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

docs/source/en/tutorials/autopipeline.md

@@ -50,7 +50,7 @@ Under the hood, [`AutoPipelineForText2Image`]:
 1. automatically detects a `"stable-diffusion"` class from the [`model_index.json`](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json) file
 2. loads the corresponding text-to-image [`StableDiffusionPipeline`] based on the `"stable-diffusion"` class name
 
-Likewise, for image-to-image, [`AutoPipelineForImage2Image`] detects a `"stable-diffusion"` checkpoint from the `model_index.json` file and it'll load the corresponding [`StableDiffusionImg2ImgPipeline`] behind the scenes. You can also pass any additional arguments specific to the pipeline class such as `strength`, which determines the amount of noise or variation added to an input image: 
+Likewise, for image-to-image, [`AutoPipelineForImage2Image`] detects a `"stable-diffusion"` checkpoint from the `model_index.json` file and it'll load the corresponding [`StableDiffusionImg2ImgPipeline`] behind the scenes. You can also pass any additional arguments specific to the pipeline class such as `strength`, which determines the amount of noise or variation added to an input image:
 
 ```py
 from diffusers import AutoPipelineForImage2Image

docs/source/en/tutorials/tutorial_overview.md

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # Overview
 
-Welcome to 🧨 Diffusers! If you're new to diffusion models and generative AI, and want to learn more, then you've come to the right place. These beginner-friendly tutorials are designed to provide a gentle introduction to diffusion models and help you understand the library fundamentals - the core components and how 🧨 Diffusers is meant to be used. 
+Welcome to 🧨 Diffusers! If you're new to diffusion models and generative AI, and want to learn more, then you've come to the right place. These beginner-friendly tutorials are designed to provide a gentle introduction to diffusion models and help you understand the library fundamentals - the core components and how 🧨 Diffusers is meant to be used.
 
 You'll learn how to use a pipeline for inference to rapidly generate things, and then deconstruct that pipeline to really understand how to use the library as a modular toolbox for building your own diffusion systems. In the next lesson, you'll learn how to train your own diffusion model to generate what you want.
 

docs/source/en/tutorials/using_peft_for_inference.md

@@ -58,7 +58,7 @@ image
 ```
 
 ![toy-face](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_8_1.png)
-    
+
 
 With the `adapter_name` parameter, it is really easy to use another adapter for inference! Load the [nerijs/pixel-art-xl](https://huggingface.co/nerijs/pixel-art-xl) adapter that has been fine-tuned to generate pixel art images, and let's call it `"pixel"`.
 
@@ -80,7 +80,7 @@ image
 ```
 
 ![pixel-art](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_12_1.png)
-    
+
 ## Combine multiple adapters
 
 You can also perform multi-adapter inference where you combine different adapter checkpoints for inference.
@@ -112,7 +112,7 @@ image
 ```
 
 ![toy-face-pixel-art](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_16_1.png)
-    
+
 Impressive! As you can see, the model was able to generate an image that mixes the characteristics of both adapters.
 
 If you want to go back to using only one adapter, use the [`~diffusers.loaders.UNet2DConditionLoadersMixin.set_adapters`] method to activate the `"toy"` adapter:

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

@@ -226,7 +226,7 @@ pipeline = AutoPipelineForText2Image.from_pretrained(
 	"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
 ).to("cuda")
 image = pipeline(
-	prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", 
+	prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
 	negative_prompt="ugly, deformed, disfigured, poor details, bad anatomy",
 ).images[0]
 image
@@ -258,7 +258,7 @@ pipeline = AutoPipelineForText2Image.from_pretrained(
 ).to("cuda")
 generator = torch.Generator(device="cuda").manual_seed(30)
 image = pipeline(
-	"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", 
+	"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
 	generator=generator,
 ).images[0]
 image

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

@@ -49,7 +49,7 @@ To ensure your pipeline and its components (`unet` and `scheduler`) can be saved
 +         self.register_modules(unet=unet, scheduler=scheduler)
 ```
 
-Cool, the `__init__` step is done and you can move to the forward pass now! 🔥 
+Cool, the `__init__` step is done and you can move to the forward pass now! 🔥
 
 ## Define the forward pass
 

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

@@ -86,7 +86,7 @@ import torch
 controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16, use_safetensors=True)
 pipe = StableDiffusionControlNetPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
+)
 
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 pipe.enable_model_cpu_offload()
@@ -146,7 +146,7 @@ import torch
 controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11f1p_sd15_depth", torch_dtype=torch.float16, use_safetensors=True)
 pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
+)
 
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 pipe.enable_model_cpu_offload()
@@ -231,7 +231,7 @@ import torch
 controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16, use_safetensors=True)
 pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
+)
 
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 pipe.enable_model_cpu_offload()

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

@@ -74,7 +74,7 @@ diffuser_pipeline = DiffusionPipeline.from_pretrained(
 diffuser_pipeline.enable_attention_slicing()
 diffuser_pipeline = diffuser_pipeline.to(device)
 
-prompt = ["a photograph of an astronaut riding a horse", 
+prompt = ["a photograph of an astronaut riding a horse",
           "Una casa en la playa",
           "Ein Hund, der Orange isst",
           "Un restaurant parisien"]

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

@@ -117,10 +117,10 @@ from pipeline_t2v_base_pixel import TextToVideoIFPipeline
 import torch
 
 pipeline = TextToVideoIFPipeline(
-    unet=unet, 
-    text_encoder=text_encoder, 
-    tokenizer=tokenizer, 
-    scheduler=scheduler, 
+    unet=unet,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    scheduler=scheduler,
     feature_extractor=feature_extractor
 )
 pipeline = pipeline.to(device="cuda")

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

@@ -160,12 +160,12 @@ Check out the [generation strategy](https://huggingface.co/docs/transformers/mai
 Load the text encoder model used by the [`StableDiffusionDiffEditPipeline`] to encode the text. You'll use the text encoder to compute the text embeddings:
 
