aMUSEd Amused is a lightweight text to image model based off of the muse architecture. Amused is particularly useful in applications that require a lightweight and fast model such as generating many images quickly at once. Amused is a vqvae token based transformer that can generate an image in fewer forward passes than many diffusion models. In contrast with muse, it uses the smaller text encoder CLIP-L/14 instead of t5-xxl. Due to its small parameter count and few forward pass generation process, amused can generate many images quickly. This benefit is seen particularly at larger batch sizes. Model Params amused-256 603M amused-512 608M  AmusedPipeline  class diffusers.AmusedPipeline  < source > ( vqvae: VQModel tokenizer: CLIPTokenizer text_encoder: CLIPTextModelWithProjection transformer: UVit2DModel scheduler: AmusedScheduler  )     __call__  < source > ( prompt: Union = None height: Optional = None width: Optional = None num_inference_steps: int = 12 guidance_scale: float = 10.0 negative_prompt: Union = None num_images_per_prompt: Optional = 1 generator: Optional = None latents: Optional = None prompt_embeds: Optional = None encoder_hidden_states: Optional = None negative_prompt_embeds: Optional = None negative_encoder_hidden_states: Optional = None output_type = 'pil' return_dict: bool = True callback: Optional = None callback_steps: int = 1 cross_attention_kwargs: Optional = None micro_conditioning_aesthetic_score: int = 6 micro_conditioning_crop_coord: Tuple = (0, 0) temperature: Union = (2, 0)  ) → ImagePipelineOutput or tuple  Parameters   prompt (str or List[str], optional) —