Text-to-Image
Diffusers
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
Inference Endpoints
Warlord-K commited on
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Initial Commit

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README.md ADDED
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+
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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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+ - bksdm
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+ - bksdm-ttiny
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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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+
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+ # Text-to-image Distillation - Warlord-K/portrait-finetuned-new
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+
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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 finetuned pipeline using the following prompts: ['Portrait of a pretty girl']:
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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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+ 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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+
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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("Warlord-K/portrait-finetuned-new", 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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+
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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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+ * Steps: 131000
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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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+
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+ "CLIPTokenizer"
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+ ]
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