 ```py
-import torch 
-from diffusers import StableDiffusionDiffEditPipeline 
+import torch
+from diffusers import StableDiffusionDiffEditPipeline
 
 pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
     "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 pipeline.enable_vae_slicing()
 

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

@@ -14,24 +14,24 @@ specific language governing permissions and limitations under the License.
 
 [[open-in-colab]]
 
-The UNet is responsible for denoising during the reverse diffusion process, and there are two distinct features in its architecture: 
+The UNet is responsible for denoising during the reverse diffusion process, and there are two distinct features in its architecture:
 
 1. Backbone features primarily contribute to the denoising process
 2. Skip features mainly introduce high-frequency features into the decoder module and can make the network overlook the semantics in the backbone features
 
-However, the skip connection can sometimes introduce unnatural image details. [FreeU](https://hf.co/papers/2309.11497) is a technique for improving image quality by rebalancing the contributions from the UNet’s skip connections and backbone feature maps. 
+However, the skip connection can sometimes introduce unnatural image details. [FreeU](https://hf.co/papers/2309.11497) is a technique for improving image quality by rebalancing the contributions from the UNet’s skip connections and backbone feature maps.
 
 FreeU is applied during inference and it does not require any additional training. The technique works for different tasks such as text-to-image, image-to-image, and text-to-video.
 
 In this guide, you will apply FreeU to the [`StableDiffusionPipeline`], [`StableDiffusionXLPipeline`], and [`TextToVideoSDPipeline`]. You need to install Diffusers from source to run the examples below.
 
 ## StableDiffusionPipeline
 
-Load the pipeline: 
+Load the pipeline:
 
 ```py
 from diffusers import DiffusionPipeline
-import torch 
+import torch
 
 pipeline = DiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, safety_checker=None
@@ -70,7 +70,7 @@ Let's see how Stable Diffusion 2 results are impacted:
 
 ```py
 from diffusers import DiffusionPipeline
-import torch 
+import torch
 
 pipeline = DiffusionPipeline.from_pretrained(
     "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, safety_checker=None
@@ -92,7 +92,7 @@ Finally, let's take a look at how FreeU affects Stable Diffusion XL results:
 
 ```py
 from diffusers import DiffusionPipeline
-import torch 
+import torch
 
 pipeline = DiffusionPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16,

docs/source/en/using-diffusers/img2img.md

@@ -27,7 +27,7 @@ from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -79,7 +79,7 @@ from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -117,7 +117,7 @@ from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -157,7 +157,7 @@ from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -204,7 +204,7 @@ from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -248,7 +248,7 @@ from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -290,7 +290,7 @@ from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -335,7 +335,7 @@ from diffusers.utils import make_image_grid
 
 pipeline = AutoPipelineForText2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -349,7 +349,7 @@ Now you can pass this generated image to the image-to-image pipeline:
 ```py
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16, use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -371,7 +371,7 @@ from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -397,7 +397,7 @@ Pass the latent output from this pipeline to the next pipeline to generate an im
 ```py
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "ogkalu/Comic-Diffusion", torch_dtype=torch.float16
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -411,7 +411,7 @@ Repeat one more time to generate the final image in a [pixel art style](https://
 ```py
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "kohbanye/pixel-art-style", torch_dtype=torch.float16
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -434,7 +434,7 @@ from diffusers.utils import make_image_grid, load_image
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -462,7 +462,7 @@ from diffusers import StableDiffusionLatentUpscalePipeline
 
 upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(
     "stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 upscaler.enable_model_cpu_offload()
 upscaler.enable_xformers_memory_efficient_attention()
 
@@ -476,7 +476,7 @@ from diffusers import StableDiffusionUpscalePipeline
 
 super_res = StableDiffusionUpscalePipeline.from_pretrained(
     "stabilityai/stable-diffusion-x4-upscaler", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 super_res.enable_model_cpu_offload()
 super_res.enable_xformers_memory_efficient_attention()
 
@@ -500,7 +500,7 @@ import torch
 
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -537,7 +537,7 @@ import torch
 controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11f1p_sd15_depth", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()
@@ -571,7 +571,7 @@ Let's apply a new [style](https://huggingface.co/nitrosocke/elden-ring-diffusion
 ```py
 pipeline = AutoPipelineForImage2Image.from_pretrained(
     "nitrosocke/elden-ring-diffusion", torch_dtype=torch.float16,
-).to("cuda")
+)
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
 pipeline.enable_xformers_memory_efficient_attention()