File size: 5,913 Bytes
8587d34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | ---
license: mit
library_name: diffusers
pipeline_tag: text-to-image
tags:
- diffusers
- image-generation
- class-conditional
- text-to-image
- imagenet
- pixelgen
- flow-matching
- pixel-space
- jit
widget:
- text: golden retriever
output:
url: PixelGen-XL-16-256/demo.png
language:
- en
---
# 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](https://github.com/Bili-Sakura/Visual-Generative-Foundation-Model-Collection/tree/main/libs/PixelGen-diffusers) in [Visual-Generative-Foundation-Model-Collection](https://github.com/Bili-Sakura/Visual-Generative-Foundation-Model-Collection).
## Available checkpoints
| Subfolder | Pipeline | Task | Resolution | Model type |
| --- | --- | --- | ---: | --- |
| [`PixelGen-XL-16-256/`](PixelGen-XL-16-256/) | `PixelGenC2IPipeline` | class-to-image | 256×256 | PixelGen-XL/16 |
| [`PixelGen-XXL-16-512-t2i/`](PixelGen-XXL-16-512-t2i/) | `PixelGenT2IPipeline` | text-to-image | 512×512 | PixelGen-XXL/16-T2I |
## Repo layout
```text
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/`](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`)
```python
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`.
```python
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`)
```python
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 > 1.0` enables classifier-free guidance over a null class token.
- `sampling_method` accepts `heun` or `euler` for C2I; T2I defaults to `adam_lm`.
- `noise_scale` defaults to `1.0` at 256×256 and `2.0` at 512×512 when not specified.
## Citation
Source paper:
- [PixelGen: Improving Pixel Diffusion with Perceptual Loss](https://arxiv.org/abs/2602.02493)
- [Hugging Face Papers page](https://huggingface.co/papers/2602.02493)
```bibtex
@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},
}
```
|