How accurate are the VRAM estimates?

They are rough planning figures at fp16 and 1024px, meant to tell you whether a workflow is comfortable, tight, or impossible on your card. Real usage shifts with resolution, sampler, and attention optimizations, so treat the number as a guide rather than a guarantee.

Which node uses the most VRAM?

After the checkpoint itself, ControlNet is usually the largest single addition because it loads a second model. IPAdapter and upscalers are next. KSampler is already accounted for in the base model, so it adds nothing extra.

How does batch size affect memory?

Model weights load once regardless of batch, but the latent activations scale with each additional image. The planner adds roughly a quarter of the base model's footprint per extra image in the batch and suggests the largest batch that still fits.

What if my workflow does not fit?

Switch to fp8 or quantized weights, enable tiled VAE and tiled upscaling, run ComfyUI with --lowvram or --medvram to offload to system RAM, or reduce batch size and resolution. The planner flags the shortfall so you know how much to cut.

ComfyUI Workflow Planner

ComfyUI workflow planner

ComfyUI’s node graph gives you total control — and total responsibility for fitting everything in VRAM. Stack a ControlNet, an IPAdapter, and an upscaler on top of an SDXL checkpoint and a 12GB card that handled the base model can fall over. This planner estimates the peak VRAM of a workflow before you wire it up, so you know in advance whether it will run, run tight, or need offloading.

How it works

The tool starts from your base model’s footprint, then adds the extra VRAM each selected node contributes — ControlNet loads a whole second model, IPAdapter pulls in an image encoder, the VAE spikes at decode, and so on. Model weights load once no matter the batch, so batch size only scales the latent activations: the planner adds about a quarter of the base footprint per extra image. It compares the total against your GPU’s VRAM, reports headroom or shortfall, and suggests the largest batch that still fits.

Tips for fitting big workflows

Quantize the checkpoint. Flux in fp8 roughly halves its 22GB fp16 footprint with little visible quality loss.
Tile the heavy nodes. Tiled VAE decode and tiled upscaling cap their memory spikes, often the difference between fitting and OOM.
Offload with flags. --medvram and --lowvram push weights to system RAM between steps; you lose speed but gain headroom.
Trim the batch first. Dropping from batch 4 to batch 1 frees the most VRAM for the least quality impact — generate sequentially if memory is tight.