first commit

diffsynth/__init__.py

@@ -0,0 +1,6 @@
+from .data import *
+from .models import *
+from .prompts import *
+from .schedulers import *
+from .pipelines import *
+from .controlnets import *

diffsynth/controlnets/__init__.py

@@ -0,0 +1,2 @@
+from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager
+from .processors import Annotator

diffsynth/controlnets/controlnet_unit.py

@@ -0,0 +1,53 @@
+import torch
+import numpy as np
+from .processors import Processor_id
+
+
+class ControlNetConfigUnit:
+    def __init__(self, processor_id: Processor_id, model_path, scale=1.0):
+        self.processor_id = processor_id
+        self.model_path = model_path
+        self.scale = scale
+
+
+class ControlNetUnit:
+    def __init__(self, processor, model, scale=1.0):
+        self.processor = processor
+        self.model = model
+        self.scale = scale
+
+
+class MultiControlNetManager:
+    def __init__(self, controlnet_units=[]):
+        self.processors = [unit.processor for unit in controlnet_units]
+        self.models = [unit.model for unit in controlnet_units]
+        self.scales = [unit.scale for unit in controlnet_units]
+
+    def process_image(self, image, processor_id=None):
+        if processor_id is None:
+            processed_image = [processor(image) for processor in self.processors]
+        else:
+            processed_image = [self.processors[processor_id](image)]
+        processed_image = torch.concat([
+            torch.Tensor(np.array(image_, dtype=np.float32) / 255).permute(2, 0, 1).unsqueeze(0)
+            for image_ in processed_image
+        ], dim=0)
+        return processed_image
+    
+    def __call__(
+        self,
+        sample, timestep, encoder_hidden_states, conditionings,
+        tiled=False, tile_size=64, tile_stride=32
+    ):
+        res_stack = None
+        for conditioning, model, scale in zip(conditionings, self.models, self.scales):
+            res_stack_ = model(
+                sample, timestep, encoder_hidden_states, conditioning,
+                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
+            )
+            res_stack_ = [res * scale for res in res_stack_]
+            if res_stack is None:
+                res_stack = res_stack_
+            else:
+                res_stack = [i + j for i, j in zip(res_stack, res_stack_)]
+        return res_stack

diffsynth/controlnets/processors.py

@@ -0,0 +1,51 @@
+from typing_extensions import Literal, TypeAlias
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    from controlnet_aux.processor import (
+        CannyDetector, MidasDetector, HEDdetector, LineartDetector, LineartAnimeDetector, OpenposeDetector
+    )
+
+
+Processor_id: TypeAlias = Literal[
+    "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile"
+]
+
+class Annotator:
+    def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None):
+        if processor_id == "canny":
+            self.processor = CannyDetector()
+        elif processor_id == "depth":
+            self.processor = MidasDetector.from_pretrained(model_path).to("cuda")
+        elif processor_id == "softedge":
+            self.processor = HEDdetector.from_pretrained(model_path).to("cuda")
+        elif processor_id == "lineart":
+            self.processor = LineartDetector.from_pretrained(model_path).to("cuda")
+        elif processor_id == "lineart_anime":
+            self.processor = LineartAnimeDetector.from_pretrained(model_path).to("cuda")
+        elif processor_id == "openpose":
+            self.processor = OpenposeDetector.from_pretrained(model_path).to("cuda")
+        elif processor_id == "tile":
+            self.processor = None
+        else:
+            raise ValueError(f"Unsupported processor_id: {processor_id}")
+        
+        self.processor_id = processor_id
+        self.detect_resolution = detect_resolution
+
+    def __call__(self, image):
+        width, height = image.size
+        if self.processor_id == "openpose":
+            kwargs = {
+                "include_body": True,
+                "include_hand": True,
+                "include_face": True
+            }
+        else:
+            kwargs = {}
+        if self.processor is not None:
+            detect_resolution = self.detect_resolution if self.detect_resolution is not None else min(width, height)
+            image = self.processor(image, detect_resolution=detect_resolution, image_resolution=min(width, height), **kwargs)
+        image = image.resize((width, height))
+        return image
+

diffsynth/data/__init__.py

@@ -0,0 +1 @@
+from .video import VideoData, save_video, save_frames

diffsynth/data/video.py

@@ -0,0 +1,148 @@
+import imageio, os
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+
+class LowMemoryVideo:
+    def __init__(self, file_name):
+        self.reader = imageio.get_reader(file_name)
+    
+    def __len__(self):
+        return self.reader.count_frames()
+
+    def __getitem__(self, item):
+        return Image.fromarray(np.array(self.reader.get_data(item))).convert("RGB")
+
+    def __del__(self):
+        self.reader.close()
+
+
+def split_file_name(file_name):
+    result = []
+    number = -1
+    for i in file_name:
+        if ord(i)>=ord("0") and ord(i)<=ord("9"):
+            if number == -1:
+                number = 0
+            number = number*10 + ord(i) - ord("0")
+        else:
+            if number != -1:
+                result.append(number)
+                number = -1
+            result.append(i)
+    if number != -1:
+        result.append(number)
+    result = tuple(result)
+    return result
+
+
+def search_for_images(folder):
+    file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")]
+    file_list = [(split_file_name(file_name), file_name) for file_name in file_list]
+    file_list = [i[1] for i in sorted(file_list)]
+    file_list = [os.path.join(folder, i) for i in file_list]
+    return file_list
+
+
+class LowMemoryImageFolder:
+    def __init__(self, folder, file_list=None):
+        if file_list is None:
+            self.file_list = search_for_images(folder)
+        else:
+            self.file_list = [os.path.join(folder, file_name) for file_name in file_list]
+    
+    def __len__(self):
+        return len(self.file_list)
+
+    def __getitem__(self, item):
+        return Image.open(self.file_list[item]).convert("RGB")
+
+    def __del__(self):
+        pass
+
+
+def crop_and_resize(image, height, width):
+    image = np.array(image)
+    image_height, image_width, _ = image.shape
+    if image_height / image_width < height / width:
+        croped_width = int(image_height / height * width)
+        left = (image_width - croped_width) // 2
+        image = image[:, left: left+croped_width]
+        image = Image.fromarray(image).resize((width, height))
+    else:
+        croped_height = int(image_width / width * height)
+        left = (image_height - croped_height) // 2
+        image = image[left: left+croped_height, :]
+        image = Image.fromarray(image).resize((width, height))
+    return image
+
+
+class VideoData:
+    def __init__(self, video_file=None, image_folder=None, height=None, width=None, **kwargs):
+        if video_file is not None:
+            self.data_type = "video"
+            self.data = LowMemoryVideo(video_file, **kwargs)
+        elif image_folder is not None:
+            self.data_type = "images"
+            self.data = LowMemoryImageFolder(image_folder, **kwargs)
+        else:
+            raise ValueError("Cannot open video or image folder")
+        self.length = None
+        self.set_shape(height, width)
+
+    def raw_data(self):
+        frames = []
+        for i in range(self.__len__()):
+            frames.append(self.__getitem__(i))
+        return frames
+
+    def set_length(self, length):
+        self.length = length
+
+    def set_shape(self, height, width):
+        self.height = height
+        self.width = width
+
+    def __len__(self):
+        if self.length is None:
+            return len(self.data)
+        else:
+            return self.length
+
+    def shape(self):
+        if self.height is not None and self.width is not None:
+            return self.height, self.width
+        else:
+            height, width, _ = self.__getitem__(0).shape
+            return height, width
+
+    def __getitem__(self, item):
+        frame = self.data.__getitem__(item)
+        width, height = frame.size
+        if self.height is not None and self.width is not None:
+            if self.height != height or self.width != width:
+                frame = crop_and_resize(frame, self.height, self.width)
+        return frame
+
+    def __del__(self):
+        pass
+
+    def save_images(self, folder):
+        os.makedirs(folder, exist_ok=True)
+        for i in tqdm(range(self.__len__()), desc="Saving images"):
+            frame = self.__getitem__(i)
+            frame.save(os.path.join(folder, f"{i}.png"))
+
+
+def save_video(frames, save_path, fps, quality=9):
+    writer = imageio.get_writer(save_path, fps=fps, quality=quality)
+    for frame in tqdm(frames, desc="Saving video"):
+        frame = np.array(frame)
+        writer.append_data(frame)
+    writer.close()
+
+def save_frames(frames, save_path):
+    os.makedirs(save_path, exist_ok=True)
+    for i, frame in enumerate(tqdm(frames, desc="Saving images")):
+        frame.save(os.path.join(save_path, f"{i}.png"))

diffsynth/extensions/ESRGAN/__init__.py

@@ -0,0 +1,118 @@
+import torch
+from einops import repeat
+from PIL import Image
+import numpy as np
+
+
+class ResidualDenseBlock(torch.nn.Module):
+
+    def __init__(self, num_feat=64, num_grow_ch=32):
+        super(ResidualDenseBlock, self).__init__()
+        self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
+        self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
+        self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        return x5 * 0.2 + x
+
+
+class RRDB(torch.nn.Module):
+
+    def __init__(self, num_feat, num_grow_ch=32):
+        super(RRDB, self).__init__()
+        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
+
+    def forward(self, x):
+        out = self.rdb1(x)
+        out = self.rdb2(out)
+        out = self.rdb3(out)
+        return out * 0.2 + x
+
+
+class RRDBNet(torch.nn.Module):
+
+    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32):
+        super(RRDBNet, self).__init__()
+        self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)])
+        self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        # upsample
+        self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        feat = x
+        feat = self.conv_first(feat)
+        body_feat = self.conv_body(self.body(feat))
+        feat = feat + body_feat
+        # upsample
+        feat = repeat(feat, "B C H W -> B C (H 2) (W 2)")
+        feat = self.lrelu(self.conv_up1(feat))
+        feat = repeat(feat, "B C H W -> B C (H 2) (W 2)")
+        feat = self.lrelu(self.conv_up2(feat))
+        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
+        return out
+
+
+class ESRGAN(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    @staticmethod
+    def from_pretrained(model_path):
+        model = RRDBNet()
+        state_dict = torch.load(model_path, map_location="cpu")["params_ema"]
+        model.load_state_dict(state_dict)
+        model.eval()
+        return ESRGAN(model)
+
+    def process_image(self, image):
+        image = torch.Tensor(np.array(image, dtype=np.float32) / 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.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8)
+        images = [Image.fromarray(image) for image in images]
+        return images
+    
+    @torch.no_grad()
+    def upscale(self, images, batch_size=4, progress_bar=lambda x:x):
+        # Preprocess
+        input_tensor = self.process_images(images)
+
+        # Interpolate
+        output_tensor = []
+        for batch_id in progress_bar(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.model.conv_first.weight.device,
+                dtype=self.model.conv_first.weight.dtype)
+            batch_output_tensor = self.model(batch_input_tensor)
+            output_tensor.append(batch_output_tensor.cpu())
+        
+        # Output
+        output_tensor = torch.concat(output_tensor, dim=0)
+
+        # To images
+        output_images = self.decode_images(output_tensor)
+        return output_images

diffsynth/extensions/FastBlend/__init__.py

@@ -0,0 +1,63 @@
+from .runners.fast import TableManager, PyramidPatchMatcher
+from PIL import Image
+import numpy as np
+import cupy as cp
+
+
+class FastBlendSmoother:
+    def __init__(self):
+        self.batch_size = 8
+        self.window_size = 64
+        self.ebsynth_config = {
+            "minimum_patch_size": 5,
+            "threads_per_block": 8,
+            "num_iter": 5,
+            "gpu_id": 0,
+            "guide_weight": 10.0,
+            "initialize": "identity",
+            "tracking_window_size": 0,
+        }
+
+    @staticmethod
+    def from_model_manager(model_manager):
+        # TODO: fetch GPU ID from model_manager
+        return FastBlendSmoother()
+
+    def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config):
+        frames_guide = [np.array(frame) for frame in frames_guide]
+        frames_style = [np.array(frame) for frame in 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,
+            **ebsynth_config
+        )
+        # left part
+        table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, batch_size, desc="FastBlend 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="FastBlend Step 2/4")
+        # right part
+        table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, batch_size, desc="FastBlend 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="FastBlend 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 = [Image.fromarray(frame.clip(0, 255).astype("uint8")) for frame in frames]
+        return frames
+    
+    def __call__(self, rendered_frames, original_frames=None, **kwargs):
+        frames = self.run(
+            original_frames, rendered_frames,
+            self.batch_size, self.window_size, self.ebsynth_config
+        )
+        mempool = cp.get_default_memory_pool()
+        pinned_mempool = cp.get_default_pinned_memory_pool()
+        mempool.free_all_blocks()
+        pinned_mempool.free_all_blocks()
+        return frames

