[`Docs`] Remove `.to('cuda')` before `.enable_model_cpu_offload()` (#5795)

Remove .to('cuda') before cpu_offload, trim trailing whitespaces

docs/source/en/using-diffusers/weighted_prompts.md

@@ -142,7 +142,7 @@ image
 ## Conjunction
 
 A conjunction diffuses each prompt independently and concatenates their results by their weighted sum. Add `.and()` to the end of a list of prompts to create a conjunction:
-  
+
 ```py
 prompt_embeds = compel_proc('["a red cat", "playing with a", "ball"].and()')
 generator = torch.Generator(device="cuda").manual_seed(55)

docs/source/en/using-diffusers/write_own_pipeline.md

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 [[open-in-colab]]
 
-🧨 Diffusers is designed to be a user-friendly and flexible toolbox for building diffusion systems tailored to your use-case. At the core of the toolbox are models and schedulers. While the [`DiffusionPipeline`] bundles these components together for convenience, you can also unbundle the pipeline and use the models and schedulers separately to create new diffusion systems. 
+🧨 Diffusers is designed to be a user-friendly and flexible toolbox for building diffusion systems tailored to your use-case. At the core of the toolbox are models and schedulers. While the [`DiffusionPipeline`] bundles these components together for convenience, you can also unbundle the pipeline and use the models and schedulers separately to create new diffusion systems.
 
 In this tutorial, you'll learn how to use models and schedulers to assemble a diffusion system for inference, starting with a basic pipeline and then progressing to the Stable Diffusion pipeline.
 
@@ -36,7 +36,7 @@ A pipeline is a quick and easy way to run a model for inference, requiring no mo
 
 That was super easy, but how did the pipeline do that? Let's breakdown the pipeline and take a look at what's happening under the hood.
 
-In the example above, the pipeline contains a [`UNet2DModel`] model and a [`DDPMScheduler`]. The pipeline denoises an image by taking random noise the size of the desired output and passing it through the model several times. At each timestep, the model predicts the *noise residual* and the scheduler uses it to predict a less noisy image. The pipeline repeats this process until it reaches the end of the specified number of inference steps. 
+In the example above, the pipeline contains a [`UNet2DModel`] model and a [`DDPMScheduler`]. The pipeline denoises an image by taking random noise the size of the desired output and passing it through the model several times. At each timestep, the model predicts the *noise residual* and the scheduler uses it to predict a less noisy image. The pipeline repeats this process until it reaches the end of the specified number of inference steps.
 
 To recreate the pipeline with the model and scheduler separately, let's write our own denoising process.
 
@@ -153,7 +153,7 @@ To speed up inference, move the models to a GPU since, unlike the scheduler, the
 
 ### Create text embeddings
 
-The next step is to tokenize the text to generate embeddings. The text is used to condition the UNet model and steer the diffusion process towards something that resembles the input prompt. 
+The next step is to tokenize the text to generate embeddings. The text is used to condition the UNet model and steer the diffusion process towards something that resembles the input prompt.
 
 <Tip>
 
@@ -284,7 +284,7 @@ Lastly, convert the image to a `PIL.Image` to see your generated image!
 
 ## Next steps
 
-From basic to complex pipelines, you've seen that all you really need to write your own diffusion system is a denoising loop. The loop should set the scheduler's timesteps, iterate over them, and alternate between calling the UNet model to predict the noise residual and passing it to the scheduler to compute the previous noisy sample. 
+From basic to complex pipelines, you've seen that all you really need to write your own diffusion system is a denoising loop. The loop should set the scheduler's timesteps, iterate over them, and alternate between calling the UNet model to predict the noise residual and passing it to the scheduler to compute the previous noisy sample.
 
 This is really what 🧨 Diffusers is designed for: to make it intuitive and easy to write your own diffusion system using models and schedulers.