Aller au contenu

ModelPrésentéExterne

SiT Diffusers

Diffusers-ready checkpoints for Scalable Interpolant Transformers (SiT), converted for local/offline use.

Sourcehuggingface.co/BiliSakura/SiT-diffusers/tree/2f02393a3079dfa6210c10302cc927f59d2cb142

TypeOtherVersionBClasse

Source du modèle

Type: Other
Taille du fichier: 319 MB
Version: vFP32
Mis à jour: 9 oct. 2026
Source: Hugging Face

Description de la source

SiT-diffusers

Diffusers-ready checkpoints for Scalable Interpolant Transformers (SiT), converted for local/offline use.

This root folder is a model collection that contains:

SiT-S-2-256
SiT-B-2-256
SiT-L-2-256
SiT-XL-2-256
SiT-XL-2-512

Each subfolder is a self-contained Diffusers model repo with:

pipeline.py
transformer/transformer_sit.py
scheduler/scheduler_config.json (FlowMatchEulerDiscreteScheduler)
transformer/diffusion_pytorch_model.safetensors
vae/diffusion_pytorch_model.safetensors

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.

Demo

SiT-XL-2-512 demo

Class-conditional sample (ImageNet class 207, golden retriever), SiT-XL/2 at 512×512, 250 steps, CFG 4.0, seed 0.

Model Paths

Use paths relative to this root README:

Model	Resolution	Local path
SiT-S/2	256x256	`./SiT-S-2-256`
SiT-B/2	256x256	`./SiT-B-2-256`
SiT-L/2	256x256	`./SiT-L-2-256`
SiT-XL/2	256x256	`./SiT-XL-2-256`
SiT-XL/2	512x512	`./SiT-XL-2-512`

Inference Demo (Diffusers)

1) Load a local subfolder checkpoint

import torch
from diffusers import DiffusionPipeline

model_path = "./SiT-XL-2-512"  # change to any path in the table above
device = "cuda" if torch.cuda.is_available() else "cpu"

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

generator = torch.Generator(device=device).manual_seed(0)

# ImageNet class example: 207 = golden retriever
print(pipe.id2label[207])
print(pipe.get_label_ids("golden retriever"))  # [207]
result = pipe(
    class_labels="golden retriever",
    height=512,
    width=512,
    num_inference_steps=250,  # official SiT comparisons commonly use 250 steps
    guidance_scale=4.0,
    generator=generator,
)

image = result.images[0]
image.save("sit_xl_512_demo.png")

2) Quick variant switch (256 models)

model_path = "./SiT-S-2-256"
# model_path = "./SiT-B-2-256"
# model_path = "./SiT-L-2-256"
# model_path = "./SiT-XL-2-256"

pipe = DiffusionPipeline.from_pretrained(model_path, trust_remote_code=True).to(device)
image = pipe(
    class_labels=207,
    height=256,
    width=256,
    num_inference_steps=250,
    guidance_scale=4.0,
    generator=generator,
).images[0]
image.save("sit_256_demo.png")

Sources

1 source

Hugging FacePrincipalVersion 2f02393a3079dfa6210c10302cc927f59d2cb142

huggingface.co/BiliSakura/SiT-diffusers/tree/2f02393a3079dfa6210c10302cc927f59d2cb142

Vérifié 9 oct.

Download files

Artefacts du modèle

5 artefacts

SiT-B-2-256/vae/diffusion_pytorch_model.safetensors

safetensors · FP32 (tensor header) · 319 MB · SHA-256 2aa1f43011b5…83ec · Hugging Face

SiT-L-2-256/vae/diffusion_pytorch_model.safetensors

safetensors · FP32 (tensor header) · 319 MB · SHA-256 2aa1f43011b5…83ec · Hugging Face

SiT-S-2-256/vae/diffusion_pytorch_model.safetensors

safetensors · FP32 (tensor header) · 319 MB · SHA-256 2aa1f43011b5…83ec · Hugging Face

SiT-XL-2-256/vae/diffusion_pytorch_model.safetensors

safetensors · FP32 (tensor header) · 319 MB · SHA-256 2aa1f43011b5…83ec · Hugging Face

SiT-XL-2-512/vae/diffusion_pytorch_model.safetensors

safetensors · FP32 (tensor header) · 319 MB · SHA-256 2aa1f43011b5…83ec · Hugging Face

Extraits de sources

2 extraits

Readme1 aoûthuggingface.co/BiliSakura/SiT-diffusers

--- library_name: diffusers pipeline_tag: unconditional-image-generation tags: - diffusers - sit - image-generation - class-conditional - imagenet license: mit inference: true widget: - output: url: SiT-XL-2-512/demo.png language: - en --- # SiT-diffusers Diffusers-ready checkpoints for **Scalable Interpolant Transformers (SiT)**, converted for local/offline use. This root folder is a model collection that contains: - `SiT-S-2-256` - `SiT-B-2-256` - `SiT-L-2-256` - `SiT-XL-2-256` - `SiT-XL-2-512` Each subfolder is a self-contained Diffusers model repo with: - `pipeline.py` - `transformer/transformer_sit.py` - `scheduler/scheduler_config.json` (`FlowMatchEulerDiscreteScheduler`) - `transformer/diffusion_pytorch_model.safetensors` - `vae/diffusion_pytorch_model.safetensors` 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. ## Demo ![SiT-XL-2-512 demo](SiT-XL-2-512/demo.png) Class-conditional sample (ImageNet class **207**, golden retriever), `SiT-XL/2` at 512×512, 250 steps, CFG 4.0, seed 0. ## Model Paths Use paths relative to this root README: | Model | Resolution | Local path | | --- | ---: | --- | | SiT-S/2 | 256x256 | `./SiT-S-2-256` | | SiT-B/2 | 256x256 | `./SiT-B-2-256` | | SiT-L/2 | 256x256 | `./SiT-L-2-256` | | SiT-XL/2 | 256x256 | `./SiT-XL-2-256` | | SiT-XL/2 | 512x512 | `./SiT-XL-2-512` | ## Inference Demo (Diffusers) ### 1) Load a local subfolder checkpoint ```python import torch from diffusers import DiffusionPipeline model_path = "./SiT-XL-2-512" # change to any path in the table above device = "cuda" if torch.cuda.is_available() else "cpu" pipe = DiffusionPipeline.from_pretrained( model_path, trust_remote_code=True, ).to(device) generator = torch.Generator(device=device).manual_seed(0) # ImageNet class example: 207 = golden retriever print(pipe.id2label[207]) print(pipe.get_label_ids("golden retriever")) # [207] result = pipe( class_labels="golden retriever", height=512, width=512, num_inference_steps=250, # official SiT comparisons commonly use 250 steps guidance_scale=4.0, generator=generator, ) image = result.images[0] image.save("sit_xl_512_demo.png") ``` ### 2) Quick variant switch (256 models) ```python model_path = "./SiT-S-2-256" # model_path = "./SiT-B-2-256" # model_path = "./SiT-L-2-256...

Note1 aoûthuggingface.co/BiliSakura/SiT-diffusers

Source context: 0 downloads · 0 likes · Pipeline unconditional-image-generation · Library diffusers · Repo BiliSakura/SiT-diffusers