diffsynth/extensions/FastBlend/api.py

@@ -0,0 +1,397 @@
+from .runners import AccurateModeRunner, FastModeRunner, BalancedModeRunner, InterpolationModeRunner, InterpolationModeSingleFrameRunner
+from .data import VideoData, get_video_fps, save_video, search_for_images
+import os
+import gradio as gr
+
+
+def check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder):
+    frames_guide = VideoData(video_guide, video_guide_folder)
+    frames_style = VideoData(video_style, video_style_folder)
+    message = ""
+    if len(frames_guide) < len(frames_style):
+        message += f"The number of frames mismatches. Only the first {len(frames_guide)} frames of style video will be used.\n"
+        frames_style.set_length(len(frames_guide))
+    elif len(frames_guide) > len(frames_style):
+        message += f"The number of frames mismatches. Only the first {len(frames_style)} frames of guide video will be used.\n"
+        frames_guide.set_length(len(frames_style))
+    height_guide, width_guide = frames_guide.shape()
+    height_style, width_style = frames_style.shape()
+    if height_guide != height_style or width_guide != width_style:
+        message += f"The shape of frames mismatches. The frames in style video will be resized to (height: {height_guide}, width: {width_guide})\n"
+        frames_style.set_shape(height_guide, width_guide)
+    return frames_guide, frames_style, message
+
+
+def smooth_video(
+    video_guide,
+    video_guide_folder,
+    video_style,
+    video_style_folder,
+    mode,
+    window_size,
+    batch_size,
+    tracking_window_size,
+    output_path,
+    fps,
+    minimum_patch_size,
+    num_iter,
+    guide_weight,
+    initialize,
+    progress = None,
+):
+    # input
+    frames_guide, frames_style, message = check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder)
+    if len(message) > 0:
+        print(message)
+    # output
+    if output_path == "":
+        if video_style is None:
+            output_path = os.path.join(video_style_folder, "output")
+        else:
+            output_path = os.path.join(os.path.split(video_style)[0], "output")
+        os.makedirs(output_path, exist_ok=True)
+        print("No valid output_path. Your video will be saved here:", output_path)
+    elif not os.path.exists(output_path):
+        os.makedirs(output_path, exist_ok=True)
+        print("Your video will be saved here:", output_path)
+    frames_path = os.path.join(output_path, "frames")
+    video_path = os.path.join(output_path, "video.mp4")
+    os.makedirs(frames_path, exist_ok=True)
+    # process
+    if mode == "Fast" or mode == "Balanced":
+        tracking_window_size = 0
+    ebsynth_config = {
+        "minimum_patch_size": minimum_patch_size,
+        "threads_per_block": 8,
+        "num_iter": num_iter,
+        "gpu_id": 0,
+        "guide_weight": guide_weight,
+        "initialize": initialize,
+        "tracking_window_size": tracking_window_size,
+    }
+    if mode == "Fast":
+        FastModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
+    elif mode == "Balanced":
+        BalancedModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
+    elif mode == "Accurate":
+        AccurateModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
+    # output
+    try:
+        fps = int(fps)
+    except:
+        fps = get_video_fps(video_style) if video_style is not None else 30
+    print("Fps:", fps)
+    print("Saving video...")
+    video_path = save_video(frames_path, video_path, num_frames=len(frames_style), fps=fps)
+    print("Success!")
+    print("Your frames are here:", frames_path)
+    print("Your video is here:", video_path)
+    return output_path, fps, video_path
+
+
+class KeyFrameMatcher:
+    def __init__(self):
+        pass
+
+    def extract_number_from_filename(self, file_name):
+        result = []
+        number = -1
+        for i in file_name:
+            if ord(i)>=ord("0") and ord(i)<=ord("9"):
+                if number == -1:
+                    number = 0
+                number = number*10 + ord(i) - ord("0")
+            else:
+                if number != -1:
+                    result.append(number)
+                    number = -1
+        if number != -1:
+            result.append(number)
+        result = tuple(result)
+        return result
+
+    def extract_number_from_filenames(self, file_names):
+        numbers = [self.extract_number_from_filename(file_name) for file_name in file_names]
+        min_length = min(len(i) for i in numbers)
+        for i in range(min_length-1, -1, -1):
+            if len(set(number[i] for number in numbers))==len(file_names):
+                return [number[i] for number in numbers]
+        return list(range(len(file_names)))
+
+    def match_using_filename(self, file_names_a, file_names_b):
+        file_names_b_set = set(file_names_b)
+        matched_file_name = []
+        for file_name in file_names_a:
+            if file_name not in file_names_b_set:
+                matched_file_name.append(None)
+            else:
+                matched_file_name.append(file_name)
+        return matched_file_name
+
+    def match_using_numbers(self, file_names_a, file_names_b):
+        numbers_a = self.extract_number_from_filenames(file_names_a)
+        numbers_b = self.extract_number_from_filenames(file_names_b)
+        numbers_b_dict = {number: file_name for number, file_name in zip(numbers_b, file_names_b)}
+        matched_file_name = []
+        for number in numbers_a:
+            if number in numbers_b_dict:
+                matched_file_name.append(numbers_b_dict[number])
+            else:
+                matched_file_name.append(None)
+        return matched_file_name
+
+    def match_filenames(self, file_names_a, file_names_b):
+        matched_file_name = self.match_using_filename(file_names_a, file_names_b)
+        if sum([i is not None for i in matched_file_name]) > 0:
+            return matched_file_name
+        matched_file_name = self.match_using_numbers(file_names_a, file_names_b)
+        return matched_file_name
+
+
+def detect_frames(frames_path, keyframes_path):
+    if not os.path.exists(frames_path) and not os.path.exists(keyframes_path):
+        return "Please input the directory of guide video and rendered frames"
+    elif not os.path.exists(frames_path):
+        return "Please input the directory of guide video"
+    elif not os.path.exists(keyframes_path):
+        return "Please input the directory of rendered frames"
+    frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)]
+    keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)]
+    if len(frames)==0:
+        return f"No images detected in {frames_path}"
+    if len(keyframes)==0:
+        return f"No images detected in {keyframes_path}"
+    matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes)
+    max_filename_length = max([len(i) for i in frames])
+    if sum([i is not None for i in matched_keyframes])==0:
+        message = ""
+        for frame, matched_keyframe in zip(frames, matched_keyframes):
+            message += frame + " " * (max_filename_length - len(frame) + 1)
+            message += "--> No matched keyframes\n"
+    else:
+        message = ""
+        for frame, matched_keyframe in zip(frames, matched_keyframes):
+            message += frame + " " * (max_filename_length - len(frame) + 1)
+            if matched_keyframe is None:
+                message += "--> [to be rendered]\n"
+            else:
+                message += f"--> {matched_keyframe}\n"
+    return message
+
+
+def check_input_for_interpolating(frames_path, keyframes_path):
+    # search for images
+    frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)]
+    keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)]
+    # match frames
+    matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes)
+    file_list = [file_name for file_name in matched_keyframes if file_name is not None]
+    index_style = [i for i, file_name in enumerate(matched_keyframes) if file_name is not None]
+    frames_guide = VideoData(None, frames_path)
+    frames_style = VideoData(None, keyframes_path, file_list=file_list)
+    # match shape
+    message = ""
+    height_guide, width_guide = frames_guide.shape()
+    height_style, width_style = frames_style.shape()
+    if height_guide != height_style or width_guide != width_style:
+        message += f"The shape of frames mismatches. The rendered keyframes will be resized to (height: {height_guide}, width: {width_guide})\n"
+        frames_style.set_shape(height_guide, width_guide)
+    return frames_guide, frames_style, index_style, message
+
+
+def interpolate_video(
+    frames_path,
+    keyframes_path,
+    output_path,
+    fps,
+    batch_size,
+    tracking_window_size,
+    minimum_patch_size,
+    num_iter,
+    guide_weight,
+    initialize,
+    progress = None,
+):
+    # input
+    frames_guide, frames_style, index_style, message = check_input_for_interpolating(frames_path, keyframes_path)
+    if len(message) > 0:
+        print(message)
+    # output
+    if output_path == "":
+        output_path = os.path.join(keyframes_path, "output")
+        os.makedirs(output_path, exist_ok=True)
+        print("No valid output_path. Your video will be saved here:", output_path)
+    elif not os.path.exists(output_path):
+        os.makedirs(output_path, exist_ok=True)
+        print("Your video will be saved here:", output_path)
+    output_frames_path = os.path.join(output_path, "frames")
+    output_video_path = os.path.join(output_path, "video.mp4")
+    os.makedirs(output_frames_path, exist_ok=True)
+    # process
+    ebsynth_config = {
+        "minimum_patch_size": minimum_patch_size,
+        "threads_per_block": 8,
+        "num_iter": num_iter,
+        "gpu_id": 0,
+        "guide_weight": guide_weight,
+        "initialize": initialize,
+        "tracking_window_size": tracking_window_size
+    }
+    if len(index_style)==1:
+        InterpolationModeSingleFrameRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path)
+    else:
+        InterpolationModeRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path)
+    try:
+        fps = int(fps)
+    except:
+        fps = 30
+    print("Fps:", fps)
+    print("Saving video...")
+    video_path = save_video(output_frames_path, output_video_path, num_frames=len(frames_guide), fps=fps)
+    print("Success!")
+    print("Your frames are here:", output_frames_path)
+    print("Your video is here:", video_path)
+    return output_path, fps, video_path
+
+
+def on_ui_tabs():
+    with gr.Blocks(analytics_enabled=False) as ui_component:
+        with gr.Tab("Blend"):
+            gr.Markdown("""
+# Blend
+
+Given a guide video and a style video, this algorithm will make the style video fluent according to the motion features of the guide video. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/208d902d-6aba-48d7-b7d5-cd120ebd306d) to see the example. Note that this extension doesn't support long videos. Please use short videos (e.g., several seconds). The algorithm is mainly designed for 512*512 resolution. Please use a larger `Minimum patch size` for higher resolution.
+            """)
+            with gr.Row():
+                with gr.Column():
+                    with gr.Tab("Guide video"):
+                        video_guide = gr.Video(label="Guide video")
+                    with gr.Tab("Guide video (images format)"):
+                        video_guide_folder = gr.Textbox(label="Guide video (images format)", value="")
+                with gr.Column():
+                    with gr.Tab("Style video"):
+                        video_style = gr.Video(label="Style video")
+                    with gr.Tab("Style video (images format)"):
+                        video_style_folder = gr.Textbox(label="Style video (images format)", value="")
+                with gr.Column():
+                    output_path = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of style video")
+                    fps = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps")
+                    video_output = gr.Video(label="Output video", interactive=False, show_share_button=True)
+            btn = gr.Button(value="Blend")
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("# Settings")
+                    mode = gr.Radio(["Fast", "Balanced", "Accurate"], label="Inference mode", value="Fast", interactive=True)
+                    window_size = gr.Slider(label="Sliding window size", value=15, minimum=1, maximum=1000, step=1, interactive=True)
+                    batch_size = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True)
+                    tracking_window_size = gr.Slider(label="Tracking window size (only for accurate mode)", value=0, minimum=0, maximum=10, step=1, interactive=True)
+                    gr.Markdown("## Advanced Settings")
+                    minimum_patch_size = gr.Slider(label="Minimum patch size (odd number)", value=5, minimum=5, maximum=99, step=2, interactive=True)
+                    num_iter = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True)
+                    guide_weight = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True)
+                    initialize = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True)
+                with gr.Column():
+                    gr.Markdown("""
+# Reference
+
+* Output directory: the directory to save the video.
+* Inference mode
+
+|Mode|Time|Memory|Quality|Frame by frame output|Description|
+|-|-|-|-|-|-|
+|Fast|■|■■■|■■|No|Blend the frames using a tree-like data structure, which requires much RAM but is fast.|
+|Balanced|■■|■|■■|Yes|Blend the frames naively.|
+|Accurate|■■■|■|■■■|Yes|Blend the frames and align them together for higher video quality. When [batch size] >= [sliding window size] * 2 + 1, the performance is the best.|
+
+* Sliding window size: our algorithm will blend the frames in a sliding windows. If the size is n, each frame will be blended with the last n frames and the next n frames. A large sliding window can make the video fluent but sometimes smoggy.
+* Batch size: a larger batch size makes the program faster but requires more VRAM.
+* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough.
+* Advanced settings
+    * Minimum patch size (odd number): the minimum patch size used for patch matching. (Default: 5)
+    * Number of iterations: the number of iterations of patch matching. (Default: 5)
+    * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10)
+    * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity)
+                    """)
+            btn.click(
+                smooth_video,
+                inputs=[
+                    video_guide,
+                    video_guide_folder,
+                    video_style,
+                    video_style_folder,
+                    mode,
+                    window_size,
+                    batch_size,
+                    tracking_window_size,
+                    output_path,
+                    fps,
+                    minimum_patch_size,
+                    num_iter,
+                    guide_weight,
+                    initialize
+                ],
+                outputs=[output_path, fps, video_output]
+            )
+        with gr.Tab("Interpolate"):
+            gr.Markdown("""
+# Interpolate
+
+Given a guide video and some rendered keyframes, this algorithm will render the remaining frames. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/3490c5b4-8f67-478f-86de-f9adc2ace16a) to see the example. The algorithm is experimental and is only tested for 512*512 resolution.
+            """)
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        with gr.Column():
+                            video_guide_folder_ = gr.Textbox(label="Guide video (images format)", value="")
+                        with gr.Column():
+                            rendered_keyframes_ = gr.Textbox(label="Rendered keyframes (images format)", value="")
+                    with gr.Row():
+                        detected_frames = gr.Textbox(label="Detected frames", value="Please input the directory of guide video and rendered frames", lines=9, max_lines=9, interactive=False)
+                    video_guide_folder_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames)
+                    rendered_keyframes_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames)
+                with gr.Column():
+                    output_path_ = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of rendered keyframes")
+                    fps_ = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps")
+                    video_output_ = gr.Video(label="Output video", interactive=False, show_share_button=True)
+            btn_ = gr.Button(value="Interpolate")
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("# Settings")
+                    batch_size_ = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True)
+                    tracking_window_size_ = gr.Slider(label="Tracking window size", value=0, minimum=0, maximum=10, step=1, interactive=True)
+                    gr.Markdown("## Advanced Settings")
+                    minimum_patch_size_ = gr.Slider(label="Minimum patch size (odd number, larger is better)", value=15, minimum=5, maximum=99, step=2, interactive=True)
+                    num_iter_ = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True)
+                    guide_weight_ = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True)
+                    initialize_ = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True)
+                with gr.Column():
+                    gr.Markdown("""
+# Reference
+
+* Output directory: the directory to save the video.
+* Batch size: a larger batch size makes the program faster but requires more VRAM.
+* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough.
+* Advanced settings
+    * Minimum patch size (odd number): the minimum patch size used for patch matching. **This parameter should be larger than that in blending. (Default: 15)**
+    * Number of iterations: the number of iterations of patch matching. (Default: 5)
+    * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10)
+    * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity)
+                    """)
+            btn_.click(
+                interpolate_video,
+                inputs=[
+                    video_guide_folder_,
+                    rendered_keyframes_,
+                    output_path_,
+                    fps_,
+                    batch_size_,
+                    tracking_window_size_,
+                    minimum_patch_size_,
+                    num_iter_,
+                    guide_weight_,
+                    initialize_,
+                ],
+                outputs=[output_path_, fps_, video_output_]
+            )
+
+        return [(ui_component, "FastBlend", "FastBlend_ui")]

diffsynth/extensions/FastBlend/cupy_kernels.py

@@ -0,0 +1,119 @@
+import cupy as cp
+
+remapping_kernel = cp.RawKernel(r'''
+extern "C" __global__
+void remap(
+    const int height,
+    const int width,
+    const int channel,
+    const int patch_size,
+    const int pad_size,
+    const float* source_style,
+    const int* nnf,
+    float* target_style
+) {
+    const int r = (patch_size - 1) / 2;
+    const int x = blockDim.x * blockIdx.x + threadIdx.x;
+    const int y = blockDim.y * blockIdx.y + threadIdx.y;
+    if (x >= height or y >= width) return;
+    const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel;
+    const int pid = (x + pad_size) * (width + pad_size * 2) + (y + pad_size);
+    const int min_px = x < r ? -x : -r;
+    const int max_px = x + r > height - 1 ? height - 1 - x : r;
+    const int min_py = y < r ? -y : -r;
+    const int max_py = y + r > width - 1 ? width - 1 - y : r;
+    int num = 0;
+    for (int px = min_px; px <= max_px; px++){
+        for (int py = min_py; py <= max_py; py++){
+            const int nid = (x + px) * width + y + py;
+            const int x_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 0] - px;
+            const int y_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 1] - py;
+            if (x_ < 0 or y_ < 0 or x_ >= height or y_ >= width)continue;
+            const int pid_ = (x_ + pad_size) * (width + pad_size * 2) + (y_ + pad_size);
+            num++;
+            for (int c = 0; c < channel; c++){
+                target_style[z + pid * channel + c] += source_style[z + pid_ * channel + c];
+            }
+        }
+    }
+    for (int c = 0; c < channel; c++){
+        target_style[z + pid * channel + c] /= num;
+    }
+}
+''', 'remap')
+
+
+patch_error_kernel = cp.RawKernel(r'''
+extern "C" __global__
+void patch_error(
+    const int height,
+    const int width,
+    const int channel,
+    const int patch_size,
+    const int pad_size,
+    const float* source,
+    const int* nnf,
+    const float* target,
+    float* error
+) {
+    const int r = (patch_size - 1) / 2;
+    const int x = blockDim.x * blockIdx.x + threadIdx.x;
+    const int y = blockDim.y * blockIdx.y + threadIdx.y;
+    const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel;
+    if (x >= height or y >= width) return;
+    const int x_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 0];
+    const int y_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 1];
+    float e = 0;
+    for (int px = -r; px <= r; px++){
+        for (int py = -r; py <= r; py++){
+            const int pid = (x + pad_size + px) * (width + pad_size * 2) + y + pad_size + py;
+            const int pid_ = (x_ + pad_size + px) * (width + pad_size * 2) + y_ + pad_size + py;
+            for (int c = 0; c < channel; c++){
+                const float diff = target[z + pid * channel + c] - source[z + pid_ * channel + c];
+                e += diff * diff;
+            }
+        }
+    }
+    error[blockIdx.z * height * width + x * width + y] = e;
+}
+''', 'patch_error')
+
+
+pairwise_patch_error_kernel = cp.RawKernel(r'''
+extern "C" __global__
+void pairwise_patch_error(
+    const int height,
+    const int width,
+    const int channel,
+    const int patch_size,
+    const int pad_size,
+    const float* source_a,
+    const int* nnf_a,
+    const float* source_b,
+    const int* nnf_b,
+    float* error
+) {
+    const int r = (patch_size - 1) / 2;
+    const int x = blockDim.x * blockIdx.x + threadIdx.x;
+    const int y = blockDim.y * blockIdx.y + threadIdx.y;
+    const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel;
+    if (x >= height or y >= width) return;
+    const int z_nnf = blockIdx.z * height * width * 2 + (x * width + y) * 2;
+    const int x_a = nnf_a[z_nnf + 0];
+    const int y_a = nnf_a[z_nnf + 1];
+    const int x_b = nnf_b[z_nnf + 0];
+    const int y_b = nnf_b[z_nnf + 1];
+    float e = 0;
+    for (int px = -r; px <= r; px++){
+        for (int py = -r; py <= r; py++){
+            const int pid_a = (x_a + pad_size + px) * (width + pad_size * 2) + y_a + pad_size + py;
+            const int pid_b = (x_b + pad_size + px) * (width + pad_size * 2) + y_b + pad_size + py;
+            for (int c = 0; c < channel; c++){
+                const float diff = source_a[z + pid_a * channel + c] - source_b[z + pid_b * channel + c];
+                e += diff * diff;
+            }
+        }
+    }
+    error[blockIdx.z * height * width + x * width + y] = e;
+}
+''', 'pairwise_patch_error')

