CIVITAI / Workflows

FLUX.2 Timestep Distillation Eight-Step Reference Image Workflow

Watch the full video first if you want to understand how this FLUX.2 timestep distillation workflow works in practice. The video shows how FLUX.2 can run through a compact eight-step generation route, how optional reference images can be connected through latent conditioning, and how to launch the workflow online without rebuilding the full ComfyUI environment locally. This ComfyUI workflow is designed for FLUX.2 timestep distillation image generation and reference-guided image editing. Its main purpose is to reduce the sampling cost of FLUX.2 while keeping the workflow clean, practical, and easy to test. Instead of using a heavy full-step FLUX.2 generation route, this workflow uses a distilled eight-step model path, making it more suitable for fast prompt testing, image editing experiments, reference-based generation, and online workflow demonstrations. The workflow is built around flux2_distilled_8step_fp8mixed.safetensors as the main diffusion model. It uses mistral_3_small_flux2_fp8.safetensors as the Flux2 text encoder, flux2-vae.safetensors as the VAE, Flux2Scheduler for step scheduling, BasicGuider for the core guidance route, KSamplerSelect with Euler sampling, RandomNoise, EmptyFlux2LatentImage, SamplerCustomAdvanced, VAEDecode, and SaveImage. The whole graph is compact, which makes the logic easier to understand than a large multi-stage image workflow. The most important idea is timestep distillation. The workflow uses an eight-step distilled FLUX.2 model, meaning the sampling process is compressed into a much smaller number of steps while still trying to preserve usable image quality, prompt following, composition, and detail. This is useful when creators need speed, repeated testing, or fast comparison between prompts and reference images. The prompt side uses a structured JSON-style editing prompt. In the uploaded workflow, the example task focuses on single-image food editing: transforming a pasta dish into a Michelin-style lobster truffle tagliatelle while preserving the bowl angle, tabletop composition, shallow depth of field, and realistic food photography base. This shows that the workflow is not only for pure text-to-image generation, but also for controlled image editing and commercial-style visual transformation. The reference image system is optional. The graph includes ReferenceLatent nodes and VAEEncode nodes. If the ReferenceLat