| # You Only Sample Once (YOSO) | |
| This algorithm was proposed in You Only Sample Once: Taming One-Step Text-To-Image Synthesis by Self-Cooperative Diffusion GANs. | |
| This model is fine-tuning from [ | |
| PixArt-XL-2-512x512](https://huggingface.co/PixArt-alpha/PixArt-XL-2-512x512), enabling one-step inference to perform text-to-image generation. | |
| We wanna highlight that the YOSO-PixArt was originally trained on 512 resolution. However, we found that we can construct a YOSO that enables generating samples with 1024 resolution by merging with [ | |
| PixArt-XL-2-1024-MS](https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS | |
| ) (Eq(15) in the paper) as follows: | |
|  | |
| The impressive performance indicates the robust generalization ability of our YOSO. | |
| ## usage | |
| ```python | |
| import torch | |
| from diffusers import PixArtAlphaPipeline, LCMScheduler, Transformer2DModel, DPMSolverMultistepScheduler | |
| transformer = Transformer2DModel.from_pretrained( | |
| "Yihong666/yoso_pixart1024", torch_dtype=torch.float16).to('cuda') | |
| pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-512x512", | |
| transformer=transformer, | |
| torch_dtype=torch.float16, use_safetensors=True) | |
| pipe = pipe.to('cuda') | |
| pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) | |
| pipe.scheduler.config.prediction_type = "v_prediction" | |
| generator = torch.manual_seed(318) | |
| imgs = pipe(prompt="Pirate ship trapped in a cosmic maelstrom nebula, rendered in cosmic beach whirlpool engine, volumetric lighting, spectacular, ambient lights, light pollution, cinematic atmosphere, art nouveau style, illustration art artwork by SenseiJaye, intricate detail.", | |
| num_inference_steps=1, | |
| num_images_per_prompt = 1, | |
| generator = generator, | |
| guidance_scale=1., | |
| )[0] | |
| imgs[0] | |
| ``` | |
|  |