diffsynth/extensions/FastBlend/data.py

@@ -0,0 +1,146 @@
+import imageio, os
+import numpy as np
+from PIL import Image
+
+
+def read_video(file_name):
+    reader = imageio.get_reader(file_name)
+    video = []
+    for frame in reader:
+        frame = np.array(frame)
+        video.append(frame)
+    reader.close()
+    return video
+
+
+def get_video_fps(file_name):
+    reader = imageio.get_reader(file_name)
+    fps = reader.get_meta_data()["fps"]
+    reader.close()
+    return fps
+
+
+def save_video(frames_path, video_path, num_frames, fps):
+    writer = imageio.get_writer(video_path, fps=fps, quality=9)
+    for i in range(num_frames):
+        frame = np.array(Image.open(os.path.join(frames_path, "%05d.png" % i)))
+        writer.append_data(frame)
+    writer.close()
+    return video_path
+
+
+class LowMemoryVideo:
+    def __init__(self, file_name):
+        self.reader = imageio.get_reader(file_name)
+    
+    def __len__(self):
+        return self.reader.count_frames()
+
+    def __getitem__(self, item):
+        return np.array(self.reader.get_data(item))
+
+    def __del__(self):
+        self.reader.close()
+
+
+def split_file_name(file_name):
+    result = []
+    number = -1
+    for i in file_name:
+        if ord(i)>=ord("0") and ord(i)<=ord("9"):
+            if number == -1:
+                number = 0
+            number = number*10 + ord(i) - ord("0")
+        else:
+            if number != -1:
+                result.append(number)
+                number = -1
+            result.append(i)
+    if number != -1:
+        result.append(number)
+    result = tuple(result)
+    return result
+
+
+def search_for_images(folder):
+    file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")]
+    file_list = [(split_file_name(file_name), file_name) for file_name in file_list]
+    file_list = [i[1] for i in sorted(file_list)]
+    file_list = [os.path.join(folder, i) for i in file_list]
+    return file_list
+
+
+def read_images(folder):
+    file_list = search_for_images(folder)
+    frames = [np.array(Image.open(i)) for i in file_list]
+    return frames
+
+
+class LowMemoryImageFolder:
+    def __init__(self, folder, file_list=None):
+        if file_list is None:
+            self.file_list = search_for_images(folder)
+        else:
+            self.file_list = [os.path.join(folder, file_name) for file_name in file_list]
+    
+    def __len__(self):
+        return len(self.file_list)
+
+    def __getitem__(self, item):
+        return np.array(Image.open(self.file_list[item]))
+
+    def __del__(self):
+        pass
+
+
+class VideoData:
+    def __init__(self, video_file, image_folder, **kwargs):
+        if video_file is not None:
+            self.data_type = "video"
+            self.data = LowMemoryVideo(video_file, **kwargs)
+        elif image_folder is not None:
+            self.data_type = "images"
+            self.data = LowMemoryImageFolder(image_folder, **kwargs)
+        else:
+            raise ValueError("Cannot open video or image folder")
+        self.length = None
+        self.height = None
+        self.width = None
+
+    def raw_data(self):
+        frames = []
+        for i in range(self.__len__()):
+            frames.append(self.__getitem__(i))
+        return frames
+
+    def set_length(self, length):
+        self.length = length
+
+    def set_shape(self, height, width):
+        self.height = height
+        self.width = width
+
+    def __len__(self):
+        if self.length is None:
+            return len(self.data)
+        else:
+            return self.length
+
+    def shape(self):
+        if self.height is not None and self.width is not None:
+            return self.height, self.width
+        else:
+            height, width, _ = self.__getitem__(0).shape
+            return height, width
+
+    def __getitem__(self, item):
+        frame = self.data.__getitem__(item)
+        height, width, _ = frame.shape
+        if self.height is not None and self.width is not None:
+            if self.height != height or self.width != width:
+                frame = Image.fromarray(frame).resize((self.width, self.height))
+                frame = np.array(frame)
+        return frame
+
+    def __del__(self):
+        pass

diffsynth/extensions/FastBlend/patch_match.py

@@ -0,0 +1,298 @@
+from .cupy_kernels import remapping_kernel, patch_error_kernel, pairwise_patch_error_kernel
+import numpy as np
+import cupy as cp
+import cv2
+
+
+class PatchMatcher:
+    def __init__(
+        self, height, width, channel, minimum_patch_size,
+        threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0,
+        random_search_steps=3, random_search_range=4,
+        use_mean_target_style=False, use_pairwise_patch_error=False,
+        tracking_window_size=0
+    ):
+        self.height = height
+        self.width = width
+        self.channel = channel
+        self.minimum_patch_size = minimum_patch_size
+        self.threads_per_block = threads_per_block
+        self.num_iter = num_iter
+        self.gpu_id = gpu_id
+        self.guide_weight = guide_weight
+        self.random_search_steps = random_search_steps
+        self.random_search_range = random_search_range
+        self.use_mean_target_style = use_mean_target_style
+        self.use_pairwise_patch_error = use_pairwise_patch_error
+        self.tracking_window_size = tracking_window_size
+
+        self.patch_size_list = [minimum_patch_size + i*2 for i in range(num_iter)][::-1]
+        self.pad_size = self.patch_size_list[0] // 2
+        self.grid = (
+            (height + threads_per_block - 1) // threads_per_block,
+            (width + threads_per_block - 1) // threads_per_block
+        )
+        self.block = (threads_per_block, threads_per_block)
+
+    def pad_image(self, image):
+        return cp.pad(image, ((0, 0), (self.pad_size, self.pad_size), (self.pad_size, self.pad_size), (0, 0)))
+
+    def unpad_image(self, image):
+        return image[:, self.pad_size: -self.pad_size, self.pad_size: -self.pad_size, :]
+
+    def apply_nnf_to_image(self, nnf, source):
+        batch_size = source.shape[0]
+        target = cp.zeros((batch_size, self.height + self.pad_size * 2, self.width + self.pad_size * 2, self.channel), dtype=cp.float32)
+        remapping_kernel(
+            self.grid + (batch_size,),
+            self.block,
+            (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target)
+        )
+        return target
+
+    def get_patch_error(self, source, nnf, target):
+        batch_size = source.shape[0]
+        error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32)
+        patch_error_kernel(
+            self.grid + (batch_size,),
+            self.block,
+            (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target, error)
+        )
+        return error
+
+    def get_pairwise_patch_error(self, source, nnf):
+        batch_size = source.shape[0]//2
+        error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32)
+        source_a, nnf_a = source[0::2].copy(), nnf[0::2].copy()
+        source_b, nnf_b = source[1::2].copy(), nnf[1::2].copy()
+        pairwise_patch_error_kernel(
+            self.grid + (batch_size,),
+            self.block,
+            (self.height, self.width, self.channel, self.patch_size, self.pad_size, source_a, nnf_a, source_b, nnf_b, error)
+        )
+        error = error.repeat(2, axis=0)
+        return error
+
+    def get_error(self, source_guide, target_guide, source_style, target_style, nnf):
+        error_guide = self.get_patch_error(source_guide, nnf, target_guide)
+        if self.use_mean_target_style:
+            target_style = self.apply_nnf_to_image(nnf, source_style)
+            target_style = target_style.mean(axis=0, keepdims=True)
+            target_style = target_style.repeat(source_guide.shape[0], axis=0)
+        if self.use_pairwise_patch_error:
+            error_style = self.get_pairwise_patch_error(source_style, nnf)
+        else:
+            error_style = self.get_patch_error(source_style, nnf, target_style)
+        error = error_guide * self.guide_weight + error_style
+        return error
+
+    def clamp_bound(self, nnf):
+        nnf[:,:,:,0] = cp.clip(nnf[:,:,:,0], 0, self.height-1)
+        nnf[:,:,:,1] = cp.clip(nnf[:,:,:,1], 0, self.width-1)
+        return nnf
+
+    def random_step(self, nnf, r):
+        batch_size = nnf.shape[0]
+        step = cp.random.randint(-r, r+1, size=(batch_size, self.height, self.width, 2), dtype=cp.int32)
+        upd_nnf = self.clamp_bound(nnf + step)
+        return upd_nnf
+
+    def neighboor_step(self, nnf, d):
+        if d==0:
+            upd_nnf = cp.concatenate([nnf[:, :1, :], nnf[:, :-1, :]], axis=1)
+            upd_nnf[:, :, :, 0] += 1
+        elif d==1:
+            upd_nnf = cp.concatenate([nnf[:, :, :1], nnf[:, :, :-1]], axis=2)
+            upd_nnf[:, :, :, 1] += 1
+        elif d==2:
+            upd_nnf = cp.concatenate([nnf[:, 1:, :], nnf[:, -1:, :]], axis=1)
+            upd_nnf[:, :, :, 0] -= 1
+        elif d==3:
+            upd_nnf = cp.concatenate([nnf[:, :, 1:], nnf[:, :, -1:]], axis=2)
+            upd_nnf[:, :, :, 1] -= 1
+        upd_nnf = self.clamp_bound(upd_nnf)
+        return upd_nnf
+        
+    def shift_nnf(self, nnf, d):
+        if d>0:
+            d = min(nnf.shape[0], d)
+            upd_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0)
+        else:
+            d = max(-nnf.shape[0], d)
+            upd_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0)
+        return upd_nnf
+    
+    def track_step(self, nnf, d):
+        if self.use_pairwise_patch_error:
+            upd_nnf = cp.zeros_like(nnf)
+            upd_nnf[0::2] = self.shift_nnf(nnf[0::2], d)
+            upd_nnf[1::2] = self.shift_nnf(nnf[1::2], d)
+        else:
+            upd_nnf = self.shift_nnf(nnf, d)
+        return upd_nnf
+
+    def C(self, n, m):
+        # not used
+        c = 1
+        for i in range(1, n+1):
+            c *= i
+        for i in range(1, m+1):
+            c //= i
+        for i in range(1, n-m+1):
+            c //= i
+        return c
+
+    def bezier_step(self, nnf, r):
+        # not used
+        n = r * 2 - 1
+        upd_nnf = cp.zeros(shape=nnf.shape, dtype=cp.float32)
+        for i, d in enumerate(list(range(-r, 0)) + list(range(1, r+1))):
+            if d>0:
+                ctl_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0)
+            elif d<0:
+                ctl_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0)
+            upd_nnf += ctl_nnf * (self.C(n, i) / 2**n)
+        upd_nnf = self.clamp_bound(upd_nnf).astype(nnf.dtype)
+        return upd_nnf
+
+    def update(self, source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf):
+        upd_err = self.get_error(source_guide, target_guide, source_style, target_style, upd_nnf)
+        upd_idx = (upd_err < err)
+        nnf[upd_idx] = upd_nnf[upd_idx]
+        err[upd_idx] = upd_err[upd_idx]
+        return nnf, err
+
+    def propagation(self, source_guide, target_guide, source_style, target_style, nnf, err):
+        for d in cp.random.permutation(4):
+            upd_nnf = self.neighboor_step(nnf, d)
+            nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
+        return nnf, err
+        
+    def random_search(self, source_guide, target_guide, source_style, target_style, nnf, err):
+        for i in range(self.random_search_steps):
+            upd_nnf = self.random_step(nnf, self.random_search_range)
+            nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
+        return nnf, err
+
+    def track(self, source_guide, target_guide, source_style, target_style, nnf, err):
+        for d in range(1, self.tracking_window_size + 1):
+            upd_nnf = self.track_step(nnf, d)
+            nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
+            upd_nnf = self.track_step(nnf, -d)
+            nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
+        return nnf, err
+
+    def iteration(self, source_guide, target_guide, source_style, target_style, nnf, err):
+        nnf, err = self.propagation(source_guide, target_guide, source_style, target_style, nnf, err)
+        nnf, err = self.random_search(source_guide, target_guide, source_style, target_style, nnf, err)
+        nnf, err = self.track(source_guide, target_guide, source_style, target_style, nnf, err)
+        return nnf, err
+
+    def estimate_nnf(self, source_guide, target_guide, source_style, nnf):
+        with cp.cuda.Device(self.gpu_id):
+            source_guide = self.pad_image(source_guide)
+            target_guide = self.pad_image(target_guide)
+            source_style = self.pad_image(source_style)
+            for it in range(self.num_iter):
+                self.patch_size = self.patch_size_list[it]
+                target_style = self.apply_nnf_to_image(nnf, source_style)
+                err = self.get_error(source_guide, target_guide, source_style, target_style, nnf)
+                nnf, err = self.iteration(source_guide, target_guide, source_style, target_style, nnf, err)
+            target_style = self.unpad_image(self.apply_nnf_to_image(nnf, source_style))
+        return nnf, target_style
+
+
+class PyramidPatchMatcher:
+    def __init__(
+        self, image_height, image_width, channel, minimum_patch_size,
+        threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0,
+        use_mean_target_style=False, use_pairwise_patch_error=False,
+        tracking_window_size=0,
+        initialize="identity"
+    ):
+        maximum_patch_size = minimum_patch_size + (num_iter - 1) * 2
+        self.pyramid_level = int(np.log2(min(image_height, image_width) / maximum_patch_size))
+        self.pyramid_heights = []
+        self.pyramid_widths = []
+        self.patch_matchers = []
+        self.minimum_patch_size = minimum_patch_size
+        self.num_iter = num_iter
+        self.gpu_id = gpu_id
+        self.initialize = initialize
+        for level in range(self.pyramid_level):
+            height = image_height//(2**(self.pyramid_level - 1 - level))
+            width = image_width//(2**(self.pyramid_level - 1 - level))
+            self.pyramid_heights.append(height)
+            self.pyramid_widths.append(width)
+            self.patch_matchers.append(PatchMatcher(
+                height, width, channel, minimum_patch_size=minimum_patch_size,
+                threads_per_block=threads_per_block, num_iter=num_iter, gpu_id=gpu_id, guide_weight=guide_weight,
+                use_mean_target_style=use_mean_target_style, use_pairwise_patch_error=use_pairwise_patch_error,
+                tracking_window_size=tracking_window_size
+            ))
+
+    def resample_image(self, images, level):
+        height, width = self.pyramid_heights[level], self.pyramid_widths[level]
+        images = images.get()
+        images_resample = []
+        for image in images:
+            image_resample = cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA)
+            images_resample.append(image_resample)
+        images_resample = cp.array(np.stack(images_resample), dtype=cp.float32)
+        return images_resample
+
+    def initialize_nnf(self, batch_size):
+        if self.initialize == "random":
+            height, width = self.pyramid_heights[0], self.pyramid_widths[0]
+            nnf = cp.stack([
+                cp.random.randint(0, height, (batch_size, height, width), dtype=cp.int32),
+                cp.random.randint(0, width, (batch_size, height, width), dtype=cp.int32)
+            ], axis=3)
+        elif self.initialize == "identity":
+            height, width = self.pyramid_heights[0], self.pyramid_widths[0]
+            nnf = cp.stack([
+                cp.repeat(cp.arange(height), width).reshape(height, width),
+                cp.tile(cp.arange(width), height).reshape(height, width)
+            ], axis=2)
+            nnf = cp.stack([nnf] * batch_size)
+        else:
+            raise NotImplementedError()
+        return nnf
+
+    def update_nnf(self, nnf, level):
+        # upscale
+        nnf = nnf.repeat(2, axis=1).repeat(2, axis=2) * 2
+        nnf[:,[i for i in range(nnf.shape[0]) if i&1],:,0] += 1
+        nnf[:,:,[i for i in range(nnf.shape[0]) if i&1],1] += 1
+        # check if scale is 2
+        height, width = self.pyramid_heights[level], self.pyramid_widths[level]
+        if height != nnf.shape[0] * 2 or width != nnf.shape[1] * 2:
+            nnf = nnf.get().astype(np.float32)
+            nnf = [cv2.resize(n, (width, height), interpolation=cv2.INTER_LINEAR) for n in nnf]
+            nnf = cp.array(np.stack(nnf), dtype=cp.int32)
+            nnf = self.patch_matchers[level].clamp_bound(nnf)
+        return nnf
+
+    def apply_nnf_to_image(self, nnf, image):
+        with cp.cuda.Device(self.gpu_id):
+            image = self.patch_matchers[-1].pad_image(image)
+            image = self.patch_matchers[-1].apply_nnf_to_image(nnf, image)
+        return image
+
+    def estimate_nnf(self, source_guide, target_guide, source_style):
+        with cp.cuda.Device(self.gpu_id):
+            if not isinstance(source_guide, cp.ndarray):
+                source_guide = cp.array(source_guide, dtype=cp.float32)
+            if not isinstance(target_guide, cp.ndarray):
+                target_guide = cp.array(target_guide, dtype=cp.float32)
+            if not isinstance(source_style, cp.ndarray):
+                source_style = cp.array(source_style, dtype=cp.float32)
+            for level in range(self.pyramid_level):
+                nnf = self.initialize_nnf(source_guide.shape[0]) if level==0 else self.update_nnf(nnf, level)
+                source_guide_ = self.resample_image(source_guide, level)
+                target_guide_ = self.resample_image(target_guide, level)
+                source_style_ = self.resample_image(source_style, level)
+                nnf, target_style = self.patch_matchers[level].estimate_nnf(
+                    source_guide_, target_guide_, source_style_, nnf
+                )
+        return nnf.get(), target_style.get()

