v1.2

diffsynth/extensions/FastBlend/runners/__init__.py

@@ -0,0 +1,4 @@
+from .accurate import AccurateModeRunner
+from .fast import FastModeRunner
+from .balanced import BalancedModeRunner
+from .interpolation import InterpolationModeRunner, InterpolationModeSingleFrameRunner

diffsynth/extensions/FastBlend/runners/accurate.py

@@ -0,0 +1,35 @@
+from ..patch_match import PyramidPatchMatcher
+import os
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+
+class AccurateModeRunner:
+    def __init__(self):
+        pass
+
+    def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Accurate Mode", save_path=None):
+        patch_match_engine = PyramidPatchMatcher(
+            image_height=frames_style[0].shape[0],
+            image_width=frames_style[0].shape[1],
+            channel=3,
+            use_mean_target_style=True,
+            **ebsynth_config
+        )
+        # run
+        n = len(frames_style)
+        for target in tqdm(range(n), desc=desc):
+            l, r = max(target - window_size, 0), min(target + window_size + 1, n)
+            remapped_frames = []
+            for i in range(l, r, batch_size):
+                j = min(i + batch_size, r)
+                source_guide = np.stack([frames_guide[source] for source in range(i, j)])
+                target_guide = np.stack([frames_guide[target]] * (j - i))
+                source_style = np.stack([frames_style[source] for source in range(i, j)])
+                _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
+                remapped_frames.append(target_style)
+            frame = np.concatenate(remapped_frames, axis=0).mean(axis=0)
+            frame = frame.clip(0, 255).astype("uint8")
+            if save_path is not None:
+                Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target))

diffsynth/extensions/FastBlend/runners/balanced.py

@@ -0,0 +1,46 @@
+from ..patch_match import PyramidPatchMatcher
+import os
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+
+class BalancedModeRunner:
+    def __init__(self):
+        pass
+
+    def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Balanced Mode", save_path=None):
+        patch_match_engine = PyramidPatchMatcher(
+            image_height=frames_style[0].shape[0],
+            image_width=frames_style[0].shape[1],
+            channel=3,
+            **ebsynth_config
+        )
+        # tasks
+        n = len(frames_style)
+        tasks = []
+        for target in range(n):
+            for source in range(target - window_size, target + window_size + 1):
+                if source >= 0 and source < n and source != target:
+                    tasks.append((source, target))
+        # run
+        frames = [(None, 1) for i in range(n)]
+        for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc):
+            tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))]
+            source_guide = np.stack([frames_guide[source] for source, target in tasks_batch])
+            target_guide = np.stack([frames_guide[target] for source, target in tasks_batch])
+            source_style = np.stack([frames_style[source] for source, target in tasks_batch])
+            _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
+            for (source, target), result in zip(tasks_batch, target_style):
+                frame, weight = frames[target]
+                if frame is None:
+                    frame = frames_style[target]
+                frames[target] = (
+                    frame * (weight / (weight + 1)) + result / (weight + 1),
+                    weight + 1
+                )
+                if weight + 1 == min(n, target + window_size + 1) - max(0, target - window_size):
+                    frame = frame.clip(0, 255).astype("uint8")
+                    if save_path is not None:
+                        Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target))
+                    frames[target] = (None, 1)

