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  1. README.md +66 -0
  2. checkpoint-10000/optimizer.bin +3 -0
  3. checkpoint-10000/random_states_0.pkl +3 -0
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  6. checkpoint-10000/unet/diffusion_pytorch_model.safetensors +3 -0
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  15. checkpoint-15000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
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  20. checkpoint-20000/unet/diffusion_pytorch_model.safetensors +3 -0
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  22. checkpoint-20000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
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  27. checkpoint-25000/unet/diffusion_pytorch_model.safetensors +3 -0
  28. checkpoint-25000/unet_ema/config.json +75 -0
  29. checkpoint-25000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
  30. checkpoint-30000/optimizer.bin +3 -0
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  36. checkpoint-30000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
  37. checkpoint-5000/optimizer.bin +3 -0
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  41. checkpoint-5000/unet/diffusion_pytorch_model.safetensors +3 -0
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  43. checkpoint-5000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
  44. feature_extractor/preprocessor_config.json +27 -0
  45. logs/text2image-fine-tune/1734086319.4684224/events.out.tfevents.1734086319.workstationv0-MS73-HB1-000.3770212.1 +3 -0
  46. logs/text2image-fine-tune/1734086319.4691591/hparams.yml +55 -0
  47. logs/text2image-fine-tune/events.out.tfevents.1734086319.workstationv0-MS73-HB1-000.3770212.0 +3 -0
  48. model_index.json +38 -0
  49. safety_checker/config.json +29 -0
  50. safety_checker/model.safetensors +3 -0
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 - hyeongjin99/pcsp4
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+
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+ This pipeline was finetuned from **stable-diffusion-v1-5/stable-diffusion-v1-5** on the **hyeongjin99/pcsp_dataset_v5** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A conceptual, high-level representation of an inverse 2D photonic crystal structure featuring circular voids, each with a radius of 122.5 and a refractive index of 1.4. When illuminated, this carefully arranged pattern reflects a red hue.']:
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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("hyeongjin99/pcsp4", torch_dtype=torch.float16)
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+ prompt = "A conceptual, high-level representation of an inverse 2D photonic crystal structure featuring circular voids, each with a radius of 122.5 and a refractive index of 1.4. When illuminated, this carefully arranged pattern reflects a red hue."
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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: 790
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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: 320
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+ * Mixed-precision: None
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+
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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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