When you load multiple pipelines that share the same model components, it makes sense to reuse the shared components instead of reloading everything into memory again, especially if your hardware is memory-constrained. For example:  
1. You generated an image with the [`StableDiffusionPipeline`] but you want to improve its quality with the [`StableDiffusionSAGPipeline`]. Both of these pipelines share the same pretrained model, so it'd be a waste of memory to load the same model twice.
2. You want to add a model component, like a [`MotionAdapter`](../api/pipelines/animatediff#animatediffpipeline), to [`AnimateDiffPipeline`] which was instantiated from an existing [`StableDiffusionPipeline`]. Again, both pipelines share the same pretrained model, so it'd be a waste of memory to load an entirely new pipeline again.  
With the [`DiffusionPipeline.from_pipe`] API, you can switch between multiple pipelines to take advantage of their different features without increasing memory-usage. It is similar to turning on and off a feature in your pipeline.  
> [!TIP]
> To switch between tasks (rather than features), use the [`~DiffusionPipeline.from_pipe`] method with the [AutoPipeline](../api/pipelines/auto_pipeline) class, which automatically identifies the pipeline class based on the task (learn more in the [AutoPipeline](../tutorials/autopipeline) tutorial).  
Let's start with a [`StableDiffusionPipeline`] and then reuse the loaded model components to create a [`StableDiffusionSAGPipeline`] to increase generation quality. You'll use the [`StableDiffusionPipeline`] with an [IP-Adapter](./ip_adapter) to generate a bear eating pizza.  
```python
from diffusers import DiffusionPipeline, StableDiffusionSAGPipeline
import torch
import gc
from diffusers.utils import load_image
from accelerate.utils import compute_module_sizes

image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png")

pipe_sd = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", torch_dtype=torch.float16)
pipe_sd.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
pipe_sd.set_ip_adapter_scale(0.6)
pipe_sd.to("cuda")

generator = torch.Generator(device="cpu").manual_seed(33)
out_sd = pipe_sd(
prompt="bear eats pizza",
negative_prompt="wrong white balance, dark, sketches,worst quality,low quality",
ip_adapter_image=image,
num_inference_steps=50,
generator=generator,
).images[0]
out_sd
```  
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/from_pipe_out_sd_0.png"/>
</div>  
For reference, you can check how much memory this process consumed.  
```python
def bytes_to_giga_bytes(bytes):
return bytes / 1024 / 1024 / 1024
print(f"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB")
"Max memory allocated: 4.406213283538818 GB"
```  
Now, reuse the same pipeline components from [`StableDiffusionPipeline`] in [`StableDiffusionSAGPipeline`] with the [`~DiffusionPipeline.from_pipe`] method.  
> [!WARNING]
> Some pipeline methods may not function properly on new pipelines created with [`~DiffusionPipeline.from_pipe`]. For instance, the [`~DiffusionPipeline.enable_model_cpu_offload`] method installs hooks on the model components based on a unique offloading sequence for each pipeline. If the models are executed in a different order in the new pipeline, the CPU offloading may not work correctly.
>
> To ensure everything works as expected, we recommend re-applying a pipeline method on a new pipeline created with [`~DiffusionPipeline.from_pipe`].  
```python
pipe_sag = StableDiffusionSAGPipeline.from_pipe(
pipe_sd
)

generator = torch.Generator(device="cpu").manual_seed(33)
out_sag = pipe_sag(
prompt="bear eats pizza",
negative_prompt="wrong white balance, dark, sketches,worst quality,low quality",
ip_adapter_image=image,
num_inference_steps=50,
generator=generator,
guidance_scale=1.0,
sag_scale=0.75
).images[0]
out_sag
```  
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/from_pipe_out_sag_1.png"/>
</div>  
If you check the memory usage, you'll see it remains the same as before because [`StableDiffusionPipeline`] and [`StableDiffusionSAGPipeline`] are sharing the same pipeline components. This allows you to use them interchangeably without any additional memory overhead.  
```py
print(f"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB")
"Max memory allocated: 4.406213283538818 GB"
```  
Let's animate the image with the [`AnimateDiffPipeline`] and also add a [`MotionAdapter`] module to the pipeline. For the [`AnimateDiffPipeline`], you need to unload the IP-Adapter first and reload it *after* you've created your new pipeline (this only applies to the [`AnimateDiffPipeline`]).  
```py
from diffusers import AnimateDiffPipeline, MotionAdapter, DDIMScheduler
from diffusers.utils import export_to_gif

pipe_sag.unload_ip_adapter()
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)

pipe_animate = AnimateDiffPipeline.from_pipe(pipe_sd, motion_adapter=adapter)
pipe_animate.scheduler = DDIMScheduler.from_config(pipe_animate.scheduler.config, beta_schedule="linear")
# load IP-Adapter and LoRA weights again
pipe_animate.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
pipe_animate.load_lora_weights("guoyww/animatediff-motion-lora-zoom-out", adapter_name="zoom-out")
pipe_animate.to("cuda")

generator = torch.Generator(device="cpu").manual_seed(33)
pipe_animate.set_adapters("zoom-out", adapter_weights=0.75)
out = pipe_animate(
prompt="bear eats pizza",
num_frames=16,
num_inference_steps=50,
ip_adapter_image=image,
generator=generator,
).frames[0]
export_to_gif(out, "out_animate.gif")
```  
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/from_pipe_out_animate_3.gif"/>
</div>  
The [`AnimateDiffPipeline`] is more memory-intensive and consumes 15GB of memory (see the [Memory-usage of from_pipe](#memory-usage-of-from_pipe) section to learn what this means for your memory-usage).  
```py
print(f"Max memory allocated: {bytes_to_giga_bytes(torch.cuda.max_memory_allocated())} GB")
"Max memory allocated: 15.178664207458496 GB"
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/loading.md