Add an alternative Karras et al. stochastic scheduler for VE models

src/diffusers/__init__.py

@@ -18,8 +18,15 @@
     get_scheduler,
 )
 from .pipeline_utils import DiffusionPipeline
-from .pipelines import DDIMPipeline, DDPMPipeline, LDMPipeline, PNDMPipeline, ScoreSdeVePipeline
-from .schedulers import DDIMScheduler, DDPMScheduler, PNDMScheduler, SchedulerMixin, ScoreSdeVeScheduler
+from .pipelines import DDIMPipeline, DDPMPipeline, KarrasVePipeline, LDMPipeline, PNDMPipeline, ScoreSdeVePipeline
+from .schedulers import (
+    DDIMScheduler,
+    DDPMScheduler,
+    KarrasVeScheduler,
+    PNDMScheduler,
+    SchedulerMixin,
+    ScoreSdeVeScheduler,
+)
 from .training_utils import EMAModel
 
 

src/diffusers/pipelines/__init__.py

@@ -4,6 +4,7 @@
 from .latent_diffusion_uncond import LDMPipeline
 from .pndm import PNDMPipeline
 from .score_sde_ve import ScoreSdeVePipeline
+from .stochatic_karras_ve import KarrasVePipeline
 
 
 if is_transformers_available():

src/diffusers/pipelines/stochatic_karras_ve/__init__.py

@@ -0,0 +1 @@
+from .pipeline_stochastic_karras_ve import KarrasVePipeline

src/diffusers/pipelines/stochatic_karras_ve/pipeline_stochastic_karras_ve.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+import torch
+
+from tqdm.auto import tqdm
+
+from ...models import UNet2DModel
+from ...pipeline_utils import DiffusionPipeline
+from ...schedulers import KarrasVeScheduler
+
+
+class KarrasVePipeline(DiffusionPipeline):
+    """
+    Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2].
+    Use Algorithm 2 and the VE column of Table 1 from [1] for reference.
+
+    [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364
+    [2] Song, Yang, et al. "Score-based generative modeling through stochastic differential equations." https://arxiv.org/abs/2011.13456
+    """
+
+    unet: UNet2DModel
+    scheduler: KarrasVeScheduler
+
+    def __init__(self, unet, scheduler):
+        super().__init__()
+        scheduler = scheduler.set_format("pt")
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(self, batch_size=1, num_inference_steps=50, generator=None, torch_device=None, output_type="pil"):
+        if torch_device is None:
+            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        img_size = self.unet.config.sample_size
+        shape = (batch_size, 3, img_size, img_size)
+
+        model = self.unet.to(torch_device)
+
+        # sample x_0 ~ N(0, sigma_0^2 * I)
+        sample = torch.randn(*shape) * self.scheduler.config.sigma_max
+        sample = sample.to(torch_device)
+
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        for t in tqdm(self.scheduler.timesteps):
+            # here sigma_t == t_i from the paper
+            sigma = self.scheduler.schedule[t]
+            sigma_prev = self.scheduler.schedule[t - 1] if t > 0 else 0
+
+            # 1. Select temporarily increased noise level sigma_hat
+            # 2. Add new noise to move from sample_i to sample_hat
+            sample_hat, sigma_hat = self.scheduler.add_noise_to_input(sample, sigma, generator=generator)
+
+            # 3. Predict the noise residual given the noise magnitude `sigma_hat`
+            # The model inputs and output are adjusted by following eq. (213) in [1].
+            model_output = (sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2)["sample"]
+
+            # 4. Evaluate dx/dt at sigma_hat
+            # 5. Take Euler step from sigma to sigma_prev
+            step_output = self.scheduler.step(model_output, sigma_hat, sigma_prev, sample_hat)
+
+            if sigma_prev != 0:
+                # 6. Apply 2nd order correction
+                # The model inputs and output are adjusted by following eq. (213) in [1].
+                model_output = (sigma_prev / 2) * model((step_output["prev_sample"] + 1) / 2, sigma_prev / 2)["sample"]
+                step_output = self.scheduler.step_correct(
+                    model_output,
+                    sigma_hat,
+                    sigma_prev,
+                    sample_hat,
+                    step_output["prev_sample"],
+                    step_output["derivative"],
+                )
+            sample = step_output["prev_sample"]
+
+        sample = (sample / 2 + 0.5).clamp(0, 1)
+        sample = sample.cpu().permute(0, 2, 3, 1).numpy()
+        if output_type == "pil":
+            sample = self.numpy_to_pil(sample)
+
+        return {"sample": sample}

src/diffusers/schedulers/__init__.py

@@ -18,6 +18,7 @@
 
 from .scheduling_ddim import DDIMScheduler
 from .scheduling_ddpm import DDPMScheduler
+from .scheduling_karras_ve import KarrasVeScheduler
 from .scheduling_pndm import PNDMScheduler
 from .scheduling_sde_ve import ScoreSdeVeScheduler
 from .scheduling_sde_vp import ScoreSdeVpScheduler

src/diffusers/schedulers/scheduling_ddpm.py

@@ -134,7 +134,7 @@ def step(
         generator=None,
     ):
         t = timestep
-        
+
         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
             model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
         else:

