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hyeongjin99
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pcsp-lora2

Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
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  1. README.md +39 -0
  2. checkpoint-1000/model.safetensors +3 -0
  3. checkpoint-1000/optimizer.bin +3 -0
  4. checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
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  19. checkpoint-12000/pytorch_lora_weights.safetensors +3 -0
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  22. checkpoint-13000/model.safetensors +3 -0
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  24. checkpoint-13000/pytorch_lora_weights.safetensors +3 -0
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  27. checkpoint-14000/model.safetensors +3 -0
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  29. checkpoint-14000/pytorch_lora_weights.safetensors +3 -0
  30. checkpoint-14000/random_states_0.pkl +3 -0
  31. checkpoint-14000/scheduler.bin +3 -0
  32. checkpoint-15000/model.safetensors +3 -0
  33. checkpoint-15000/optimizer.bin +3 -0
  34. checkpoint-15000/pytorch_lora_weights.safetensors +3 -0
  35. checkpoint-15000/random_states_0.pkl +3 -0
  36. checkpoint-15000/scheduler.bin +3 -0
  37. checkpoint-16000/model.safetensors +3 -0
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  39. checkpoint-16000/pytorch_lora_weights.safetensors +3 -0
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  42. checkpoint-17000/model.safetensors +3 -0
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  47. checkpoint-18000/model.safetensors +3 -0
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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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+ tags:
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+ - stable-diffusion
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+ - stable-diffusion-diffusers
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+ - text-to-image
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+ - diffusers
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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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+ # LoRA text2image fine-tuning - hyeongjin99/pcsp-lora2
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+ These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were fine-tuned on the hyeongjin99/pcsp_dataset_v3 dataset. 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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+
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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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