diffsynth/extensions/FastBlend/runners/fast.py

@@ -0,0 +1,141 @@
+from ..patch_match import PyramidPatchMatcher
+import functools, os
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+
+class TableManager:
+    def __init__(self):
+        pass
+
+    def task_list(self, n):
+        tasks = []
+        max_level = 1
+        while (1<<max_level)<=n:
+            max_level += 1
+        for i in range(n):
+            j = i
+            for level in range(max_level):
+                if i&(1<<level):
+                    continue
+                j |= 1<<level
+                if j>=n:
+                    break
+                meta_data = {
+                    "source": i,
+                    "target": j,
+                    "level": level + 1
+                }
+                tasks.append(meta_data)
+        tasks.sort(key=functools.cmp_to_key(lambda u, v: u["level"]-v["level"]))
+        return tasks
+    
+    def build_remapping_table(self, frames_guide, frames_style, patch_match_engine, batch_size, desc=""):
+        n = len(frames_guide)
+        tasks = self.task_list(n)
+        remapping_table = [[(frames_style[i], 1)] for i in range(n)]
+        for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc):
+            tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))]
+            source_guide = np.stack([frames_guide[task["source"]] for task in tasks_batch])
+            target_guide = np.stack([frames_guide[task["target"]] for task in tasks_batch])
+            source_style = np.stack([frames_style[task["source"]] for task in tasks_batch])
+            _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
+            for task, result in zip(tasks_batch, target_style):
+                target, level = task["target"], task["level"]
+                if len(remapping_table[target])==level:
+                    remapping_table[target].append((result, 1))
+                else:
+                    frame, weight = remapping_table[target][level]
+                    remapping_table[target][level] = (
+                        frame * (weight / (weight + 1)) + result / (weight + 1),
+                        weight + 1
+                    )
+        return remapping_table
+
+    def remapping_table_to_blending_table(self, table):
+        for i in range(len(table)):
+            for j in range(1, len(table[i])):
+                frame_1, weight_1 = table[i][j-1]
+                frame_2, weight_2 = table[i][j]
+                frame = (frame_1 + frame_2) / 2
+                weight = weight_1 + weight_2
+                table[i][j] = (frame, weight)
+        return table
+
+    def tree_query(self, leftbound, rightbound):
+        node_list = []
+        node_index = rightbound
+        while node_index>=leftbound:
+            node_level = 0
+            while (1<<node_level)&node_index and node_index-(1<<node_level+1)+1>=leftbound:
+                node_level += 1
+            node_list.append((node_index, node_level))
+            node_index -= 1<<node_level
+        return node_list
+
+    def process_window_sum(self, frames_guide, blending_table, patch_match_engine, window_size, batch_size, desc=""):
+        n = len(blending_table)
+        tasks = []
+        frames_result = []
+        for target in range(n):
+            node_list = self.tree_query(max(target-window_size, 0), target)
+            for source, level in node_list:
+                if source!=target:
+                    meta_data = {
+                        "source": source,
+                        "target": target,
+                        "level": level
+                    }
+                    tasks.append(meta_data)
+                else:
+                    frames_result.append(blending_table[target][level])
+        for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc):
+            tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))]
+            source_guide = np.stack([frames_guide[task["source"]] for task in tasks_batch])
+            target_guide = np.stack([frames_guide[task["target"]] for task in tasks_batch])
+            source_style = np.stack([blending_table[task["source"]][task["level"]][0] for task in tasks_batch])
+            _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
+            for task, frame_2 in zip(tasks_batch, target_style):
+                source, target, level = task["source"], task["target"], task["level"]
+                frame_1, weight_1 = frames_result[target]
+                weight_2 = blending_table[source][level][1]
+                weight = weight_1 + weight_2
+                frame = frame_1 * (weight_1 / weight) + frame_2 * (weight_2 / weight)
+                frames_result[target] = (frame, weight)
+        return frames_result
+
+
+class FastModeRunner:
+    def __init__(self):
+        pass
+
+    def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, save_path=None):
+        frames_guide = frames_guide.raw_data()
+        frames_style = frames_style.raw_data()
+        table_manager = TableManager()
+        patch_match_engine = PyramidPatchMatcher(
+            image_height=frames_style[0].shape[0],
+            image_width=frames_style[0].shape[1],
+            channel=3,
+            **ebsynth_config
+        )
+        # left part
+        table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, batch_size, desc="Fast Mode Step 1/4")
+        table_l = table_manager.remapping_table_to_blending_table(table_l)
+        table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, window_size, batch_size, desc="Fast Mode Step 2/4")
+        # right part
+        table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, batch_size, desc="Fast Mode Step 3/4")
+        table_r = table_manager.remapping_table_to_blending_table(table_r)
+        table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, window_size, batch_size, desc="Fast Mode Step 4/4")[::-1]
+        # merge
+        frames = []
+        for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r):
+            weight_m = -1
+            weight = weight_l + weight_m + weight_r
+            frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight)
+            frames.append(frame)
+        frames = [frame.clip(0, 255).astype("uint8") for frame in frames]
+        if save_path is not None:
+            for target, frame in enumerate(frames):
+                Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target))