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -0,0 +1,127 @@
+# Copyright 2022 NVIDIA and 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.
+
+
+from typing import Union
+
+import numpy as np
+import torch
+
+from ..configuration_utils import ConfigMixin, register_to_config
+from .scheduling_utils import SchedulerMixin
+
+
+class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
+    """
+    Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2].
+    Use Algorithm 2 and the VE column of Table 1 from [1] for reference.
+
+    [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364
+    [2] Song, Yang, et al. "Score-based generative modeling through stochastic differential equations." https://arxiv.org/abs/2011.13456
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        sigma_min=0.02,
+        sigma_max=100,
+        s_noise=1.007,
+        s_churn=80,
+        s_min=0.05,
+        s_max=50,
+        tensor_format="pt",
+    ):
+        """
+        For more details on the parameters, see the original paper's Appendix E.:
+        "Elucidating the Design Space of Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364.
+        The grid search values used to find the optimal {s_noise, s_churn, s_min, s_max} for a specific model
+        are described in Table 5 of the paper.
+
+        Args:
+            sigma_min (`float`): minimum noise magnitude
+            sigma_max (`float`): maximum noise magnitude
+            s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling.
+                A reasonable range is [1.000, 1.011].
+            s_churn (`float`): the parameter controlling the overall amount of stochasticity.
+                A reasonable range is [0, 100].
+            s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity).
+                A reasonable range is [0, 10].
+            s_max (`float`): the end value of the sigma range where we add noise.
+                A reasonable range is [0.2, 80].
+        """
+        # setable values
+        self.num_inference_steps = None
+        self.timesteps = None
+        self.schedule = None  # sigma(t_i)
+
+        self.tensor_format = tensor_format
+        self.set_format(tensor_format=tensor_format)
+
+    def set_timesteps(self, num_inference_steps):
+        self.num_inference_steps = num_inference_steps
+        self.timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()
+        self.schedule = [
+            (self.sigma_max * (self.sigma_min**2 / self.sigma_max**2) ** (i / (num_inference_steps - 1)))
+            for i in self.timesteps
+        ]
+        self.schedule = np.array(self.schedule, dtype=np.float32)
+
+        self.set_format(tensor_format=self.tensor_format)
+
+    def add_noise_to_input(self, sample, sigma, generator=None):
+        """
+        Explicit Langevin-like "churn" step of adding noise to the sample according to
+        a factor gamma_i ≥ 0 to reach a higher noise level sigma_hat = sigma_i + gamma_i*sigma_i.
+        """
+        if self.s_min <= sigma <= self.s_max:
+            gamma = min(self.s_churn / self.num_inference_steps, 2**0.5 - 1)
+        else:
+            gamma = 0
+
+        # sample eps ~ N(0, S_noise^2 * I)
+        eps = self.s_noise * torch.randn(sample.shape, generator=generator).to(sample.device)
+        sigma_hat = sigma + gamma * sigma
+        sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
+
+        return sample_hat, sigma_hat
+
+    def step(
+        self,
+        model_output: Union[torch.FloatTensor, np.ndarray],
+        sigma_hat: float,
+        sigma_prev: float,
+        sample_hat: Union[torch.FloatTensor, np.ndarray],
+    ):
+        pred_original_sample = sample_hat + sigma_hat * model_output
+        derivative = (sample_hat - pred_original_sample) / sigma_hat
+        sample_prev = sample_hat + (sigma_prev - sigma_hat) * derivative
+
+        return {"prev_sample": sample_prev, "derivative": derivative}
+
+    def step_correct(
+        self,
+        model_output: Union[torch.FloatTensor, np.ndarray],
+        sigma_hat: float,
+        sigma_prev: float,
+        sample_hat: Union[torch.FloatTensor, np.ndarray],
+        sample_prev: Union[torch.FloatTensor, np.ndarray],
+        derivative: Union[torch.FloatTensor, np.ndarray],
+    ):
+        pred_original_sample = sample_prev + sigma_prev * model_output
+        derivative_corr = (sample_prev - pred_original_sample) / sigma_prev
+        sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
+        return {"prev_sample": sample_prev, "derivative": derivative_corr}
+
+    def add_noise(self, original_samples, noise, timesteps):
+        raise NotImplementedError()

tests/test_modeling_utils.py

@@ -29,6 +29,8 @@
     DDIMScheduler,
     DDPMPipeline,
     DDPMScheduler,
+    KarrasVePipeline,
+    KarrasVeScheduler,
     LDMPipeline,
     LDMTextToImagePipeline,
     PNDMPipeline,
@@ -920,3 +922,19 @@ def test_ddpm_ddim_equality_batched(self):
 
         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(ddpm_images - ddim_images).max() < 1e-1
+
+    @slow
+    def test_karras_ve_pipeline(self):
+        model_id = "google/ncsnpp-celebahq-256"
+        model = UNet2DModel.from_pretrained(model_id)
+        scheduler = KarrasVeScheduler(tensor_format="pt")
+
+        pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
+
+        generator = torch.manual_seed(0)
+        image = pipe(num_inference_steps=20, generator=generator, output_type="numpy")["sample"]
+
+        image_slice = image[0, -3:, -3:, -1]
+        assert image.shape == (1, 256, 256, 3)
+        expected_slice = np.array([0.26815, 0.1581, 0.2658, 0.23248, 0.1550, 0.2539, 0.1131, 0.1024, 0.0837])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2