rename to scheduling

src/diffusers/__init__.py

@@ -11,9 +11,7 @@ from .models.unet_ldm import UNetLDMModel
 from .pipeline_utils import DiffusionPipeline
 from .pipelines import DDIM, DDPM, GLIDE, LatentDiffusion
 
-from .schedulers import SchedulerMixin
-from .schedulers.scheduling_ddim import DDIMScheduler
-from .schedulers.scheduling_ddpm import DDPMScheduler
+from .schedulers import SchedulerMixin, DDIMScheduler, DDPMScheduler
 
 from .schedulers.classifier_free_guidance import ClassifierFreeGuidanceScheduler
 from .schedulers.glide_ddim import GlideDDIMScheduler

src/diffusers/schedulers/__init__.py

@@ -18,7 +18,7 @@
 
 from .scheduling_ddim import DDIMScheduler
 from .scheduling_ddpm import DDPMScheduler
-from .schedulers_utils import SchedulerMixin
+from .scheduling_utils import SchedulerMixin
 
 from .classifier_free_guidance import ClassifierFreeGuidanceScheduler
 from .glide_ddim import GlideDDIMScheduler

src/diffusers/schedulers/glide_ddim.py

@@ -16,7 +16,7 @@ import torch
 from torch import nn
 
 from ..configuration_utils import ConfigMixin
-from .schedulers_utils import betas_for_alpha_bar, linear_beta_schedule
+from .scheduling_utils import betas_for_alpha_bar, linear_beta_schedule
 
 
 SAMPLING_CONFIG_NAME = "scheduler_config.json"

src/diffusers/schedulers/scheduling_ddim.py

@@ -16,7 +16,7 @@ import math
 import numpy as np
 
 from ..configuration_utils import ConfigMixin
-from .schedulers_utils import SchedulerMixin, betas_for_alpha_bar, linear_beta_schedule
+from .scheduling_utils import SchedulerMixin, betas_for_alpha_bar, linear_beta_schedule
 
 
 class DDIMScheduler(SchedulerMixin, ConfigMixin):

src/diffusers/schedulers/scheduling_ddpm.py

@@ -16,7 +16,7 @@ import math
 import numpy as np
 
 from ..configuration_utils import ConfigMixin
-from .schedulers_utils import SchedulerMixin, betas_for_alpha_bar, linear_beta_schedule
+from .scheduling_utils import SchedulerMixin, betas_for_alpha_bar, linear_beta_schedule
 
 
 class DDPMScheduler(SchedulerMixin, ConfigMixin):

src/diffusers/schedulers/scheduling_plms.py

@@ -0,0 +1,144 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+
+import numpy as np
+
+from ..configuration_utils import ConfigMixin
+from .schedulers_utils import SchedulerMixin, betas_for_alpha_bar, linear_beta_schedule
+
+
+class DDIMScheduler(SchedulerMixin, ConfigMixin):
+    def __init__(
+        self,
+        timesteps=1000,
+        beta_start=0.0001,
+        beta_end=0.02,
+        beta_schedule="linear",
+        clip_predicted_image=True,
+        tensor_format="np",
+    ):
+        super().__init__()
+        self.register(
+            timesteps=timesteps,
+            beta_start=beta_start,
+            beta_end=beta_end,
+            beta_schedule=beta_schedule,
+        )
+        self.timesteps = int(timesteps)
+        self.clip_image = clip_predicted_image
+
+        if beta_schedule == "linear":
+            self.betas = linear_beta_schedule(timesteps, beta_start=beta_start, beta_end=beta_end)
+        elif beta_schedule == "squaredcos_cap_v2":
+            # GLIDE cosine schedule
+            self.betas = betas_for_alpha_bar(
+                timesteps,
+                lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+            )
+        else:
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
+        self.one = np.array(1.0)
+
+        self.set_format(tensor_format=tensor_format)
+
+    #        alphas_cumprod_prev = torch.nn.functional.pad(alphas_cumprod[:-1], (1, 0), value=1.0)
+    # TODO(PVP) - check how much of these is actually necessary!
+    # LDM only uses "fixed_small"; glide seems to use a weird mix of the two, ...
+    # https://github.com/openai/glide-text2im/blob/69b530740eb6cef69442d6180579ef5ba9ef063e/glide_text2im/gaussian_diffusion.py#L246
+    #        variance = betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
+    #        if variance_type == "fixed_small":
+    #            log_variance = torch.log(variance.clamp(min=1e-20))
+    #        elif variance_type == "fixed_large":
+    #            log_variance = torch.log(torch.cat([variance[1:2], betas[1:]], dim=0))
+    #
+    #
+    #        self.register_buffer("log_variance", log_variance.to(torch.float32))
+
+    def get_alpha(self, time_step):
+        return self.alphas[time_step]
+
+    def get_beta(self, time_step):
+        return self.betas[time_step]
+
+    def get_alpha_prod(self, time_step):
+        if time_step < 0:
+            return self.one
+        return self.alphas_cumprod[time_step]
+
+    def get_orig_t(self, t, num_inference_steps):
+        if t < 0:
+            return -1
+        return self.timesteps // num_inference_steps * t
+
+    def get_variance(self, t, num_inference_steps):
+        orig_t = self.get_orig_t(t, num_inference_steps)
+        orig_prev_t = self.get_orig_t(t - 1, num_inference_steps)
+
+        alpha_prod_t = self.get_alpha_prod(orig_t)
+        alpha_prod_t_prev = self.get_alpha_prod(orig_prev_t)
+        beta_prod_t = 1 - alpha_prod_t
+        beta_prod_t_prev = 1 - alpha_prod_t_prev
+
+        variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
+
+        return variance
+
+    def step(self, residual, image, t, num_inference_steps, eta):
+        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
+        # Ideally, read DDIM paper in-detail understanding
+
+        # Notation (<variable name> -> <name in paper>
+        # - pred_noise_t -> e_theta(x_t, t)
+        # - pred_original_image -> f_theta(x_t, t) or x_0
+        # - std_dev_t -> sigma_t
+        # - eta -> η
+        # - pred_image_direction -> "direction pointingc to x_t"
+        # - pred_prev_image -> "x_t-1"
+
+        # 1. get actual t and t-1
+        orig_t = self.get_orig_t(t, num_inference_steps)
+        orig_prev_t = self.get_orig_t(t - 1, num_inference_steps)
+
+        # 2. compute alphas, betas
+        alpha_prod_t = self.get_alpha_prod(orig_t)
+        alpha_prod_t_prev = self.get_alpha_prod(orig_prev_t)
+        beta_prod_t = 1 - alpha_prod_t
+
+        # 3. compute predicted original image from predicted noise also called
+        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+        pred_original_image = (image - beta_prod_t ** (0.5) * residual) / alpha_prod_t ** (0.5)
+
+        # 4. Clip "predicted x_0"
+        if self.clip_image:
+            pred_original_image = self.clip(pred_original_image, -1, 1)
+
+        # 5. compute variance: "sigma_t(η)" -> see formula (16)
+        # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
+        variance = self.get_variance(t, num_inference_steps)
+        std_dev_t = eta * variance ** (0.5)
+
+        # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+        pred_image_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * residual
+
+        # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+        pred_prev_image = alpha_prod_t_prev ** (0.5) * pred_original_image + pred_image_direction
+
+        return pred_prev_image
+
+    def __len__(self):
+        return self.timesteps