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_blocks_aggressive offload. PiDFinalize converts the PiD result into the final enhanced image, which is saved separately from the native baseline.

Compared with ordinary FLUX.2 Dev generation, this workflow is not only a simple upscale. It keeps the native output for comparison, captures a PiD-ready latent, and uses PiD to rebuild high-resolution structure in a more integrated way. This is useful for product photography, commercial posters, premium packaging visuals, sharp concept art, high-detail social media covers, RunningHub showcases, and Civitai workflow publishing.

Main features:

• FLUX.2 Dev PiD direct 4K workflow

• FLUX.2 Dev fp8 mixed UNet route

• Mistral Flux2 fp8 text encoder

• Flux2 VAE decoding

• 1024×1024 base latent generation

• PiDTextPrompt unified prompt and caption input

• FluxGuidance 4 configuration

• PiDKSamplerCapture latent capture

• 50-step Euler / simple sampling setup

• Capture step 45 for PiD preparation

• PiDPrepare with Flux2 backbone

• 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 with a strong prompt that clearly defines the subject, material, lighting, camera style, typography, texture, and final commercial use case. Keep the base canvas at 1024×1024 for the first test. Run the native FLUX.2 Dev lane first and check whether the main composition, label text, reflections, and product structure are correct. If the base image is weak, adjust the prompt before entering the PiD lane. Once the native result is stable, let PiDKSamplerCapture pass the captured latent and sigma into PiDPrepare, then run PiDSample and compare the native baseline with the enhanced output. If the result becomes too heavy or unstable, keep PiD steps at 4 and rely on cleanup / offload settings instead of pushing more aggressive enhancement.

⚙️ RunningHub Workflow

Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2059961769686294530?inviteCode=rh-v1111

If the results meet your expectations, you can later deploy it locally for customization.

🎁 Fan Benefits: Register to get 1000 points + daily login 100 points — enjoy 4090 performance and 48 GB super power!

📺 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/

☕ Support Me on Ko-fi

If you find my content helpful and want to support future creations, you can buy me a coffee ☕.
Every bit of support helps me keep creating — just like a spark that can ignite a blazing flame.
👉 Ko-fi: https://ko-fi.com/aiksk

💼 Business Contact

For collaboration or inquiries, please contact aiksk95 on WeChat.

⚙️打开下方链接即可在线体验，无需安装。
👉 工作流： https://www.runninghub.ai/post/2059961769686294530?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。

🎁 粉丝福利： 注册即送 1000 积分，每日登录 100 积分，畅玩 4090 体验 48 G 超级性能！

📺 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。