madtune/pixeldit-diffusers

PixelDiT 1.3B — Diffusers-Compatible Pipeline

Two RTX 3060s. Infinite Lore. Zero Fear.

Unofficial HuggingFace diffusers-compatible conversion of NVIDIA's PixelDiT-1300M-1024px with dual text encoder support (Gemma-2-2B + Qwen3-2B), LoRA training, and ComfyUI integration.

All credit for the model architecture and weights goes to NVIDIA Research. This repo provides the pipeline wrapper, Qwen encoder integration, LoRA tooling, and scripts.

I do not own this model. Original weights, architecture, and training are the work of NVIDIA Research. For non-commercial use only (NSCLv1).

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Gallery — IP-Adapter style transfer (SigLIP only, no text prompt)

All generated with madtune/pixeldit-controlnet — IP-Adapter only, zero text conditioning.

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What is PixelDiT?

PixelDiT is a 1.3B parameter pixel-space diffusion transformer — no VAE, generates images directly in pixel space. Runs on 4GB VRAM.

• Architecture: MMDiT patch blocks + pixel pathway (PiT blocks)
• Text encoders: Gemma-2-2B (photorealistic) or Qwen3-2B (creative/fantasy)
• Native resolution: 1024×1024 (non-square supported)
• Samplers: Euler (default), Heun, LCM
• Minimum steps: 45–50 — below 45 produces garbage output
• LoRA: full PEFT-compatible LoRA training + inference

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Install

python3 -m venv .venv && source .venv/bin/activate
pip install torch --index-url https://download.pytorch.org/whl/cu121
pip install "diffusers>=0.31.0" "transformers>=4.40.0,<5.0.0" accelerate safetensors pillow peft
git clone https://github.com/madtunebk/pixeldit-diffusers
cd pixeldit-diffusers
python scripts/setup_diffusers_pixeldit.py

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Quick Start

# Gemma encoder (photorealistic, default)
python generate.py --prompt "a viking warrior on a cliff at sunset, cinematic"

# Portrait mode
python generate.py --height 1280 --width 768 --steps 60 --cfg 8.5 --prompt "your prompt"

# LCM fast mode (8 steps)
python generate.py --scheduler lcm --steps 8 --cfg 2.0 --prompt "your prompt"

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Python API

import torch
from diffusers import PixelDiTPipeline


pipe = PixelDiTPipeline.from_pretrained("madtune/pixeldit-diffusers",  torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()

image = pipe(
    "a viking warrior on a cliff overlooking the stormy sea at sunset",
    negative_prompt="blurry, low quality, deformed, watermark",
    height=1024, width=1024,
    num_inference_steps=50,
    guidance_scale=7.5,
).images[0]

image.save("out.jpg")

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ComfyUI

ln -s /path/to/pixeldit-diffusers/comfyui_pixeldit /path/to/ComfyUI/custom_nodes/comfyui_pixeldit

Three nodes under PixelDiT category:

• PixelDiT Text Encoder — load Gemma or any compatible encoder
• PixelDiT Model Loader — loads transformer from HF
• PixelDiT Sampler — prompt → image, all params exposed

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Scripts

Script	Purpose
generate.py	Main generation script
scripts/upscale_images.py	RealESRGAN 4× upscale before LoRA precompute
scripts/setup_diffusers_pixeldit.py	Install pipeline into active venv's diffusers

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Credits

• Original model & all credit: NVIDIA Research
• Paper: PixelDiT: Pixel-Space Diffusion Transformers for Text-to-Image Generation — NVIDIA
• This repo: unofficial diffusers conversion, Qwen integration, LoRA tooling only