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End of training

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  1. .gitattributes +1 -0
  2. README.md +68 -0
  3. checkpoint-12000/optimizer.bin +3 -0
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  29. logs/text2image-fine-tune/1764969941.5745482/events.out.tfevents.1764969941.54e56bc8fc38.974.1 +3 -0
  30. logs/text2image-fine-tune/1764969941.5764635/hparams.yml +55 -0
  31. logs/text2image-fine-tune/1764970003.7036204/events.out.tfevents.1764970003.54e56bc8fc38.1462.1 +3 -0
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  33. logs/text2image-fine-tune/events.out.tfevents.1764969938.54e56bc8fc38.974.0 +3 -0
  34. logs/text2image-fine-tune/events.out.tfevents.1764970001.54e56bc8fc38.1462.0 +3 -0
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  39. text_encoder/config.json +25 -0
  40. text_encoder/model.safetensors +3 -0
  41. tokenizer/merges.txt +0 -0
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  44. tokenizer/vocab.json +0 -0
  45. unet/config.json +68 -0
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  47. vae/config.json +38 -0
  48. vae/diffusion_pytorch_model.safetensors +3 -0
  49. val_imgs_grid.png +3 -0
.gitattributes CHANGED
@@ -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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  *.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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+ val_imgs_grid.png filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
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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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+ ---
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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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+
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+
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+ # Text-to-image finetuning - ButterChicken98/soybean_controlnet_sd15_v1
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+
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+ This pipeline was finetuned from **stable-diffusion-v1-5/stable-diffusion-v1-5** on the **ButterChicken98/soybean-disease-controlnet** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A soybean leaf with early symptoms of Aerial Blight, showing small water-soaked spots at the leaf tip.']:
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+
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+ ![val_imgs_grid](./val_imgs_grid.png)
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+
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+
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+ ## Pipeline usage
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+
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+ You can use the pipeline like so:
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+
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+ ```python
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+ from diffusers import DiffusionPipeline
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+ import torch
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+
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+ pipeline = DiffusionPipeline.from_pretrained("ButterChicken98/soybean_controlnet_sd15_v1", torch_dtype=torch.float16)
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+ prompt = "A soybean leaf with early symptoms of Aerial Blight, showing 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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+
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+ ## Training info
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+
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+ These are the key hyperparameters used during training:
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+
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+ * Epochs: 115
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+ * Learning rate: 5e-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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+
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+
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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/dd5fnkki).
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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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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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+
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+ #### Limitations and bias
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+
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+ [TODO: provide examples of latent issues and potential remediations]
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+
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+ ## Training details
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+
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+ [TODO: describe the data used to train the model]
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