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

diffsynth/extensions/RIFE/__init__.py

@@ -0,0 +1,241 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+
+
+def warp(tenInput, tenFlow, device):
+    backwarp_tenGrid = {}
+    k = (str(tenFlow.device), str(tenFlow.size()))
+    if k not in backwarp_tenGrid:
+        tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view(
+            1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
+        tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view(
+            1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
+        backwarp_tenGrid[k] = torch.cat(
+            [tenHorizontal, tenVertical], 1).to(device)
+
+    tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
+                         tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
+
+    g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
+    return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)
+
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),        
+        nn.PReLU(out_planes)
+    )
+
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, c=64):
+        super(IFBlock, self).__init__()
+        self.conv0 = nn.Sequential(conv(in_planes, c//2, 3, 2, 1), conv(c//2, c, 3, 2, 1),)
+        self.convblock0 = nn.Sequential(conv(c, c), conv(c, c))
+        self.convblock1 = nn.Sequential(conv(c, c), conv(c, c))
+        self.convblock2 = nn.Sequential(conv(c, c), conv(c, c))
+        self.convblock3 = nn.Sequential(conv(c, c), conv(c, c))
+        self.conv1 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 4, 4, 2, 1))
+        self.conv2 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 1, 4, 2, 1))
+
+    def forward(self, x, flow, scale=1):
+        x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
+        flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale
+        feat = self.conv0(torch.cat((x, flow), 1))
+        feat = self.convblock0(feat) + feat
+        feat = self.convblock1(feat) + feat
+        feat = self.convblock2(feat) + feat
+        feat = self.convblock3(feat) + feat        
+        flow = self.conv1(feat)
+        mask = self.conv2(feat)
+        flow = F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale
+        mask = F.interpolate(mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
+        return flow, mask
+
+
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(7+4, c=90)
+        self.block1 = IFBlock(7+4, c=90)
+        self.block2 = IFBlock(7+4, c=90)
+        self.block_tea = IFBlock(10+4, c=90)
+
+    def forward(self, x, scale_list=[4, 2, 1], training=False):
+        if training == False:
+            channel = x.shape[1] // 2
+            img0 = x[:, :channel]
+            img1 = x[:, channel:]
+        flow_list = []
+        merged = []
+        mask_list = []
+        warped_img0 = img0
+        warped_img1 = img1
+        flow = (x[:, :4]).detach() * 0
+        mask = (x[:, :1]).detach() * 0
+        block = [self.block0, self.block1, self.block2]
+        for i in range(3):
+            f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i])
+            f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i])
+            flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
+            mask = mask + (m0 + (-m1)) / 2
+            mask_list.append(mask)
+            flow_list.append(flow)
+            warped_img0 = warp(img0, flow[:, :2], device=x.device)
+            warped_img1 = warp(img1, flow[:, 2:4], device=x.device)
+            merged.append((warped_img0, warped_img1))
+        '''
+        c0 = self.contextnet(img0, flow[:, :2])
+        c1 = self.contextnet(img1, flow[:, 2:4])
+        tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
+        res = tmp[:, 1:4] * 2 - 1
+        '''
+        for i in range(3):
+            mask_list[i] = torch.sigmoid(mask_list[i])
+            merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])    
+        return flow_list, mask_list[2], merged
+    
+    def state_dict_converter(self):
+        return IFNetStateDictConverter()
+
+
+class IFNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {k.replace("module.", ""): v for k, v in state_dict.items()}
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+
+
+class RIFEInterpolater:
+    def __init__(self, model, device="cuda"):
+        self.model = model
+        self.device = device
+        # IFNet only does not support float16
+        self.torch_dtype = torch.float32
+
+    @staticmethod
+    def from_model_manager(model_manager):
+        return RIFEInterpolater(model_manager.RIFE, device=model_manager.device)
+
+    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 add_interpolated_images(self, images, interpolated_images):
+        output_images = []
+        for image, interpolated_image in zip(images, interpolated_images):
+            output_images.append(image)
+            output_images.append(interpolated_image)
+        output_images.append(images[-1])
+        return output_images
+    
+
+    @torch.no_grad()
+    def interpolate_(self, images, scale=1.0):
+        input_tensor = self.process_images(images)
+        input_tensor = torch.cat((input_tensor[:-1], input_tensor[1:]), dim=1)
+        input_tensor = input_tensor.to(device=self.device, dtype=self.torch_dtype)
+        flow, mask, merged = self.model(input_tensor, [4/scale, 2/scale, 1/scale])
+        output_images = self.decode_images(merged[2].cpu())
+        if output_images[0].size != images[0].size:
+            output_images = [image.resize(images[0].size) for image in output_images]
+        return output_images
+    
+
+    @torch.no_grad()
+    def interpolate(self, images, scale=1.0, batch_size=4, num_iter=1, progress_bar=lambda x:x):
+        # Preprocess
+        processed_images = self.process_images(images)
+
+        for iter in range(num_iter):
+            # Input
+            input_tensor = torch.cat((processed_images[:-1], processed_images[1:]), dim=1)
+
+            # Interpolate
+            output_tensor = []
+            for batch_id in progress_bar(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
+            output_tensor = torch.concat(output_tensor, dim=0).clip(0, 1)
+            processed_images = self.add_interpolated_images(processed_images, output_tensor)
+            processed_images = torch.stack(processed_images)
+
+        # To images
+        output_images = self.decode_images(processed_images)
+        if output_images[0].size != images[0].size:
+            output_images = [image.resize(images[0].size) for image in output_images]
+        return output_images
+
+
+class RIFESmoother(RIFEInterpolater):
+    def __init__(self, model, device="cuda"):
+        super(RIFESmoother, self).__init__(model, device=device)
+
+    @staticmethod
+    def from_model_manager(model_manager):
+        return RIFESmoother(model_manager.RIFE, device=model_manager.device)
+    
+    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, scale=1.0, batch_size=4, num_iter=1, **kwargs):
+        # Preprocess
+        processed_images = self.process_images(rendered_frames)
+
+        for iter in range(num_iter):
+            # Input
+            input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1)
+
+            # Interpolate
+            output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size)
+            
+            # Blend
+            input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1)
+            output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size)
+
+            # Add to frames
+            processed_images[1:-1] = output_tensor
+
+        # 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/models/__init__.py

@@ -0,0 +1,562 @@
+import torch, os
+from safetensors import safe_open
+from typing_extensions import Literal, TypeAlias
+from typing import List
+
+from .downloader import download_from_huggingface, download_from_modelscope
+
+from .sd_text_encoder import SDTextEncoder
+from .sd_unet import SDUNet
+from .sd_vae_encoder import SDVAEEncoder
+from .sd_vae_decoder import SDVAEDecoder
+from .sd_lora import SDLoRA
+
+from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
+from .sdxl_unet import SDXLUNet
+from .sdxl_vae_decoder import SDXLVAEDecoder
+from .sdxl_vae_encoder import SDXLVAEEncoder
+
+from .sd_controlnet import SDControlNet
+
+from .sd_motion import SDMotionModel
+from .sdxl_motion import SDXLMotionModel
+
+from .svd_image_encoder import SVDImageEncoder
+from .svd_unet import SVDUNet
+from .svd_vae_decoder import SVDVAEDecoder
+from .svd_vae_encoder import SVDVAEEncoder
+
+from .sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder
+from .sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
+
+from .hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
+from .hunyuan_dit import HunyuanDiT
+
+
+preset_models_on_huggingface = {
+    "HunyuanDiT": [
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"),
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"),
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"),
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"),
+    ],
+    "stable-video-diffusion-img2vid-xt": [
+        ("stabilityai/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"),
+    ],
+    "ExVideo-SVD-128f-v1": [
+        ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"),
+    ],
+}
+preset_models_on_modelscope = {
+    "HunyuanDiT": [
+        ("modelscope/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"),
+        ("modelscope/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"),
+        ("modelscope/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"),
+        ("modelscope/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"),
+    ],
+    "stable-video-diffusion-img2vid-xt": [
+        ("AI-ModelScope/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"),
+    ],
+    "ExVideo-SVD-128f-v1": [
+        ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"),
+    ],
+}
+Preset_model_id: TypeAlias = Literal[
+    "HunyuanDiT",
+    "stable-video-diffusion-img2vid-xt",
+    "ExVideo-SVD-128f-v1"
+]
+Preset_model_website: TypeAlias = Literal[
+    "HuggingFace",
+    "ModelScope",
+]
+website_to_preset_models = {
+    "HuggingFace": preset_models_on_huggingface,
+    "ModelScope": preset_models_on_modelscope,
+}
+website_to_download_fn = {
+    "HuggingFace": download_from_huggingface,
+    "ModelScope": download_from_modelscope,
+}
+
+
+class ModelManager:
+    def __init__(
+        self,
+        torch_dtype=torch.float16,
+        device="cuda",
+        model_id_list: List[Preset_model_id] = [],
+        downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"],
+        file_path_list: List[str] = [],
+    ):
+        self.torch_dtype = torch_dtype
+        self.device = device
+        self.model = {}
+        self.model_path = {}
+        self.textual_inversion_dict = {}
+        downloaded_files = self.download_models(model_id_list, downloading_priority)
+        self.load_models(downloaded_files + file_path_list)
+
+    def download_models(
+        self,
+        model_id_list: List[Preset_model_id] = [],
+        downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"],
+    ):
+        downloaded_files = []
+        for model_id in model_id_list:
+            for website in downloading_priority:
+                if model_id in website_to_preset_models[website]:
+                    for model_id, origin_file_path, local_dir in website_to_preset_models[website][model_id]:
+                        # Check if the file is downloaded.
+                        file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path))
+                        if file_to_download in downloaded_files:
+                            continue
+                        # Download
+                        website_to_download_fn[website](model_id, origin_file_path, local_dir)
+                        if os.path.basename(origin_file_path) in os.listdir(local_dir):
+                            downloaded_files.append(file_to_download)
+        return downloaded_files
+
+    def is_stable_video_diffusion(self, state_dict):
+        param_name = "model.diffusion_model.output_blocks.9.1.time_stack.0.norm_in.weight"
+        return param_name in state_dict
+
+    def is_RIFE(self, state_dict):
+        param_name = "block_tea.convblock3.0.1.weight"
+        return param_name in state_dict or ("module." + param_name) in state_dict
+
+    def is_beautiful_prompt(self, state_dict):
+        param_name = "transformer.h.9.self_attention.query_key_value.weight"
+        return param_name in state_dict
+
+    def is_stabe_diffusion_xl(self, state_dict):
+        param_name = "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight"
+        return param_name in state_dict
+
+    def is_stable_diffusion(self, state_dict):
+        if self.is_stabe_diffusion_xl(state_dict):
+            return False
+        param_name = "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight"
+        return param_name in state_dict
+    
+    def is_controlnet(self, state_dict):
+        param_name = "control_model.time_embed.0.weight"
+        param_name_2 = "mid_block.resnets.1.time_emb_proj.weight" # For controlnets in diffusers format
+        return param_name in state_dict or param_name_2 in state_dict
+    
+    def is_animatediff(self, state_dict):
+        param_name = "mid_block.motion_modules.0.temporal_transformer.proj_out.weight"
+        return param_name in state_dict
+    
+    def is_animatediff_xl(self, state_dict):
+        param_name = "up_blocks.2.motion_modules.2.temporal_transformer.transformer_blocks.0.ff_norm.weight"
+        return param_name in state_dict
+    
+    def is_sd_lora(self, state_dict):
+        param_name = "lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2.lora_up.weight"
+        return param_name in state_dict
+    
+    def is_translator(self, state_dict):
+        param_name = "model.encoder.layers.5.self_attn_layer_norm.weight"
+        return param_name in state_dict and len(state_dict) == 254
+    
+    def is_ipadapter(self, state_dict):
+        return "image_proj" in state_dict and "ip_adapter" in state_dict and state_dict["image_proj"]["proj.weight"].shape == torch.Size([3072, 1024])
+    
+    def is_ipadapter_image_encoder(self, state_dict):
+        param_name = "vision_model.encoder.layers.31.self_attn.v_proj.weight"
+        return param_name in state_dict and len(state_dict) == 521
+    
+    def is_ipadapter_xl(self, state_dict):
+        return "image_proj" in state_dict and "ip_adapter" in state_dict and state_dict["image_proj"]["proj.weight"].shape == torch.Size([8192, 1280])
+    
+    def is_ipadapter_xl_image_encoder(self, state_dict):
+        param_name = "vision_model.encoder.layers.47.self_attn.v_proj.weight"
+        return param_name in state_dict and len(state_dict) == 777
+    
+    def is_hunyuan_dit_clip_text_encoder(self, state_dict):
+        param_name = "bert.encoder.layer.23.attention.output.dense.weight"
+        return param_name in state_dict
+    
+    def is_hunyuan_dit_t5_text_encoder(self, state_dict):
+        param_name = "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"
+        return param_name in state_dict
+    
+    def is_hunyuan_dit(self, state_dict):
+        param_name = "final_layer.adaLN_modulation.1.weight"
+        return param_name in state_dict
+    
+    def is_diffusers_vae(self, state_dict):
+        param_name = "quant_conv.weight"
+        return param_name in state_dict
+    
+    def is_ExVideo_StableVideoDiffusion(self, state_dict):
+        param_name = "blocks.185.positional_embedding.embeddings"
+        return param_name in state_dict
+    
+    def load_stable_video_diffusion(self, state_dict, components=None, file_path="", add_positional_conv=None):
+        component_dict = {
+            "image_encoder": SVDImageEncoder,
+            "unet": SVDUNet,
+            "vae_decoder": SVDVAEDecoder,
+            "vae_encoder": SVDVAEEncoder,
+        }
+        if components is None:
+            components = ["image_encoder", "unet", "vae_decoder", "vae_encoder"]
+        for component in components:
+            if component == "unet":
+                self.model[component] = component_dict[component](add_positional_conv=add_positional_conv)
+                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict, add_positional_conv=add_positional_conv), strict=False)
+            else:
+                self.model[component] = component_dict[component]()
+                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
+            self.model[component].to(self.torch_dtype).to(self.device)
+            self.model_path[component] = file_path
+    
+    def load_stable_diffusion(self, state_dict, components=None, file_path=""):
+        component_dict = {
+            "text_encoder": SDTextEncoder,
+            "unet": SDUNet,
+            "vae_decoder": SDVAEDecoder,
+            "vae_encoder": SDVAEEncoder,
+            "refiner": SDXLUNet,
+        }
+        if components is None:
+            components = ["text_encoder", "unet", "vae_decoder", "vae_encoder"]
+        for component in components:
+            if component == "text_encoder":
+                # Add additional token embeddings to text encoder
+                token_embeddings = [state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"]]
+                for keyword in self.textual_inversion_dict:
+                    _, embeddings = self.textual_inversion_dict[keyword]
+                    token_embeddings.append(embeddings.to(dtype=token_embeddings[0].dtype))
+                token_embeddings = torch.concat(token_embeddings, dim=0)
+                state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"] = token_embeddings
+                self.model[component] = component_dict[component](vocab_size=token_embeddings.shape[0])
+                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
+                self.model[component].to(self.torch_dtype).to(self.device)
+            else:
+                self.model[component] = component_dict[component]()
+                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
+                self.model[component].to(self.torch_dtype).to(self.device)
+            self.model_path[component] = file_path
+
+    def load_stable_diffusion_xl(self, state_dict, components=None, file_path=""):
+        component_dict = {
+            "text_encoder": SDXLTextEncoder,
+            "text_encoder_2": SDXLTextEncoder2,
+            "unet": SDXLUNet,
+            "vae_decoder": SDXLVAEDecoder,
+            "vae_encoder": SDXLVAEEncoder,
+        }
+        if components is None:
+            components = ["text_encoder", "text_encoder_2", "unet", "vae_decoder", "vae_encoder"]
+        for component in components:
+            self.model[component] = component_dict[component]()
+            self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
+            if component in ["vae_decoder", "vae_encoder"]:
+                # These two model will output nan when float16 is enabled.
+                # The precision problem happens in the last three resnet blocks.
+                # I do not know how to solve this problem.
+                self.model[component].to(torch.float32).to(self.device)
+            else:
+                self.model[component].to(self.torch_dtype).to(self.device)
+            self.model_path[component] = file_path
+
+    def load_controlnet(self, state_dict, file_path=""):
+        component = "controlnet"
+        if component not in self.model:
+            self.model[component] = []
+            self.model_path[component] = []
+        model = SDControlNet()
+        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component].append(model)
+        self.model_path[component].append(file_path)
+
+    def load_animatediff(self, state_dict, file_path=""):
+        component = "motion_modules"
+        model = SDMotionModel()
+        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_animatediff_xl(self, state_dict, file_path=""):
+        component = "motion_modules_xl"
+        model = SDXLMotionModel()
+        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_beautiful_prompt(self, state_dict, file_path=""):
+        component = "beautiful_prompt"
+        from transformers import AutoModelForCausalLM
+        model_folder = os.path.dirname(file_path)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_folder, state_dict=state_dict, local_files_only=True, torch_dtype=self.torch_dtype
+        ).to(self.device).eval()
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_RIFE(self, state_dict, file_path=""):
+        component = "RIFE"
+        from ..extensions.RIFE import IFNet
+        model = IFNet().eval()
+        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
+        model.to(torch.float32).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_sd_lora(self, state_dict, alpha):
+        SDLoRA().add_lora_to_text_encoder(self.model["text_encoder"], state_dict, alpha=alpha, device=self.device)
+        SDLoRA().add_lora_to_unet(self.model["unet"], state_dict, alpha=alpha, device=self.device)
+
+    def load_translator(self, state_dict, file_path=""):
+        # This model is lightweight, we do not place it on GPU.
+        component = "translator"
+        from transformers import AutoModelForSeq2SeqLM
+        model_folder = os.path.dirname(file_path)
+        model = AutoModelForSeq2SeqLM.from_pretrained(model_folder).eval()
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_ipadapter(self, state_dict, file_path=""):
+        component = "ipadapter"
+        model = SDIpAdapter()
+        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_ipadapter_image_encoder(self, state_dict, file_path=""):
+        component = "ipadapter_image_encoder"
+        model = IpAdapterCLIPImageEmbedder()
+        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_ipadapter_xl(self, state_dict, file_path=""):
+        component = "ipadapter_xl"
+        model = SDXLIpAdapter()
+        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_ipadapter_xl_image_encoder(self, state_dict, file_path=""):
+        component = "ipadapter_xl_image_encoder"
+        model = IpAdapterXLCLIPImageEmbedder()
+        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_hunyuan_dit_clip_text_encoder(self, state_dict, file_path=""):
+        component = "hunyuan_dit_clip_text_encoder"
+        model = HunyuanDiTCLIPTextEncoder()
+        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_hunyuan_dit_t5_text_encoder(self, state_dict, file_path=""):
+        component = "hunyuan_dit_t5_text_encoder"
+        model = HunyuanDiTT5TextEncoder()
+        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_hunyuan_dit(self, state_dict, file_path=""):
+        component = "hunyuan_dit"
+        model = HunyuanDiT()
+        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_diffusers_vae(self, state_dict, file_path=""):
+        # TODO: detect SD and SDXL
+        component = "vae_encoder"
+        model = SDXLVAEEncoder()
+        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+        component = "vae_decoder"
+        model = SDXLVAEDecoder()
+        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
+        model.to(self.torch_dtype).to(self.device)
+        self.model[component] = model
+        self.model_path[component] = file_path
+
+    def load_ExVideo_StableVideoDiffusion(self, state_dict, file_path=""):
+        unet_state_dict = self.model["unet"].state_dict()
+        self.model["unet"].to("cpu")
+        del self.model["unet"]
+        add_positional_conv = state_dict["blocks.185.positional_embedding.embeddings"].shape[0]
+        self.model["unet"] = SVDUNet(add_positional_conv=add_positional_conv)
+        self.model["unet"].load_state_dict(unet_state_dict, strict=False)
+        self.model["unet"].load_state_dict(state_dict, strict=False)
+        self.model["unet"].to(self.torch_dtype).to(self.device)
+
+    def search_for_embeddings(self, state_dict):
+        embeddings = []
+        for k in state_dict:
+            if isinstance(state_dict[k], torch.Tensor):
+                embeddings.append(state_dict[k])
+            elif isinstance(state_dict[k], dict):
+                embeddings += self.search_for_embeddings(state_dict[k])
+        return embeddings
+
+    def load_textual_inversions(self, folder):
+        # Store additional tokens here
+        self.textual_inversion_dict = {}
+
+        # Load every textual inversion file
+        for file_name in os.listdir(folder):
+            if file_name.endswith(".txt"):
+                continue
+            keyword = os.path.splitext(file_name)[0]
+            state_dict = load_state_dict(os.path.join(folder, file_name))
+
+            # Search for embeddings
+            for embeddings in self.search_for_embeddings(state_dict):
+                if len(embeddings.shape) == 2 and embeddings.shape[1] == 768:
+                    tokens = [f"{keyword}_{i}" for i in range(embeddings.shape[0])]
+                    self.textual_inversion_dict[keyword] = (tokens, embeddings)
+                    break
+        
+    def load_model(self, file_path, components=None, lora_alphas=[]):
+        state_dict = load_state_dict(file_path, torch_dtype=self.torch_dtype)
+        if self.is_stable_video_diffusion(state_dict):
+            self.load_stable_video_diffusion(state_dict, file_path=file_path)
+        elif self.is_animatediff(state_dict):
+            self.load_animatediff(state_dict, file_path=file_path)
+        elif self.is_animatediff_xl(state_dict):
+            self.load_animatediff_xl(state_dict, file_path=file_path)
+        elif self.is_controlnet(state_dict):
+            self.load_controlnet(state_dict, file_path=file_path)
+        elif self.is_stabe_diffusion_xl(state_dict):
+            self.load_stable_diffusion_xl(state_dict, components=components, file_path=file_path)
+        elif self.is_stable_diffusion(state_dict):
+            self.load_stable_diffusion(state_dict, components=components, file_path=file_path)
+        elif self.is_sd_lora(state_dict):
+            self.load_sd_lora(state_dict, alpha=lora_alphas.pop(0))
+        elif self.is_beautiful_prompt(state_dict):
+            self.load_beautiful_prompt(state_dict, file_path=file_path)
+        elif self.is_RIFE(state_dict):
+            self.load_RIFE(state_dict, file_path=file_path)
+        elif self.is_translator(state_dict):
+            self.load_translator(state_dict, file_path=file_path)
+        elif self.is_ipadapter(state_dict):
+            self.load_ipadapter(state_dict, file_path=file_path)
+        elif self.is_ipadapter_image_encoder(state_dict):
+            self.load_ipadapter_image_encoder(state_dict, file_path=file_path)
+        elif self.is_ipadapter_xl(state_dict):
+            self.load_ipadapter_xl(state_dict, file_path=file_path)
+        elif self.is_ipadapter_xl_image_encoder(state_dict):
+            self.load_ipadapter_xl_image_encoder(state_dict, file_path=file_path)
+        elif self.is_hunyuan_dit_clip_text_encoder(state_dict):
+            self.load_hunyuan_dit_clip_text_encoder(state_dict, file_path=file_path)
+        elif self.is_hunyuan_dit_t5_text_encoder(state_dict):
+            self.load_hunyuan_dit_t5_text_encoder(state_dict, file_path=file_path)
+        elif self.is_hunyuan_dit(state_dict):
+            self.load_hunyuan_dit(state_dict, file_path=file_path)
+        elif self.is_diffusers_vae(state_dict):
+            self.load_diffusers_vae(state_dict, file_path=file_path)
+        elif self.is_ExVideo_StableVideoDiffusion(state_dict):
+            self.load_ExVideo_StableVideoDiffusion(state_dict, file_path=file_path)
+
+    def load_models(self, file_path_list, lora_alphas=[]):
+        for file_path in file_path_list:
+            self.load_model(file_path, lora_alphas=lora_alphas)
+        
+    def to(self, device):
+        for component in self.model:
+            if isinstance(self.model[component], list):
+                for model in self.model[component]:
+                    model.to(device)
+            else:
+                self.model[component].to(device)
+        torch.cuda.empty_cache()
+
+    def get_model_with_model_path(self, model_path):
+        for component in self.model_path:
+            if isinstance(self.model_path[component], str):
+                if os.path.samefile(self.model_path[component], model_path):
+                    return self.model[component]
+            elif isinstance(self.model_path[component], list):
+                for i, model_path_ in enumerate(self.model_path[component]):
+                    if os.path.samefile(model_path_, model_path):
+                        return self.model[component][i]
+        raise ValueError(f"Please load model {model_path} before you use it.")
+    
+    def __getattr__(self, __name):
+        if __name in self.model:
+            return self.model[__name]
+        else:
+            return super.__getattribute__(__name)
+
+
+def load_state_dict(file_path, torch_dtype=None):
+    if file_path.endswith(".safetensors"):
+        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype)
+    else:
+        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype)
+
+
+def load_state_dict_from_safetensors(file_path, torch_dtype=None):
+    state_dict = {}
+    with safe_open(file_path, framework="pt", device="cpu") as f:
+        for k in f.keys():
+            state_dict[k] = f.get_tensor(k)
+            if torch_dtype is not None:
+                state_dict[k] = state_dict[k].to(torch_dtype)
+    return state_dict
+
+
+def load_state_dict_from_bin(file_path, torch_dtype=None):
+    state_dict = torch.load(file_path, map_location="cpu")
+    if torch_dtype is not None:
+        for i in state_dict:
+            if isinstance(state_dict[i], torch.Tensor):
+                state_dict[i] = state_dict[i].to(torch_dtype)
+    return state_dict
+
+
+def search_parameter(param, state_dict):
+    for name, param_ in state_dict.items():
+        if param.numel() == param_.numel():
+            if param.shape == param_.shape:
+                if torch.dist(param, param_) < 1e-6:
+                    return name
+            else:
+                if torch.dist(param.flatten(), param_.flatten()) < 1e-6:
+                    return name
+    return None
+
+
+def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False):
+    matched_keys = set()
+    with torch.no_grad():
+        for name in source_state_dict:
+            rename = search_parameter(source_state_dict[name], target_state_dict)
+            if rename is not None:
+                print(f'"{name}": "{rename}",')
+                matched_keys.add(rename)
+            elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0:
+                length = source_state_dict[name].shape[0] // 3
+                rename = []
+                for i in range(3):
+                    rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict))
+                if None not in rename:
+                    print(f'"{name}": {rename},')
+                    for rename_ in rename:
+                        matched_keys.add(rename_)
+    for name in target_state_dict:
+        if name not in matched_keys:
+            print("Cannot find", name, target_state_dict[name].shape)