diffsynth/extensions/FastBlend/runners/interpolation.py

@@ -0,0 +1,121 @@
+from ..patch_match import PyramidPatchMatcher
+import os
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+
+class InterpolationModeRunner:
+    def __init__(self):
+        pass
+
+    def get_index_dict(self, index_style):
+        index_dict = {}
+        for i, index in enumerate(index_style):
+            index_dict[index] = i
+        return index_dict
+
+    def get_weight(self, l, m, r):
+        weight_l, weight_r = abs(m - r), abs(m - l)
+        if weight_l + weight_r == 0:
+            weight_l, weight_r = 0.5, 0.5
+        else:
+            weight_l, weight_r = weight_l / (weight_l + weight_r), weight_r / (weight_l + weight_r)
+        return weight_l, weight_r
+
+    def get_task_group(self, index_style, n):
+        task_group = []
+        index_style = sorted(index_style)
+        # first frame
+        if index_style[0]>0:
+            tasks = []
+            for m in range(index_style[0]):
+                tasks.append((index_style[0], m, index_style[0]))
+            task_group.append(tasks)
+        # middle frames
+        for l, r in zip(index_style[:-1], index_style[1:]):
+            tasks = []
+            for m in range(l, r):
+                tasks.append((l, m, r))
+            task_group.append(tasks)
+        # last frame
+        tasks = []
+        for m in range(index_style[-1], n):
+            tasks.append((index_style[-1], m, index_style[-1]))
+        task_group.append(tasks)
+        return task_group
+
+    def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None):
+        patch_match_engine = PyramidPatchMatcher(
+            image_height=frames_style[0].shape[0],
+            image_width=frames_style[0].shape[1],
+            channel=3,
+            use_mean_target_style=False,
+            use_pairwise_patch_error=True,
+            **ebsynth_config
+        )
+        # task
+        index_dict = self.get_index_dict(index_style)
+        task_group = self.get_task_group(index_style, len(frames_guide))
+        # run
+        for tasks in task_group:
+            index_start, index_end = min([i[1] for i in tasks]), max([i[1] for i in tasks])
+            for batch_id in tqdm(range(0, len(tasks), batch_size), desc=f"Rendering frames {index_start}...{index_end}"):
+                tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))]
+                source_guide, target_guide, source_style = [], [], []
+                for l, m, r in tasks_batch:
+                    # l -> m
+                    source_guide.append(frames_guide[l])
+                    target_guide.append(frames_guide[m])
+                    source_style.append(frames_style[index_dict[l]])
+                    # r -> m
+                    source_guide.append(frames_guide[r])
+                    target_guide.append(frames_guide[m])
+                    source_style.append(frames_style[index_dict[r]])
+                source_guide = np.stack(source_guide)
+                target_guide = np.stack(target_guide)
+                source_style = np.stack(source_style)
+                _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
+                if save_path is not None:
+                    for frame_l, frame_r, (l, m, r) in zip(target_style[0::2], target_style[1::2], tasks_batch):
+                        weight_l, weight_r = self.get_weight(l, m, r)
+                        frame = frame_l * weight_l + frame_r * weight_r
+                        frame = frame.clip(0, 255).astype("uint8")
+                        Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % m))
+
+
+class InterpolationModeSingleFrameRunner:
+    def __init__(self):
+        pass
+
+    def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None):
+        # check input
+        tracking_window_size = ebsynth_config["tracking_window_size"]
+        if tracking_window_size * 2 >= batch_size:
+            raise ValueError("batch_size should be larger than track_window_size * 2")
+        frame_style = frames_style[0]
+        frame_guide = frames_guide[index_style[0]]
+        patch_match_engine = PyramidPatchMatcher(
+            image_height=frame_style.shape[0],
+            image_width=frame_style.shape[1],
+            channel=3,
+            **ebsynth_config
+        )
+        # run
+        frame_id, n = 0, len(frames_guide)
+        for i in tqdm(range(0, n, batch_size - tracking_window_size * 2), desc=f"Rendering frames 0...{n}"):
+            if i + batch_size > n:
+                l, r = max(n - batch_size, 0), n
+            else:
+                l, r = i, i + batch_size
+            source_guide = np.stack([frame_guide] * (r-l))
+            target_guide = np.stack([frames_guide[i] for i in range(l, r)])
+            source_style = np.stack([frame_style] * (r-l))
+            _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
+            for i, frame in zip(range(l, r), target_style):
+                if i==frame_id:
+                    frame = frame.clip(0, 255).astype("uint8")
+                    Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % frame_id))
+                    frame_id += 1
+                if r < n and r-frame_id <= tracking_window_size:
+                    break