Watch the full video first if you want to understand how this FLUX.1 Dev + PiD workflow works in practice. The video shows how a 1024 base image can be generated and then pushed into a PiD-based 4K enhancement lane, how the native baseline compares with the enhanced result, and how to launch the workflow online without building a local ComfyUI setup.
This ComfyUI workflow is designed for FLUX.1 Dev 4K image generation using PiD as the high-resolution enhancement stage. Its main purpose is to create a strong FLUX.1 Dev base image first, capture the correct intermediate latent state, and then send it into a PiD 2K-to-4K refinement pipeline for sharper, richer, and more detailed final output.
The workflow is built around flux1-dev-fp8.safetensors as the main checkpoint. It uses a 1024×1024 base latent, CLIP text encoding, FluxGuidance, PiDTextPrompt, PiDKSamplerCapture, PiDPrepare, PiDSample, PiDFinalize, VAEDecode, and SaveImage. The graph also includes a native FLUX output lane and a PiD enhanced output lane, making it useful for direct comparison between the standard generation result and the 4K-enhanced version.
The first part of the workflow is the FLUX.1 Dev base generation. The prompt is written into PiDTextPrompt and then passed into the positive prompt encoder. FluxGuidance is set to 3.5, while CFG is set to 1.0, which is important because FLUX Dev does not use the negative prompt in the same way as older CFG-heavy models. The workflow note also makes this clear: for FLUX Dev and Schnell, CFG should stay at 1.0 because negative prompting is ignored when CFG is 1.0.
The sampling stage uses PiDKSamplerCapture instead of a normal KSampler. This node generates the native FLUX latent while also capturing a PiD-ready latent at the selected capture step. In this workflow, the setup uses 28 steps, Euler sampler, simple scheduler, denoise 1.0, and capture_step 26. This is a practical setting because it allows the base image to develop enough structure before the PiD stage receives the captured latent.
After the base latent is captured, PiDPrepare converts it into a PiD-compatible preparation object. The workflow is configured for the FLUX backbone, 2kto4k PiD checkpoint type, scale 4, auto download enabled, and cleanup after prepare enabled. Then PiDSample runs the high-resolution PiD pass with 4 PiD steps, CFG scale 1.0, fixed seed, aggressive cleanup, and sequential block offload. Finally, PiDFinalize converts the sampled PiD result into the enhanced final image.
Compared with ordinary FLUX generation, this workflow is not just a simple upscale. It uses a dedicated PiD refinement pass to improve detail density, texture clarity, and high-resolution visual structure. Compared with traditional external upscalers, it is more integrated into the generation pipeline because it starts from the captured model latent and uses the original caption as part of the PiD preparation logic.
This workflow is suitable for fantasy key art, cinematic illustrations, character posters, concept art, product-style visuals, high-detail portraits, game art, premium social media covers, RunningHub showcases, and Civitai workflow publishing.
Main features:
FLUX.1 Dev 4K image generation workflow
1024 base latent generation route
FLUX.1 Dev fp8 checkpoint support
FluxGuidance 3.5 configuration
CFG 1.0 FLUX-style setup
PiDTextPrompt unified prompt input
PiDKSamplerCapture intermediate latent capture
28-step Euler / simple base sampling
Capture step 26 for PiD preparation
PiDPrepare FLUX backbone support
PiD 2K-to-4K enhancement route
PiDSample with 4 PiD steps
Sequential block offload and aggressive cleanup
Native baseline and enhanced output comparison
Suggested workflow:
Start by writing a strong visual prompt with a clear subject, environment, style direction, lighting, and detail target. Keep the base latent at 1024×1024 for the first test, then run the native FLUX lane to confirm that the composition is correct. If the base result is weak, adjust the prompt before entering PiD. Once the native image has the right structure, let PiDKSamplerCapture send the captured latent into PiDPrepare and PiDSample. Use the native saved image as your baseline and the PiD finalized image as your enhanced 4K output. If the final result is too heavy or unstable, keep PiD steps at 4 and rely on cleanup / offload settings instead of forcing more aggressive parameters.
⚙️ RunningHub Workflow
Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2059961777689030657?inviteCode=rh-v1111
If the results meet your expectations, you can later deploy it locally for customization.
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📺 Bilibili Updates (Mainland China & Asia-Pacific)
If you’re in the Asia-Pacific region, you can watch the video below to see the workflow demonstration and creative breakdown.
📺 Bilibili Video: https://www.bilibili.com/video/BV1tsVH6kEBJ/
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⚙️打开下方链接即可在线体验,无需安装。
👉 工作流: https://www.runninghub.ai/post/2059961777689030657?inviteCode=rh-v1111
如果觉得效果理想,你也可以在本地进行自定义部署。
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📺 Bilibili 更新(中国大陆及南亚太地区)
如果你在中国大陆或南亚太地区,可以通过下方视频查看该工作流的实测效果与构思讲解。
📺 B站视频: https://www.bilibili.com/video/BV1tsVH6kEBJ/
我会在 夸克网盘 持续更新模型资源:
👉 https://pan.quark.cn/s/20c6f6f8d87b
这些资源主要面向本地用户,方便进行创作与学习。
