BiliSakura/PixelFlow-diffusers

Self-contained PixelFlow checkpoints for Hugging Face diffusers. Each variant folder ships its own pipeline.py, component modules, and weights.

Available checkpoints

Subfolder	Pipeline	Task	Resolution	Params
`PixelFlow-256/`	`PixelFlowPipeline`	class-to-image	256×256	677M
`PixelFlow-T2I/`	`PixelFlowT2IPipeline`	text-to-image	1024×1024	882M

Repo layout

BiliSakura/PixelFlow-diffusers/
├── README.md
├── PixelFlow-256/
│   ├── pipeline.py
│   ├── model_index.json
│   ├── scheduler/scheduler_config.json
│   └── transformer/
└── PixelFlow-T2I/
    ├── pipeline.py
    ├── model_index.json
    ├── scheduler/scheduler_config.json
    ├── text_encoder/
    ├── tokenizer/
    └── transformer/

Each variant is self-contained. The scheduler/ folder contains scheduler_config.json and scheduling_pixelflow.py with PixelFlowScheduler.

No shared helper modules at inference time; only PyPI diffusers plus the local variant directory.

ImageNet class labels

For class-conditional PixelFlow-256/, id2label is embedded in PixelFlow-256/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, 40 steps ([10, 10, 10, 10]).

Class-to-image:

python demo_inference_c2i.py

Text-to-image:

python demo_inference_t2i.py

Load from a local clone

Class-to-image (`PixelFlow-256`)

from pathlib import Path
import torch
from diffusers import DiffusionPipeline

model_dir = Path("./PixelFlow-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=[10, 10, 10, 10],
    guidance_scale=4.0,
    generator=generator,
).images[0]
image.save("demo.png")

Text-to-image (`PixelFlow-T2I`)

Uses google/flan-t5-xl when text_encoder/ is not bundled.

from pathlib import Path
import torch
from diffusers import DiffusionPipeline

model_dir = Path("./PixelFlow-T2I").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",
    height=1024,
    width=1024,
    num_inference_steps=[10, 10, 10, 10],
    guidance_scale=4.0,
    generator=generator,
).images[0]
image.save("demo.png")