DSD Image Generator

This is the payoff node. You hand it a photo of a subject - a character, a product, your dog - plus a sentence, and it generates that subject in a brand-new context, no per-instance training required. Where IP-Adapter and PuLID inject image embeddings through attention, DSD (Diffusion Self-Distillation) goes bigger: it's a research model from primecai that the community met with a now-famous "End of LoRA?" thread on r/StableDiffusion in March 2025. The honest read on that thread, even from fans: it threatens character LoRAs, not style LoRAs, and it eats VRAM (people were reporting roughly 24GB just to run it). It's a zero-shot subject-preservation model built on a FLUX.1-schnell base - not a speed hack.

How it works

DSD generates on a wide canvas. Your reference image gets resized to height × width/2 and slotted into the left half; the model denoises the right half conditioned on it. The reference is VAE-encoded and fed into the transformer as a conditioning signal, and the usual single CFG scale is split in two - one direction pulls the image toward your reference (image_guidance_scale), the other toward your text (text_guidance_scale). When decoding finishes, the canvas is cut down the middle: the right half is your generated image, the left half is returned as reference_image so you can eyeball exactly what the model was preserving.

That two-scale CFG is the thing to play with. If your subject drifts, raise image_guidance_scale; if your text prompt gets ignored, raise text_guidance_scale. The overall guidance_scale (3.5 default) stays in charge of prompt adherence.

The inputs that matter

• dsd_model - comes from the DSD Model Downloader or Loader; nothing else will plug in.
• image - your subject photo. Feed it something clean and well-lit; garbage in, garbage subject.
• prompt / negative_prompt - multiline. The model strips newlines itself.
• seed - 0 means "surprise me." The node rolls a random one and returns it on the seed output so you can lock in a keeper.
• width / height - default 1024×512. Here's the trap: that's the combined canvas width, so at defaults you actually get two 512×512 panels, not a 1024-wide image. Expect the output to be width/2 square.
• num_inference_steps - 28 default; lower it if you're memory-constrained.
• use_gemini_prompt - cosmetic. It doesn't call Gemini itself (the Enhancer node does that); it only toggles the status text. What actually matters is wiring the enhanced prompt into this node's prompt input.
• resize_params (optional) - from DSD Resize Selector; controls how your photo is crammed into the reference slot. Leave it unconnected and you get the same resize_and_center_crop behavior anyway.

The outputs are image (the real result, wire it to a Save Image), reference_image (the left panel), and seed.

Installing and first run

Install the pack, then grab the model - see the DSD Model Downloader article for the full dance. Fair warning: your first run of this node triggers two big downloads (the DSD transformer plus the FLUX.1-schnell base it loads behind the scenes), and generation is heavy. The README's own memory advice is the right ladder: stay on bfloat16, drop resolution, cut steps, and only reach for the CPU-offload options on the loader if you're truly VRAM-starved.

If you get CUDA out-of-memory, it's not a bug in the node - it's the nature of running a FLUX-sized conditional model locally. Drop to 512×512 and 20 steps and see if it breathes.

Wiring the whole thing

Load → (Downloader or Loader) → Image Generator, Load Image → Image Generator, and the two dsd_model/image wires are the whole graph. For better subject descriptions, route the Gemini Enhancer's output into prompt. That's the entire intended workflow, and it's refreshingly small.