diffsynth/models/attention.py

@@ -0,0 +1,89 @@
+import torch
+from einops import rearrange
+
+
+def low_version_attention(query, key, value, attn_bias=None):
+    scale = 1 / query.shape[-1] ** 0.5
+    query = query * scale
+    attn = torch.matmul(query, key.transpose(-2, -1))
+    if attn_bias is not None:
+        attn = attn + attn_bias
+    attn = attn.softmax(-1)
+    return attn @ value
+
+
+class Attention(torch.nn.Module):
+
+    def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False):
+        super().__init__()
+        dim_inner = head_dim * num_heads
+        kv_dim = kv_dim if kv_dim is not None else q_dim
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q)
+        self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out)
+
+    def interact_with_ipadapter(self, hidden_states, q, ip_k, ip_v, scale=1.0):
+        batch_size = q.shape[0]
+        ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v)
+        hidden_states = hidden_states + scale * ip_hidden_states
+        return hidden_states
+
+    def torch_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None):
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        batch_size = encoder_hidden_states.shape[0]
+
+        q = self.to_q(hidden_states)
+        k = self.to_k(encoder_hidden_states)
+        v = self.to_v(encoder_hidden_states)
+
+        q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        if qkv_preprocessor is not None:
+            q, k, v = qkv_preprocessor(q, k, v)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        if ipadapter_kwargs is not None:
+            hidden_states = self.interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+
+        hidden_states = self.to_out(hidden_states)
+
+        return hidden_states
+    
+    def xformers_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        q = self.to_q(hidden_states)
+        k = self.to_k(encoder_hidden_states)
+        v = self.to_v(encoder_hidden_states)
+
+        q = rearrange(q, "b f (n d) -> (b n) f d", n=self.num_heads)
+        k = rearrange(k, "b f (n d) -> (b n) f d", n=self.num_heads)
+        v = rearrange(v, "b f (n d) -> (b n) f d", n=self.num_heads)
+
+        if attn_mask is not None:
+            hidden_states = low_version_attention(q, k, v, attn_bias=attn_mask)
+        else:
+            import xformers.ops as xops
+            hidden_states = xops.memory_efficient_attention(q, k, v)
+        hidden_states = rearrange(hidden_states, "(b n) f d -> b f (n d)", n=self.num_heads)
+
+        hidden_states = hidden_states.to(q.dtype)
+        hidden_states = self.to_out(hidden_states)
+
+        return hidden_states
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None):
+        return self.torch_forward(hidden_states, encoder_hidden_states=encoder_hidden_states, attn_mask=attn_mask, ipadapter_kwargs=ipadapter_kwargs, qkv_preprocessor=qkv_preprocessor)

diffsynth/models/downloader.py

@@ -0,0 +1,278 @@
+from huggingface_hub import hf_hub_download
+from http.cookiejar import CookieJar
+from pathlib import Path
+from typing import Dict, Optional, List, Union
+import copy, uuid, requests, io, platform, pickle, os, urllib
+from requests.adapters import Retry
+from tqdm import tqdm
+
+
+def _get_sep(path):
+    if isinstance(path, bytes):
+        return b'/'
+    else:
+        return '/'
+
+
+def expanduser(path):
+    """Expand ~ and ~user constructions.  If user or $HOME is unknown,
+    do nothing."""
+    path = os.fspath(path)
+    if isinstance(path, bytes):
+        tilde = b'~'
+    else:
+        tilde = '~'
+    if not path.startswith(tilde):
+        return path
+    sep = _get_sep(path)
+    i = path.find(sep, 1)
+    if i < 0:
+        i = len(path)
+    if i == 1:
+        if 'HOME' not in os.environ:
+            import pwd
+            try:
+                userhome = pwd.getpwuid(os.getuid()).pw_dir
+            except KeyError:
+                # bpo-10496: if the current user identifier doesn't exist in the
+                # password database, return the path unchanged
+                return path
+        else:
+            userhome = os.environ['HOME']
+    else:
+        import pwd
+        name = path[1:i]
+        if isinstance(name, bytes):
+            name = str(name, 'ASCII')
+        try:
+            pwent = pwd.getpwnam(name)
+        except KeyError:
+            # bpo-10496: if the user name from the path doesn't exist in the
+            # password database, return the path unchanged
+            return path
+        userhome = pwent.pw_dir
+    if isinstance(path, bytes):
+        userhome = os.fsencode(userhome)
+        root = b'/'
+    else:
+        root = '/'
+    userhome = userhome.rstrip(root)
+    return (userhome + path[i:]) or root
+
+
+
+class ModelScopeConfig:
+    DEFAULT_CREDENTIALS_PATH = Path.home().joinpath('.modelscope', 'credentials')
+    path_credential = expanduser(DEFAULT_CREDENTIALS_PATH)
+    COOKIES_FILE_NAME = 'cookies'
+    GIT_TOKEN_FILE_NAME = 'git_token'
+    USER_INFO_FILE_NAME = 'user'
+    USER_SESSION_ID_FILE_NAME = 'session'
+
+    @staticmethod
+    def make_sure_credential_path_exist():
+        os.makedirs(ModelScopeConfig.path_credential, exist_ok=True)
+
+    @staticmethod
+    def get_user_session_id():
+        session_path = os.path.join(ModelScopeConfig.path_credential,
+                                    ModelScopeConfig.USER_SESSION_ID_FILE_NAME)
+        session_id = ''
+        if os.path.exists(session_path):
+            with open(session_path, 'rb') as f:
+                session_id = str(f.readline().strip(), encoding='utf-8')
+                return session_id
+        if session_id == '' or len(session_id) != 32:
+            session_id = str(uuid.uuid4().hex)
+            ModelScopeConfig.make_sure_credential_path_exist()
+            with open(session_path, 'w+') as wf:
+                wf.write(session_id)
+
+        return session_id
+
+    @staticmethod
+    def get_user_agent(user_agent: Union[Dict, str, None] = None, ) -> str:
+        """Formats a user-agent string with basic info about a request.
+
+        Args:
+            user_agent (`str`, `dict`, *optional*):
+                The user agent info in the form of a dictionary or a single string.
+
+        Returns:
+            The formatted user-agent string.
+        """
+
+        # include some more telemetrics when executing in dedicated
+        # cloud containers
+        MODELSCOPE_CLOUD_ENVIRONMENT = 'MODELSCOPE_ENVIRONMENT'
+        MODELSCOPE_CLOUD_USERNAME = 'MODELSCOPE_USERNAME'
+        env = 'custom'
+        if MODELSCOPE_CLOUD_ENVIRONMENT in os.environ:
+            env = os.environ[MODELSCOPE_CLOUD_ENVIRONMENT]
+        user_name = 'unknown'
+        if MODELSCOPE_CLOUD_USERNAME in os.environ:
+            user_name = os.environ[MODELSCOPE_CLOUD_USERNAME]
+
+        ua = 'modelscope/%s; python/%s; session_id/%s; platform/%s; processor/%s; env/%s; user/%s' % (
+            "1.15.0",
+            platform.python_version(),
+            ModelScopeConfig.get_user_session_id(),
+            platform.platform(),
+            platform.processor(),
+            env,
+            user_name,
+        )
+        if isinstance(user_agent, dict):
+            ua += '; ' + '; '.join(f'{k}/{v}' for k, v in user_agent.items())
+        elif isinstance(user_agent, str):
+            ua += '; ' + user_agent
+        return ua
+    
+    @staticmethod
+    def get_cookies():
+        cookies_path = os.path.join(ModelScopeConfig.path_credential,
+                                    ModelScopeConfig.COOKIES_FILE_NAME)
+        if os.path.exists(cookies_path):
+            with open(cookies_path, 'rb') as f:
+                cookies = pickle.load(f)
+                return cookies
+        return None
+
+
+
+def modelscope_http_get_model_file(
+    url: str,
+    local_dir: str,
+    file_name: str,
+    file_size: int,
+    cookies: CookieJar,
+    headers: Optional[Dict[str, str]] = None,
+):
+    """Download remote file, will retry 5 times before giving up on errors.
+
+    Args:
+        url(str):
+            actual download url of the file
+        local_dir(str):
+            local directory where the downloaded file stores
+        file_name(str):
+            name of the file stored in `local_dir`
+        file_size(int):
+            The file size.
+        cookies(CookieJar):
+            cookies used to authentication the user, which is used for downloading private repos
+        headers(Dict[str, str], optional):
+            http headers to carry necessary info when requesting the remote file
+
+    Raises:
+        FileDownloadError: File download failed.
+
+    """
+    get_headers = {} if headers is None else copy.deepcopy(headers)
+    get_headers['X-Request-ID'] = str(uuid.uuid4().hex)
+    temp_file_path = os.path.join(local_dir, file_name)
+    # retry sleep 0.5s, 1s, 2s, 4s
+    retry = Retry(
+        total=5,
+        backoff_factor=1,
+        allowed_methods=['GET'])
+    while True:
+        try:
+            progress = tqdm(
+                unit='B',
+                unit_scale=True,
+                unit_divisor=1024,
+                total=file_size,
+                initial=0,
+                desc='Downloading',
+            )
+            partial_length = 0
+            if os.path.exists(
+                    temp_file_path):  # download partial, continue download
+                with open(temp_file_path, 'rb') as f:
+                    partial_length = f.seek(0, io.SEEK_END)
+                    progress.update(partial_length)
+            if partial_length > file_size:
+                break
+            get_headers['Range'] = 'bytes=%s-%s' % (partial_length,
+                                                    file_size - 1)
+            with open(temp_file_path, 'ab') as f:
+                r = requests.get(
+                    url,
+                    stream=True,
+                    headers=get_headers,
+                    cookies=cookies,
+                    timeout=60)
+                r.raise_for_status()
+                for chunk in r.iter_content(
+                        chunk_size=1024 * 1024 * 1):
+                    if chunk:  # filter out keep-alive new chunks
+                        progress.update(len(chunk))
+                        f.write(chunk)
+            progress.close()
+            break
+        except (Exception) as e:  # no matter what happen, we will retry.
+            retry = retry.increment('GET', url, error=e)
+            retry.sleep()
+
+
+def get_endpoint():
+    MODELSCOPE_URL_SCHEME = 'https://'
+    DEFAULT_MODELSCOPE_DOMAIN = 'www.modelscope.cn'
+    modelscope_domain = os.getenv('MODELSCOPE_DOMAIN',
+                                  DEFAULT_MODELSCOPE_DOMAIN)
+    return MODELSCOPE_URL_SCHEME + modelscope_domain
+
+
+def get_file_download_url(model_id: str, file_path: str, revision: str):
+    """Format file download url according to `model_id`, `revision` and `file_path`.
+    e.g., Given `model_id=john/bert`, `revision=master`, `file_path=README.md`,
+    the resulted download url is: https://modelscope.cn/api/v1/models/john/bert/repo?Revision=master&FilePath=README.md
+
+    Args:
+        model_id (str): The model_id.
+        file_path (str): File path
+        revision (str): File revision.
+
+    Returns:
+        str: The file url.
+    """
+    file_path = urllib.parse.quote_plus(file_path)
+    revision = urllib.parse.quote_plus(revision)
+    download_url_template = '{endpoint}/api/v1/models/{model_id}/repo?Revision={revision}&FilePath={file_path}'
+    return download_url_template.format(
+        endpoint=get_endpoint(),
+        model_id=model_id,
+        revision=revision,
+        file_path=file_path,
+    )
+
+
+def download_from_modelscope(model_id, origin_file_path, local_dir):
+    os.makedirs(local_dir, exist_ok=True)
+    if os.path.basename(origin_file_path) in os.listdir(local_dir):
+        print(f"{os.path.basename(origin_file_path)} has been already in {local_dir}.")
+        return
+    else:
+        print(f"Start downloading {os.path.join(local_dir, os.path.basename(origin_file_path))}")
+    headers = {'user-agent': ModelScopeConfig.get_user_agent(user_agent=None)}
+    cookies = ModelScopeConfig.get_cookies()
+    url = get_file_download_url(model_id=model_id, file_path=origin_file_path, revision="master")
+    modelscope_http_get_model_file(
+        url,
+        local_dir,
+        os.path.basename(origin_file_path),
+        file_size=0,
+        headers=headers,
+        cookies=cookies
+    )
+
+
+def download_from_huggingface(model_id, origin_file_path, local_dir):
+    os.makedirs(local_dir, exist_ok=True)
+    if os.path.basename(origin_file_path) in os.listdir(local_dir):
+        print(f"{os.path.basename(origin_file_path)} has been already in {local_dir}.")
+        return
+    else:
+        print(f"Start downloading {os.path.join(local_dir, os.path.basename(origin_file_path))}")
+    hf_hub_download(model_id, origin_file_path, local_dir=local_dir)

