```bash
--prediction_type="v_prediction"
```  
3. change the sampler to always start from the last timestep  
```py
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
```  
4. rescale classifier-free guidance to prevent over-exposure  
```py
image = pipe(prompt, guidance_rescale=0.7).images[0]
```  
For example:  
```py
from diffusers import DiffusionPipeline, DDIMScheduler
import torch