoint-18000/pytorch_lora_weights.safetensors +3 -0
  21. checkpoint-18000/random_states_0.pkl +3 -0
  22. checkpoint-19000/pytorch_lora_weights.safetensors +3 -0
  23. checkpoint-19000/random_states_0.pkl +3 -0
  24. checkpoint-2000/pytorch_lora_weights.safetensors +3 -0
  25. checkpoint-2000/random_states_0.pkl +3 -0
  26. checkpoint-20000/pytorch_lora_weights.safetensors +3 -0
  27. checkpoint-20000/random_states_0.pkl +3 -0
  28. checkpoint-3000/pytorch_lora_weights.safetensors +3 -0
  29. checkpoint-3000/random_states_0.pkl +3 -0
  30. checkpoint-4000/pytorch_lora_weights.safetensors +3 -0
  31. checkpoint-4000/random_states_0.pkl +3 -0
  32. checkpoint-5000/pytorch_lora_weights.safetensors +3 -0
  33. checkpoint-5000/random_states_0.pkl +3 -0
  34. checkpoint-6000/pytorch_lora_weights.safetensors +3 -0
  35. checkpoint-6000/random_states_0.pkl +3 -0
  36. checkpoint-7000/pytorch_lora_weights.safetensors +3 -0
  37. checkpoint-7000/random_states_0.pkl +3 -0
  38. checkpoint-8000/pytorch_lora_weights.safetensors +3 -0
  39. checkpoint-8000/random_states_0.pkl +3 -0
  40. checkpoint-9000/pytorch_lora_weights.safetensors +3 -0
  41. checkpoint-9000/random_states_0.pkl +3 -0
  42. image_control.png +0 -0
  43. images_0.png +0 -0
  44. images_1.png +0 -0
  45. images_2.png +0 -0
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README.md ADDED
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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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+
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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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+
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+
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+ # control-lora-IsraelBenDavid/orientation-control-lora-only-x
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+
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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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+
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+ prompt: A soccer ball with a solid background
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+ ![images_0)](./images_0.png)
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+ prompt: A soccer ball with a solid background
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+ ![images_1)](./images_1.png)
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+ prompt: A soccer ball with a solid background
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+ ![images_2)](./images_2.png)
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+ prompt: A soccer ball with a solid background
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+ ![images_3)](./images_3.png)
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+ prompt: A credit card with a solid background
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+ ![images_4)](./images_4.png)
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+ prompt: A remote control with a solid background
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+ ![images_5)](./images_5.png)
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+ prompt: A bed with a solid background
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+ ![images_6)](./images_6.png)
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+ prompt: A car with a solid background
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+ ![images_7)](./images_7.png)
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+
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+
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+ ## License
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+
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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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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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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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+
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+ #### Limitations and bias
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+
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+ [TODO: provide examples of latent issues and potential remediations]
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+
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+ ## Training details
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+
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+ [TODO: describe the data used to train the model]
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