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Cataloged from unsloth/ERNIE-Image-Turbo-GGUF
All models by unsloth →This is a GGUF quantized version of ERNIE-Image-Turbo.
unsloth/ERNIE-Image-Turbo-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance.
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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:
ERNIE-Image: Our SFT model, delivers stronger general-purpose capability and instruction fidelity in typically 50 inference steps.
ERNIE-Image-Turbo: Our Turbo model, optimized by DMD and RL, achieves faster speed and higher aesthetics in only 8 inference steps.
Install the latest version of diffusers:
Install the latest version of sglang:
Start the server:
Send a generation request:
pip install git+https://github.com/huggingface/diffusersimport torchfrom diffusers import ErnieImagePipeline
pipe = ErnieImagePipeline.from_pretrained( "Baidu/ERNIE-Image-Turbo", torch_dtype=torch.bfloat16,).to("cuda")
image = pipe( prompt="This is a photograph depicting an urban street scene. Shot at eye level, it shows a covered pedestrian or commercial street. Slightly below the center of the frame, a cyclist rides away from the camera toward the background, appearing as a dark silhouette against backlighting with indistinct details. The ground is paved with regular square tiles, bisected by a prominent tactile paving strip running through the scene, whose raised textures are clearly visible under the light. Light streams in diagonally from the right side of the frame, creating a strong backlight effect with a distinct Tyndall effect—visible light beams illuminating dust or vapor in the air and casting long shadows across the street. Several pedestrians appear on the left side and in the distance, some with their backs to the camera and others walking sideways, all rendered as silhouettes or semi-silhouettes. The overall color palette is warm, dominated by golden yellows and dark browns, evoking the atmosphere of dusk or early morning.", height=1264, width=848, num_inference_steps=8, guidance_scale=1.0, use_pe=True # use prompt enhancer).images[0]
image.save("output.png")git clone https://github.com/sgl-project/sglang.gitsglang serve --model-path baidu/ERNIE-Image-Turbocurl -X POST http://localhost:30000/generate \ -H "Content-Type: application/json" \ -d '{ "prompt": "This is a photograph depicting an urban street scene. Shot at eye level, it shows a covered pedestrian or commercial street. Slightly below the center of the frame, a cyclist rides away from the camera toward the background, appearing as a dark silhouette against backlighting with indistinct details. The ground is paved with regular square tiles, bisected by a prominent tactile paving strip running through the scene, whose raised textures are clearly visible under the light. Light streams in diagonally from the right side of the frame, creating a strong backlight effect with a distinct Tyndall effect—visible light beams illuminating dust or vapor in the air and casting long shadows across the street. Several pedestrians appear on the left side and in the distance, some with their backs to the camera and others walking sideways, all rendered as silhouettes or semi-silhouettes. The overall color palette is warm, dominated by golden yellows and dark browns, evoking the atmosphere of dusk or early morning.", "height": 1264, "width": 848, "num_inference_steps": 8, "guidance_scale": 1.0, "use_pe": true }' \ --output output.png