End of training

README.md

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+---
+base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
+library_name: diffusers
+license: creativeml-openrail-m
+inference: true
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+- diffusers-training
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# Text-to-image finetuning - hyeongjin99/pcsp3
+
+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.']: 
+
+![val_imgs_grid](./val_imgs_grid.png)
+
+
+## Pipeline usage
+
+You can use the pipeline like so:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipeline = DiffusionPipeline.from_pretrained("hyeongjin99/pcsp3", torch_dtype=torch.float16)
+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."
+image = pipeline(prompt).images[0]
+image.save("my_image.png")
+```
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* Epochs: 790
+* Learning rate: 1e-05
+* Batch size: 1
+* Gradient accumulation steps: 4
+* Image resolution: 320
+* Mixed-precision: None
+
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# TODO: add an example code snippet for running this diffusion pipeline
+```
+
+#### Limitations and bias
+
+[TODO: provide examples of latent issues and potential remediations]
+
+## Training details
+
+[TODO: describe the data used to train the model]

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