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pribadihcr
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trained_project-32_medium

Text-to-Image
Diffusers
diffusers-training
lora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Model card Files
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  1. README.md +62 -0
  2. checkpoint-1000/optimizer.bin +3 -0
  3. checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
  4. checkpoint-1000/random_states_0.pkl +3 -0
  5. checkpoint-1000/scaler.pt +3 -0
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  32. checkpoint-4000/optimizer.bin +3 -0
  33. checkpoint-4000/pytorch_lora_weights.safetensors +3 -0
  34. checkpoint-4000/random_states_0.pkl +3 -0
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README.md ADDED
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+ ---
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+ base_model: stabilityai/stable-diffusion-xl-base-1.0
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+ library_name: diffusers
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+ license: openrail++
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+ tags:
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+ - text-to-image
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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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+ - stable-diffusion-xl
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+ - stable-diffusion-xl-diffusers
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+ instance_prompt: a photo of sks pcb
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+ widget: []
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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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+ # SDXL LoRA DreamBooth - pribadihcr/trained_project-32_medium
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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 pribadihcr/trained_project-32_medium LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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+
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+ The weights were trained using [DreamBooth](https://dreambooth.github.io/).
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+
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+ LoRA for the text encoder was enabled: False.
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+
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+ Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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+
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+ ## Trigger words
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+
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+ You should use a photo of sks pcb to trigger the image generation.
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+
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+ ## Download model
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+
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+ Weights for this model are available in Safetensors format.
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+
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+ [Download](pribadihcr/trained_project-32_medium/tree/main) them in the Files & versions tab.
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+
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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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