Instructions to use nupurkmr9/penguin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nupurkmr9/penguin with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nupurkmr9/penguin", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <new1> cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Upload . with huggingface_hub
Browse files- <new1>.bin +1 -1
- checkpoint-250/custom_checkpoint_0.pkl +2 -2
- checkpoint-250/optimizer.bin +2 -2
- checkpoint-250/pytorch_model.bin +2 -2
- checkpoint-250/pytorch_model_1.bin +1 -1
- checkpoint-250/random_states_0.pkl +1 -1
- image_0.png +0 -0
- image_1.png +0 -0
- logs/custom-diffusion/1681795251.2276833/events.out.tfevents.1681795251.grogu-1-37.eth.154795.1 +3 -0
- logs/custom-diffusion/1681795251.2317605/hparams.yml +55 -0
- logs/custom-diffusion/events.out.tfevents.1681795251.grogu-1-37.eth.154795.0 +3 -0
- pytorch_custom_diffusion_weights.bin +2 -2
<new1>.bin
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checkpoint-250/optimizer.bin
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checkpoint-250/random_states_0.pkl
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image_0.png
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image_1.png
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logs/custom-diffusion/1681795251.2276833/events.out.tfevents.1681795251.grogu-1-37.eth.154795.1
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logs/custom-diffusion/1681795251.2317605/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: ./real_reg/samples_cat/
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class_prompt: cat
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concepts_list: null
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dataloader_num_workers: 2
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enable_xformers_memory_efficient_attention: true
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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: nupurkmr9/penguin
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hub_token: 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
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initializer_token: ktn+pll+ucd
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instance_data_dir: ./data/cat
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instance_prompt: photo of a <new1> cat
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learning_rate: 1.0e-05
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local_rank: 0
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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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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: path-to-save-model
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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: 1.0
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push_to_hub: true
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real_prior: true
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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: <new1> cat playing with a ball
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validation_steps: 500
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with_prior_preservation: true
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logs/custom-diffusion/events.out.tfevents.1681795251.grogu-1-37.eth.154795.0
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size 953353
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pytorch_custom_diffusion_weights.bin
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