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  1. cpen.ai › ai-disturbance-textureTry AI Disturbance Texture & Save your Art from AI - Cpen AI

    AI disturbance texture is a revolutionary technique utilized in digital art programs, particularly drawing apps. Its primary function is to shield an artist’s distinct style from being replicated by AI image generation tools.

  2. Imágenes

    • Disturbance Generator (Texture)
    • Disturbance Generator (Texture)
    • Disturbance Digital Art by Norma Jean Lipert - Fine Art America
    • Disturbance Generator (Texture)
    • Disturbance Generator (Texture)

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  3. ibispaint.com › lecture › index62. AI Disturbance - How to use ibisPaint

    The AI Disturbance applies noise to illustrations to interfere with the fine-tuning of image-generating AIs (such as Stable Diffusion) using technologies like LoRA and DreamBooth.

  4. poly.cam › tools › ai-texture-generatorAI Texture Generator for Blender, Unreal, Unity | Polycam

    Polycam's free AI Texture Generator is like a magic wand for anyone into 3D modeling, gaming, or animation. It can turn your creative ideas into reality by generating incredibly realistic textures based on a simple text prompt.

  5. johannesballing.users.earthengine.app › view › forest-disturbance-textureForest Disturbance Texture

    Forest Disturbance Texture ... more_vert

  6. www.filterforge.com › filters › 15805Disturbance Generator (Texture) - Filter Forge

    Disturbance Generator texture, created by Boogie Jack in Filter Forge. This is a random disturbance generator, creating intricate twists and weaves of blobs, strings, and holes.

  7. www.freepik.es › fotos-vectores-gratis › textura-multicolorImágenes de Textura Multicolor - Descarga gratuita en Freepik

    Encuentra y descarga recursos gráficos gratuitos de Textura Multicolor. +97.000 Vectores, fotos de stock y archivos PSD. Gratis para uso comercial Imágenes de gran calidad.

  8. www.sciencedirect.com › science › articleHow textural features can improve SAR-based tropical forest...

    1 de nov. de 2023 · The method was found to be robust across a variety of forest disturbance types. The largest reduction of omission errors and greatest improvement of timeliness was evident for sites with large unfragmented disturbance patches (e.g., large-scale clearings, fires and mining).