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flux-dev-controlnet-qr-code

Text-to-Image
Diffusers
Safetensors
flux
flux-diffusers
controlnet
diffusers-training
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End of training

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  1. README.md +43 -0
  2. checkpoint-1000/flux_controlnet/config.json +20 -0
  3. checkpoint-1000/flux_controlnet/diffusion_pytorch_model.safetensors +3 -0
  4. checkpoint-1000/optimizer.bin +3 -0
  5. checkpoint-1000/random_states_0.pkl +3 -0
  6. checkpoint-1000/scheduler.bin +3 -0
  7. checkpoint-1500/flux_controlnet/config.json +20 -0
  8. checkpoint-1500/flux_controlnet/diffusion_pytorch_model.safetensors +3 -0
  9. checkpoint-1500/optimizer.bin +3 -0
  10. checkpoint-1500/random_states_0.pkl +3 -0
  11. checkpoint-1500/scheduler.bin +3 -0
  12. checkpoint-500/flux_controlnet/config.json +20 -0
  13. checkpoint-500/flux_controlnet/diffusion_pytorch_model.safetensors +3 -0
  14. checkpoint-500/optimizer.bin +3 -0
  15. checkpoint-500/random_states_0.pkl +3 -0
  16. checkpoint-500/scheduler.bin +3 -0
  17. config.json +20 -0
  18. diffusion_pytorch_model.safetensors +3 -0
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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+ 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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+ - controlnet
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+ - diffusers-training
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+ inference: true
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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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+ # controlnet-lucataco/flux-dev-controlnet-qr-code
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+
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+ These are controlnet weights trained on black-forest-labs/FLUX.1-dev with new type of conditioning.
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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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+ "num_single_layers": 0,
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+ "patch_size": 1,
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+ "pooled_projection_dim": 768
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+ }
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