CHATS

Name: CHATS
Author: AIDC-AI

296

1 language

license:apache-2.0

AIDC-AI

Image Model

OTHER

2502.12579B params

New

296 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

5593GB+ RAM

Mobile

Laptop

Server

Quick Summary

CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation (ICML2025) CHATS is a next-generation framework that unifies hu...

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

2331GB+ RAM

Code Examples

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

🛠️ Quick Startpythonpytorch

import torch
from pipeline import ChatsSDXLPipeline

# Load CHATS-SDXL pipeline
pipe = ChatsSDXLPipeline.from_pretrained(
        "AIDC-AI/CHATS",
        torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate images
images = pipe(
    prompt=["A serene mountain lake at sunset"],
    num_inference_steps=50,
    guidance_scale=5,
    seed=0
)

# Save outputs
for i, img in enumerate(images):
    img.save(f"output_{i}.png")

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

📚 Citationbibtex

@inproceedings{fu2025chats,
title={CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation},
author={Fu, Minghao and Wang, Guo-Hua and Cao, Liangfu and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu},
booktitle={International Conference on Machine Learning (ICML)},
year={2025}
}

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