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mrt-model

Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
lora
template:sd-lora
sd3
sd3-diffusers
Model card Files
xet
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End of training

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  1. README.md +43 -0
  2. checkpoint-1000/optimizer.bin +3 -0
  3. checkpoint-1000/random_states_0.pkl +3 -0
  4. checkpoint-1000/scaler.pt +3 -0
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  6. checkpoint-1000/text_encoder/config.json +25 -0
  7. checkpoint-1000/text_encoder/model.safetensors +3 -0
  8. checkpoint-1000/unet/config.json +68 -0
  9. checkpoint-1000/unet/diffusion_pytorch_model.safetensors +3 -0
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  15. checkpoint-500/text_encoder/model.safetensors +3 -0
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  17. checkpoint-500/unet/diffusion_pytorch_model.safetensors +3 -0
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  45. logs/dreambooth/events.out.tfevents.1738938877.atenea.2137224.0 +3 -0
  46. model_index.json +38 -0
  47. safety_checker/config.json +29 -0
  48. safety_checker/model.safetensors +3 -0
  49. scheduler/scheduler_config.json +15 -0
  50. text_encoder/config.json +25 -0
README.md ADDED
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+ ---
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+ base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
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+ library_name: diffusers
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+ license: creativeml-openrail-m
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+ inference: true
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+ instance_prompt: a photo of mrt
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+ tags:
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+ - text-to-image
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+ - dreambooth
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+ - diffusers-training
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+ - stable-diffusion
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+ - stable-diffusion-diffusers
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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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+ # DreamBooth - MarcosRodrigo/mrt-model
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+
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+ This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of mrt using [DreamBooth](https://dreambooth.github.io/).
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+ You can find some example images in the following.
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+
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+
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+
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+ DreamBooth for the text encoder was enabled: True.
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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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