update smoothers

diffsynth/processors/FastBlend.py

@@ -0,0 +1,142 @@
+from PIL import Image
+import cupy as cp
+import numpy as np
+from tqdm import tqdm
+from ..extensions.FastBlend.patch_match import PyramidPatchMatcher
+from ..extensions.FastBlend.runners.fast import TableManager
+from .base import VideoProcessor
+
+
+class FastBlendSmoother(VideoProcessor):
+    def __init__(
+        self,
+        inference_mode="fast", batch_size=8, window_size=60,
+        minimum_patch_size=5, threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, initialize="identity", tracking_window_size=0
+    ):
+        self.inference_mode = inference_mode
+        self.batch_size = batch_size
+        self.window_size = window_size
+        self.ebsynth_config = {
+            "minimum_patch_size": minimum_patch_size,
+            "threads_per_block": threads_per_block,
+            "num_iter": num_iter,
+            "gpu_id": gpu_id,
+            "guide_weight": guide_weight,
+            "initialize": initialize,
+            "tracking_window_size": tracking_window_size
+        }
+
+    @staticmethod
+    def from_model_manager(model_manager, **kwargs):
+        # TODO: fetch GPU ID from model_manager
+        return FastBlendSmoother(**kwargs)
+
+    def inference_fast(self, frames_guide, frames_style):
+        table_manager = TableManager()
+        patch_match_engine = PyramidPatchMatcher(
+            image_height=frames_style[0].shape[0],
+            image_width=frames_style[0].shape[1],
+            channel=3,
+            **self.ebsynth_config
+        )
+        # left part
+        table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, self.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, self.window_size, self.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, self.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, self.window_size, self.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]
+        frames = [Image.fromarray(frame) for frame in frames]
+        return frames
+    
+    def inference_balanced(self, frames_guide, frames_style):
+        patch_match_engine = PyramidPatchMatcher(
+            image_height=frames_style[0].shape[0],
+            image_width=frames_style[0].shape[1],
+            channel=3,
+            **self.ebsynth_config
+        )
+        output_frames = []
+        # tasks
+        n = len(frames_style)
+        tasks = []
+        for target in range(n):
+            for source in range(target - self.window_size, target + self.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), self.batch_size), desc="Balanced Mode"):
+            tasks_batch = tasks[batch_id: min(batch_id+self.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 + self.window_size + 1) - max(0, target - self.window_size):
+                    frame = frame.clip(0, 255).astype("uint8")
+                    output_frames.append(Image.fromarray(frame))
+                    frames[target] = (None, 1)
+        return output_frames
+    
+    def inference_accurate(self, frames_guide, frames_style):
+        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,
+            **self.ebsynth_config
+        )
+        output_frames = []
+        # run
+        n = len(frames_style)
+        for target in tqdm(range(n), desc="Accurate Mode"):
+            l, r = max(target - self.window_size, 0), min(target + self.window_size + 1, n)
+            remapped_frames = []
+            for i in range(l, r, self.batch_size):
+                j = min(i + self.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")
+            output_frames.append(Image.fromarray(frame))
+        return output_frames
+    
+    def release_vram(self):
+        mempool = cp.get_default_memory_pool()
+        pinned_mempool = cp.get_default_pinned_memory_pool()
+        mempool.free_all_blocks()
+        pinned_mempool.free_all_blocks()
+    
+    def __call__(self, rendered_frames, original_frames=None, **kwargs):
+        rendered_frames = [np.array(frame) for frame in rendered_frames]
+        original_frames = [np.array(frame) for frame in original_frames]
+        if self.inference_mode == "fast":
+            output_frames = self.inference_fast(original_frames, rendered_frames)
+        elif self.inference_mode == "balanced":
+            output_frames = self.inference_balanced(original_frames, rendered_frames)
+        elif self.inference_mode == "accurate":
+            output_frames = self.inference_accurate(original_frames, rendered_frames)
+        else:
+            raise ValueError("inference_mode must be fast, balanced or accurate")
+        self.release_vram()
+        return output_frames

