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+ # DiffSketcher
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+
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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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+
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+ ## Model Description
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+
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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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+
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+ ## Usage
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+
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+ ```python
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+ from diffusers import DiffusionPipeline
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+
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+ # Load the pipeline
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+ pipeline = DiffusionPipeline.from_pretrained("jree423/diffsketcher")
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+
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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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+
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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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+
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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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+
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+ # Save the image
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+ image.save("sunset_sketch.png")
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+ ```
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+
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+ ## Parameters
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+
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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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+
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+ ## Citation
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+
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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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+ ```