End of training
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- README.md +46 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scaler.pt +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-1000/unet/config.json +68 -0
- checkpoint-1000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-1000/unet_ema/config.json +75 -0
- checkpoint-1000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-10000/optimizer.bin +3 -0
- checkpoint-10000/random_states_0.pkl +3 -0
- checkpoint-10000/scaler.pt +3 -0
- checkpoint-10000/scheduler.bin +3 -0
- checkpoint-10000/unet/config.json +68 -0
- checkpoint-10000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-10000/unet_ema/config.json +75 -0
- checkpoint-10000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-10500/optimizer.bin +3 -0
- checkpoint-10500/random_states_0.pkl +3 -0
- checkpoint-10500/scaler.pt +3 -0
- checkpoint-10500/scheduler.bin +3 -0
- checkpoint-10500/unet/config.json +68 -0
- checkpoint-10500/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-10500/unet_ema/config.json +75 -0
- checkpoint-10500/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-11000/optimizer.bin +3 -0
- checkpoint-11000/random_states_0.pkl +3 -0
- checkpoint-11000/scaler.pt +3 -0
- checkpoint-11000/scheduler.bin +3 -0
- checkpoint-11000/unet/config.json +68 -0
- checkpoint-11000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-11000/unet_ema/config.json +75 -0
- checkpoint-11000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-11500/optimizer.bin +3 -0
- checkpoint-11500/random_states_0.pkl +3 -0
- checkpoint-11500/scaler.pt +3 -0
- checkpoint-11500/scheduler.bin +3 -0
- checkpoint-11500/unet/config.json +68 -0
- checkpoint-11500/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-11500/unet_ema/config.json +75 -0
- checkpoint-11500/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-12000/optimizer.bin +3 -0
- checkpoint-12000/random_states_0.pkl +3 -0
- checkpoint-12000/scaler.pt +3 -0
- checkpoint-12000/scheduler.bin +3 -0
- checkpoint-12000/unet/config.json +68 -0
- checkpoint-12000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-12000/unet_ema/config.json +75 -0
- checkpoint-12000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-12500/optimizer.bin +3 -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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- None
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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 - gremlin97/runs3
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This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A photo of a crop field']:
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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("gremlin97/runs3", torch_dtype=torch.float16)
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prompt = "A photo of a crop field"
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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-05
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* Batch size: 1
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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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checkpoint-1000/optimizer.bin
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checkpoint-1000/unet/config.json
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{
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"_diffusers_version": "0.25.0.dev0",
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"act_fn": "silu",
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"CrossAttnDownBlock2D",
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"resnet_out_scale_factor": 1.0,
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"up_block_types": [
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|
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}
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checkpoint-1000/unet/diffusion_pytorch_model.safetensors
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checkpoint-1000/unet_ema/config.json
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checkpoint-10000/unet/config.json
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