CIVITAI / Workflows

SD3.5 Large PiD Direct 4K Image Generation Workflow

Watch the full video first if you want to understand how this SD3.5 Large + PiD workflow works in practice. The video shows how a 1024 base image can be generated with SD3.5 Large, how the PiD enhancement stage pushes it toward 4K output, and how to launch the workflow online without building a local ComfyUI environment. This ComfyUI workflow is designed for SD3.5 Large direct 4K image generation using PiD as the high-resolution enhancement stage. Its main purpose is to create a clean SD3.5 Large base image first, capture the correct latent state during sampling, and then send that latent into a PiD 2K-to-4K refinement pipeline. The result is a more detailed and more production-oriented output than a simple native image generation pass. The workflow is built around sd3.5_large_fp8_scaled.safetensors as the main checkpoint. It uses a 1024×1024 EmptySD3LatentImage as the base canvas, CLIPTextEncode for positive and negative conditioning, PiDTextPrompt for unified prompt and caption handling, PiDKSamplerCapture for SD3 latent generation and PiD latent capture, PiDPrepare for preparing the captured latent, PiDSample for the PiD refinement pass, PiDFinalize for final image conversion, VAEDecode for native baseline output, and SaveImage for both comparison outputs. The first lane is the native SD3.5 Large generation route. The prompt is written into PiDTextPrompt, then sent into the positive prompt encoder and also used as the caption for PiD preparation. The negative prompt suppresses common image problems such as deformation, blur, low resolution, bad anatomy, extra fingers, JPEG artifacts, and watermarks. This makes the workflow suitable for both artistic generation and cleaner comparison testing. The key generation node is PiDKSamplerCapture. Unlike a normal KSampler, this node produces the final native latent while also capturing a PiD-ready latent at a selected capture step. In this workflow, the sampling setup uses 25 steps, CFG 4.4, Euler sampler, sgm_uniform scheduler, denoise 1.0, and capture_step 23. The captured PiD sigma is automatically sent into PiDPrepare, which helps the PiD stage receive the correct sampling-state information instead of relying on manual sigma guessing. After capture, PiDPrepare is configured with the SD3 backbone, 2kto4k PiD checkpoint type, scale 4, auto download enabled, and cleanup after prepare enabled. PiDSample then p