End of training
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- README.md +66 -0
- checkpoint-10000/optimizer.bin +3 -0
- checkpoint-10000/random_states_0.pkl +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-15000/optimizer.bin +3 -0
- checkpoint-15000/random_states_0.pkl +3 -0
- checkpoint-15000/scheduler.bin +3 -0
- checkpoint-15000/unet/config.json +68 -0
- checkpoint-15000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-15000/unet_ema/config.json +75 -0
- checkpoint-15000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-20000/optimizer.bin +3 -0
- checkpoint-20000/random_states_0.pkl +3 -0
- checkpoint-20000/scheduler.bin +3 -0
- checkpoint-20000/unet/config.json +68 -0
- checkpoint-20000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-20000/unet_ema/config.json +75 -0
- checkpoint-20000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-25000/optimizer.bin +3 -0
- checkpoint-25000/random_states_0.pkl +3 -0
- checkpoint-25000/scheduler.bin +3 -0
- checkpoint-25000/unet/config.json +68 -0
- checkpoint-25000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-25000/unet_ema/config.json +75 -0
- checkpoint-25000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-30000/optimizer.bin +3 -0
- checkpoint-30000/random_states_0.pkl +3 -0
- checkpoint-30000/scheduler.bin +3 -0
- checkpoint-30000/unet/config.json +68 -0
- checkpoint-30000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-30000/unet_ema/config.json +75 -0
- checkpoint-30000/unet_ema/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
- checkpoint-5000/unet/config.json +68 -0
- checkpoint-5000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-5000/unet_ema/config.json +75 -0
- checkpoint-5000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- feature_extractor/preprocessor_config.json +27 -0
- logs/text2image-fine-tune/1734086319.4684224/events.out.tfevents.1734086319.workstationv0-MS73-HB1-000.3770212.1 +3 -0
- logs/text2image-fine-tune/1734086319.4691591/hparams.yml +55 -0
- logs/text2image-fine-tune/events.out.tfevents.1734086319.workstationv0-MS73-HB1-000.3770212.0 +3 -0
- model_index.json +38 -0
- safety_checker/config.json +29 -0
- safety_checker/model.safetensors +3 -0
README.md
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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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<!-- 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 - hyeongjin99/pcsp4
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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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## 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("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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## Training info
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These are the key hyperparameters used during training:
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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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## 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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[TODO: describe the data used to train the model]
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checkpoint-10000/optimizer.bin
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checkpoint-10000/unet/config.json
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"down_block_types": [
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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checkpoint-10000/unet/diffusion_pytorch_model.safetensors
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checkpoint-10000/unet_ema/config.json
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checkpoint-15000/scheduler.bin
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checkpoint-15000/unet/config.json
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|
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|
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|
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|
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|
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|
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|
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|
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model_index.json
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safety_checker/config.json
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|
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|
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safety_checker/model.safetensors
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