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VXP_Anima_ScreenCap_Turbo V1

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VXP_Anima_ScreenCap_Turbo V1 - AI Model cover image
Caricato su
18 ago 2026, 15:01
Utilizzi
2.4k
Reviews
Excellent  (3)
Passaggi di campionamento

12

Metodo di campionamento

Euler

Scala CFG

3

Negativo

lowres, bad anatomy, bad hands, bad hand, text, error, extra finger, missing fingers, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, worst quality, low quality, artist name, blurry, censor, fused limbs, fused fingers, realistic skin texture, realistic

Permessi
  • Allow image generation & sharing
  • Permettete agli utenti di scaricare il vostro modello
  • Usi commerciali

Descrizione

VXP_Anima_ScreenCap_Turbo V1 is my latest image-generation model, built on the new Anima architecture and powered by DiT (Diffusion Transformer) technology.

Unlike traditional SDXL models, which rely primarily on a U-Net backbone, DiT models process latent image patches through Transformer blocks. This modern and highly scalable architecture provides stronger global-context awareness, improved prompt interpretation, and better coherence when generating complex scenes.

The model supports both conventional tag-based prompts and detailed natural-language descriptions, allowing you to choose whichever prompting style best suits your workflow.

Its extensive built-in knowledge includes more than 35,000 characters from anime and video games, together with approximately 15,000 recognized artist styles. This makes it possible to reproduce a wide variety of characters and visual styles directly through prompting, often reducing the need for additional LoRAs.

VXP_Anima_ScreenCap_Turbo V1 has also been distilled using Turbo technology. It is optimized to produce high-quality images in as few as 12 sampling steps, significantly reducing generation cost while already operating at PixAI’s minimum generation-cost tier.

You can explore the complete catalogue of characters and artist styles recognized by the model,including example images and their corresponding prompt tags, on : https://animadex.net/

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