We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space. The original codebase of this paper can be found at ermongroup/ddim, and you can contact the author on tsong.me.  Tips The paper Common Diffusion Noise Schedules and Sample Steps are Flawed claims that a mismatch between the training and inference settings leads to suboptimal inference generation results for Stable Diffusion. To fix this, the authors propose: 🧪 This is an experimental feature! rescale the noise schedule to enforce zero terminal signal-to-noise ratio (SNR)   Copied pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, rescale_betas_zero_snr=True) train a model with v_prediction (add the following argument to the train_text_to_image.py or train_text_to_image_lora.py scripts)   Copied --prediction_type="v_prediction" change the sampler to always start from the last timestep   Copied pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") rescale classifier-free guidance to prevent over-exposure   Copied image = pipe(prompt, guidance_rescale=0.7).images[0] For example:   Copied from diffusers import DiffusionPipeline, DDIMScheduler