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--- |
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license: creativeml-openrail-m |
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base_model: SG161222/Realistic_Vision_V4.0 |
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datasets: |
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- recastai/LAION-art-EN-improved-captions |
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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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inference: true |
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--- |
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# Text-to-image Distillation |
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This pipeline was distilled from **SG161222/Realistic_Vision_V4.0** on a Subset of **recastai/LAION-art-EN-improved-captions** dataset. Below are some example images generated with the tiny-sd model. |
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![val_imgs_grid](./grid_tiny.png) |
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This Pipeline is based upon [the paper](https://arxiv.org/pdf/2305.15798.pdf). Training Code can be found [here](https://github.com/segmind/BKSDM). |
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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("segmind/tiny-sd-mxtune", torch_dtype=torch.float16) |
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prompt = "Portrait of a pretty girl" |
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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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* Steps: 125000 |
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* Learning rate: 1e-4 |
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* Batch size: 32 |
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* Gradient accumulation steps: 4 |
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* Image resolution: 512 |
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* Mixed-precision: fp16 |
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## Finetune info |
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These are the key hyperparameters used during training: |
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* Steps: 8000 / 100000 |
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* Learning rate: 1e-5 |
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* Batch size: 24 |
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* Gradient accumulation steps: 1 |
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* Image resolution: 768 |
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* Mixed-precision: fp16 |
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## Speed Comparision |
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We have observed that the distilled models are upto 80% faster than the Base SD1.5 Models. Below is a comparision on an A100 80GB. |
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![graph](./graph.png) |
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![comparision](./comparision1.png) |
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Below is the code for benchmarking the speeds |
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