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README.md
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# DiffSketcher
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This is a Hugging Face implementation of [DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models](https://github.com/ximinng/DiffSketcher).
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## Model Description
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DiffSketcher is a novel approach for synthesizing vector sketches from text prompts by leveraging the power of latent diffusion models. It extracts cross-attention maps from a pre-trained text-to-image diffusion model and uses them to guide the optimization of vector sketches.
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## Usage
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```python
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from diffusers import DiffusionPipeline
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# Load the pipeline
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pipeline = DiffusionPipeline.from_pretrained("jree423/diffsketcher")
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# Generate a vector sketch
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result = pipeline(
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prompt="A beautiful sunset over the mountains",
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negative_prompt="ugly, blurry",
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num_paths=96,
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token_ind=4,
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num_iter=800,
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guidance_scale=7.5,
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width=1.5,
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seed=42
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)
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# Access the SVG string and rendered image
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svg_string = result["svg"]
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image = result["image"]
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# Save the SVG
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with open("sunset_sketch.svg", "w") as f:
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f.write(svg_string)
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# Save the image
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image.save("sunset_sketch.png")
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```
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## Parameters
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- `prompt` (str): The text prompt to guide the sketch generation.
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- `negative_prompt` (str, optional): Negative text prompt for guidance.
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- `num_paths` (int, optional): Number of paths to use in the sketch. Default is 96.
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- `token_ind` (int, optional): Token index for attention. Default is 4.
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- `num_iter` (int, optional): Number of optimization iterations. Default is 800.
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- `guidance_scale` (float, optional): Scale for classifier-free guidance. Default is 7.5.
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- `width` (float, optional): Stroke width. Default is 1.5.
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- `seed` (int, optional): Random seed for reproducibility.
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- `return_dict` (bool, optional): Whether to return a dict or tuple. Default is True.
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- `output_type` (str, optional): Output type, one of "pil", "np", or "svg". Default is "pil".
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## Citation
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```bibtex
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@article{xing2023diffsketcher,
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title={DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models},
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author={Xing, Ximing and Xie, Chuang and Qiao, Yu and Xu, Hongteng},
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journal={arXiv preprint arXiv:2306.14685},
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year={2023}
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}
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```
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