[Pytorch] pytorch only timesteps

docs/source/api/schedulers.mdx

@@ -36,7 +36,7 @@ This allows for rapid experimentation and cleaner abstractions in the code, wher
 To this end, the design of schedulers is such that:
 
 - Schedulers can be used interchangeably between diffusion models in inference to find the preferred trade-off between speed and generation quality.
-- Schedulers are currently by default in PyTorch, but are designed to be framework independent (partial Numpy support currently exists).
+- Schedulers are currently by default in PyTorch, but are designed to be framework independent (partial Jax support currently exists).
 
 
 ## API

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -278,11 +278,8 @@ def __call__(
         self.scheduler.set_timesteps(num_inference_steps)
 
         # Some schedulers like PNDM have timesteps as arrays
-        # It's more optimzed to move all timesteps to correct device beforehand
-        if torch.is_tensor(self.scheduler.timesteps):
-            timesteps_tensor = self.scheduler.timesteps.to(self.device)
-        else:
-            timesteps_tensor = torch.tensor(self.scheduler.timesteps.copy(), device=self.device)
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
 
         # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
         if isinstance(self.scheduler, LMSDiscreteScheduler):

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -304,7 +304,10 @@ def __call__(
         latents = init_latents
 
         t_start = max(num_inference_steps - init_timestep + offset, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps = self.scheduler.timesteps[t_start:].to(self.device)
 
         for i, t in enumerate(self.progress_bar(timesteps)):
             t_index = t_start + i

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -342,7 +342,10 @@ def __call__(
         latents = init_latents
 
         t_start = max(num_inference_steps - init_timestep + offset, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps = self.scheduler.timesteps[t_start:].to(self.device)
 
         for i, t in tqdm(enumerate(timesteps)):
             t_index = t_start + i

src/diffusers/schedulers/README.md

@@ -2,7 +2,7 @@
 
 - Schedulers are the algorithms to use diffusion models in inference as well as for training. They include the noise schedules and define algorithm-specific diffusion steps.
 - Schedulers can be used interchangeable between diffusion models in inference to find the preferred trade-off between speed and generation quality.
-- Schedulers are available in numpy, but can easily be transformed into PyTorch.
+- Schedulers are available in PyTorch and Jax.
 
 ## API
 

src/diffusers/schedulers/scheduling_ddim.py

@@ -154,7 +154,7 @@ def __init__(
 
         # setable values
         self.num_inference_steps = None
-        self.timesteps = np.arange(0, num_train_timesteps)[::-1]
+        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
 
     def _get_variance(self, timestep, prev_timestep):
         alpha_prod_t = self.alphas_cumprod[timestep]
@@ -166,7 +166,7 @@ def _get_variance(self, timestep, prev_timestep):
 
         return variance
 
-    def set_timesteps(self, num_inference_steps: int, **kwargs):
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None, **kwargs):
         """
         Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
 
@@ -183,7 +183,8 @@ def set_timesteps(self, num_inference_steps: int, **kwargs):
         step_ratio = self.config.num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
-        self.timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1]
+        timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy()
+        self.timesteps = torch.from_numpy(timesteps).to(device)
         self.timesteps += offset
 
     def step(

src/diffusers/schedulers/scheduling_ddpm.py

@@ -142,11 +142,11 @@ def __init__(
 
         # setable values
         self.num_inference_steps = None
-        self.timesteps = np.arange(0, num_train_timesteps)[::-1]
+        self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
 
         self.variance_type = variance_type
 
-    def set_timesteps(self, num_inference_steps: int):
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
         """
         Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
 
@@ -156,9 +156,10 @@ def set_timesteps(self, num_inference_steps: int):
         """
         num_inference_steps = min(self.config.num_train_timesteps, num_inference_steps)
         self.num_inference_steps = num_inference_steps
-        self.timesteps = np.arange(
+        timesteps = np.arange(
             0, self.config.num_train_timesteps, self.config.num_train_timesteps // self.num_inference_steps
-        )[::-1]
+        )[::-1].copy()
+        self.timesteps = torch.from_numpy(timesteps).to(device)
 
     def _get_variance(self, t, predicted_variance=None, variance_type=None):
         alpha_prod_t = self.alphas_cumprod[t]

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -97,10 +97,10 @@ def __init__(
 
         # setable values
         self.num_inference_steps: int = None
-        self.timesteps: np.ndarray = None
+        self.timesteps: np.IntTensor = None
         self.schedule: torch.FloatTensor = None  # sigma(t_i)
 
