End of training
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- 10epoch-h/.gitattributes +36 -0
- 10epoch-h/README.md +46 -0
- 10epoch-h/feature_extractor/preprocessor_config.json +28 -0
- 10epoch-h/logs/text2image-fine-tune/1699306835.1319811/events.out.tfevents.1699306835.DESKTOP-TNN0PMP.8128.1 +3 -0
- 10epoch-h/logs/text2image-fine-tune/1699306835.1339812/hparams.yml +50 -0
- 10epoch-h/logs/text2image-fine-tune/1699312817.2914586/events.out.tfevents.1699312817.DESKTOP-TNN0PMP.10224.1 +3 -0
- 10epoch-h/logs/text2image-fine-tune/1699312817.2934585/hparams.yml +50 -0
- 10epoch-h/logs/text2image-fine-tune/1699314601.5908923/events.out.tfevents.1699314601.DESKTOP-TNN0PMP.2164.1 +3 -0
- 10epoch-h/logs/text2image-fine-tune/1699314601.5918913/hparams.yml +50 -0
- 10epoch-h/logs/text2image-fine-tune/1699412326.680156/events.out.tfevents.1699412326.DESKTOP-TNN0PMP.18100.1 +3 -0
- 10epoch-h/logs/text2image-fine-tune/1699412326.6821554/hparams.yml +50 -0
- 10epoch-h/logs/text2image-fine-tune/1699452581.0122697/events.out.tfevents.1699452581.DESKTOP-TNN0PMP.12764.1 +3 -0
- 10epoch-h/logs/text2image-fine-tune/1699452581.0132704/hparams.yml +50 -0
- 10epoch-h/logs/text2image-fine-tune/1699558210.3350325/events.out.tfevents.1699558210.DESKTOP-TNN0PMP.12724.1 +3 -0
- 10epoch-h/logs/text2image-fine-tune/1699558210.3370333/hparams.yml +50 -0
- 10epoch-h/logs/text2image-fine-tune/events.out.tfevents.1699306835.DESKTOP-TNN0PMP.8128.0 +3 -0
- 10epoch-h/logs/text2image-fine-tune/events.out.tfevents.1699312817.DESKTOP-TNN0PMP.10224.0 +3 -0
- 10epoch-h/logs/text2image-fine-tune/events.out.tfevents.1699314601.DESKTOP-TNN0PMP.2164.0 +3 -0
- 10epoch-h/logs/text2image-fine-tune/events.out.tfevents.1699412326.DESKTOP-TNN0PMP.18100.0 +3 -0
- 10epoch-h/logs/text2image-fine-tune/events.out.tfevents.1699452581.DESKTOP-TNN0PMP.12764.0 +3 -0
- 10epoch-h/logs/text2image-fine-tune/events.out.tfevents.1699558210.DESKTOP-TNN0PMP.12724.0 +3 -0
- 10epoch-h/model_index.json +34 -0
- 10epoch-h/safety_checker/config.json +28 -0
- 10epoch-h/safety_checker/model.safetensors +3 -0
- 10epoch-h/scheduler/scheduler_config.json +15 -0
- 10epoch-h/text_encoder/config.json +25 -0
- 10epoch-h/text_encoder/model.safetensors +3 -0
- 10epoch-h/tokenizer/merges.txt +0 -0
- 10epoch-h/tokenizer/special_tokens_map.json +24 -0
- 10epoch-h/tokenizer/tokenizer_config.json +33 -0
- 10epoch-h/tokenizer/vocab.json +0 -0
- 10epoch-h/unet/config.json +67 -0
- 10epoch-h/unet/diffusion_pytorch_model.safetensors +3 -0
- 10epoch-h/vae/config.json +32 -0
- 10epoch-h/vae/diffusion_pytorch_model.safetensors +3 -0
- 10epoch-h/val_imgs_grid.png +3 -0
- README.md +2 -2
- checkpoint-4620000/optimizer.bin +3 -0
- checkpoint-4620000/random_states_0.pkl +3 -0
- checkpoint-4620000/scaler.pt +3 -0
- checkpoint-4620000/scheduler.bin +3 -0
- checkpoint-4620000/unet/config.json +67 -0
- checkpoint-4620000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-4640000/optimizer.bin +3 -0
- checkpoint-4640000/random_states_0.pkl +3 -0
- checkpoint-4640000/scaler.pt +3 -0
- checkpoint-4640000/scheduler.bin +3 -0
- checkpoint-4640000/unet/config.json +67 -0
- checkpoint-4640000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-4660000/optimizer.bin +3 -0
10epoch-h/.gitattributes
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10epoch-h/README.md
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---
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license: creativeml-openrail-m
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base_model: runwayml/stable-diffusion-v1-5
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datasets:
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- iamkaikai/amazing_logos_v4
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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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inference: true
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---
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# Text-to-image finetuning - iamkaikai/amazing-logos-v5
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This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **iamkaikai/amazing_logos_v4** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Simple elegant logo for Mandarin Oriental, Fan Hong kong Lines Paper, Hospitality, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges, black and white', 'Simple elegant logo for AltVest Investments, alternative investments financial services, Finance, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for PeckCerativeHorz2.jpg, peck horizontal trends branding bold photography analysis packaging vertical products circle discovery identity color creative exhibition direction P graphics julian research, , successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', "Simple elegant logo for Johns Creek Shirts, printing T's art Apparel screen tshirt summer T t shirts, Apparel, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges", 'Simple elegant logo for MGD, Human Circle MGD dots Resources SRP 3D brown, Human Resources, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for Indooroopilly Uniting Church, abstract initials people swirl letter I letter U letter C giving community soft friendly purple blue red, Religious, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for Hacker, Douglas, & Company, accountant Hollywood law H filmstrip attorney HDC film, law, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for Windmill unused #5, windmill property community shapes quilt blades houses colorful carlsbad homes circle whimsical estate housing real, housing development, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for The Duck Store, track track and field sports athletics tree logo badge, Sports Apparel, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for InGenious Fitness, G Ball Green Blue, Fitness, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for KickCharge Creative, seating safety man driver person figure hardhat S initial sign, Transportation, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for Chickasaw Nation, water drop laundry, Commercial Laundry Services, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for NBA Properties, Inc., basketball sports branding team entertainment philadelphia star patriotic, Sports Entertainment, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for North Asheville Tailgate Market Veggie Sub Mark, culinary cheese Initials combo organic serif vegetable radish Farmers eggplant inspirations2023 tailgate food market submark asheville farm kale modern unique sanserif veggie , farmers market, culinary, food, retail, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges', 'Simple elegant logo for A. Diethelm, A Circle Line Switzerland Triangle, Painting Tools and Supplies, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges, black and white', 'Simple elegant logo for Grupo Altair Publicidad, Circle Lines Venezuela, Publishing, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges, black and white']:
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![val_imgs_grid](./val_imgs_grid.png)
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## Pipeline usage
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You can use the pipeline like so:
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```python
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from diffusers import DiffusionPipeline
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import torch
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pipeline = DiffusionPipeline.from_pretrained("iamkaikai/amazing-logos-v5", torch_dtype=torch.float16)
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prompt = "Simple elegant logo for Mandarin Oriental, Fan Hong kong Lines Paper, Hospitality, successful vibe, minimalist, thought-provoking, abstract, recognizable, relatable, sharp, vector art, even edges, black and white"
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image = pipeline(prompt).images[0]
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image.save("my_image.png")
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```
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## Training info
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These are the key hyperparameters used during training:
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* Epochs: 10
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* Learning rate: 1e-07
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* Batch size: 1
