[Schedulers Refactoring] Phase 1: timesteps and scaling

src/diffusers/__init__.py

@@ -31,6 +31,7 @@
     from .pipeline_utils import DiffusionPipeline
     from .pipelines import DDIMPipeline, DDPMPipeline, KarrasVePipeline, LDMPipeline, PNDMPipeline, ScoreSdeVePipeline
     from .schedulers import (
+        BaseScheduler,
         DDIMScheduler,
         DDPMScheduler,
         KarrasVeScheduler,

src/diffusers/dependency_versions_table.py

@@ -17,7 +17,6 @@
     "jaxlib": "jaxlib>=0.1.65,<=0.3.6",
     "modelcards": "modelcards>=0.1.4",
     "numpy": "numpy",
-    "onnxruntime": "onnxruntime",
     "onnxruntime-gpu": "onnxruntime-gpu",
     "pytest": "pytest",
     "pytest-timeout": "pytest-timeout",

src/diffusers/pipeline_utils.py

@@ -43,6 +43,7 @@
 LOADABLE_CLASSES = {
     "diffusers": {
         "ModelMixin": ["save_pretrained", "from_pretrained"],
+        "BaseScheduler": ["save_config", "from_config"],
         "SchedulerMixin": ["save_config", "from_config"],
         "DiffusionPipeline": ["save_pretrained", "from_pretrained"],
         "OnnxRuntimeModel": ["save_pretrained", "from_pretrained"],

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -82,14 +82,15 @@ def __call__(
         # set step values
         self.scheduler.set_timesteps(num_inference_steps)
 
-        for t in self.progress_bar(self.scheduler.timesteps):
+        for step in self.progress_bar(self.scheduler.timesteps):
             # 1. predict noise model_output
+            t = self.scheduler.get_noise_condition(step)
             model_output = self.unet(image, t).sample
 
             # 2. predict previous mean of image x_t-1 and add variance depending on eta
             # eta corresponds to η in paper and should be between [0, 1]
             # do x_t -> x_t-1
-            image = self.scheduler.step(model_output, t, image, eta).prev_sample
+            image = self.scheduler.step(model_output, step, image, eta).prev_sample
 
         image = (image / 2 + 0.5).clamp(0, 1)
         image = image.cpu().permute(0, 2, 3, 1).numpy()

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -75,12 +75,13 @@ def __call__(
         # set step values
         self.scheduler.set_timesteps(1000)
 
-        for t in self.progress_bar(self.scheduler.timesteps):
+        for step in self.progress_bar(self.scheduler.timesteps):
             # 1. predict noise model_output
+            t = self.scheduler.get_noise_condition(step)
             model_output = self.unet(image, t).sample
 
             # 2. compute previous image: x_t -> t_t-1
-            image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample
+            image = self.scheduler.step(model_output, step, image, generator=generator).prev_sample
 
         image = (image / 2 + 0.5).clamp(0, 1)
         image = image.cpu().permute(0, 2, 3, 1).numpy()

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -117,6 +117,7 @@ def __call__(
             generator=generator,
         )
         latents = latents.to(self.device)
+        latents = self.scheduler.scale_initial_noise(latents)
 
         self.scheduler.set_timesteps(num_inference_steps)
 
@@ -127,7 +128,7 @@ def __call__(
         if accepts_eta:
             extra_kwargs["eta"] = eta
 
-        for t in self.progress_bar(self.scheduler.timesteps):
+        for step in self.progress_bar(self.scheduler.timesteps):
             if guidance_scale == 1.0:
                 # guidance_scale of 1 means no guidance
                 latents_input = latents
@@ -140,14 +141,16 @@ def __call__(
                 context = torch.cat([uncond_embeddings, text_embeddings])
 
             # predict the noise residual
+            latents_input = self.scheduler.scale_model_input(latents_input, step)
+            t = self.scheduler.get_noise_condition(step)
             noise_pred = self.unet(latents_input, t, encoder_hidden_states=context).sample
             # perform guidance
             if guidance_scale != 1.0:
                 noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample
+            latents = self.scheduler.step(noise_pred, step, latents, **extra_kwargs).prev_sample
 