-    def set_timesteps(self, num_inference_steps: int):
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
         """
         Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
 
@@ -110,15 +110,16 @@ def set_timesteps(self, num_inference_steps: int):
 
         """
         self.num_inference_steps = num_inference_steps
-        self.timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()
+        timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()
+        self.timesteps = torch.from_numpy(timesteps).to(device)
         schedule = [
             (
                 self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in self.timesteps
         ]
-        self.schedule = torch.tensor(schedule, dtype=torch.float32)
+        self.schedule = torch.tensor(schedule, dtype=torch.float32, device=device)
 
     def add_noise_to_input(
         self, sample: torch.FloatTensor, sigma: float, generator: Optional[torch.Generator] = None

src/diffusers/schedulers/scheduling_pndm.py

@@ -147,7 +147,7 @@ def __init__(
         self.plms_timesteps = None
         self.timesteps = None
 
-    def set_timesteps(self, num_inference_steps: int, **kwargs) -> torch.FloatTensor:
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None, **kwargs):
         """
         Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
 
@@ -184,7 +184,8 @@ def set_timesteps(self, num_inference_steps: int, **kwargs) -> torch.FloatTensor
                 ::-1
             ].copy()  # we copy to avoid having negative strides which are not supported by torch.from_numpy
 
-        self.timesteps = np.concatenate([self.prk_timesteps, self.plms_timesteps]).astype(np.int64)
+        timesteps = np.concatenate([self.prk_timesteps, self.plms_timesteps]).astype(np.int64)
+        self.timesteps = torch.from_numpy(timesteps).to(device)
 
         self.ets = []
         self.counter = 0

src/diffusers/schedulers/scheduling_sde_ve.py

@@ -89,7 +89,9 @@ def __init__(
 
         self.set_sigmas(num_train_timesteps, sigma_min, sigma_max, sampling_eps)
 
-    def set_timesteps(self, num_inference_steps: int, sampling_eps: float = None):
+    def set_timesteps(
+        self, num_inference_steps: int, sampling_eps: float = None, device: Union[str, torch.device] = None
+    ):
         """
         Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
 
@@ -101,7 +103,7 @@ def set_timesteps(self, num_inference_steps: int, sampling_eps: float = None):
         """
         sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
 
-        self.timesteps = torch.linspace(1, sampling_eps, num_inference_steps)
+        self.timesteps = torch.linspace(1, sampling_eps, num_inference_steps, device=device)
 
     def set_sigmas(
         self, num_inference_steps: int, sigma_min: float = None, sigma_max: float = None, sampling_eps: float = None

src/diffusers/schedulers/scheduling_sde_vp.py

@@ -14,9 +14,8 @@
 
 # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
 
-# TODO(Patrick, Anton, Suraj) - make scheduler framework independent and clean-up a bit
-
 import math
+from typing import Union
 
 import torch
 
@@ -52,8 +51,8 @@ def __init__(self, num_train_timesteps=2000, beta_min=0.1, beta_max=20, sampling
         self.discrete_sigmas = None
         self.timesteps = None
 
-    def set_timesteps(self, num_inference_steps):
-        self.timesteps = torch.linspace(1, self.config.sampling_eps, num_inference_steps)
+    def set_timesteps(self, num_inference_steps, device: Union[str, torch.device] = None):
+        self.timesteps = torch.linspace(1, self.config.sampling_eps, num_inference_steps, device=device)
 
     def step_pred(self, score, x, t, generator=None):
         if self.timesteps is None:

tests/test_scheduler.py

@@ -354,7 +354,7 @@ def test_steps_offset(self):
         scheduler_config = self.get_scheduler_config(steps_offset=1)
         scheduler = scheduler_class(**scheduler_config)
         scheduler.set_timesteps(5)
-        assert np.equal(scheduler.timesteps, np.array([801, 601, 401, 201, 1])).all()
+        assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1]))
 
     def test_betas(self):
         for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):
@@ -568,10 +568,12 @@ def test_steps_offset(self):
         scheduler_config = self.get_scheduler_config(steps_offset=1)
         scheduler = scheduler_class(**scheduler_config)
         scheduler.set_timesteps(10)
-        assert np.equal(
+        assert torch.equal(
             scheduler.timesteps,
-            np.array([901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]),
-        ).all()
+            torch.LongTensor(
+                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]
+            ),
+        )
 
     def test_betas(self):
         for beta_start, beta_end in zip([0.0001, 0.001], [0.002, 0.02]):