# Control image brightness
The Stable Diffusion pipeline is mediocre at generating images that are either very bright or dark as explained in the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) paper. The solutions proposed in the paper are currently implemented in the [`DDIMScheduler`] which you can use to improve the lighting in your images.
💡 Take a look at the paper linked above for more details about the proposed solutions!
One of the solutions is to train a model with *v prediction* and *v loss*. Add the following flag to the [`train_text_to_image.py`](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) or [`train_text_to_image_lora.py`](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) scripts to enable `v_prediction`:
```bash
--prediction_type="v_prediction"
```
For example, let's use the [`ptx0/pseudo-journey-v2`](https://huggingface.co/ptx0/pseudo-journey-v2) checkpoint which has been finetuned with `v_prediction`.
Next, configure the following parameters in the [`DDIMScheduler`]:
1. `rescale_betas_zero_snr=True`, rescales the noise schedule to zero terminal signal-to-noise ratio (SNR)
2. `timestep_spacing="trailing"`, starts sampling from the last timestep
```py
from diffusers import DiffusionPipeline, DDIMScheduler
pipeline = DiffusionPipeline.from_pretrained("ptx0/pseudo-journey-v2", use_safetensors=True)
# switch the scheduler in the pipeline to use the DDIMScheduler
pipeline.scheduler = DDIMScheduler.from_config(
pipeline.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing="trailing"
)
pipeline.to("cuda")
```
Finally, in your call to the pipeline, set `guidance_rescale` to prevent overexposure:
```py
prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"
image = pipeline(prompt, guidance_rescale=0.7).images[0]
image
```
