Flux.1: Text-to-Image Graph in ComfyUI
Blueprint for clean base generation from positive and negative prompts with Flux.1 (8 GB–16 GB VRAM) using EmptyLatentImage → KSampler → VAE Decode → Save Image.
Updated Oct 17, 2026 · 1 min read
- Model Architecture
- Flux.1
- Workflow Task
- Text-to-Image Graph
- VRAM Requirement
- 8 GB–16 GB
- Primary Loader
- Load Diffusion Model + DualCLIPLoader
- Model Directory
- models/unet/
Node graph blueprint: Flux.1 × Text-to-Image Graph
This route pairs **Flux.1** with a **Text-to-Image Graph** setup designed for clean base generation from positive and negative prompts. Place your model weights in `models/unet/` and start the graph with **Load Diffusion Model + DualCLIPLoader**.
Core wiring chain: `EmptyLatentImage → KSampler → VAE Decode → Save Image`. Connect your positive and negative conditioning from CLIP Text Encode into KSampler and decode to RGB pixels.
VRAM tuning and execution tips on Windows
- **Memory target (8 GB–16 GB):** If your GPU sits at 8 GB or 12 GB, use FP8 or GGUF Q4/Q5 weights and swap in VAE Decode (Tiled) to avoid end-of-pass memory spikes.
- **Workflow persistence:** Every image saved by this route embeds the full JSON graph inside the PNG metadata — drag the output back onto ComfyUI Studio anytime to resume.
- **Get the app:** Download the Windows desktop or portable build from /download.
FAQs
Which loader node do I use for Flux.1 in a Text-to-Image Graph?
Use `Load Diffusion Model + DualCLIPLoader` with weights stored in `models/unet/`, then wire the output into `EmptyLatentImage → KSampler → VAE Decode → Save Image`.
How much VRAM does Flux.1 need for Text-to-Image Graph?
Target VRAM is 8 GB–16 GB. On 8 GB cards, load quantized FP8 or GGUF weights and enable tiled VAE decoding.
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