Flux Kontext Dev GGUF (Low VRAM) w/ Detail Daemon & Upscaling
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I've created a simple Flux Kontext Dev GGUF workflow designed specifically for low VRAM systems. By leveraging the GGUF versions of the Kontext model, you can achieve fast, high-quality image generation even on GPUs with as little as 4–6GB of VRAM.
This setup includes:
- The Flux Kontext Dev GGUF model for efficient generation
- Integrated detail enhancement (Detail Daemon) and upscaling nodes to boost your final output quality
If you want a full walkthrough of the workflow and installation process—including manual setup, recommended
settings, and tips for both local and cloud deployment—check out my YouTube tutorial linked below:
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- latest (6 months ago)
Node Details
Primitive Nodes (11)
DetailDaemonSamplerNode (1)
EmptySD3LatentImage (1)
FluxGuidance (1)
FluxKontextImageScale (1)
ModelSamplingFlux (1)
Note (2)
PrimitiveNode (2)
ReferenceLatent (1)
UnetLoaderGGUF (1)
Custom Nodes (15)
ComfyUI
- VAEDecode (1)
- VAELoader (1)
- SamplerCustomAdvanced (1)
- VAEEncode (1)
- BasicGuider (1)
- RandomNoise (1)
- DualCLIPLoader (1)
- ImageScaleBy (1)
- CLIPTextEncode (1)
- LoadImage (1)
- SaveImage (1)
- ImageUpscaleWithModel (1)
- UpscaleModelLoader (1)
- KSamplerSelect (1)
- BasicScheduler (1)
Model Details
Checkpoints (0)
LoRAs (0)