Merge remote-tracking branch 'origin/main'

_

+# 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 torch
+
+import tqdm
+
+from ..pipeline_utils import DiffusionPipeline
+
+
+class PNDM(DiffusionPipeline):
+    def __init__(self, unet, noise_scheduler):
+        super().__init__()
+        noise_scheduler = noise_scheduler.set_format("pt")
+        self.register_modules(unet=unet, noise_scheduler=noise_scheduler)
+
+    def __call__(self, batch_size=1, generator=None, torch_device=None, num_inference_steps=50):
+        # eta corresponds to η in paper and should be between [0, 1]
+        if torch_device is None:
+            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        num_trained_timesteps = self.noise_scheduler.timesteps
+        inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps)
+
+        self.unet.to(torch_device)
+
+        # Sample gaussian noise to begin loop
+        image = torch.randn(
+            (batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution),
+            generator=generator,
+        )
+        image = image.to(torch_device)
+
+        seq = list(inference_step_times)
+        seq_next = [-1] + list(seq[:-1])
+        model = self.unet
+
+        warmup_steps = [len(seq) - (i // 4 + 1) for i in range(3 * 4)]
+
+        ets = []
+        prev_image = image
+        for i, step_idx in enumerate(warmup_steps):
+            i = seq[step_idx]
+            j = seq_next[step_idx]
+
+            t = (torch.ones(image.shape[0]) * i)
+            t_next = (torch.ones(image.shape[0]) * j)
+
+            residual = model(image.to("cuda"), t.to("cuda"))
+            residual = residual.to("cpu")
+
+            image = image.to("cpu")
+            image = self.noise_scheduler.transfer(prev_image.to("cpu"), t_list[0], t_list[1], residual)
+
+            if i % 4 == 0:
+                ets.append(residual)
+                prev_image = image
+
+        for 
+
+        ets = []
+        step_idx = len(seq) - 1
+        while step_idx >= 0:
+            i = seq[step_idx]
+            j = seq_next[step_idx]
+
+            t = (torch.ones(image.shape[0]) * i)
+            t_next = (torch.ones(image.shape[0]) * j)
+
+            residual = model(image.to("cuda"), t.to("cuda"))
+            residual = residual.to("cpu")
+
+            t_list = [t, (t+t_next)/2, t_next]
+
+            ets.append(residual)
+            if len(ets) <= 3:
+                image = image.to("cpu")
+                x_2 = self.noise_scheduler.transfer(image.to("cpu"), t_list[0], t_list[1], residual)
+
+                e_2 = model(x_2.to("cuda"), t_list[1].to("cuda")).to("cpu")
+                x_3 = self.noise_scheduler.transfer(image, t_list[0], t_list[1], e_2)
+                e_3 = model(x_3.to("cuda"), t_list[1].to("cuda")).to("cpu")
+                x_4 = self.noise_scheduler.transfer(image, t_list[0], t_list[2], e_3)
+                e_4 = model(x_4.to("cuda"), t_list[2].to("cuda")).to("cpu")
+                residual = (1 / 6) * (residual + 2 * e_2 + 2 * e_3 + e_4)
+            else:
+                residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
+
+            img_next = self.noise_scheduler.transfer(image.to("cpu"), t, t_next, residual)
+            image = img_next
+
+            step_idx = step_idx - 1
+
+#            if len(prev_noises) in [1, 2]:
+#                t = (t + t_next) / 2
+#            elif len(prev_noises) == 3:
+#                t = t_next / 2
+
+#            if len(prev_noises) == 0:
+#                ets.append(residual)
+#
+#            if len(ets) > 3:
+#                residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
+#                step_idx = step_idx - 1
+#            elif len(ets) <= 3 and len(prev_noises) == 3:
+#                residual = (1 / 6) * (prev_noises[-3] + 2 * prev_noises[-2] + 2 * prev_noises[-1] + residual)
+#                prev_noises = []
+#                step_idx = step_idx - 1
+#            elif len(ets) <= 3 and len(prev_noises) < 3:
+#                prev_noises.append(residual)
+#                if len(prev_noises) < 2:
+#                    t_next = (t + t_next) / 2
+#
+#            image = self.noise_scheduler.transfer(image.to("cpu"), t, t_next, residual)
+
+        return image
+
+        # 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"
+#        for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
+            # 1. predict noise residual
+#            with torch.no_grad():
+#                residual = self.unet(image, inference_step_times[t])
+#
+            # 2. predict previous mean of image x_t-1
+#            pred_prev_image = self.noise_scheduler.step(residual, image, t, num_inference_steps, eta)
+#
+            # 3. optionally sample variance
+#            variance = 0
+#            if eta > 0:
+#                noise = torch.randn(image.shape, generator=generator).to(image.device)
+#                variance = self.noise_scheduler.get_variance(t, num_inference_steps).sqrt() * eta * noise
+#
+            # 4. set current image to prev_image: x_t -> x_t-1
+#            image = pred_prev_image + variance

src/diffusers/pipelines/pipeline_pndm.py

@@ -44,67 +44,55 @@ class PNDM(DiffusionPipeline):
         )
         image = image.to(torch_device)
 
