End of training
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- README.md +48 -0
- checkpoint-1200/optimizer.bin +3 -0
- checkpoint-1200/random_states_0.pkl +3 -0
- checkpoint-1200/scaler.pt +3 -0
- checkpoint-1200/scheduler.bin +3 -0
- checkpoint-1200/unet/config.json +68 -0
- checkpoint-1200/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-1200/unet_ema/config.json +75 -0
- checkpoint-1200/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-1500/optimizer.bin +3 -0
- checkpoint-1500/random_states_0.pkl +3 -0
- checkpoint-1500/scaler.pt +3 -0
- checkpoint-1500/scheduler.bin +3 -0
- checkpoint-1500/unet/config.json +68 -0
- checkpoint-1500/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-1500/unet_ema/config.json +75 -0
- checkpoint-1500/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-1800/optimizer.bin +3 -0
- checkpoint-1800/random_states_0.pkl +3 -0
- checkpoint-1800/scaler.pt +3 -0
- checkpoint-1800/scheduler.bin +3 -0
- checkpoint-1800/unet/config.json +68 -0
- checkpoint-1800/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-1800/unet_ema/config.json +75 -0
- checkpoint-1800/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-300/optimizer.bin +3 -0
- checkpoint-300/random_states_0.pkl +3 -0
- checkpoint-300/scaler.pt +3 -0
- checkpoint-300/scheduler.bin +3 -0
- checkpoint-300/unet/config.json +68 -0
- checkpoint-300/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-300/unet_ema/config.json +75 -0
- checkpoint-300/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-600/optimizer.bin +3 -0
- checkpoint-600/random_states_0.pkl +3 -0
- checkpoint-600/scaler.pt +3 -0
- checkpoint-600/scheduler.bin +3 -0
- checkpoint-600/unet/config.json +68 -0
- checkpoint-600/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-600/unet_ema/config.json +75 -0
- checkpoint-600/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-900/optimizer.bin +3 -0
- checkpoint-900/random_states_0.pkl +3 -0
- checkpoint-900/scaler.pt +3 -0
- checkpoint-900/scheduler.bin +3 -0
- checkpoint-900/unet/config.json +68 -0
- checkpoint-900/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-900/unet_ema/config.json +75 -0
- checkpoint-900/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- feature_extractor/preprocessor_config.json +27 -0
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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- linearts_0805
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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 - SungjinWi/linearts_10820_segments
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This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **linearts_0805** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A coloring page of penguin riding motorbike - penugin riding honda motorbike']:
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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("SungjinWi/linearts_10820_segments", torch_dtype=torch.float16)
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prompt = "A coloring page of penguin riding motorbike - penugin riding honda motorbike"
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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: 12
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* Learning rate: 1e-05
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* Batch size: 4
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* Gradient accumulation steps: 4
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* Image resolution: 512
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* Mixed-precision: fp16
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More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/inu-attention/text2image-fine-tune/runs/mlu91jx7).
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checkpoint-1200/optimizer.bin
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checkpoint-1200/random_states_0.pkl
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checkpoint-1200/scaler.pt
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checkpoint-1200/unet/config.json
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"down_block_types": [
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"mid_block_type": "UNetMidBlock2DCrossAttn",
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checkpoint-1200/unet_ema/config.json
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checkpoint-1500/scheduler.bin
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checkpoint-1500/unet/config.json
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