         # scale and decode the image latents with vae
         latents = 1 / 0.18215 * latents

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -63,6 +63,7 @@ def __call__(
             generator=generator,
         )
         latents = latents.to(self.device)
+        latents = self.scheduler.scale_initial_noise(latents)
 
         self.scheduler.set_timesteps(num_inference_steps)
 
@@ -73,11 +74,13 @@ def __call__(
         if accepts_eta:
             extra_kwargs["eta"] = eta
 
-        for t in self.progress_bar(self.scheduler.timesteps):
+        for step in self.progress_bar(self.scheduler.timesteps):
             # predict the noise residual
+            latents = self.scheduler.scale_model_input(latents, step)
+            t = self.scheduler.get_noise_condition(step)
             noise_prediction = self.unet(latents, t).sample
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample
+            latents = self.scheduler.step(noise_prediction, step, latents, **extra_kwargs).prev_sample
 
         # decode the image latents with the VAE
         image = self.vqvae.decode(latents).sample

src/diffusers/pipelines/pndm/pipeline_pndm.py

@@ -79,12 +79,14 @@ def __call__(
             generator=generator,
         )
         image = image.to(self.device)
+        image = self.scheduler.scale_initial_noise(image)
 
         self.scheduler.set_timesteps(num_inference_steps)
-        for t in self.progress_bar(self.scheduler.timesteps):
+        for step in self.progress_bar(self.scheduler.timesteps):
+            t = self.scheduler.get_noise_condition(step)
             model_output = self.unet(image, t).sample
 
-            image = self.scheduler.step(model_output, t, image).prev_sample
+            image = self.scheduler.step(model_output, step, image).prev_sample
 
         image = (image / 2 + 0.5).clamp(0, 1)
         image = image.cpu().permute(0, 2, 3, 1).numpy()

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -46,6 +46,14 @@ class StableDiffusionPipeline(DiffusionPipeline):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
 
+    vae: AutoencoderKL
+    text_encoder: CLIPTextModel
+    tokenizer: CLIPTokenizer
+    unet: UNet2DConditionModel
+    scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
+    safety_checker: StableDiffusionSafetyChecker
+    feature_extractor: CLIPFeatureExtractor
+
     def __init__(
         self,
         vae: AutoencoderKL,
@@ -230,14 +238,11 @@ def __call__(
             if latents.shape != latents_shape:
                 raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
         latents = latents.to(self.device)
+        latents = self.scheduler.scale_initial_noise(latents)
 
         # set timesteps
         self.scheduler.set_timesteps(num_inference_steps)
 
-        # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
-        if isinstance(self.scheduler, LMSDiscreteScheduler):
-            latents = latents * self.scheduler.sigmas[0]
-
         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
@@ -247,13 +252,11 @@ def __call__(
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
+        for step in self.progress_bar(self.scheduler.timesteps):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                sigma = self.scheduler.sigmas[i]
-                # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
-                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, step)
+            t = self.scheduler.get_noise_condition(step)
 
             # predict the noise residual
             noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
@@ -264,10 +267,7 @@ def __call__(
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
-            else:
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+            latents = self.scheduler.step(noise_pred, step, latents, **extra_step_kwargs).prev_sample
 
         # scale and decode the image latents with vae
         latents = 1 / 0.18215 * latents

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -206,13 +206,7 @@ def __call__(
         offset = self.scheduler.config.get("steps_offset", 0)
         init_timestep = int(num_inference_steps * strength) + offset
         init_timestep = min(init_timestep, num_inference_steps)
-        if isinstance(self.scheduler, LMSDiscreteScheduler):
-            timesteps = torch.tensor(
-                [num_inference_steps - init_timestep] * batch_size, dtype=torch.long, device=self.device
-            )
-        else:
-            timesteps = self.scheduler.timesteps[-init_timestep]
-            timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
+        timesteps = torch.tensor([init_timestep - 1] * batch_size, dtype=torch.long, device=self.device)
 
