[WIP][Docs] Use DiffusionPipeline Instead of Child Classes when Loading Pipeline (#2809)

* Change the docs to use the parent DiffusionPipeline class when loading a checkpoint using from_pretrained() instead of a child class (e.g. StableDiffusionPipeline) where possible.

* Run make style to fix style issues.

* Change more docs to use DiffusionPipeline rather than a subclass.

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/en/optimization/fp16.mdx

@@ -45,7 +45,10 @@ torch.backends.cuda.matmul.allow_tf32 = True
 To save more GPU memory and get more speed, you can load and run the model weights directly in half precision. This involves loading the float16 version of the weights, which was saved to a branch named `fp16`, and telling PyTorch to use the `float16` type when loading them:
 
 ```Python
-pipe = StableDiffusionPipeline.from_pretrained(
+import torch
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
     
     torch_dtype=torch.float16,
@@ -72,13 +75,13 @@ For even additional memory savings, you can use a sliced version of attention th
   each head which can save a significant amount of memory.
 </Tip>
 
-To perform the attention computation sequentially over each head, you only need to invoke [`~StableDiffusionPipeline.enable_attention_slicing`] in your pipeline before inference, like here:
+To perform the attention computation sequentially over each head, you only need to invoke [`~DiffusionPipeline.enable_attention_slicing`] in your pipeline before inference, like here:
 
 ```Python
 import torch
-from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained(
+pipe = DiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
     
     torch_dtype=torch.float16,
@@ -402,10 +405,10 @@ To leverage it just make sure you have:
  - Cuda available
  - [Installed the xformers library](xformers).
 ```python
-from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline
 import torch
 
-pipe = StableDiffusionPipeline.from_pretrained(
+pipe = DiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float16,
 ).to("cuda")

docs/source/en/optimization/mps.mdx

@@ -35,9 +35,9 @@ The snippet below demonstrates how to use the `mps` backend using the familiar `
 We strongly recommend you use PyTorch 2 or better, as it solves a number of problems like the one described in the previous tip.
 
 ```python
-from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 pipe = pipe.to("mps")
 
 # Recommended if your computer has < 64 GB of RAM

docs/source/en/optimization/torch2.0.mdx

@@ -35,9 +35,9 @@ pip install --upgrade torch torchvision diffusers
 
     ```Python
     import torch
-    from diffusers import StableDiffusionPipeline
+    from diffusers import DiffusionPipeline
 
-    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+    pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
     pipe = pipe.to("cuda")
 
     prompt = "a photo of an astronaut riding a horse on mars"
@@ -48,10 +48,10 @@ pip install --upgrade torch torchvision diffusers
 
     ```Python
     import torch
-    from diffusers import StableDiffusionPipeline
+    from diffusers import DiffusionPipeline
     from diffusers.models.attention_processor import AttnProcessor2_0
 
-    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
+    pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
     pipe.unet.set_attn_processor(AttnProcessor2_0())
 
     prompt = "a photo of an astronaut riding a horse on mars"
@@ -68,11 +68,9 @@ pip install --upgrade torch torchvision diffusers
 
     ```python
     import torch
-    from diffusers import StableDiffusionPipeline
+    from diffusers import DiffusionPipeline
 
-    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(
-        "cuda"
-    )
+    pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
     pipe.unet = torch.compile(pipe.unet)
 
     batch_size = 10

docs/source/en/quicktour.mdx

@@ -141,7 +141,7 @@ Different schedulers come with different denoising speeds and quality trade-offs
 ```py
 >>> from diffusers import EulerDiscreteScheduler
 
->>> pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
 >>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
 ```
 

docs/source/en/stable_diffusion.mdx

@@ -47,9 +47,9 @@ Let's load the pipeline.
 ## Speed Optimization                                                                                                                                                                                                                                                                                                                                                                                                                                                                       
 
 ``` python                                                                                                                                                                                                                                    
-from diffusers import StableDiffusionPipeline                                                                                                                                                                                                 
+from diffusers import DiffusionPipeline                                                                                                                                                                                                 
                                                                                                                                                                                                                                               
-pipe = StableDiffusionPipeline.from_pretrained(model_id)                                                                                                                                                                                      
+pipe = DiffusionPipeline.from_pretrained(model_id)                                                                                                                                                                                      
 ```                                                                                                                                                                                                                                           
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             
 We aim at generating a beautiful photograph of an *old warrior chief* and will later try to find the best prompt to generate such a photograph. For now, let's keep the prompt simple:                                                                                                                                                                                                                                                                                                      
@@ -88,7 +88,7 @@ The default run we did above used full float32 precision and ran the default num
 ``` python                                                                                                                                                                                                                                    
 import torch                                                                                                                                                                                                                                  
 
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)                                                                                                                                                           
+pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)                                                                                                                                                           
 pipe = pipe.to("cuda")                                                                                                                                                                                                                        
 ```                                                                                                                                                                                                                                           
 

docs/source/en/training/dreambooth.mdx

@@ -457,11 +457,11 @@ If you have **`"accelerate>=0.16.0"`** installed, you can use the following code
 inference from an intermediate checkpoint:
 
 ```python
-from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline
 import torch
 
 model_id = "path_to_saved_model"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
+pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
 
 prompt = "A photo of sks dog in a bucket"
 image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

docs/source/en/using-diffusers/using_safetensors.mdx

@@ -75,9 +75,9 @@ And we're equipped with dealing with it.
 Then in order to use the model, even before the branch gets accepted by the original author you can do:
 
 ```python
-from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline
 
-pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", revision="refs/pr/22")
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", revision="refs/pr/22")
 ```
 
 or you can test it directly online with this [space](https://huggingface.co/spaces/diffusers/check_pr).