End of training
Browse files- <new1>.bin +3 -0
- README.md +22 -0
- checkpoint-250/custom_checkpoint_0.pkl +3 -0
- checkpoint-250/optimizer.bin +3 -0
- checkpoint-250/pytorch_model.bin +3 -0
- checkpoint-250/pytorch_model_1.bin +3 -0
- checkpoint-250/random_states_0.pkl +3 -0
- checkpoint-250/scheduler.bin +3 -0
- logs/custom-diffusion/1701683136.3099444/events.out.tfevents.1701683136.cudahpc06.3324720.1 +3 -0
- logs/custom-diffusion/1701683136.3115225/hparams.yml +57 -0
- logs/custom-diffusion/events.out.tfevents.1701683136.cudahpc06.3324720.0 +3 -0
- pytorch_custom_diffusion_weights.bin +3 -0
<new1>.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:8595f5c8f2f52ea43d655ec0a26bcd3bb32ff27774471485e4d06b98acb17044
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README.md
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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: photo of a <new1> morning glory
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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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# Custom Diffusion - SidXXD/single-F-5-morning_glory
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These are Custom Diffusion adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on photo of a <new1> morning glory using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
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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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checkpoint-250/custom_checkpoint_0.pkl
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checkpoint-250/optimizer.bin
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checkpoint-250/pytorch_model.bin
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checkpoint-250/pytorch_model_1.bin
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checkpoint-250/random_states_0.pkl
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checkpoint-250/scheduler.bin
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logs/custom-diffusion/1701683136.3099444/events.out.tfevents.1701683136.cudahpc06.3324720.1
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logs/custom-diffusion/1701683136.3115225/hparams.yml
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adam_beta1: 0.9
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adam_beta2: 0.999
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adam_epsilon: 1.0e-08
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adam_weight_decay: 0.01
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allow_tf32: false
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center_crop: false
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checkpointing_steps: 250
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checkpoints_total_limit: null
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class_data_dir: null
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class_prompt: null
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concepts_list: null
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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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gradient_checkpointing: false
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hflip: true
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hub_model_id: null
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hub_token: null
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initializer_token: ktn+pll+ucd
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instance_data_dir: ./data/flowers/flw_1
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instance_prompt: photo of a <new1> morning glory
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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: 250
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mixed_precision: null
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modifier_token: <new1>
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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/single-F-5-morning_glory
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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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push_to_hub: true
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real_prior: false
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report_to: tensorboard
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resolution: 512
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resume_from_checkpoint: null
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revision: null
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sample_batch_size: 4
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scale_lr: true
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seed: 42
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set_grads_to_none: false
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tokenizer_name: null
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train_batch_size: 2
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use_8bit_adam: false
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validation_prompt: null
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validation_steps: 50
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variant: null
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with_prior_preservation: false
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logs/custom-diffusion/events.out.tfevents.1701683136.cudahpc06.3324720.0
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size 20834
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pytorch_custom_diffusion_weights.bin
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