End of training

.gitattributes

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README.md

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+
+---
+license: creativeml-openrail-m
+base_model: kandinsky-community/kandinsky-2-2-prior
+datasets:
+- JeonghyunLee/abnormal_metadata_v6
+tags:
+- kandinsky
+- text-to-image
+- diffusers
+- diffusers-training
+inference: true
+---
+    
+# Finetuning - JeonghyunLee/kandi2-prior-abnormal_images
+
+This pipeline was finetuned from **kandinsky-community/kandinsky-2-2-prior** on the **JeonghyunLee/abnormal_metadata_v6** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['An abnormal image. This is real microscopic image of integrated circuits. Add Scratch.']: 
+
+![val_imgs_grid](./val_imgs_grid.png)
+
+
+## Pipeline usage
+
+You can use the pipeline like so:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipe_prior = DiffusionPipeline.from_pretrained("JeonghyunLee/kandi2-prior-abnormal_images", torch_dtype=torch.float16)
+pipe_t2i = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
+prompt = "An abnormal image. This is real microscopic image of integrated circuits. Add Scratch."
+image_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple()
+image = pipe_t2i(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds).images[0]
+image.save("my_image.png")
+```
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* Epochs: 3
+* Learning rate: 1e-05
+* Batch size: 1
+* Gradient accumulation steps: 4
+* Image resolution: 768
+* Mixed-precision: None
+
+
+More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/wjdgus7458-/text2image-fine-tune/runs/g64fkz57).

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