diffsynth/processors/PILEditor.py

@@ -0,0 +1,28 @@
+from PIL import ImageEnhance
+from .base import VideoProcessor
+
+
+class ContrastEditor(VideoProcessor):
+    def __init__(self, rate=1.5):
+        self.rate = rate
+
+    @staticmethod
+    def from_model_manager(model_manager, **kwargs):
+        return ContrastEditor(**kwargs)
+    
+    def __call__(self, rendered_frames, **kwargs):
+        rendered_frames = [ImageEnhance.Contrast(i).enhance(self.rate) for i in rendered_frames]
+        return rendered_frames
+
+
+class SharpnessEditor(VideoProcessor):
+    def __init__(self, rate=1.5):
+        self.rate = rate
+
+    @staticmethod
+    def from_model_manager(model_manager, **kwargs):
+        return SharpnessEditor(**kwargs)
+    
+    def __call__(self, rendered_frames, **kwargs):
+        rendered_frames = [ImageEnhance.Sharpness(i).enhance(self.rate) for i in rendered_frames]
+        return rendered_frames

diffsynth/processors/RIFE.py

@@ -0,0 +1,77 @@
+import torch
+import numpy as np
+from PIL import Image
+from .base import VideoProcessor
+
+
+class RIFESmoother(VideoProcessor):
+    def __init__(self, model, device="cuda", scale=1.0, batch_size=4, interpolate=True):
+        self.model = model
+        self.device = device
+
+        # IFNet only does not support float16
+        self.torch_dtype = torch.float32
+
+        # Other parameters
+        self.scale = scale
+        self.batch_size = batch_size
+        self.interpolate = interpolate
+
+    @staticmethod
+    def from_model_manager(model_manager, **kwargs):
+        return RIFESmoother(model_manager.RIFE, device=model_manager.device, **kwargs)
+    
+    def process_image(self, image):
+        width, height = image.size
+        if width % 32 != 0 or height % 32 != 0:
+            width = (width + 31) // 32
+            height = (height + 31) // 32
+            image = image.resize((width, height))
+        image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1)
+        return image
+    
+    def process_images(self, images):
+        images = [self.process_image(image) for image in images]
+        images = torch.stack(images)
+        return images
+    
+    def decode_images(self, images):
+        images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8)
+        images = [Image.fromarray(image) for image in images]
+        return images
+    
+    def process_tensors(self, input_tensor, scale=1.0, batch_size=4):
+        output_tensor = []
+        for batch_id in range(0, input_tensor.shape[0], batch_size):
+            batch_id_ = min(batch_id + batch_size, input_tensor.shape[0])
+            batch_input_tensor = input_tensor[batch_id: batch_id_]
+            batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype)
+            flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale])
+            output_tensor.append(merged[2].cpu())
+        output_tensor = torch.concat(output_tensor, dim=0)
+        return output_tensor
+
+    @torch.no_grad()
+    def __call__(self, rendered_frames, **kwargs):
+        # Preprocess
+        processed_images = self.process_images(rendered_frames)
+
+        # Input
+        input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1)
+
+        # Interpolate
+        output_tensor = self.process_tensors(input_tensor, scale=self.scale, batch_size=self.batch_size)
+        
+        if self.interpolate:
+            # Blend
+            input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1)
+            output_tensor = self.process_tensors(input_tensor, scale=self.scale, batch_size=self.batch_size)
+            processed_images[1:-1] = output_tensor
+        else:
+            processed_images[1:-1] = (processed_images[1:-1] + output_tensor) / 2
+
+        # To images
+        output_images = self.decode_images(processed_images)
+        if output_images[0].size != rendered_frames[0].size:
+            output_images = [image.resize(rendered_frames[0].size) for image in output_images]
+        return output_images

diffsynth/processors/base.py

@@ -0,0 +1,6 @@
+class VideoProcessor:
+    def __init__(self):
+        pass
+
+    def __call__(self):
+        raise NotImplementedError

diffsynth/processors/sequencial_processor.py

@@ -0,0 +1,15 @@
+from .base import VideoProcessor
+
+
+class SequencialProcessor(VideoProcessor):
+    def __init__(self, processors=[]):
+        self.processors = processors
+
+    @staticmethod
+    def from_model_manager(model_manager, **kwargs):
+        return SequencialProcessor(**kwargs)
+    
+    def __call__(self, rendered_frames, **kwargs):
+        for processor in self.processors:
+            rendered_frames = processor(rendered_frames, **kwargs)
+        return rendered_frames