Instructions to use neonforestmist/clover-image-tiny-monet-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neonforestmist/clover-image-tiny-monet-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("neonforestmist/Clover-Image-Tiny", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("neonforestmist/clover-image-tiny-monet-lora") prompt = "Monet Style, a nice autumn background" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
End of training
Browse files- .gitattributes +4 -0
- README.md +43 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scaler.pt +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-750/optimizer.bin +3 -0
- checkpoint-750/pytorch_lora_weights.safetensors +3 -0
- checkpoint-750/random_states_0.pkl +3 -0
- checkpoint-750/scaler.pt +3 -0
- checkpoint-750/scheduler.bin +3 -0
- image_0.png +3 -0
- image_1.png +3 -0
- image_2.png +3 -0
- image_3.png +3 -0
- logs/text2image-fine-tune/1785451532.846119/events.out.tfevents.1785451532.j-neonforestmist-6a6bd34fb36a6516e96a341f-9w1wbkxn-24d40-d76v4.126.1 +3 -0
- logs/text2image-fine-tune/1785451532.8475652/hparams.yml +52 -0
- logs/text2image-fine-tune/events.out.tfevents.1785451532.j-neonforestmist-6a6bd34fb36a6516e96a341f-9w1wbkxn-24d40-d76v4.126.0 +3 -0
- pytorch_lora_weights.safetensors +3 -0
.gitattributes
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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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image_0.png filter=lfs diff=lfs merge=lfs -text
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image_1.png filter=lfs diff=lfs merge=lfs -text
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image_2.png filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: neonforestmist/Clover-Image-Tiny
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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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<!-- 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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# LoRA text2image fine-tuning - neonforestmist/clover-image-tiny-monet-lora
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These are LoRA adaption weights for neonforestmist/Clover-Image-Tiny. The weights were fine-tuned on the neonforestmist/GPT_Monet_Style_Images dataset. You can find some example images in the following.
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## Intended uses & limitations
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#### How to use
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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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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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checkpoint-1000/optimizer.bin
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size 13933067
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checkpoint-750/optimizer.bin
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checkpoint-750/scheduler.bin
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size 1401
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image_0.png
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Git LFS Details
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image_1.png
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Git LFS Details
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image_2.png
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Git LFS Details
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image_3.png
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Git LFS Details
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logs/text2image-fine-tune/1785451532.846119/events.out.tfevents.1785451532.j-neonforestmist-6a6bd34fb36a6516e96a341f-9w1wbkxn-24d40-d76v4.126.1
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oid sha256:e4b97bdd1573b92d89940ec2fcc7be47e49fa30cc47f50e9f83dfb4b3c8e5a9d
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size 2702
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logs/text2image-fine-tune/1785451532.8475652/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: false
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checkpointing_steps: 250
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checkpoints_total_limit: 2
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dataloader_num_workers: 2
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dataset_config_name: null
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dataset_name: neonforestmist/GPT_Monet_Style_Images
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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: neonforestmist/clover-image-tiny-monet-lora
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hub_token: null
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image_column: image
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image_interpolation_mode: lanczos
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learning_rate: 0.0001
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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: 100
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max_grad_norm: 1.0
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max_train_samples: null
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max_train_steps: 1000
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mixed_precision: fp16
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noise_offset: 0
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num_train_epochs: 10
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num_validation_images: 4
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output_dir: /tmp/monet
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prediction_type: null
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pretrained_model_name_or_path: neonforestmist/Clover-Image-Tiny
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push_to_hub: true
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random_flip: false
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rank: 16
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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: 63b0e9f6be9c00888ff464f342a9ef052bf76681
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scale_lr: false
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seed: 20260730
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snr_gamma: 5.0
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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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validation_epochs: 999
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validation_prompt: Monet Style, a small blue cat resting beside a lily pond, luminous
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garden light, loose impressionist brushwork
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variant: null
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logs/text2image-fine-tune/events.out.tfevents.1785451532.j-neonforestmist-6a6bd34fb36a6516e96a341f-9w1wbkxn-24d40-d76v4.126.0
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pytorch_lora_weights.safetensors
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size 6927128
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