diffsynth/models/hunyuan_dit.py

@@ -0,0 +1,451 @@
+from .attention import Attention
+from .tiler import TileWorker
+from einops import repeat, rearrange
+import math
+import torch
+
+
+class HunyuanDiTRotaryEmbedding(torch.nn.Module):
+
+    def __init__(self, q_norm_shape=88, k_norm_shape=88, rotary_emb_on_k=True):
+        super().__init__()
+        self.q_norm = torch.nn.LayerNorm((q_norm_shape,), elementwise_affine=True, eps=1e-06)
+        self.k_norm = torch.nn.LayerNorm((k_norm_shape,), elementwise_affine=True, eps=1e-06)
+        self.rotary_emb_on_k = rotary_emb_on_k
+        self.k_cache, self.v_cache = [], []
+
+    def reshape_for_broadcast(self, freqs_cis, x):
+        ndim = x.ndim
+        shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
+
+    def rotate_half(self, x):
+        x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)
+        return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+
+    def apply_rotary_emb(self, xq, xk, freqs_cis):
+        xk_out = None
+        cos, sin = self.reshape_for_broadcast(freqs_cis, xq)
+        cos, sin = cos.to(xq.device), sin.to(xq.device)
+        xq_out = (xq.float() * cos + self.rotate_half(xq.float()) * sin).type_as(xq)
+        if xk is not None:
+            xk_out = (xk.float() * cos + self.rotate_half(xk.float()) * sin).type_as(xk)
+        return xq_out, xk_out
+
+    def forward(self, q, k, v, freqs_cis_img, to_cache=False):
+        # norm
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        # RoPE
+        if self.rotary_emb_on_k:
+            q, k = self.apply_rotary_emb(q, k, freqs_cis_img)
+        else:
+            q, _ = self.apply_rotary_emb(q, None, freqs_cis_img)
+        
+        if to_cache:
+            self.k_cache.append(k)
+            self.v_cache.append(v)
+        elif len(self.k_cache) > 0 and len(self.v_cache) > 0:
+            k = torch.concat([k] + self.k_cache, dim=2)
+            v = torch.concat([v] + self.v_cache, dim=2)
+            self.k_cache, self.v_cache = [], []
+        return q, k, v
+
+
+class FP32_Layernorm(torch.nn.LayerNorm):
+    def forward(self, inputs):
+        origin_dtype = inputs.dtype
+        return torch.nn.functional.layer_norm(inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps).to(origin_dtype)
+
+
+class FP32_SiLU(torch.nn.SiLU):
+    def forward(self, inputs):
+        origin_dtype = inputs.dtype
+        return torch.nn.functional.silu(inputs.float(), inplace=False).to(origin_dtype)
+    
+
+class HunyuanDiTFinalLayer(torch.nn.Module):
+    def __init__(self, final_hidden_size=1408, condition_dim=1408, patch_size=2, out_channels=8):
+        super().__init__()
+        self.norm_final = torch.nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = torch.nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = torch.nn.Sequential(
+            FP32_SiLU(),
+            torch.nn.Linear(condition_dim, 2 * final_hidden_size, bias=True)
+        )
+
+    def modulate(self, x, shift, scale):
+        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+    def forward(self, hidden_states, condition_emb):
+        shift, scale = self.adaLN_modulation(condition_emb).chunk(2, dim=1)
+        hidden_states = self.modulate(self.norm_final(hidden_states), shift, scale)
+        hidden_states = self.linear(hidden_states)
+        return hidden_states
+
+
+class HunyuanDiTBlock(torch.nn.Module):
+
+    def __init__(
+        self,
+        hidden_dim=1408,
+        condition_dim=1408,
+        num_heads=16,
+        mlp_ratio=4.3637,
+        text_dim=1024,
+        skip_connection=False
+    ):
+        super().__init__()
+        self.norm1 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True)
+        self.rota1 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads)
+        self.attn1 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, bias_q=True, bias_kv=True, bias_out=True)
+        self.norm2 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True)
+        self.rota2 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads, rotary_emb_on_k=False)
+        self.attn2 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, kv_dim=text_dim, bias_q=True, bias_kv=True, bias_out=True)
+        self.norm3 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True)
+        self.modulation = torch.nn.Sequential(FP32_SiLU(), torch.nn.Linear(condition_dim, hidden_dim, bias=True))
+        self.mlp = torch.nn.Sequential(
+            torch.nn.Linear(hidden_dim, int(hidden_dim*mlp_ratio), bias=True),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(int(hidden_dim*mlp_ratio), hidden_dim, bias=True)
+        )
+        if skip_connection:
+            self.skip_norm = FP32_Layernorm((hidden_dim * 2,), eps=1e-6, elementwise_affine=True)
+            self.skip_linear = torch.nn.Linear(hidden_dim * 2, hidden_dim, bias=True)
+        else:
+            self.skip_norm, self.skip_linear = None, None
+
+    def forward(self, hidden_states, condition_emb, text_emb, freq_cis_img, residual=None, to_cache=False):
+        # Long Skip Connection
+        if self.skip_norm is not None and self.skip_linear is not None:
+            hidden_states = torch.cat([hidden_states, residual], dim=-1)
+            hidden_states = self.skip_norm(hidden_states)
+            hidden_states = self.skip_linear(hidden_states)
+
+        # Self-Attention
+        shift_msa = self.modulation(condition_emb).unsqueeze(dim=1)
+        attn_input = self.norm1(hidden_states) + shift_msa
+        hidden_states = hidden_states + self.attn1(attn_input, qkv_preprocessor=lambda q, k, v: self.rota1(q, k, v, freq_cis_img, to_cache=to_cache))
+
+        # Cross-Attention
+        attn_input = self.norm3(hidden_states)
+        hidden_states = hidden_states + self.attn2(attn_input, text_emb, qkv_preprocessor=lambda q, k, v: self.rota2(q, k, v, freq_cis_img))
+
+        # FFN Layer
+        mlp_input = self.norm2(hidden_states)
+        hidden_states = hidden_states + self.mlp(mlp_input)
+        return hidden_states
+    
+
+class AttentionPool(torch.nn.Module):
+    def __init__(self, spacial_dim, embed_dim, num_heads, output_dim = None):
+        super().__init__()
+        self.positional_embedding = torch.nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5)
+        self.k_proj = torch.nn.Linear(embed_dim, embed_dim)
+        self.q_proj = torch.nn.Linear(embed_dim, embed_dim)
+        self.v_proj = torch.nn.Linear(embed_dim, embed_dim)
+        self.c_proj = torch.nn.Linear(embed_dim, output_dim or embed_dim)
+        self.num_heads = num_heads
+
+    def forward(self, x):
+        x = x.permute(1, 0, 2)  # NLC -> LNC
+        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (L+1)NC
+        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (L+1)NC
+        x, _ = torch.nn.functional.multi_head_attention_forward(
+            query=x[:1], key=x, value=x,
+            embed_dim_to_check=x.shape[-1],
+            num_heads=self.num_heads,
+            q_proj_weight=self.q_proj.weight,
+            k_proj_weight=self.k_proj.weight,
+            v_proj_weight=self.v_proj.weight,
+            in_proj_weight=None,
+            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
+            bias_k=None,
+            bias_v=None,
+            add_zero_attn=False,
+            dropout_p=0,
+            out_proj_weight=self.c_proj.weight,
+            out_proj_bias=self.c_proj.bias,
+            use_separate_proj_weight=True,
+            training=self.training,
+            need_weights=False
+        )
+        return x.squeeze(0)
+    
+
+class PatchEmbed(torch.nn.Module):
+    def __init__(
+        self,
+        patch_size=(2, 2),
+        in_chans=4,
+        embed_dim=1408,
+        bias=True,
+    ):
+        super().__init__()
+        self.proj = torch.nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+
+    def forward(self, x):
+        x = self.proj(x)
+        x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
+        return x
+    
+
+def timestep_embedding(t, dim, max_period=10000, repeat_only=False):
+    # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+    if not repeat_only:
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period)
+            * torch.arange(start=0, end=half, dtype=torch.float32)
+            / half
+        ).to(device=t.device)   # size: [dim/2], 一个指数衰减的曲线
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+    else:
+        embedding = repeat(t, "b -> b d", d=dim)
+    return embedding
+    
+
+class TimestepEmbedder(torch.nn.Module):
+    def __init__(self, hidden_size=1408, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = torch.nn.Sequential(
+            torch.nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    def forward(self, t):
+        t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class HunyuanDiT(torch.nn.Module):
+    def __init__(self, num_layers_down=21, num_layers_up=19, in_channels=4, out_channels=8, hidden_dim=1408, text_dim=1024, t5_dim=2048, text_length=77, t5_length=256):
+        super().__init__()
+
+        # Embedders
+        self.text_emb_padding = torch.nn.Parameter(torch.randn(text_length + t5_length, text_dim, dtype=torch.float32))
+        self.t5_embedder = torch.nn.Sequential(
+            torch.nn.Linear(t5_dim, t5_dim * 4, bias=True),
+            FP32_SiLU(),
+            torch.nn.Linear(t5_dim * 4, text_dim, bias=True),
+        )
+        self.t5_pooler = AttentionPool(t5_length, t5_dim, num_heads=8, output_dim=1024)
+        self.style_embedder = torch.nn.Parameter(torch.randn(hidden_dim))
+        self.patch_embedder = PatchEmbed(in_chans=in_channels)
+        self.timestep_embedder = TimestepEmbedder()
+        self.extra_embedder = torch.nn.Sequential(
+            torch.nn.Linear(256 * 6 + 1024 + hidden_dim, hidden_dim * 4),
+            FP32_SiLU(),
+            torch.nn.Linear(hidden_dim * 4, hidden_dim),
+        )
+
+        # Transformer blocks
+        self.num_layers_down = num_layers_down
+        self.num_layers_up = num_layers_up
+        self.blocks = torch.nn.ModuleList(
+            [HunyuanDiTBlock(skip_connection=False) for _ in range(num_layers_down)] + \
+            [HunyuanDiTBlock(skip_connection=True) for _ in range(num_layers_up)]
+        )
+
+        # Output layers
+        self.final_layer = HunyuanDiTFinalLayer()
+        self.out_channels = out_channels
+
+    def prepare_text_emb(self, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5):
+        text_emb_mask = text_emb_mask.bool()
+        text_emb_mask_t5 = text_emb_mask_t5.bool()
+        text_emb_t5 = self.t5_embedder(text_emb_t5)
+        text_emb = torch.cat([text_emb, text_emb_t5], dim=1)
+        text_emb_mask = torch.cat([text_emb_mask, text_emb_mask_t5], dim=-1)
+        text_emb = torch.where(text_emb_mask.unsqueeze(2), text_emb, self.text_emb_padding.to(text_emb))
+        return text_emb
+    
+    def prepare_extra_emb(self, text_emb_t5, timestep, size_emb, dtype, batch_size):
+        # Text embedding
+        pooled_text_emb_t5 = self.t5_pooler(text_emb_t5)
+
+        # Timestep embedding
+        timestep_emb = self.timestep_embedder(timestep)
+
+        # Size embedding
+        size_emb = timestep_embedding(size_emb.view(-1), 256).to(dtype)
+        size_emb = size_emb.view(-1, 6 * 256)
+
+        # Style embedding
+        style_emb = repeat(self.style_embedder, "D -> B D", B=batch_size)
+
+        # Concatenate all extra vectors
+        extra_emb = torch.cat([pooled_text_emb_t5, size_emb, style_emb], dim=1)
+        condition_emb = timestep_emb + self.extra_embedder(extra_emb)
+
+        return condition_emb
+
+    def unpatchify(self, x, h, w):
+        return rearrange(x, "B (H W) (P Q C) -> B C (H P) (W Q)", H=h, W=w, P=2, Q=2)
+    
+    def build_mask(self, data, is_bound):
+        _, _, H, W = data.shape
+        h = repeat(torch.arange(H), "H -> H W", H=H, W=W)
+        w = repeat(torch.arange(W), "W -> H W", H=H, W=W)
+        border_width = (H + W) // 4
+        pad = torch.ones_like(h) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else h + 1,
+            pad if is_bound[1] else H - h,
+            pad if is_bound[2] else w + 1,
+            pad if is_bound[3] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=data.dtype, device=data.device)
+        mask = rearrange(mask, "H W -> 1 H W")
+        return mask
+    
+    def tiled_block_forward(self, block, hidden_states, condition_emb, text_emb, freq_cis_img, residual, torch_dtype, data_device, computation_device, tile_size, tile_stride):
+        B, C, H, W = hidden_states.shape
+
+        weight = torch.zeros((1, 1, H, W), dtype=torch_dtype, device=data_device)
+        values = torch.zeros((B, C, H, W), dtype=torch_dtype, device=data_device)
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, tile_stride):
+            for w in range(0, W, tile_stride):
+                if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W):
+                    continue
+                h_, w_ = h + tile_size, w + tile_size
+                if h_ > H: h, h_ = H - tile_size, H
+                if w_ > W: w, w_ = W - tile_size, W
+                tasks.append((h, h_, w, w_))
+
+        # Run
+        for hl, hr, wl, wr in tasks:
+            hidden_states_batch = hidden_states[:, :, hl:hr, wl:wr].to(computation_device)
+            hidden_states_batch = rearrange(hidden_states_batch, "B C H W -> B (H W) C")
+            if residual is not None:
+                residual_batch = residual[:, :, hl:hr, wl:wr].to(computation_device)
+                residual_batch = rearrange(residual_batch, "B C H W -> B (H W) C")
+            else:
+                residual_batch = None
+
+            # Forward
+            hidden_states_batch = block(hidden_states_batch, condition_emb, text_emb, freq_cis_img, residual_batch).to(data_device)
+            hidden_states_batch = rearrange(hidden_states_batch, "B (H W) C -> B C H W", H=hr-hl)
+
+            mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W))
+            values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask
+            weight[:, :, hl:hr, wl:wr] += mask
+        values /= weight
+        return values
+
+    def forward(
+        self, hidden_states, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5, timestep, size_emb, freq_cis_img,
+        tiled=False, tile_size=64, tile_stride=32,
+        to_cache=False,
+        use_gradient_checkpointing=False,
+    ):
+        # Embeddings
+        text_emb = self.prepare_text_emb(text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5)
+        condition_emb = self.prepare_extra_emb(text_emb_t5, timestep, size_emb, hidden_states.dtype, hidden_states.shape[0])
+        
+        # Input
+        height, width = hidden_states.shape[-2], hidden_states.shape[-1]
+        hidden_states = self.patch_embedder(hidden_states)
+
+        # Blocks
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+        if tiled:
+            hidden_states = rearrange(hidden_states, "B (H W) C -> B C H W", H=height//2)
+            residuals = []
+            for block_id, block in enumerate(self.blocks):
+                residual = residuals.pop() if block_id >= self.num_layers_down else None
+                hidden_states = self.tiled_block_forward(
+                    block, hidden_states, condition_emb, text_emb, freq_cis_img, residual,
+                    torch_dtype=hidden_states.dtype, data_device=hidden_states.device, computation_device=hidden_states.device,
+                    tile_size=tile_size, tile_stride=tile_stride
+                )
+                if block_id < self.num_layers_down - 2:
+                    residuals.append(hidden_states)
+            hidden_states = rearrange(hidden_states, "B C H W -> B (H W) C")
+        else:
+            residuals = []
+            for block_id, block in enumerate(self.blocks):
+                residual = residuals.pop() if block_id >= self.num_layers_down else None
+                if self.training and use_gradient_checkpointing:
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        hidden_states, condition_emb, text_emb, freq_cis_img, residual,
+                        use_reentrant=False,
+                    )
+                else:
+                    hidden_states = block(hidden_states, condition_emb, text_emb, freq_cis_img, residual, to_cache=to_cache)
+                if block_id < self.num_layers_down - 2:
+                    residuals.append(hidden_states)
+
+        # Output
+        hidden_states = self.final_layer(hidden_states, condition_emb)
+        hidden_states = self.unpatchify(hidden_states, height//2, width//2)
+        hidden_states, _ = hidden_states.chunk(2, dim=1)
+        return hidden_states
+    
+    def state_dict_converter(self):
+        return HunyuanDiTStateDictConverter()
+
+
+
+class HunyuanDiTStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            name_ = name
+            name_ = name_.replace(".default_modulation.", ".modulation.")
+            name_ = name_.replace(".mlp.fc1.", ".mlp.0.")
+            name_ = name_.replace(".mlp.fc2.", ".mlp.2.")
+            name_ = name_.replace(".attn1.q_norm.", ".rota1.q_norm.")
+            name_ = name_.replace(".attn2.q_norm.", ".rota2.q_norm.")
+            name_ = name_.replace(".attn1.k_norm.", ".rota1.k_norm.")
+            name_ = name_.replace(".attn2.k_norm.", ".rota2.k_norm.")
+            name_ = name_.replace(".q_proj.", ".to_q.")
+            name_ = name_.replace(".out_proj.", ".to_out.")
+            name_ = name_.replace("text_embedding_padding", "text_emb_padding")
+            name_ = name_.replace("mlp_t5.0.", "t5_embedder.0.")
+            name_ = name_.replace("mlp_t5.2.", "t5_embedder.2.")
+            name_ = name_.replace("pooler.", "t5_pooler.")
+            name_ = name_.replace("x_embedder.", "patch_embedder.")
+            name_ = name_.replace("t_embedder.", "timestep_embedder.")
+            name_ = name_.replace("t5_pooler.to_q.", "t5_pooler.q_proj.")
+            name_ = name_.replace("style_embedder.weight", "style_embedder")
+            if ".kv_proj." in name_:
+                param_k = param[:param.shape[0]//2]
+                param_v = param[param.shape[0]//2:]
+                state_dict_[name_.replace(".kv_proj.", ".to_k.")] = param_k
+                state_dict_[name_.replace(".kv_proj.", ".to_v.")] = param_v
+            elif ".Wqkv." in name_:
+                param_q = param[:param.shape[0]//3]
+                param_k = param[param.shape[0]//3:param.shape[0]//3*2]
+                param_v = param[param.shape[0]//3*2:]
+                state_dict_[name_.replace(".Wqkv.", ".to_q.")] = param_q
+                state_dict_[name_.replace(".Wqkv.", ".to_k.")] = param_k
+                state_dict_[name_.replace(".Wqkv.", ".to_v.")] = param_v
+            elif "style_embedder" in name_:
+                state_dict_[name_] = param.squeeze()
+            else:
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/hunyuan_dit_text_encoder.py

