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ButterChicken98
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pv_db_v1

Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Inference Endpoints
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End of training

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  1. .gitattributes +4 -0
  2. README.md +47 -0
  3. checkpoint-1000/optimizer.bin +3 -0
  4. checkpoint-1000/random_states_0.pkl +3 -0
  5. checkpoint-1000/scheduler.bin +3 -0
  6. checkpoint-1000/text_encoder/config.json +25 -0
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  44. checkpoint-500/unet/diffusion_pytorch_model.safetensors +3 -0
  45. feature_extractor/preprocessor_config.json +27 -0
  46. image_0.png +3 -0
  47. image_1.png +3 -0
  48. image_2.png +3 -0
  49. image_3.png +3 -0
  50. logs/dreambooth/1741169017.3739688/events.out.tfevents.1741169017.f57da3430a9c.1376.1 +3 -0
.gitattributes CHANGED
@@ -33,3 +33,7 @@ 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 ADDED
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+ ---
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+ base_model: CompVis/stable-diffusion-v1-4
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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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+ instance_prompt: a photo of yellowleafCurlVirus leaf
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+ tags:
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+ - text-to-image
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+ - dreambooth
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+ - diffusers-training
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+ - stable-diffusion
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+ - stable-diffusion-diffusers
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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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+ # DreamBooth - ButterChicken98/pv_db_v1
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+
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+ This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of yellowleafCurlVirus leaf using [DreamBooth](https://dreambooth.github.io/).
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+ You can find some example images in the following.
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+
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+ ![img_0](./image_0.png)
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+ ![img_1](./image_1.png)
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+ ![img_2](./image_2.png)
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+ ![img_3](./image_3.png)
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+
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+
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+ DreamBooth for the text encoder was enabled: True.
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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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