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End of training

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  1. <new1>.bin +3 -0
  2. <new2>.bin +3 -0
  3. <new3>.bin +3 -0
  4. <new4>.bin +3 -0
  5. <new5>.bin +3 -0
  6. README.md +22 -0
  7. checkpoint-1000/custom_checkpoint_0.pkl +3 -0
  8. checkpoint-1000/optimizer.bin +3 -0
  9. checkpoint-1000/pytorch_model.bin +3 -0
  10. checkpoint-1000/pytorch_model_1.bin +3 -0
  11. checkpoint-1000/random_states_0.pkl +3 -0
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  13. checkpoint-1250/custom_checkpoint_0.pkl +3 -0
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  38. logs/custom-diffusion/1701432466.071947/hparams.yml +57 -0
  39. logs/custom-diffusion/events.out.tfevents.1701432466.cudahpc06.2217003.0 +3 -0
  40. pytorch_custom_diffusion_weights.bin +3 -0
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+
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+ ---
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+ license: creativeml-openrail-m
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+ base_model: CompVis/stable-diffusion-v1-4
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+ instance_prompt: None
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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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+ - custom-diffusion
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+ inference: true
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+ ---
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+
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+ # Custom Diffusion - SidXXD/model-multi-5-flowers
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+
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+ These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on None using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
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+
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+ For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
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+ adam_beta1: 0.9
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+ concepts_list: ./concept_list.json
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+ dataloader_num_workers: 2
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+ enable_xformers_memory_efficient_attention: false
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+ freeze_model: crossattn_kv
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+ gradient_accumulation_steps: 1
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+ initializer_token: ktn+pll+ucd+xdd+xxd
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+ instance_data_dir: null
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+ learning_rate: 1.0e-05
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+ local_rank: -1
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+ logging_dir: logs
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+ lr_scheduler: constant
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+ lr_warmup_steps: 0
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+ max_grad_norm: 1.0
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+ max_train_steps: 1250
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+ mixed_precision: null
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+ modifier_token: <new1>+<new2>+<new3>+<new4>+<new5>
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+ no_safe_serialization: true
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+ noaug: false
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+ num_class_images: 200
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+ num_train_epochs: 1
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+ num_validation_images: 2
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+ output_dir: model-multi-5-flowers
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+ pretrained_model_name_or_path: CompVis/stable-diffusion-v1-4
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+ prior_generation_precision: null
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+ prior_loss_weight: 0.0
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+ resolution: 512
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