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BiliSakura / PixelGen-diffusers
Model's Last Updated: June 21 2026
text-to-image
Introduction of PixelGen-diffusers
Model Details of PixelGen-diffusers
BiliSakura/PixelGen-diffusers
Self-contained PixelGen checkpoints for Hugging Face diffusers. Each variant folder ships its own pipeline code, component modules, and weights.
Converted from upstream PixelGen checkpoints using PixelGen-diffusers in Visual-Generative-Foundation-Model-Collection .
Available checkpoints
| Subfolder | Pipeline | Task | Resolution | Model type |
|---|---|---|---|---|
PixelGen-XL-16-256/
|
PixelGenC2IPipeline
|
class-to-image | 256×256 | PixelGen-XL/16 |
PixelGen-XXL-16-512-t2i/
|
PixelGenT2IPipeline
|
text-to-image | 512×512 | PixelGen-XXL/16-T2I |
Repo layout
BiliSakura/PixelGen-diffusers/
├── README.md
├── PixelGen-XL-16-256/
│ ├── pipeline.py
│ ├── model_index.json
│ ├── demo.png
│ ├── scheduler/
│ │ ├── scheduler_config.json
│ │ └── scheduling_pixelgen.py
│ └── transformer/
│ ├── config.json
│ └── transformer_jit.py
└── PixelGen-XXL-16-512-t2i/
├── pipeline.py
├── model_index.json
├── conversion_metadata.json
├── scheduler/
│ ├── scheduler_config.json
│ └── scheduling_pixelgen.py
├── text_encoder/
├── tokenizer/
└── transformer/
├── config.json
├── diffusion_pytorch_model.safetensors
└── transformer_jit_t2i.py
Each class-conditional variant is self-contained: load with
custom_pipeline=.../pipeline.py
and
trust_remote_code=True
. PixelGen denoises directly in pixel space (no VAE).
ImageNet class labels
For
PixelGen-XL-16-256/
,
id2label
is embedded in
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
Class 207 — golden retriever, 256×256, 50 steps,
guidance_scale=2.25
, Heun solver,
timeshift=2.0
.
Load from Hugging Face
Class-to-image (
PixelGen-XL-16-256
)
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"BiliSakura/PixelGen-diffusers/PixelGen-XL-16-256",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
).to("cuda")
print(pipe.id2label[207])
print(pipe.get_label_ids("golden retriever"))
generator = torch.Generator(device="cuda").manual_seed(0)
images = pipe(
class_labels="golden retriever",
num_inference_steps=50,
guidance_scale=2.25,
generator=generator,
).images
Text-to-image (
PixelGen-XXL-16-512-t2i
)
Uses a bundled Qwen3 text encoder when
text_encoder/
is present; otherwise downloads from the path recorded in
conversion_metadata.json
.
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"BiliSakura/PixelGen-diffusers/PixelGen-XXL-16-512-t2i",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
)
generator = torch.Generator(device="cuda").manual_seed(42)
images = pipe(
prompt="A golden retriever playing in a sunny garden",
num_inference_steps=50,
guidance_scale=4.0,
generator=generator,
).images
Load from a local clone
Class-to-image (
PixelGen-XL-16-256
)
from pathlib import Path
import torch
from diffusers import DiffusionPipeline
model_dir = Path("./PixelGen-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,
).to("cuda")
generator = torch.Generator(device="cuda").manual_seed(0)
image = pipe(
class_labels="golden retriever",
num_inference_steps=50,
guidance_scale=2.25,
generator=generator,
).images[0]
image.save("demo.png")
Recommended inference settings
| Variant | Steps | CFG scale | Solver | Timeshift | CFG interval |
|---|---|---|---|---|---|
PixelGen-XL-16-256
|
50 | 2.25 | heun | 2.0 | [0.1, 0.9] |
PixelGen-XXL-16-512-t2i
|
25 | 4.0 | adam_lm | 3.0 | [0.0, 1.0] |
height
and
width
are fixed by each checkpoint's
sample_size
. Custom sizes are not supported for these exports.
Interface notes
-
Class-conditional generation uses
class_labels(integer ImageNet id or English synonym). -
guidance_scale > 1.0enables classifier-free guidance over a null class token. -
sampling_methodacceptsheunoreulerfor C2I; T2I defaults toadam_lm. -
noise_scaledefaults to1.0at 256×256 and2.0at 512×512 when not specified.
Citation
Source paper:
@article{ma2026pixelgen,
title={PixelGen: Improving Pixel Diffusion with Perceptual Loss},
author={Zehong Ma and Ruihan Xu and Shiliang Zhang},
year={2026},
eprint={2602.02493},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2602.02493},
}
Runs of BiliSakura PixelGen-diffusers on huggingface.co
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More Information About PixelGen-diffusers huggingface.co Model
Url of PixelGen-diffusers
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Provider of PixelGen-diffusers huggingface.co
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