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ERNIE-Image-Turbo-GGUF

unsloth · ERNIE-Image-Turbo-GGUF

This is a GGUF quantized version of ERNIE-Image-Turbo. unsloth/ERNIE-Image-Turbo-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - Uses tooling from ComfyUI-GGUF by city96. ERNIE-Image-Turbo is an open text-to-image generation model developed by the ERNIE-Image team at Baidu. It is the distilled release of ERNIE-Image, built on the same single-stream Diffusion Transformer (DiT) family and designed for fast generation with strong fidelity in only 8 inference steps. The model retains strong controllability in practical generation scenarios where accurate content realization matters as much as aesthetics. In particular, ERNIE-Image-Turbo remains strong on complex instruction following, text rendering, and structured image generation, making it well suited for posters, comics, multi-panel layouts, and other content creation tasks that require both visual quality and efficiency. It also supports a broad range of visual styles, including realistic photography, design-oriented imagery, and stylized aesthetic outputs. **Highlights:** - **Fast and efficient**: As the distilled checkpoint of ERNIE-Image, ERNIE-Image-Turbo delivers strong generation quality with only 8 inference steps, making it suitable for latency-sensitive applications. - **Text rendering**: ERNIE-Image-Turbo performs well on dense, long-form, and layout-sensitive text, making it a strong choice for posters, infographics, UI-like images, and other text-heavy visual content. - **Instruction following**: The model is able to follow complex prompts involving multiple objects, detailed relationships, and knowledge-intensive descriptions with strong reliability. - **Structured generation**: ERNIE-Image-Turbo is effective for structured visual tasks such as posters, comics, storyboards, and multi-panel compositions, where layout and organization are critical. - **Style coverage**: In addition to clean and readable design-oriented outputs, the model also supports realistic photography and distinctive stylized aesthetics, including softer and more cinematic visual tones. - **Practical deployment**: Thanks to its compact size, ERNIE-Image-Turbo

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이미지 생성license:apache-2.0
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정보 업데이트2026/11/2 19:16:44
OVERVIEW

모델 소개

This is a GGUF quantized version of ERNIE-Image-Turbo. unsloth/ERNIE-Image-Turbo-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - Uses tooling from ComfyUI-GGUF by city96. ERNIE-Image-Turbo is an open text-to-image generation model developed by the ERNIE-Image team at Baidu. It is the distilled release of ERNIE-Image, built on the same single-stream Diffusion Transformer (DiT) family and designed for fast generation with strong fidelity in only 8 inference steps. The model retains strong controllability in practical generation scenarios where accurate content realization matters as much as aesthetics. In particular, ERNIE-Image-Turbo remains strong on complex instruction following, text rendering, and structured image generation, making it well suited for posters, comics, multi-panel layouts, and other content creation tasks that require both visual quality and efficiency. It also supports a broad range of visual styles, including realistic photography, design-oriented imagery, and stylized aesthetic outputs. **Highlights:** - **Fast and efficient**: As the distilled checkpoint of ERNIE-Image, ERNIE-Image-Turbo delivers strong generation quality with only 8 inference steps, making it suitable for latency-sensitive applications. - **Text rendering**: ERNIE-Image-Turbo performs well on dense, long-form, and layout-sensitive text, making it a strong choice for posters, infographics, UI-like images, and other text-heavy visual content. - **Instruction following**: The model is able to follow complex prompts involving multiple objects, detailed relationships, and knowledge-intensive descriptions with strong reliability. - **Structured generation**: ERNIE-Image-Turbo is effective for structured visual tasks such as posters, comics, storyboards, and multi-panel compositions, where layout and organization are critical. - **Style coverage**: In addition to clean and readable design-oriented outputs, the model also supports realistic photography and distinctive stylized aesthetics, including softer and more cinematic visual tones. - **Practical deployment**: Thanks to its compact size, ERNIE-Image-Turbo

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