MinDalle Image Generator

MinDalleNode drops the 2022-era DALL·E Mini - the model that eventually became Craiyon - into your ComfyUI graph. You know the look: soggy faces, wrong fingers, a dreamy incoherence that half of Reddit now actively chases. This is the node people point to when someone asks how to get that "fucked-up 2022 aesthetic" back, and honestly, it's the honest answer. It won't make you pretty images. It makes that look, faithfully.

What you're actually getting

Under the hood this is a thin wrapper around min-dalle, Devin Kuprel's clean PyTorch implementation of the original open-source DALL·E Mini. That architecture is a sequence-to-sequence BART-style model that encodes your prompt and decodes VQGAN image tokens, all the way to a fixed 256×256 output. No latent diffusion, no checkpoint in your models/ folder - the min-dalle package downloads and loads its own weights into its own cache the first time the node runs.

The wrapper itself is gloriously simple: it caches the model on the class so you're not re-downloading per generation, reloads only when you flip the mega toggle, hardcodes temperature=1.0, single-image output, and exposes just five knobs. The README is upfront that the code was "mostly written by AI, and then fixed and made to actually work" - it's a one-commit project from May 2025, public domain, and it shows. It's a time capsule, not a product.

The inputs that matter

• prompt - plain text, no CLIP syntax or embeddings. It takes what you give it.
• seed - for reproducibility. Default 42.
• top_k - sampling cutoff at 256 by default; lower it toward e.g. 32 and output gets more degenerate and repetitive, higher loosens it.
• supercondition_factor - DALL·E Mini's "super-conditioning," a prompt-strength multiplier. Default 16; dropping it makes generations drift further from your prompt.
• mega - true loads the ~1.6B-parameter Mega model (the good one), false loads the smaller ~400M Mini. Default on.

The single output is a PIL_IMAGE - a raw PIL image, not the IMAGE tensor ComfyUI normally passes around. That one sentence is the whole game.

Installing it

Via ComfyUI Manager, search "ComfyUI-MinDalle" (or "MinDalle") and hit install. Or the manual route:

cd ComfyUI/custom_nodes
git clone https://github.com/Yahweasel/ComfyUI-MinDalle
cd ComfyUI-MinDalle
pip install -r requirements.txt

That requirements file is literally one line: min-dalle. Restart ComfyUI and you're done. There are no model files to fetch by hand - but brace yourself, because the first run downloads the model itself, several gigabytes for Mega, with no progress bar (is_verbose=False is hardcoded). It'll look hung for a while. It isn't.

The gotcha that will bite you

The PIL_IMAGE output won't plug into a SaveImage or anything else standard. You need a converter - the README points at the "PIL to Image" node in ComfyUI-Ib-CustomNodes. The bundled example workflow shows the intended chain: MinDalleNode → PILToImage → ImageUpscaleWithModel (4x-ESRGAN) → SaveImage. The upscaler isn't optional garnish, it's necessary - 256×256 straight out is tiny. And yes, that's a round trip through PIL that the README cheerfully calls "how efficient!"

What to expect in practice

Watch your VRAM: the node pins dtype=torch.float32 no matter what your card supports, so Mega alone is pushing 6-7GB of weights before activations. On a modest card, flip mega off or don't bother. Note that toggling mega forces a full model reload, so don't flip it mid-workflow casually.

Biggest honest caveat: nobody in the community has confirmed this still boots clean on a 2026 ComfyUI. It gets recommended in nostalgia threads - the r/comfyui "old VQGAN / Disco Diffusion" post and the "fucked-up aesthetic" thread both point here - but the replies also shrug about whether anything that old still runs, and the unpinned min-dalle dependency is exactly the kind of thing that rots. Use it for the look, for teaching, for a laugh. If you need a good image, reach for something from this decade.