Each variant embeds English id2label directly in model_index.json (DiT-style), so class labels can be passed as ImageNet ids or English synonym strings.

pipe.id2label — id → English label (comma-separated synonyms)
pipe.get_label_ids("golden retriever") — English label → id

Demo

Class-conditional sample (ImageNet class 207, golden retriever), DeCo-XL/16 at 512×512, 100 steps, CFG 5.0, seed 42.

Model Paths

Use paths relative to this root README:

Model	Resolution	Source checkpoint	Local path
DeCo-XL/16	256×256	`imagenet256_epoch800.ckpt` (EMA)	`./DeCo-XL-16-256`
DeCo-XL/16	512×512	`imagenet512_epoch340.ckpt` (EMA)	`./DeCo-XL-16-512`
DeCo-XXL/16	512×512 t2i	`t2i_DeCo.ckpt` (EMA)	`./DeCo-XXL-16-512-t2i`

Inference Demo (Diffusers)

1) Load a local subfolder checkpoint

python

import torch
from diffusers import DiffusionPipeline

model_path = "./DeCo-XL-16-512"  # change to ./DeCo-XL-16-256 for 256px
device = "cuda" if torch.cuda.is_available() else "cpu"

pipe = DiffusionPipeline.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).to(device)

generator = torch.Generator(device=device).manual_seed(42)

# ImageNet class example: 207 = golden retriever
print(pipe.id2label[207])
print(pipe.get_label_ids("golden retriever"))  # [207]

result = pipe(
    class_labels="golden retriever",
    num_inference_steps=100,
    guidance_scale=5.0,  # use 3.2 for DeCo-XL-16-256
    generator=generator,
)

image = result.images[0]
image.save("deco_xl_512_demo.png")

2) Quick variant switch (256 model)

python

model_path = "./DeCo-XL-16-256"

pipe = DiffusionPipeline.from_pretrained(model_path, trust_remote_code=True).to(device)
image = pipe(
    class_labels=207,
    num_inference_steps=100,
    guidance_scale=3.2,
    generator=generator,
).images[0]
image.save("deco_xl_256_demo.png")

Integer class ids, batched labels, and optional batch_size for repeating a single label are also supported.

3) Text-to-image (`DeCo-XXL-16-512-t2i` / `t2i_DeCo.ckpt`)

Use the AdamLM scheduler defaults from official DeCo (not the c2i 100-step / CFG 5.0 recipe):

python

import torch
from diffusers import DiffusionPipeline

model_path = "./DeCo-XXL-16-512-t2i"
device = "cuda" if torch.cuda.is_available() else "cpu"

pipe = DiffusionPipeline.from_pretrained(
    model_path,
    trust_remote_code=True,
    custom_pipeline=f"{model_path}/pipeline.py",
    torch_dtype=torch.bfloat16,
).to(device)

# Bundled ./text_encoder (Qwen3-1.7B weights + tokenizer). Pipeline loads both from that folder.
# Denoiser runs in float32 during __call__ (matches official GenEval predict).

image = pipe(
    prompt="a golden retriever playing in the snow, high quality photograph",
    negative_prompt="Unrealistic, JPEG artifacts.",
    num_inference_steps=25,
    guidance_scale=4.0,
    timeshift=3.0,
    generator=torch.Generator(device="cpu").manual_seed(42),
).images[0]
image.save("deco_t2i_demo.png")

BiliSakura/DeCo-diffusers

DeCo-diffusers

Demo

Model Paths

Inference Demo (Diffusers)

1) Load a local subfolder checkpoint

2) Quick variant switch (256 model)

3) Text-to-image (DeCo-XXL-16-512-t2i / t2i_DeCo.ckpt)

BiliSakura/
DeCo-diffusers

3) Text-to-image (`DeCo-XXL-16-512-t2i` / `t2i_DeCo.ckpt`)