End of training
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- .gitattributes +1 -0
- README.md +48 -0
- checkpoint-100000/optimizer.bin +3 -0
- checkpoint-100000/random_states_0.pkl +3 -0
- checkpoint-100000/scaler.pt +3 -0
- checkpoint-100000/scheduler.bin +3 -0
- checkpoint-100000/unet/config.json +65 -0
- checkpoint-100000/unet/diffusion_pytorch_model.bin +3 -0
- checkpoint-30000/optimizer.bin +3 -0
- checkpoint-30000/random_states_0.pkl +3 -0
- checkpoint-30000/scaler.pt +3 -0
- checkpoint-30000/scheduler.bin +3 -0
- checkpoint-30000/unet/config.json +65 -0
- checkpoint-30000/unet/diffusion_pytorch_model.bin +3 -0
- checkpoint-40000/optimizer.bin +3 -0
- checkpoint-40000/random_states_0.pkl +3 -0
- checkpoint-40000/scaler.pt +3 -0
- checkpoint-40000/scheduler.bin +3 -0
- checkpoint-40000/unet/config.json +65 -0
- checkpoint-40000/unet/diffusion_pytorch_model.bin +3 -0
- checkpoint-50000/optimizer.bin +3 -0
- checkpoint-50000/random_states_0.pkl +3 -0
- checkpoint-50000/scaler.pt +3 -0
- checkpoint-50000/scheduler.bin +3 -0
- checkpoint-50000/unet/config.json +65 -0
- checkpoint-50000/unet/diffusion_pytorch_model.bin +3 -0
- checkpoint-60000/optimizer.bin +3 -0
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- checkpoint-60000/scaler.pt +3 -0
- checkpoint-60000/scheduler.bin +3 -0
- checkpoint-60000/unet/config.json +65 -0
- checkpoint-60000/unet/diffusion_pytorch_model.bin +3 -0
- checkpoint-70000/optimizer.bin +3 -0
- checkpoint-70000/random_states_0.pkl +3 -0
- checkpoint-70000/scaler.pt +3 -0
- checkpoint-70000/scheduler.bin +3 -0
- checkpoint-70000/unet/config.json +65 -0
- checkpoint-70000/unet/diffusion_pytorch_model.bin +3 -0
- checkpoint-80000/optimizer.bin +3 -0
- checkpoint-80000/random_states_0.pkl +3 -0
- checkpoint-80000/scaler.pt +3 -0
- checkpoint-80000/scheduler.bin +3 -0
- checkpoint-80000/unet/config.json +65 -0
- checkpoint-80000/unet/diffusion_pytorch_model.bin +3 -0
- checkpoint-90000/optimizer.bin +3 -0
- checkpoint-90000/random_states_0.pkl +3 -0
- checkpoint-90000/scaler.pt +3 -0
- checkpoint-90000/scheduler.bin +3 -0
- checkpoint-90000/unet/config.json +65 -0
- checkpoint-90000/unet/diffusion_pytorch_model.bin +3 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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val_imgs_grid.png filter=lfs diff=lfs merge=lfs -text
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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_v3
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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-v2
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This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **iamkaikai/amazing_logos_v3** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Simple elegant logo for Digital Art, D A square terminal tech, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white', 'Simple elegant logo for Digital Art, D A arrow terminal tech, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white', 'Simple elegant logo for Digital Art, D A dots terminal tech, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white', 'Simple elegant logo for Digital Art, D A portal terminal tech, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white', 'Simple elegant logo for Digital Art, D A tech terminal tech, successful vibe, minimalist, thought-provoking, abstract, recognizable', 'Simple elegant logo for Digital Art, D A programming terminal tech, successful vibe, minimalist, thought-provoking, abstract, recognizable', 'Simple elegant logo for Digital Art, D A art terminal tech, successful vibe, minimalist, thought-provoking, abstract, recognizable', 'Simple elegant logo for Digital Art, D A digital terminal tech, successful vibe, minimalist, thought-provoking, abstract, recognizable', 'Simple elegant logo for DA, > A terminal tech, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white', 'Simple elegant logo for DA, D A portal art, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white', 'Simple elegant logo for DA, art tech school, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white', 'Simple elegant logo for DA, art tech school, successful vibe, minimalist, thought-provoking, abstract, recognizable', 'Simple elegant logo for DA, art tech school digital, successful vibe, minimalist, thought-provoking, abstract, recognizable', 'Simple elegant logo for DA, art tech school digital, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white', 'Simple elegant logo for DA, D A art tech, successful vibe, minimalist, thought-provoking, abstract, recognizable', 'Simple elegant logo for DA, D A digital art school, successful vibe, minimalist, thought-provoking, abstract, recognizable']:
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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-v2", torch_dtype=torch.float16)
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prompt = "Simple elegant logo for Digital Art, D A square terminal tech, successful vibe, minimalist, thought-provoking, abstract, recognizable, 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: 7
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* Learning rate: 1e-07
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* Batch size: 8
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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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More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/iam-kai-kai/text2image-fine-tune/runs/v2iba1q6).
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checkpoint-100000/optimizer.bin
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