Subfolder	Pipeline	Task	Resolution	Source checkpoint	gFID	Params
`PixelDiT-T2I-1024/`	`PixelDiTT2IPipeline`	text-to-image	1024×1024	`pixeldit_t2i_v1.pth`	—	~1.3B
`PixelDiT-XL-16-256/`	`PixelDiTPipeline`	class-to-image	256×256	`imagenet256_pixeldit_xl_epoch320.ckpt`	1.61	~700M
`PixelDiT-XL-16-512/`	`PixelDiTPipeline`	class-to-image	512×512	`imagenet512_pixeldit_xl.ckpt`	1.81	~700M

Repo layout

text

BiliSakura/PixelDiT-diffusers/
├── README.md
├── demo_inference.py
├── PixelDiT-T2I-1024/
│   ├── pipeline.py
│   ├── model_index.json
│   ├── demo.png
│   ├── scheduler/scheduler_config.json
│   └── transformer/
├── PixelDiT-XL-16-256/
│   ├── pipeline.py
│   ├── model_index.json
│   ├── demo.png
│   ├── scheduler/scheduler_config.json
│   └── transformer/
└── PixelDiT-XL-16-512/
    ├── pipeline.py
    ├── model_index.json
    ├── scheduler/scheduler_config.json
    └── transformer/

Each variant is self-contained. The scheduler/ folder uses built-in FlowMatchEulerDiscreteScheduler from PyPI diffusers. No shared helper modules at inference time beyond the local variant directory.

ImageNet class labels

id2label is embedded in each variant's model_index.json (DiT-style).

pipe.id2label — inspect id → English label correspondence
pipe.labels — reverse map (English synonym → id)
pipe.get_label_ids("golden retriever")
pipe(class_labels="golden retriever", ...) — string labels resolved automatically

Demo

Text-to-image — "A golden retriever playing in a sunny garden", 1024×1024, 50 steps, guidance_scale=2.75.

python demo_inference_t2i.py

Class 207 — golden retriever, 256×256, 100 steps, guidance_scale=2.75, CFG interval [0.1, 0.9].

python demo_inference.py

Load from a local clone

Text-to-image 1024×1024 (`PixelDiT-T2I-1024`)

python

from pathlib import Path
import torch
from diffusers import DiffusionPipeline

model_dir = Path("./PixelDiT-T2I-1024").resolve()
pipe = DiffusionPipeline.from_pretrained(
    str(model_dir),
    local_files_only=True,
    custom_pipeline=str(model_dir / "pipeline.py"),
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
    prompt="A golden retriever playing in a sunny garden",
    negative_prompt="low quality, worst quality, over-saturated, blurry, deformed, watermark",
    height=1024,
    width=1024,
    num_inference_steps=50,
    guidance_scale=2.75,
    generator=generator,
).images[0]
image.save("demo.png")

Gemma text encoder (google/gemma-2-2b-it) is downloaded on first run unless bundled under text_encoder/.

ImageNet 256×256 (`PixelDiT-XL-16-256`)

python

from pathlib import Path
import torch
from diffusers import DiffusionPipeline

model_dir = Path("./PixelDiT-XL-16-256").resolve()
pipe = DiffusionPipeline.from_pretrained(
    str(model_dir),
    local_files_only=True,
    custom_pipeline=str(model_dir / "pipeline.py"),
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

print(pipe.id2label[207])
print(pipe.get_label_ids("golden retriever"))

generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
    class_labels="golden retriever",
    height=256,
    width=256,
    num_inference_steps=100,
    guidance_scale=2.75,
    guidance_interval_min=0.1,
    guidance_interval_max=0.9,
    generator=generator,
).images[0]
image.save("demo.png")

ImageNet 512×512 (`PixelDiT-XL-16-512`)

python

from pathlib import Path
import torch
from diffusers import DiffusionPipeline

model_dir = Path("./PixelDiT-XL-16-512").resolve()
pipe = DiffusionPipeline.from_pretrained(
    str(model_dir),
    local_files_only=True,
    custom_pipeline=str(model_dir / "pipeline.py"),
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

generator = torch.Generator(device="cuda").manual_seed(42)
image = pipe(
    class_labels=207,
    height=512,
    width=512,
    num_inference_steps=100,
    guidance_scale=3.5,
    guidance_interval_min=0.1,
    guidance_interval_max=1.0,
    generator=generator,
).images[0]
image.save("demo.png")

Recommended inference settings

Variant	Steps	CFG scale	Scheduler shift	CFG interval
`PixelDiT-T2I-1024`	50	2.75	4.0	[0.0, 1.0]
`PixelDiT-XL-16-256`	100	2.75	1.0	[0.1, 0.9]
`PixelDiT-XL-16-512`	100	3.5	2.0	[0.1, 1.0]

PixelDiT denoises directly in pixel space (no VAE). height and width must be divisible by the patch size (16).

Conversion

bash

cd libs/PixelDiT-diffusers

python scripts/convert_pixeldit_t2i_to_diffusers.py \
  --checkpoint /path/to/pixeldit_t2i_v1.pth \
  --config /path/to/config.json \
  --output /path/to/PixelDiT-T2I-1024 \
  --sample-size 1024 \
  --scheduler-shift 4.0 \
  --check-load

python scripts/convert_pixeldit_to_diffusers.py \
  --checkpoint /path/to/imagenet256_pixeldit_xl_epoch320.ckpt \
  --output /path/to/PixelDiT-XL-16-256 \
  --model-size pixeldit-xl \
  --sample-size 256 \
  --scheduler-shift 1.0 \
  --check-load \
  --id2label /path/to/id2label_en.json

Citation

bibtex

@inproceedings{yu2025pixeldit,
      title={PixelDiT: Pixel Diffusion Transformers for Image Generation},
      author={Yongsheng Yu and Wei Xiong and Weili Nie and Yichen Sheng and Shiqiu Liu and Jiebo Luo},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
      year={2026},
}

License

Weights are converted from NVIDIA checkpoints released under the NSCLv1 License. Use for non-commercial research and evaluation only.

BiliSakura/PixelDiT-diffusers

BiliSakura/PixelDiT-diffusers

Available checkpoints

Repo layout

ImageNet class labels

Demo

Load from a local clone

Text-to-image 1024×1024 (PixelDiT-T2I-1024)

ImageNet 256×256 (PixelDiT-XL-16-256)

ImageNet 512×512 (PixelDiT-XL-16-512)

Recommended inference settings

Conversion

Citation

License

BiliSakura/
PixelDiT-diffusers

Text-to-image 1024×1024 (`PixelDiT-T2I-1024`)

ImageNet 256×256 (`PixelDiT-XL-16-256`)

ImageNet 512×512 (`PixelDiT-XL-16-512`)