Fix CPU Offloading Usage & Typos (#8230)

* Fix typos

* Fix `pipe.enable_model_cpu_offload()` usage

* Fix cpu offloading

* Update numbers

README.md

@@ -77,7 +77,7 @@ Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggi
 
 ## Quickstart
 
-Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 22000+ checkpoints):
+Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 25.000+ checkpoints):
 
 ```python
 from diffusers import DiffusionPipeline
@@ -219,7 +219,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
 - https://github.com/deep-floyd/IF
 - https://github.com/bentoml/BentoML
 - https://github.com/bmaltais/kohya_ss
-- +9000 other amazing GitHub repositories 💪
+- +11.000 other amazing GitHub repositories 💪
 
 Thank you for using us ❤️.
 

docs/source/en/optimization/tgate.md

@@ -6,7 +6,7 @@ Before you begin, make sure you install T-GATE.
 
 ```bash
 pip install tgate
-pip install -U pytorch diffusers transformers accelerate DeepCache
+pip install -U torch diffusers transformers accelerate DeepCache
 ```
 
 

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

@@ -249,13 +249,13 @@ controlnet = ControlNetModel.from_pretrained(
     controlnet_id,
     torch_dtype=torch.float16,
     variant="fp16",
-).to(device)
+)
 pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
     base_model_id,
     controlnet=controlnet,
     torch_dtype=torch.float16,
     variant="fp16",
-).to(device)
+)
 pipe.enable_model_cpu_offload()
 
 pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
@@ -301,13 +301,13 @@ controlnet = ControlNetModel.from_pretrained(
     controlnet_id,
     torch_dtype=torch.float16,
     variant="fp16",
-).to(device)
+)
 pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
     base_model_id,
     controlnet=controlnet,
     torch_dtype=torch.float16,
     variant="fp16",
-).to(device)
+)
 pipe.enable_model_cpu_offload()
 
 pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)

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

@@ -230,7 +230,7 @@ from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
-).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()
@@ -255,7 +255,7 @@ from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
     "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
-).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()
@@ -296,7 +296,7 @@ from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
-).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()
@@ -319,7 +319,7 @@ from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
     "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
-).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()

examples/community/README.md

@@ -1032,7 +1032,7 @@ image = pipe().images[0]
 
 Make sure you have @crowsonkb's <https://github.com/crowsonkb/k-diffusion> installed:
 
-```
+```sh
 pip install k-diffusion
 ```
 
@@ -1854,13 +1854,13 @@ To use this pipeline, you need to:
 
 You can simply use pip to install IPEX with the latest version.
 
-```python
+```sh
 python -m pip install intel_extension_for_pytorch
 ```
 
 **Note:** To install a specific version, run with the following command:
 
-```
+```sh
 python -m pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
 ```
 
@@ -1958,13 +1958,13 @@ To use this pipeline, you need to:
 
 You can simply use pip to install IPEX with the latest version.
 
-```python
+```sh
 python -m pip install intel_extension_for_pytorch
 ```
 
 **Note:** To install a specific version, run with the following command:
 
-```
+```sh
 python -m pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
 ```
 
@@ -3010,8 +3010,8 @@ This code implements a pipeline for the Stable Diffusion model, enabling the div
 
 ### Sample Code
 
-```
-from from examples.community.regional_prompting_stable_diffusion import RegionalPromptingStableDiffusionPipeline
+```py
+from examples.community.regional_prompting_stable_diffusion import RegionalPromptingStableDiffusionPipeline
 pipe = RegionalPromptingStableDiffusionPipeline.from_single_file(model_path, vae=vae)
 
 rp_args = {
@@ -4131,7 +4131,7 @@ This implementation is based on [Diffusers](https://huggingface.co/docs/diffuser
 
 ## Example Usage
 
-```
+```py
 import os
 import torch
 

examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py

@@ -896,7 +896,6 @@ def main():
         images = []
         if args.validation_prompts is not None:
             logger.info("Running inference for collecting generated images...")
-            pipeline = pipeline.to(accelerator.device)
             pipeline.torch_dtype = weight_dtype
             pipeline.set_progress_bar_config(disable=True)
             pipeline.enable_model_cpu_offload()

tests/lora/test_lora_layers_sd.py

@@ -642,7 +642,7 @@ class LoraIntegrationTests(unittest.TestCase):
         This test simply checks that loading a LoRA with an empty network alpha works fine
         See: https://github.com/huggingface/diffusers/issues/5606
         """
-        pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to(torch_device)
+        pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
         pipeline.enable_sequential_cpu_offload()
         civitai_path = hf_hub_download("ybelkada/test-ahi-civitai", "ahi_lora_weights.safetensors")
         pipeline.load_lora_weights(civitai_path, adapter_name="ahri")

tests/pipelines/i2vgen_xl/test_i2vgenxl.py

@@ -243,7 +243,6 @@ class I2VGenXLPipelineSlowTests(unittest.TestCase):
 
     def test_i2vgen_xl(self):
         pipe = I2VGenXLPipeline.from_pretrained("ali-vilab/i2vgen-xl", torch_dtype=torch.float16, variant="fp16")
-        pipe = pipe.to(torch_device)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
         image = load_image(

tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py

@@ -612,10 +612,10 @@ class IPAdapterSDXLIntegrationTests(IPAdapterNightlyTestsMixin):
     def test_instant_style_multiple_masks(self):
         image_encoder = CLIPVisionModelWithProjection.from_pretrained(
             "h94/IP-Adapter", subfolder="models/image_encoder", torch_dtype=torch.float16
-        ).to("cuda")
+        )
         pipeline = StableDiffusionXLPipeline.from_pretrained(
             "RunDiffusion/Juggernaut-XL-v9", torch_dtype=torch.float16, image_encoder=image_encoder, variant="fp16"
-        ).to("cuda")
+        )
         pipeline.enable_model_cpu_offload()
 
         pipeline.load_ip_adapter(

tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py

@@ -420,7 +420,6 @@ class StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase):
         pipe.scheduler = DDIMScheduler.from_config(
             pipe.scheduler.config, timestep_spacing="trailing", rescale_betas_zero_snr=True
         )
-        pipe.to(torch_device)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
 

tests/pipelines/stable_video_diffusion/test_stable_video_diffusion.py

@@ -534,7 +534,6 @@ class StableVideoDiffusionPipelineSlowTests(unittest.TestCase):
             variant="fp16",
             torch_dtype=torch.float16,
         )
-        pipe = pipe.to(torch_device)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
         image = load_image(