         # add noise to latents using the timesteps
         noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
@@ -265,17 +259,11 @@ def __call__(
         latents = init_latents
 
         t_start = max(num_inference_steps - init_timestep + offset, 0)
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps[t_start:])):
-            t_index = t_start + i
-
+        for step in self.progress_bar(self.scheduler.timesteps[t_start:]):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-            # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                sigma = self.scheduler.sigmas[t_index]
-                # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
-                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, step)
+            t = self.scheduler.get_noise_condition(step)
 
             # predict the noise residual
             noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
@@ -286,10 +274,7 @@ def __call__(
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                latents = self.scheduler.step(noise_pred, t_index, latents, **extra_step_kwargs).prev_sample
-            else:
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+            latents = self.scheduler.step(noise_pred, step, latents, **extra_step_kwargs).prev_sample
 
         # scale and decode the image latents with vae
         latents = 1 / 0.18215 * latents

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -6,7 +6,6 @@
 import torch
 
 import PIL
-from tqdm.auto import tqdm
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
@@ -240,13 +239,7 @@ def __call__(
         offset = self.scheduler.config.get("steps_offset", 0)
         init_timestep = int(num_inference_steps * strength) + offset
         init_timestep = min(init_timestep, num_inference_steps)
-        if isinstance(self.scheduler, LMSDiscreteScheduler):
-            timesteps = torch.tensor(
-                [num_inference_steps - init_timestep] * batch_size, dtype=torch.long, device=self.device
-            )
-        else:
-            timesteps = self.scheduler.timesteps[-init_timestep]
-            timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
+        timesteps = torch.tensor([init_timestep - 1] * batch_size, dtype=torch.long, device=self.device)
 
         # add noise to latents using the timesteps
         noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
@@ -298,14 +291,11 @@ def __call__(
 
         latents = init_latents
         t_start = max(num_inference_steps - init_timestep + offset, 0)
-        for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
-            t_index = t_start + i
+        for step in self.progress_bar(self.scheduler.timesteps[t_start:]):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                sigma = self.scheduler.sigmas[t_index]
-                # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
-                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, step)
+            t = self.scheduler.get_noise_condition(step)
 
             # predict the noise residual
             noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
@@ -316,14 +306,9 @@ def __call__(
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                latents = self.scheduler.step(noise_pred, t_index, latents, **extra_step_kwargs).prev_sample
-                # masking
-                init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.LongTensor([t_index]))
-            else:
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-                # masking
-                init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.LongTensor([t]))
+            latents = self.scheduler.step(noise_pred, step, latents, **extra_step_kwargs).prev_sample
+            # masking
+            init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor(step))
 
             latents = (init_latents_proper * mask) + (latents * (1 - mask))
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_onnx.py

@@ -109,6 +109,7 @@ def __call__(
             latents = np.random.randn(*latents_shape).astype(np.float32)
         elif latents.shape != latents_shape:
             raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+        latents = self.scheduler.scale_initial_noise(latents)
 
         # set timesteps
         self.scheduler.set_timesteps(num_inference_steps)
@@ -126,13 +127,11 @@ def __call__(
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
+        for step in self.progress_bar(self.scheduler.timesteps):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                sigma = self.scheduler.sigmas[i]
-                # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
-                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, step)
+            t = self.scheduler.get_noise_condition(step)
 
             # predict the noise residual
             noise_pred = self.unet(
@@ -146,10 +145,7 @@ def __call__(
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
-            else:
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+            latents = self.scheduler.step(noise_pred, step, latents, **extra_step_kwargs).prev_sample
 
         # scale and decode the image latents with vae
         latents = 1 / 0.18215 * latents

src/diffusers/schedulers/__init__.py

@@ -23,7 +23,7 @@
     from .scheduling_pndm import PNDMScheduler
     from .scheduling_sde_ve import ScoreSdeVeScheduler
     from .scheduling_sde_vp import ScoreSdeVpScheduler
-    from .scheduling_utils import SchedulerMixin
+    from .scheduling_utils import BaseScheduler, SchedulerMixin
 else:
     from ..utils.dummy_pt_objects import *  # noqa F403