FLUX.1 with ControlNet enables guided image generation — sketch-to-image, depth-conditioned, pose-controlled. Each ControlNet adds VRAM pressure on top of an already memory-hungry base model.
TL;DR
FLUX.1 dev FP8 + 1 ControlNet (~3 GB) fits on a 16 GB card; FP16 + 2+ ControlNets needs 24+ GB. RTX 5090 32 GB is the comfortable home; 5060 Ti 16 GB workable with FP8 + single ControlNet.
FLUX.1 ControlNet variants
- FLUX.1-Canny: edge-conditioned generation
- FLUX.1-Depth: depth-map-conditioned
- FLUX.1-Pose: human-pose-conditioned
- FLUX.1-Tile: tile-conditioned (for upscaling)
- InstantX FLUX-Union: community multi-mode ControlNet
VRAM math
| Component | FP16 | FP8 |
|---|---|---|
| FLUX.1 dev base | 24 GB | 12 GB |
| + 1 ControlNet | +3 GB | +1.5 GB |
| + 2 ControlNets | +6 GB | +3 GB |
| + IP-Adapter | +0.5 GB | +0.3 GB |
| + LoRA | +0.2 GB | +0.2 GB |
| Typical peak | ~30 GB | ~16 GB |
Hardware sizing
| Workflow | Min GPU |
|---|---|
| FLUX.1 dev FP8 + 1 ControlNet | RTX 5060 Ti 16 GB |
| FLUX.1 dev FP16 + 1 ControlNet | RTX 5090 32 GB |
| FLUX.1 dev FP16 + 2 ControlNets + 2 LoRAs | RTX 6000 Pro 96 GB |
| Batch 4 + ControlNet | RTX 6000 Pro 96 GB |
ComfyUI workflow
Standard pattern in ComfyUI:
- Load FLUX.1 dev checkpoint (FP8)
- Load Canny ControlNet model
- Apply Canny preprocessor to input image
- Connect Apply ControlNet node
- Sample with 25 steps Euler
- VAE decode
Verdict
For FLUX.1 + ControlNet, the RTX 5090 is the comfortable home. The 5060 Ti works for single-ControlNet FP8 workflows. For multi-LoRA + multi-ControlNet stacks, 6000 Pro.
Bottom line
FLUX.1 with ControlNet is one of the strongest open-weight image-gen pipelines available. RTX 5090 is the right GPU. See best GPU for FLUX.