@@ -0,0 +1,161 @@
+from transformers import BertModel, BertConfig, T5EncoderModel, T5Config
+import torch
+
+
+
+class HunyuanDiTCLIPTextEncoder(BertModel):
+    def __init__(self):
+        config = BertConfig(
+            _name_or_path = "",
+            architectures = ["BertModel"],
+            attention_probs_dropout_prob = 0.1,
+            bos_token_id = 0,
+            classifier_dropout = None,
+            directionality = "bidi",
+            eos_token_id = 2,
+            hidden_act = "gelu",
+            hidden_dropout_prob = 0.1,
+            hidden_size = 1024,
+            initializer_range = 0.02,
+            intermediate_size = 4096,
+            layer_norm_eps = 1e-12,
+            max_position_embeddings = 512,
+            model_type = "bert",
+            num_attention_heads = 16,
+            num_hidden_layers = 24,
+            output_past = True,
+            pad_token_id = 0,
+            pooler_fc_size = 768,
+            pooler_num_attention_heads = 12,
+            pooler_num_fc_layers = 3,
+            pooler_size_per_head = 128,
+            pooler_type = "first_token_transform",
+            position_embedding_type = "absolute",
+            torch_dtype = "float32",
+            transformers_version = "4.37.2",
+            type_vocab_size = 2,
+            use_cache = True,
+            vocab_size = 47020
+        )
+        super().__init__(config, add_pooling_layer=False)
+        self.eval()
+
+    def forward(self, input_ids, attention_mask, clip_skip=1):
+        input_shape = input_ids.size()
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device
+
+        past_key_values_length = 0
+
+        if attention_mask is None:
+            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
+
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=None,
+            token_type_ids=None,
+            inputs_embeds=None,
+            past_key_values_length=0,
+        )
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=None,
+            encoder_hidden_states=None,
+            encoder_attention_mask=None,
+            past_key_values=None,
+            use_cache=False,
+            output_attentions=False,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+        all_hidden_states = encoder_outputs.hidden_states
+        prompt_emb = all_hidden_states[-clip_skip]
+        if clip_skip > 1:
+            mean, std = all_hidden_states[-1].mean(), all_hidden_states[-1].std()
+            prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean
+        return prompt_emb
+
+    def state_dict_converter(self):
+        return HunyuanDiTCLIPTextEncoderStateDictConverter()
+
+
+
+class HunyuanDiTT5TextEncoder(T5EncoderModel):
+    def __init__(self):
+        config = T5Config(
+            _name_or_path = "../HunyuanDiT/t2i/mt5",
+            architectures = ["MT5ForConditionalGeneration"],
+            classifier_dropout = 0.0,
+            d_ff = 5120,
+            d_kv = 64,
+            d_model = 2048,
+            decoder_start_token_id = 0,
+            dense_act_fn = "gelu_new",
+            dropout_rate = 0.1,
+            eos_token_id = 1,
+            feed_forward_proj = "gated-gelu",
+            initializer_factor = 1.0,
+            is_encoder_decoder = True,
+            is_gated_act = True,
+            layer_norm_epsilon = 1e-06,
+            model_type = "t5",
+            num_decoder_layers = 24,
+            num_heads = 32,
+            num_layers = 24,
+            output_past = True,
+            pad_token_id = 0,
+            relative_attention_max_distance = 128,
+            relative_attention_num_buckets = 32,
+            tie_word_embeddings = False,
+            tokenizer_class = "T5Tokenizer",
+            transformers_version = "4.37.2",
+            use_cache = True,
+            vocab_size = 250112
+        )
+        super().__init__(config)
+        self.eval()
+
+    def forward(self, input_ids, attention_mask, clip_skip=1):
+        outputs = super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+        )
+        prompt_emb = outputs.hidden_states[-clip_skip]
+        if clip_skip > 1:
+            mean, std = outputs.hidden_states[-1].mean(), outputs.hidden_states[-1].std()
+            prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean
+        return prompt_emb
+    
+    def state_dict_converter(self):
+        return HunyuanDiTT5TextEncoderStateDictConverter()
+
+
+
+class HunyuanDiTCLIPTextEncoderStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {name[5:]: param for name, param in state_dict.items() if name.startswith("bert.")}
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+
+
+class HunyuanDiTT5TextEncoderStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("encoder.")}
+        state_dict_["shared.weight"] = state_dict["shared.weight"]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/sd_controlnet.py

