End of training
Browse filesThis view is limited to 50 files because it contains too many changes.
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- .gitattributes +1 -0
- README.md +68 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/random_states_0.pkl +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-1500/optimizer.bin +3 -0
- checkpoint-1500/random_states_0.pkl +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-2000/optimizer.bin +3 -0
- checkpoint-2000/random_states_0.pkl +3 -0
- checkpoint-2000/scheduler.bin +3 -0
- checkpoint-2000/unet/config.json +68 -0
- checkpoint-2000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-2500/optimizer.bin +3 -0
- checkpoint-2500/random_states_0.pkl +3 -0
- checkpoint-2500/scheduler.bin +3 -0
- checkpoint-2500/unet/config.json +68 -0
- checkpoint-2500/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-3000/optimizer.bin +3 -0
- checkpoint-3000/random_states_0.pkl +3 -0
- checkpoint-3000/scheduler.bin +3 -0
- checkpoint-3000/unet/config.json +68 -0
- checkpoint-3000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-3500/optimizer.bin +3 -0
- checkpoint-3500/random_states_0.pkl +3 -0
- checkpoint-3500/scheduler.bin +3 -0
- checkpoint-3500/unet/config.json +68 -0
- checkpoint-3500/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-4000/optimizer.bin +3 -0
- checkpoint-4000/random_states_0.pkl +3 -0
- checkpoint-4000/scheduler.bin +3 -0
- checkpoint-4000/unet/config.json +68 -0
- checkpoint-4000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-4500/optimizer.bin +3 -0
- checkpoint-4500/random_states_0.pkl +3 -0
- checkpoint-4500/scheduler.bin +3 -0
- checkpoint-4500/unet/config.json +68 -0
- checkpoint-4500/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-500/optimizer.bin +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scheduler.bin +3 -0
- checkpoint-500/unet/config.json +68 -0
- checkpoint-500/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-5000/optimizer.bin +3 -0
- checkpoint-5000/random_states_0.pkl +3 -0
- checkpoint-5000/scheduler.bin +3 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ 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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README.md
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---
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| 2 |
+
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
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| 3 |
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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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| 9 |
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- text-to-image
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- diffusers
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- diffusers-training
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| 12 |
+
---
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| 13 |
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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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# Text-to-image finetuning - ButterChicken98/dec_logs_ab_v3_sqrt
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This pipeline was finetuned from **stable-diffusion-v1-5/stable-diffusion-v1-5** on the **ButterChicken98/soyabean_aerial_plus_healthy** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A soybean leaf with early symptoms of Aerial Blight disease, showing very small water-soaked spots at the leaf tip.']:
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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("ButterChicken98/dec_logs_ab_v3_sqrt", torch_dtype=torch.float16)
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prompt = "A soybean leaf with early symptoms of Aerial Blight disease, showing very small water-soaked spots at the leaf tip."
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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: 29
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* Learning rate: 1e-05
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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: None
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More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/butterchicken-iit-indore/text2image-fine-tune/runs/3asm2a6f).
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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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| 67 |
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| 68 |
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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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checkpoint-1000/random_states_0.pkl
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size 1000
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checkpoint-1000/unet/config.json
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{
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"cross_attention_dim": 768,
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"down_block_types": [
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"resnet_out_scale_factor": 1.0,
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"transformer_layers_per_block": 1,
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| 60 |
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"up_block_types": [
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"UpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D"
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],
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"upcast_attention": false,
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}
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checkpoint-1000/unet/diffusion_pytorch_model.safetensors
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checkpoint-1500/random_states_0.pkl
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checkpoint-1500/unet/config.json
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{
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|
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|
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|
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"down_block_types": [
|
| 25 |
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"CrossAttnDownBlock2D",
|
| 26 |
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"CrossAttnDownBlock2D",
|
| 27 |
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"CrossAttnDownBlock2D",
|
| 28 |
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"DownBlock2D"
|
| 29 |
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],
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| 30 |
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"downsample_padding": 1,
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"dropout": 0.0,
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"mid_block_type": "UNetMidBlock2DCrossAttn",
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"norm_eps": 1e-05,
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checkpoint-2000/unet/config.json
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checkpoint-2500/unet/config.json
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checkpoint-3500/unet/config.json
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