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Getting in a bit of background info
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patil-suraj
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| To start, use the [`DiffusionPipeline`] for quick inference and sample generations! | ||
| Textual Inversion is a technique for capturing novel concepts from a small number of example images in a way that can later be used to control text-to-image pipelines. It does so by learning new 'words' in the embedding space of the pipeline's text encoder. These special words can then be used within text prompts to achieve very fine-grained control of the resulting images. | ||
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| _By using just 3-5 images you can teach new concepts to a model such as Stable Diffusion for personalized image generation ([image source](https://github.com/rinongal/textual_inversion))._ | ||
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| This technique was introduced in [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://arxiv.org/abs/2208.01618). The paper demonstrated the concept using a [latent diffusion model](https://github.com/CompVis/latent-diffusion) but the idea has since been applied to other variants such as [Stable Diffusion](https://huggingface.co/docs/diffusers/main/en/conceptual/stable_diffusion). | ||
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| ## How It Works | ||
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| _Architecture Overview from the [textual inversion blog post](https://textual-inversion.github.io/)_ | ||
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| Before a text prompt can be used in a diffusion model, it must first be processed into a numerical representation. This typically involves tokenizing the text, converting each token to an embedding and then feeding those embeddings through a model (typically a transformer) whose output will be used as the conditioning for the diffusion model. | ||
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| Textual inversion learns a new token embedding (v* in the diagram above). A prompt (that includes a token which will be mapped to this new embedding) is used in conjunction with a noised version of one or more training images as inputs to the generator model, which attempts to predict the denoised version of the image. The embedding is optimized based on how well the model does at this task - an embedding that better captures the object or style shown by the training images will give more useful information to the diffusion model and thus result in a lower denoising loss. After many steps (typically several thousand) with a variety of prompt and image variants the learned embedding should hopefully capture the essence of the new concept being taught. |
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Getting in a bit of background info as a starting point for these docs