unsloth/ERNIE-Image-Turbo-GGUF

text-to-image model by unsloth

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This is a GGUF quantized version of ERNIE-Image-Turbo. unsloth/ERNIE-Image-Turbo-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. 🤗 ERNIE-Image  |  🤗 ERNIE-Image-Turbo  |  🖥️ Huggingface Demo  |  🖥️ AI Studio Demo  |  📖 Blog  |  🖼️ Art Gallery 💬 WeChat(微信)  |  🫨 Discord  |  🏷️ X 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…

unsloth/ERNIE-Image-Turbo-GGUF on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-09-24T06:49:31.306Z

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Unsloth Dynamic 2.0

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Model overview

unsloth/ERNIE-Image-Turbo-GGUF is a text-to-image model repository published by unsloth on Hugging Face. The source reports the ggml library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task: text-to-image
Library: ggml
Recent downloads (30 days): 53,779
Cumulative likes: 231
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: apache-2.0
Access: Not gated by Hugging Face
Created: 2026-04-14T19:30:06.000Z
Last modified: 2026-04-14T23:56:23.000Z

Compatibility and lineage

Language coverage was not reported.

Reported base models: baidu/ERNIE-Image-Turbo

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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