).to("cuda") To use this approach, you need to define the number of timesteps for each model to run through their respective stages. For the base model, this is controlled by the denoising_end parameter and for the refiner model, it is controlled by the denoising_start parameter. The denoising_end and denoising_start parameters should be a float between 0 and 1. These parameters are represented as a proportion of discrete timesteps as defined by the scheduler. If you’re also using the strength parameter, it’ll be ignored because the number of denoising steps is determined by the discrete timesteps the model is trained on and the declared fractional cutoff. Let’s set denoising_end=0.8 so the base model performs the first 80% of denoising the high-noise timesteps and set denoising_start=0.8 so the refiner model performs the last 20% of denoising the low-noise timesteps. The base model output should be in latent space instead of a PIL image.   Copied prompt = "A majestic lion jumping from a big stone at night"