SD3.5-Large-IP-Adapter
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SD3.5 Large IP Adapter
Overview :
The SD3.5-Large-IP-Adapter is an IP adapter developed by the InstantX Team, based on the Stable Diffusion 3.5 Large model. This model analogizes image processing to text processing, boasting strong image generation capabilities and the potential for enhanced quality and effects through adapter technology. Its significance lies in promoting the advancement of image generation technology, particularly in creative work and artistic expression. Background information indicates that the model is a sponsored project by Hugging Face and fal.ai, adhering to the stabilityai-ai-community licensing agreement.
Target Users :
The target audience includes researchers, developers, and artists in the field of image generation. This product is suitable for them as it provides a powerful tool for generating high-quality images and serves as an innovative component in the research and creative processes.
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Use Cases
Using the SD3.5-Large-IP-Adapter to generate images in specific styles or themes.
In artistic creation, leveraging the model to produce images with creative elements.
In education, serving as a teaching tool to help students understand image generation technology.
Features
? IP adapter technology based on the Stable Diffusion 3.5 Large model, enhancing image generation quality.
? Utilizing google/siglip-so400m-patch14-384 to encode images for improved performance.
? Implementing TimeResampler technology for image projection processing.
? Setting the number of image tokens to 64 to optimize model processing capabilities.
? Supporting high-resolution image generation, though sensitive to generation parameters.
? Providing code examples for easy local deployment and usage.
? Adhering to the stabilityai-ai-community licensing agreement to ensure legal compliance.
How to Use
1. Import the necessary libraries, such as torch and PIL.
2. Load the SD3.5-Large-IP-Adapter model from the Hugging Face model library.
3. Initialize the model, including setting the image encoder path and the number of image tokens.
4. Prepare the reference image and convert it to RGB format.
5. Configure the generation parameters, such as image size, prompt words, negative prompts, etc.
6. Call the model to generate images and obtain the results.
7. Save the generated images locally.
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