CIVITAI / Workflows

FLUX.2 Dev PiD Direct 4K Image Generation Workflow

Watch the full video first if you want to understand how this FLUX.2 Dev + PiD workflow works in practice. The video shows how a 1024 base image can be generated with FLUX.2 Dev, how the PiD 2K-to-4K enhancement lane improves the final image, and how to launch the workflow online without building a local ComfyUI environment. This ComfyUI workflow is designed for FLUX.2 Dev direct 4K image generation using PiD as the high-resolution enhancement stage. Its main purpose is to create a strong FLUX.2 Dev base image first, capture the correct latent state during sampling, and then send that latent into a PiD refinement pipeline for a cleaner and more detailed final output. The workflow is built around flux2_dev_fp8mixed.safetensors as the main UNet model. It uses mistral_3_small_flux2_fp8.safetensors as the Flux2 text encoder, flux2-vae.safetensors as the VAE, and EmptyFlux2LatentImage for the base canvas. The base resolution is controlled through separate width and height primitive nodes, both set to 1024, giving the workflow a clean 1024×1024 starting point before the 2K-to-4K PiD pass. The prompt is handled through PiDTextPrompt. This node sends the same text into the positive prompt encoder and also provides the caption for PiD preparation. In this workflow, the prompt is written for a high-end commercial product photograph, with a luxury skincare bottle, wet black stone, cyan and gold rim lighting, crisp reflections, tiny water droplets, clean product label text, and premium advertising composition. This makes the workflow especially suitable for testing detail, typography, packaging clarity, lighting control, and product-style realism. The main FLUX.2 generation stage uses CLIPTextEncode, FluxGuidance, and PiDKSamplerCapture. FluxGuidance is set to 4, while the sampler route uses 50 steps, CFG 4, Euler sampler, simple scheduler, denoise 1.0, and capture_step 45. PiDKSamplerCapture outputs both the final native latent and a PiD latent. The native latent is decoded through the Flux2 VAE and saved as the baseline result, while the PiD latent is sent into the enhancement lane. The PiD lane is configured with the Flux2 backbone, 2kto4k checkpoint type, scale 4, auto download enabled, and cleanup after prepare enabled. PiDSample then performs the high-resolution enhancement pass with 4 PiD steps, CFG scale 1.0, fixed seed, aggressive cleanup, and sequential_b