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* Gradient accumulation steps: 1
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* Image resolution: 512
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* Mixed-precision: fp16
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10epoch-h/feature_extractor/preprocessor_config.json
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{
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"crop_size": {
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"height": 224,
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"width": 224
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},
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"do_center_crop": true,
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"feature_extractor_type": "CLIPFeatureExtractor",
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"image_mean": [
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0.48145466,
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0.4578275,
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0.40821073
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],
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"image_processor_type": "CLIPImageProcessor",
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"image_std": [
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0.26862954,
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0.26130258,
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0.27577711
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],
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"shortest_edge": 224
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}
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}
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10epoch-h/logs/text2image-fine-tune/1699306835.1319811/events.out.tfevents.1699306835.DESKTOP-TNN0PMP.8128.1
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size 2414
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10epoch-h/logs/text2image-fine-tune/1699306835.1339812/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: true
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cache_dir: null
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caption_column: text
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center_crop: true
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checkpointing_steps: 10000
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checkpoints_total_limit: null
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dataloader_num_workers: 0
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dataset_config_name: null
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dataset_name: iamkaikai/amazing_logos_v4
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enable_xformers_memory_efficient_attention: false
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gradient_accumulation_steps: 1
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gradient_checkpointing: false
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hub_model_id: amazing-logos-v5
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hub_token: null
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image_column: image
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input_perturbation: 0
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learning_rate: 1.0e-07
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local_rank: -1
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logging_dir: logs
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lr_scheduler: cosine
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lr_warmup_steps: 400000
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max_grad_norm: 1.0
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+
max_train_samples: null
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max_train_steps: 3972510
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mixed_precision: fp16
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+
noise_offset: 0
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+
non_ema_revision: null
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num_train_epochs: 10
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output_dir: amazing-logos-v5
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prediction_type: null
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pretrained_model_name_or_path: runwayml/stable-diffusion-v1-5
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push_to_hub: true
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random_flip: false
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report_to: tensorboard
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resolution: 512
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resume_from_checkpoint: checkpoint-3240000
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revision: null
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scale_lr: false
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seed: 6666
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snr_gamma: null
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tracker_project_name: text2image-fine-tune
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train_batch_size: 1
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train_data_dir: null
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use_8bit_adam: false
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use_ema: false
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validation_epochs: 1
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10epoch-h/logs/text2image-fine-tune/1699312817.2914586/events.out.tfevents.1699312817.DESKTOP-TNN0PMP.10224.1
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version https://git-lfs.github.com/spec/v1
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size 2414
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10epoch-h/logs/text2image-fine-tune/1699312817.2934585/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: true
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cache_dir: null
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caption_column: text
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center_crop: true
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checkpointing_steps: 10000
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checkpoints_total_limit: null
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dataloader_num_workers: 0
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dataset_config_name: null
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dataset_name: iamkaikai/amazing_logos_v4
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enable_xformers_memory_efficient_attention: false
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gradient_accumulation_steps: 1
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gradient_checkpointing: false
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hub_model_id: amazing-logos-v5
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hub_token: null
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image_column: image
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input_perturbation: 0
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learning_rate: 1.0e-07
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README.md
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These are the key hyperparameters used during training:
|
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|
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* Epochs:
|
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checkpoint-4620000/unet/config.json
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{
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checkpoint-4620000/unet/diffusion_pytorch_model.safetensors
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checkpoint-4640000/optimizer.bin
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checkpoint-4640000/random_states_0.pkl
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checkpoint-4640000/scaler.pt
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checkpoint-4640000/scheduler.bin
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checkpoint-4640000/unet/config.json
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checkpoint-4640000/unet/diffusion_pytorch_model.safetensors
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