ZImageTurboPiDv2 +Color transfer from lora image as PiD tends to raise the gamma +Optional SageAttention +ModelSampling shift to PiD model <span style="background-color:rgb(26, 27, 30);color:rgb(193, 194, 197);font-family:-apple-system, BlinkMacSystemFont, &quot;Segoe UI&quot;, Roboto, Helvetica, Arial, sans-serif, &quot;Apple Color Emoji&quot;, &quot;Segoe UI Emoji&quot;;font-size:16px">Update your ComfyUI for PixelDiT support For models/text encoder <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/PixelDiT">https://huggingface.co/Comfy-Org/PixelDiT</a> PiD reformulates the latent-to-pixel decoder as a conditional pixel-space diffusion model, unifying decoding and upsampling into a single generative module. It denoises directly in high-resolution pixel space and produces a super-resolved image in one pass. This repository hosts the released decoder checkpoints, plus the encoder/decoder ("VAE") weights they depend on. This workflow takes a ZImageTurbo/ZImage generation latent from 512x512&gt;2048x2048/1024x1024&gt;4096x4096 or 16:9, 4:3, 3:4 aspect ratios. RTX 3060 12gb + 32gb ram 60 seconds upscale+decode for 1024 model 768x1024&gt;3072x4096 8 seconds upscale+decode for 512 model 432x576&gt;1728x2304

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