[Better scheduler docs] Improve usage examples of schedulers

README.md

@@ -131,11 +131,7 @@ it before the pipeline and pass it to `from_pretrained`.
 ```python
 from diffusers import LMSDiscreteScheduler
 
-lms = LMSDiscreteScheduler(
-    beta_start=0.00085, 
-    beta_end=0.012, 
-    beta_schedule="scaled_linear"
-)
+lms = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4", 

docs/source/quicktour.mdx

@@ -122,7 +122,7 @@ you could use it as follows:
 ```python
 >>> from diffusers import LMSDiscreteScheduler
 
->>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+>>> scheduler = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
 >>> generator = StableDiffusionPipeline.from_pretrained(
 ...     "CompVis/stable-diffusion-v1-4", scheduler=scheduler, use_auth_token=AUTH_TOKEN

src/diffusers/pipeline_flax_utils.py

@@ -271,7 +271,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         >>> # Download pipeline, but overwrite scheduler
         >>> from diffusers import LMSDiscreteScheduler
 
-        >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        >>> scheduler = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
         >>> pipeline = FlaxDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler)
         ```
         """

src/diffusers/pipeline_utils.py

@@ -343,7 +343,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         >>> # Download pipeline, but overwrite scheduler
         >>> from diffusers import LMSDiscreteScheduler
 
-        >>> scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        >>> scheduler = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
         >>> pipeline = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=scheduler)
         ```
         """

src/diffusers/pipelines/stable_diffusion/README.md

@@ -72,7 +72,7 @@ image.save("astronaut_rides_horse.png")
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline, DDIMScheduler
 
-scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
+scheduler =  DDIMScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4", 
@@ -91,11 +91,7 @@ image.save("astronaut_rides_horse.png")
 # make sure you're logged in with `huggingface-cli login`
 from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
 
-lms = LMSDiscreteScheduler(
-    beta_start=0.00085, 
-    beta_end=0.012, 
-    beta_schedule="scaled_linear"
-)
+lms = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4", 

tests/test_pipelines.py

@@ -512,7 +512,7 @@ def test_stable_diffusion_no_safety_checker(self):
     def test_stable_diffusion_k_lms(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         unet = self.dummy_cond_unet
-        scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        scheduler = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
         vae = self.dummy_vae
         bert = self.dummy_text_encoder
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
@@ -557,7 +557,7 @@ def test_stable_diffusion_k_lms(self):
     def test_stable_diffusion_attention_chunk(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         unet = self.dummy_cond_unet
-        scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        scheduler = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
         vae = self.dummy_vae
         bert = self.dummy_text_encoder
         tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
@@ -842,7 +842,7 @@ def test_stable_diffusion_img2img_multiple_init_images(self):
     def test_stable_diffusion_img2img_k_lms(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         unet = self.dummy_cond_unet
-        scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        scheduler = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
         vae = self.dummy_vae
         bert = self.dummy_text_encoder
@@ -1943,7 +1943,7 @@ def test_stable_diffusion_img2img_pipeline_k_lms(self):
         init_image = init_image.resize((768, 512))
         expected_image = np.array(expected_image, dtype=np.float32) / 255.0
 
-        lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        lms = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
         model_id = "CompVis/stable-diffusion-v1-4"
         pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
@@ -2032,7 +2032,7 @@ def test_stable_diffusion_inpaint_pipeline_k_lms(self):
         )
         expected_image = np.array(expected_image, dtype=np.float32) / 255.0
 
-        lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        lms = LMSDiscreteScheduler.from_config("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
 
         model_id = "CompVis/stable-diffusion-v1-4"
         pipe = StableDiffusionInpaintPipeline.from_pretrained(