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End of training

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  1. .gitattributes +4 -0
  2. README.md +90 -0
  3. checkpoint-100/optimizer.bin +3 -0
  4. checkpoint-100/pytorch_lora_weights.safetensors +3 -0
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  31. checkpoint-500/scaler.pt +3 -0
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  33. checkpoint-600/optimizer.bin +3 -0
  34. checkpoint-600/pytorch_lora_weights.safetensors +3 -0
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  49. checkpoint-900/pytorch_lora_weights.safetensors +3 -0
  50. checkpoint-900/random_states_0.pkl +3 -0
.gitattributes CHANGED
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ image_0.png filter=lfs diff=lfs merge=lfs -text
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+ image_1.png filter=lfs diff=lfs merge=lfs -text
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+ image_2.png filter=lfs diff=lfs merge=lfs -text
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+ image_3.png filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ base_model: stabilityai/stable-diffusion-3-medium-diffusers
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+ library_name: diffusers
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+ license: other
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+ instance_prompt: photo of a A@K man
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+ widget:
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+ - text: photo of a A@K man in a restaurant
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+ output:
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+ url: image_0.png
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+ - text: photo of a A@K man in a restaurant
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+ output:
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+ url: image_1.png
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+ - text: photo of a A@K man in a restaurant
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+ output:
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+ url: image_2.png
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+ - text: photo of a A@K man in a restaurant
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+ output:
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+ url: image_3.png
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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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+ - template:sd-lora
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+ - sd3
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+ - sd3-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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+ # SD3 DreamBooth LoRA - mtreinik/output-20241204_005457
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+
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+ <Gallery />
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+
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+ ## Model description
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+
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+ These are mtreinik/output-20241204_005457 DreamBooth LoRA weights for stabilityai/stable-diffusion-3-medium-diffusers.
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+
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+ The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md).
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+
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+ Was LoRA for the text encoder enabled? False.
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+
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+ ## Trigger words
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+
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+ You should use `photo of a A@K man` to trigger the image generation.
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+
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+ ## Download model
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+
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+ [Download the *.safetensors LoRA](mtreinik/output-20241204_005457/tree/main) in the Files & versions tab.
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+
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+ ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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+
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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(stabilityai/stable-diffusion-3-medium-diffusers, torch_dtype=torch.float16).to('cuda')
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+ pipeline.load_lora_weights('mtreinik/output-20241204_005457', weight_name='pytorch_lora_weights.safetensors')
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+ image = pipeline('photo of a A@K man in a restaurant').images[0]
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+ ```
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+
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+ ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
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+
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+ - **LoRA**: download **[`diffusers_lora_weights.safetensors` here 💾](/mtreinik/output-20241204_005457/blob/main/diffusers_lora_weights.safetensors)**.
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+ - Rename it and place it on your `models/Lora` folder.
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+ - On AUTOMATIC1111, load the LoRA by adding `<lora:your_new_name:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
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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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+
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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/stabilityai/stable-diffusion-3-medium/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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