This workflow is designed for creators who demand high-fidelity results without the heavy hardware tax. By leveraging the Ernie-Image-Turbo model in GGUF format, this setup provides a streamlined, fast, and VRAM-efficient path to professional-grade AI imagery. Key Features GGUF Optimization: Optimized for lower VRAM usage via the UnetLoaderGGUF node, allowing high-end generation on mid-range...
This workflow is designed for creators who demand high-fidelity results without the heavy hardware tax. By leveraging the Ernie-Image-Turbo model in GGUF format, this setup provides a streamlined, fast, and VRAM-efficient path to professional-grade AI imagery.
Key Features
GGUF Optimization: Optimized for lower VRAM usage via the UnetLoaderGGUF node, allowing high-end generation on mid-range hardware.
Turbo Performance: Uses the res_2s sampler with only 8 steps, delivering hyper-realistic results in a fraction of the time.
High-Resolution Ready: Defaulted to a 720x1024 portrait ratio, perfect for cinematic character shots and detailed textures.
Advanced CLIP Integration: Utilizes the ministral-3-3b CLIP model for superior prompt adherence and nuanced understanding of complex instructions.
Components & Nodes
Model: ernie-image-turbo-Q8_0.gguf
VAE: flux2-vae.safetensors
CLIP: ministral-3-3b.safetensors
Sampler: KSampler configured for speed (res_2s / simple scheduler).
How to Use
Ensure you have the ComfyUI-GGUF custom nodes installed.
Place your GGUF model in the models/unet folder and CLIP/VAE in their respective directories.
Input your prompt into the PrimitiveStringMultiline node and hit generate to see the "Turbo" efficiency in action.
This workflow is designed for creators who demand high-fidelity results without the heavy hardware tax. By leveraging the Ernie-Image-Turbo model in GGUF format, this setup provides a streamlined, fast, and VRAM-efficient path to professional-grade AI…
This workflow is designed for creators who demand high-fidelity results without the heavy hardware tax. By leveraging the Ernie-Image-Turbo model in GGUF format, this setup provides a streamlined, fast, and VRAM-efficient path to professional-grade AI…