-        seq = inference_step_times
+        seq = list(inference_step_times)
         seq_next = [-1] + list(seq[:-1])
         model = self.unet
 
+        warmup_time_steps = list(reversed([(t + 5) // 10 * 10 for t in range(seq[-4], seq[-1], 5)]))
+
+        cur_residual = 0
+        prev_image = image
         ets = []
-        for i, j in zip(reversed(seq), reversed(seq_next)):
-            t = (torch.ones(image.shape[0]) * i)
-            t_next = (torch.ones(image.shape[0]) * j)
+        for i in range(len(warmup_time_steps)):
+            t = warmup_time_steps[i] * torch.ones(image.shape[0])
+            t_next = (warmup_time_steps[i + 1] if i < len(warmup_time_steps) - 1 else warmup_time_steps[-1]) * torch.ones(image.shape[0])
 
             residual = model(image.to("cuda"), t.to("cuda"))
             residual = residual.to("cpu")
 
-            t_list = [t, (t+t_next)/2, t_next]
-
-            if len(ets) <= 2:
+            if i % 4 == 0:
+                cur_residual += 1 / 6 * residual
                 ets.append(residual)
+                prev_image = image
+            elif (i - 1) % 4 == 0:
+                cur_residual += 1 / 3 * residual
+            elif (i - 2) % 4 == 0:
+                cur_residual += 1 / 3 * residual
+            elif (i - 3) % 4 == 0:
+                cur_residual += 1 / 6 * residual
+                residual = cur_residual
+                cur_residual = 0
+
             image = image.to("cpu")
-                x_2 = self.noise_scheduler.transfer(image, t_list[0], t_list[1], residual)
-                e_2 = model(x_2.to("cuda"), t_list[1].to("cuda")).to("cpu")
-                x_3 = self.noise_scheduler.transfer(image, t_list[0], t_list[1], e_2)
-                e_3 = model(x_3.to("cuda"), t_list[1].to("cuda")).to("cpu")
-                x_4 = self.noise_scheduler.transfer(image, t_list[0], t_list[2], e_3)
-                e_4 = model(x_4.to("cuda"), t_list[2].to("cuda")).to("cpu")
-                residual = (1 / 6) * (residual + 2 * e_2 + 2 * e_3 + e_4)
-            else:
+            t_2 = warmup_time_steps[4 * (i // 4)] * torch.ones(image.shape[0])
+            image = self.noise_scheduler.transfer(prev_image.to("cpu"), t_2, t_next, residual)
+
+        step_idx = len(seq) - 4
+        while step_idx >= 0:
+            i = seq[step_idx]
+            j = seq_next[step_idx]
+
+            t = (torch.ones(image.shape[0]) * i)
+            t_next = (torch.ones(image.shape[0]) * j)
+
+            residual = model(image.to("cuda"), t.to("cuda"))
+            residual = residual.to("cpu")
             ets.append(residual)
             residual = (1 / 24) * (55 * ets[-1] - 59 * ets[-2] + 37 * ets[-3] - 9 * ets[-4])
 
             img_next = self.noise_scheduler.transfer(image.to("cpu"), t, t_next, residual)
-
-#            with torch.no_grad():
-#                t_start, t_end = t_next, t
-#                img_next, ets = self.noise_scheduler.step(image, t_start, t_end, model, ets)
-
             image = img_next
 
-        return image
+            step_idx = step_idx - 1
 
-        # 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"
-#        for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
-            # 1. predict noise residual
-#            with torch.no_grad():
-#                residual = self.unet(image, inference_step_times[t])
-#
-            # 2. predict previous mean of image x_t-1
-#            pred_prev_image = self.noise_scheduler.step(residual, image, t, num_inference_steps, eta)
-#
-            # 3. optionally sample variance
-#            variance = 0
-#            if eta > 0:
-#                noise = torch.randn(image.shape, generator=generator).to(image.device)
-#                variance = self.noise_scheduler.get_variance(t, num_inference_steps).sqrt() * eta * noise
-#
-            # 4. set current image to prev_image: x_t -> x_t-1
-#            image = pred_prev_image + variance
+        return image