Diagnose Forge performance before you tune it

First identify whether the delay is model load, every sampling step, VAE decode/save, or the browser interface. Then reproduce with the exact model, commit and extensions-off workload. “Forge is slow” is not one diagnosable state.

The first request can include model loading, patching, file reads and cache setup. Measure cold load and first generation separately, then compare repeated warm runs. If only the first FLUX run stalls, keep the full console log because historical reports describe that distinct symptom.

Begin with the correct preset and a passing base workload. Remove extensions and add-ons, keep the model and generation settings fixed, and find the slow phase. Only tune GPU Weights or swap when memory evidence points there.

There is no universal setting across GPUs, model families, formats, dimensions and environments. The safest baseline is the preset value with Automatic, Queue and CPU, followed by one controlled change.

No. Original Forge code warns that maximum weight allocation can leave no GPU memory for matrix computation and trigger fallback, OOM or a large slowdown.

The model or workload may not fit the chosen dedicated-memory allocation, or Shared was selected as the swap location. Shared memory is not extra dedicated VRAM and can make transfer or system-memory pressure part of the run.

Not by itself. Measure the phase, console memory lines, warm iteration rate, dedicated/shared memory and CPU activity together. A single utilization percentage cannot distinguish transfer, decode, waiting or fallback.

s/it is seconds per diffusion iteration, so lower is faster. it/s is iterations per second, so higher is faster. Do not compare the raw numbers as if both units move in the same direction.

Our editorial protocol is one documented cold run, one warm-up, then at least three unchanged warm runs with every result retained. This is a repeatability rule for this guide, not an official Forge minimum.

A prompt alone is insufficient. Lock the model hash, VAE, sampler, scheduler, steps, dimensions, batch, seed, precision, extensions and environment, then report cold and warm phases separately.

Record both commits and reproduce with the same model hash, settings and environment after disabling extensions. The official reporting post requests full before/after console logs; a date or “latest” label is not enough.

Not as the first move. The flags exist in the inspected code, but the original announcements describe device-specific gains and failure risks. Establish a default baseline, change one flag, and keep the result only if stability and output also pass.

More headroom can reduce offload for a fixed workload, but speed still depends on GPU architecture, model format, precision, software environment and the actual task. VRAM capacity alone is not a benchmark.

A VRAM tier does not determine one expected speed. Publish the GPU model, exact model files, dimensions, steps, memory settings and warm-run unit; otherwise a time from another 6 GB or 12 GB system is not a valid target.

Keep model-load time separate from sampling. Record the exact checkpoint and companion files, storage format, disk location, previous loaded model, console movement lines and whether the next unchanged warm generation is normal.

No universal claim is supported. Local research contains both faster and conflicting community results. A meaningful comparison requires the same artifacts, workload, environment and multiple warm runs.

The generation interface and console expose timing and memory evidence. Preserve the full console, the generation parameters and screenshots together; a cropped iteration rate is not a complete performance record.