@@ -0,0 +1,587 @@
+import torch
+from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler
+from .tiler import TileWorker
+
+
+class ControlNetConditioningLayer(torch.nn.Module):
+    def __init__(self, channels = (3, 16, 32, 96, 256, 320)):
+        super().__init__()
+        self.blocks = torch.nn.ModuleList([])
+        self.blocks.append(torch.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1))
+        self.blocks.append(torch.nn.SiLU())
+        for i in range(1, len(channels) - 2):
+            self.blocks.append(torch.nn.Conv2d(channels[i], channels[i], kernel_size=3, padding=1))
+            self.blocks.append(torch.nn.SiLU())
+            self.blocks.append(torch.nn.Conv2d(channels[i], channels[i+1], kernel_size=3, padding=1, stride=2))
+            self.blocks.append(torch.nn.SiLU())
+        self.blocks.append(torch.nn.Conv2d(channels[-2], channels[-1], kernel_size=3, padding=1))
+
+    def forward(self, conditioning):
+        for block in self.blocks:
+            conditioning = block(conditioning)
+        return conditioning
+
+
+class SDControlNet(torch.nn.Module):
+    def __init__(self, global_pool=False):
+        super().__init__()
+        self.time_proj = Timesteps(320)
+        self.time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(320, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
+
+        self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320))
+
+        self.blocks = torch.nn.ModuleList([
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768),
+            PushBlock(),
+            ResnetBlock(320, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768),
+            PushBlock(),
+            DownSampler(320),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768),
+            PushBlock(),
+            ResnetBlock(640, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768),
+            PushBlock(),
+            DownSampler(640),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(640, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768),
+            PushBlock(),
+            DownSampler(1280),
+            PushBlock(),
+            # DownBlock2D
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock(),
+            # UNetMidBlock2DCrossAttn
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768),
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock()
+        ])
+
+        self.controlnet_blocks = torch.nn.ModuleList([
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
+        ])
+
+        self.global_pool = global_pool
+
+    def forward(
+        self,
+        sample, timestep, encoder_hidden_states, conditioning,
+        tiled=False, tile_size=64, tile_stride=32,
+    ):
+        # 1. time
+        time_emb = self.time_proj(timestep[None]).to(sample.dtype)
+        time_emb = self.time_embedding(time_emb)
+        time_emb = time_emb.repeat(sample.shape[0], 1)
+
+        # 2. pre-process
+        height, width = sample.shape[2], sample.shape[3]
+        hidden_states = self.conv_in(sample) + self.controlnet_conv_in(conditioning)
+        text_emb = encoder_hidden_states
+        res_stack = [hidden_states]
+
+        # 3. blocks
+        for i, block in enumerate(self.blocks):
+            if tiled and not isinstance(block, PushBlock):
+                _, _, inter_height, _ = hidden_states.shape
+                resize_scale = inter_height / height
+                hidden_states = TileWorker().tiled_forward(
+                    lambda x: block(x, time_emb, text_emb, res_stack)[0],
+                    hidden_states,
+                    int(tile_size * resize_scale),
+                    int(tile_stride * resize_scale),
+                    tile_device=hidden_states.device,
+                    tile_dtype=hidden_states.dtype
+                )
+            else:
+                hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack)
+
+        # 4. ControlNet blocks
+        controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)]
+
+        # pool
+        if self.global_pool:
+            controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack]
+
+        return controlnet_res_stack
+
+    def state_dict_converter(self):
+        return SDControlNetStateDictConverter()
+
+
+class SDControlNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 
+            'ResnetBlock', 'AttentionBlock', 'ResnetBlock',
+            'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock'
+        ]
+
+        # controlnet_rename_dict
+        controlnet_rename_dict = {
+            "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight",
+            "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias",
+            "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight",
+            "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias",
+            "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight",
+            "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias",
+            "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight",
+            "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias",
+            "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight",
+            "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias",
+            "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight",
+            "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias",
+            "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight",
+            "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias",
+            "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight",
+            "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias",
+        }
+
+        # Rename each parameter
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
+        last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            if names[0] in ["conv_in", "conv_norm_out", "conv_out"]:
+                pass
+            elif name in controlnet_rename_dict:
+                names = controlnet_rename_dict[name].split(".")
+            elif names[0] == "controlnet_down_blocks":
+                names[0] = "controlnet_blocks"
+            elif names[0] == "controlnet_mid_block":
+                names = ["controlnet_blocks", "12", names[-1]]
+            elif names[0] in ["time_embedding", "add_embedding"]:
+                if names[0] == "add_embedding":
+                    names[0] = "add_time_embedding"
+                names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
+            elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
+                if names[0] == "mid_block":
+                    names.insert(1, "0")
+                block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
+                block_type_with_id = ".".join(names[:4])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:4])
+                names = ["blocks", str(block_id[block_type])] + names[4:]
+                if "ff" in names:
+                    ff_index = names.index("ff")
+                    component = ".".join(names[ff_index:ff_index+3])
+                    component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
+                    names = names[:ff_index] + [component] + names[ff_index+3:]
+                if "to_out" in names:
+                    names.pop(names.index("to_out") + 1)
+            else:
+                raise ValueError(f"Unknown parameters: {name}")
+            rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if ".proj_in." in name or ".proj_out." in name:
+                param = param.squeeze()
+            if rename_dict[name] in [
+                "controlnet_blocks.1.bias", "controlnet_blocks.2.bias", "controlnet_blocks.3.bias", "controlnet_blocks.5.bias", "controlnet_blocks.6.bias",
+                "controlnet_blocks.8.bias", "controlnet_blocks.9.bias", "controlnet_blocks.10.bias", "controlnet_blocks.11.bias", "controlnet_blocks.12.bias"
+            ]:
+                continue
+            state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        if "mid_block.resnets.1.time_emb_proj.weight" in state_dict:
+            # For controlnets in diffusers format
+            return self.from_diffusers(state_dict)
+        rename_dict = {
+            "control_model.time_embed.0.weight": "time_embedding.0.weight",
+            "control_model.time_embed.0.bias": "time_embedding.0.bias",
+            "control_model.time_embed.2.weight": "time_embedding.2.weight",
+            "control_model.time_embed.2.bias": "time_embedding.2.bias",
+            "control_model.input_blocks.0.0.weight": "conv_in.weight",
+            "control_model.input_blocks.0.0.bias": "conv_in.bias",
+            "control_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",
+            "control_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",
+            "control_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight",
+            "control_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias",
+            "control_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",
+            "control_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",
+            "control_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight",
+            "control_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias",
+            "control_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight",
+            "control_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias",
+            "control_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight",
+            "control_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias",
+            "control_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight",
+            "control_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.1.transformer_blocks.0.attn2.to_q.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.1.transformer_blocks.0.attn2.to_v.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.1.transformer_blocks.0.norm1.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.1.transformer_blocks.0.norm2.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.1.transformer_blocks.0.norm2.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.1.transformer_blocks.0.norm3.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.1.transformer_blocks.0.norm3.bias",
+            "control_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight",
+            "control_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias",
+            "control_model.input_blocks.2.0.in_layers.0.weight": "blocks.3.norm1.weight",
+            "control_model.input_blocks.2.0.in_layers.0.bias": "blocks.3.norm1.bias",
+            "control_model.input_blocks.2.0.in_layers.2.weight": "blocks.3.conv1.weight",
+            "control_model.input_blocks.2.0.in_layers.2.bias": "blocks.3.conv1.bias",
+            "control_model.input_blocks.2.0.emb_layers.1.weight": "blocks.3.time_emb_proj.weight",
+            "control_model.input_blocks.2.0.emb_layers.1.bias": "blocks.3.time_emb_proj.bias",
+            "control_model.input_blocks.2.0.out_layers.0.weight": "blocks.3.norm2.weight",
+            "control_model.input_blocks.2.0.out_layers.0.bias": "blocks.3.norm2.bias",
+            "control_model.input_blocks.2.0.out_layers.3.weight": "blocks.3.conv2.weight",
+            "control_model.input_blocks.2.0.out_layers.3.bias": "blocks.3.conv2.bias",
+            "control_model.input_blocks.2.1.norm.weight": "blocks.4.norm.weight",
+            "control_model.input_blocks.2.1.norm.bias": "blocks.4.norm.bias",
+            "control_model.input_blocks.2.1.proj_in.weight": "blocks.4.proj_in.weight",
+            "control_model.input_blocks.2.1.proj_in.bias": "blocks.4.proj_in.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_q.weight": "blocks.4.transformer_blocks.0.attn1.to_q.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_k.weight": "blocks.4.transformer_blocks.0.attn1.to_k.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_v.weight": "blocks.4.transformer_blocks.0.attn1.to_v.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.4.transformer_blocks.0.attn1.to_out.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.4.transformer_blocks.0.attn1.to_out.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.4.transformer_blocks.0.act_fn.proj.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.4.transformer_blocks.0.act_fn.proj.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.weight": "blocks.4.transformer_blocks.0.ff.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.bias": "blocks.4.transformer_blocks.0.ff.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_q.weight": "blocks.4.transformer_blocks.0.attn2.to_q.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight": "blocks.4.transformer_blocks.0.attn2.to_k.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_v.weight": "blocks.4.transformer_blocks.0.attn2.to_v.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.4.transformer_blocks.0.attn2.to_out.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.4.transformer_blocks.0.attn2.to_out.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm1.weight": "blocks.4.transformer_blocks.0.norm1.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm1.bias": "blocks.4.transformer_blocks.0.norm1.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm2.weight": "blocks.4.transformer_blocks.0.norm2.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm2.bias": "blocks.4.transformer_blocks.0.norm2.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm3.weight": "blocks.4.transformer_blocks.0.norm3.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm3.bias": "blocks.4.transformer_blocks.0.norm3.bias",
+            "control_model.input_blocks.2.1.proj_out.weight": "blocks.4.proj_out.weight",
+            "control_model.input_blocks.2.1.proj_out.bias": "blocks.4.proj_out.bias",
+            "control_model.input_blocks.3.0.op.weight": "blocks.6.conv.weight",
+            "control_model.input_blocks.3.0.op.bias": "blocks.6.conv.bias",
+            "control_model.input_blocks.4.0.in_layers.0.weight": "blocks.8.norm1.weight",
+            "control_model.input_blocks.4.0.in_layers.0.bias": "blocks.8.norm1.bias",
+            "control_model.input_blocks.4.0.in_layers.2.weight": "blocks.8.conv1.weight",
+            "control_model.input_blocks.4.0.in_layers.2.bias": "blocks.8.conv1.bias",
+            "control_model.input_blocks.4.0.emb_layers.1.weight": "blocks.8.time_emb_proj.weight",
+            "control_model.input_blocks.4.0.emb_layers.1.bias": "blocks.8.time_emb_proj.bias",
+            "control_model.input_blocks.4.0.out_layers.0.weight": "blocks.8.norm2.weight",
+            "control_model.input_blocks.4.0.out_layers.0.bias": "blocks.8.norm2.bias",
+            "control_model.input_blocks.4.0.out_layers.3.weight": "blocks.8.conv2.weight",
+            "control_model.input_blocks.4.0.out_layers.3.bias": "blocks.8.conv2.bias",
+            "control_model.input_blocks.4.0.skip_connection.weight": "blocks.8.conv_shortcut.weight",
+            "control_model.input_blocks.4.0.skip_connection.bias": "blocks.8.conv_shortcut.bias",
+            "control_model.input_blocks.4.1.norm.weight": "blocks.9.norm.weight",
+            "control_model.input_blocks.4.1.norm.bias": "blocks.9.norm.bias",
+            "control_model.input_blocks.4.1.proj_in.weight": "blocks.9.proj_in.weight",
+            "control_model.input_blocks.4.1.proj_in.bias": "blocks.9.proj_in.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q.weight": "blocks.9.transformer_blocks.0.attn1.to_q.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.9.transformer_blocks.0.attn1.to_k.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v.weight": "blocks.9.transformer_blocks.0.attn1.to_v.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.9.transformer_blocks.0.attn1.to_out.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.9.transformer_blocks.0.attn1.to_out.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.9.transformer_blocks.0.act_fn.proj.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.9.transformer_blocks.0.act_fn.proj.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.weight": "blocks.9.transformer_blocks.0.ff.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.bias": "blocks.9.transformer_blocks.0.ff.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_q.weight": "blocks.9.transformer_blocks.0.attn2.to_q.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight": "blocks.9.transformer_blocks.0.attn2.to_k.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_v.weight": "blocks.9.transformer_blocks.0.attn2.to_v.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.9.transformer_blocks.0.attn2.to_out.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.9.transformer_blocks.0.attn2.to_out.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm1.weight": "blocks.9.transformer_blocks.0.norm1.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm1.bias": "blocks.9.transformer_blocks.0.norm1.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm2.weight": "blocks.9.transformer_blocks.0.norm2.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm2.bias": "blocks.9.transformer_blocks.0.norm2.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm3.weight": "blocks.9.transformer_blocks.0.norm3.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm3.bias": "blocks.9.transformer_blocks.0.norm3.bias",
+            "control_model.input_blocks.4.1.proj_out.weight": "blocks.9.proj_out.weight",
+            "control_model.input_blocks.4.1.proj_out.bias": "blocks.9.proj_out.bias",
+            "control_model.input_blocks.5.0.in_layers.0.weight": "blocks.11.norm1.weight",
+            "control_model.input_blocks.5.0.in_layers.0.bias": "blocks.11.norm1.bias",
+            "control_model.input_blocks.5.0.in_layers.2.weight": "blocks.11.conv1.weight",
+            "control_model.input_blocks.5.0.in_layers.2.bias": "blocks.11.conv1.bias",
+            "control_model.input_blocks.5.0.emb_layers.1.weight": "blocks.11.time_emb_proj.weight",
+            "control_model.input_blocks.5.0.emb_layers.1.bias": "blocks.11.time_emb_proj.bias",
+            "control_model.input_blocks.5.0.out_layers.0.weight": "blocks.11.norm2.weight",
+            "control_model.input_blocks.5.0.out_layers.0.bias": "blocks.11.norm2.bias",
+            "control_model.input_blocks.5.0.out_layers.3.weight": "blocks.11.conv2.weight",
+            "control_model.input_blocks.5.0.out_layers.3.bias": "blocks.11.conv2.bias",
+            "control_model.input_blocks.5.1.norm.weight": "blocks.12.norm.weight",
+            "control_model.input_blocks.5.1.norm.bias": "blocks.12.norm.bias",
+            "control_model.input_blocks.5.1.proj_in.weight": "blocks.12.proj_in.weight",
+            "control_model.input_blocks.5.1.proj_in.bias": "blocks.12.proj_in.bias",
+            "control_model.input_blocks.5.1.transformer_blocks.0.attn1.to_q.weight": "blocks.12.transformer_blocks.0.attn1.to_q.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.attn1.to_k.weight": "blocks.12.transformer_blocks.0.attn1.to_k.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.attn1.to_v.weight": "blocks.12.transformer_blocks.0.attn1.to_v.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.12.transformer_blocks.0.attn1.to_out.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.12.transformer_blocks.0.attn1.to_out.bias",
+            "control_model.input_blocks.5.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.12.transformer_blocks.0.act_fn.proj.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.12.transformer_blocks.0.act_fn.proj.bias",
+            "control_model.input_blocks.5.1.transformer_blocks.0.ff.net.2.weight": "blocks.12.transformer_blocks.0.ff.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.ff.net.2.bias": "blocks.12.transformer_blocks.0.ff.bias",
+            "control_model.input_blocks.5.1.transformer_blocks.0.attn2.to_q.weight": "blocks.12.transformer_blocks.0.attn2.to_q.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.attn2.to_k.weight": "blocks.12.transformer_blocks.0.attn2.to_k.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.attn2.to_v.weight": "blocks.12.transformer_blocks.0.attn2.to_v.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.12.transformer_blocks.0.attn2.to_out.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.12.transformer_blocks.0.attn2.to_out.bias",
+            "control_model.input_blocks.5.1.transformer_blocks.0.norm1.weight": "blocks.12.transformer_blocks.0.norm1.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.norm1.bias": "blocks.12.transformer_blocks.0.norm1.bias",
+            "control_model.input_blocks.5.1.transformer_blocks.0.norm2.weight": "blocks.12.transformer_blocks.0.norm2.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.norm2.bias": "blocks.12.transformer_blocks.0.norm2.bias",
+            "control_model.input_blocks.5.1.transformer_blocks.0.norm3.weight": "blocks.12.transformer_blocks.0.norm3.weight",
+            "control_model.input_blocks.5.1.transformer_blocks.0.norm3.bias": "blocks.12.transformer_blocks.0.norm3.bias",
+            "control_model.input_blocks.5.1.proj_out.weight": "blocks.12.proj_out.weight",
+            "control_model.input_blocks.5.1.proj_out.bias": "blocks.12.proj_out.bias",
+            "control_model.input_blocks.6.0.op.weight": "blocks.14.conv.weight",
+            "control_model.input_blocks.6.0.op.bias": "blocks.14.conv.bias",
+            "control_model.input_blocks.7.0.in_layers.0.weight": "blocks.16.norm1.weight",
+            "control_model.input_blocks.7.0.in_layers.0.bias": "blocks.16.norm1.bias",
+            "control_model.input_blocks.7.0.in_layers.2.weight": "blocks.16.conv1.weight",
+            "control_model.input_blocks.7.0.in_layers.2.bias": "blocks.16.conv1.bias",
+            "control_model.input_blocks.7.0.emb_layers.1.weight": "blocks.16.time_emb_proj.weight",
+            "control_model.input_blocks.7.0.emb_layers.1.bias": "blocks.16.time_emb_proj.bias",
+            "control_model.input_blocks.7.0.out_layers.0.weight": "blocks.16.norm2.weight",
+            "control_model.input_blocks.7.0.out_layers.0.bias": "blocks.16.norm2.bias",
+            "control_model.input_blocks.7.0.out_layers.3.weight": "blocks.16.conv2.weight",
+            "control_model.input_blocks.7.0.out_layers.3.bias": "blocks.16.conv2.bias",
+            "control_model.input_blocks.7.0.skip_connection.weight": "blocks.16.conv_shortcut.weight",
+            "control_model.input_blocks.7.0.skip_connection.bias": "blocks.16.conv_shortcut.bias",
+            "control_model.input_blocks.7.1.norm.weight": "blocks.17.norm.weight",
+            "control_model.input_blocks.7.1.norm.bias": "blocks.17.norm.bias",
+            "control_model.input_blocks.7.1.proj_in.weight": "blocks.17.proj_in.weight",
+            "control_model.input_blocks.7.1.proj_in.bias": "blocks.17.proj_in.bias",
+            "control_model.input_blocks.7.1.transformer_blocks.0.attn1.to_q.weight": "blocks.17.transformer_blocks.0.attn1.to_q.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.attn1.to_k.weight": "blocks.17.transformer_blocks.0.attn1.to_k.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.attn1.to_v.weight": "blocks.17.transformer_blocks.0.attn1.to_v.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.17.transformer_blocks.0.attn1.to_out.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.17.transformer_blocks.0.attn1.to_out.bias",
+            "control_model.input_blocks.7.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.17.transformer_blocks.0.act_fn.proj.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.17.transformer_blocks.0.act_fn.proj.bias",
+            "control_model.input_blocks.7.1.transformer_blocks.0.ff.net.2.weight": "blocks.17.transformer_blocks.0.ff.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.ff.net.2.bias": "blocks.17.transformer_blocks.0.ff.bias",
+            "control_model.input_blocks.7.1.transformer_blocks.0.attn2.to_q.weight": "blocks.17.transformer_blocks.0.attn2.to_q.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.attn2.to_k.weight": "blocks.17.transformer_blocks.0.attn2.to_k.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.attn2.to_v.weight": "blocks.17.transformer_blocks.0.attn2.to_v.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.17.transformer_blocks.0.attn2.to_out.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.17.transformer_blocks.0.attn2.to_out.bias",
+            "control_model.input_blocks.7.1.transformer_blocks.0.norm1.weight": "blocks.17.transformer_blocks.0.norm1.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.norm1.bias": "blocks.17.transformer_blocks.0.norm1.bias",
+            "control_model.input_blocks.7.1.transformer_blocks.0.norm2.weight": "blocks.17.transformer_blocks.0.norm2.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.norm2.bias": "blocks.17.transformer_blocks.0.norm2.bias",
+            "control_model.input_blocks.7.1.transformer_blocks.0.norm3.weight": "blocks.17.transformer_blocks.0.norm3.weight",
+            "control_model.input_blocks.7.1.transformer_blocks.0.norm3.bias": "blocks.17.transformer_blocks.0.norm3.bias",
+            "control_model.input_blocks.7.1.proj_out.weight": "blocks.17.proj_out.weight",
+            "control_model.input_blocks.7.1.proj_out.bias": "blocks.17.proj_out.bias",
+            "control_model.input_blocks.8.0.in_layers.0.weight": "blocks.19.norm1.weight",
+            "control_model.input_blocks.8.0.in_layers.0.bias": "blocks.19.norm1.bias",
+            "control_model.input_blocks.8.0.in_layers.2.weight": "blocks.19.conv1.weight",
+            "control_model.input_blocks.8.0.in_layers.2.bias": "blocks.19.conv1.bias",
+            "control_model.input_blocks.8.0.emb_layers.1.weight": "blocks.19.time_emb_proj.weight",
+            "control_model.input_blocks.8.0.emb_layers.1.bias": "blocks.19.time_emb_proj.bias",
+            "control_model.input_blocks.8.0.out_layers.0.weight": "blocks.19.norm2.weight",
+            "control_model.input_blocks.8.0.out_layers.0.bias": "blocks.19.norm2.bias",
+            "control_model.input_blocks.8.0.out_layers.3.weight": "blocks.19.conv2.weight",
+            "control_model.input_blocks.8.0.out_layers.3.bias": "blocks.19.conv2.bias",
+            "control_model.input_blocks.8.1.norm.weight": "blocks.20.norm.weight",
+            "control_model.input_blocks.8.1.norm.bias": "blocks.20.norm.bias",
+            "control_model.input_blocks.8.1.proj_in.weight": "blocks.20.proj_in.weight",
+            "control_model.input_blocks.8.1.proj_in.bias": "blocks.20.proj_in.bias",
+            "control_model.input_blocks.8.1.transformer_blocks.0.attn1.to_q.weight": "blocks.20.transformer_blocks.0.attn1.to_q.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.attn1.to_k.weight": "blocks.20.transformer_blocks.0.attn1.to_k.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.attn1.to_v.weight": "blocks.20.transformer_blocks.0.attn1.to_v.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.20.transformer_blocks.0.attn1.to_out.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.20.transformer_blocks.0.attn1.to_out.bias",
+            "control_model.input_blocks.8.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.20.transformer_blocks.0.act_fn.proj.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.20.transformer_blocks.0.act_fn.proj.bias",
+            "control_model.input_blocks.8.1.transformer_blocks.0.ff.net.2.weight": "blocks.20.transformer_blocks.0.ff.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.ff.net.2.bias": "blocks.20.transformer_blocks.0.ff.bias",
+            "control_model.input_blocks.8.1.transformer_blocks.0.attn2.to_q.weight": "blocks.20.transformer_blocks.0.attn2.to_q.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.attn2.to_k.weight": "blocks.20.transformer_blocks.0.attn2.to_k.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.attn2.to_v.weight": "blocks.20.transformer_blocks.0.attn2.to_v.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.20.transformer_blocks.0.attn2.to_out.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.20.transformer_blocks.0.attn2.to_out.bias",
+            "control_model.input_blocks.8.1.transformer_blocks.0.norm1.weight": "blocks.20.transformer_blocks.0.norm1.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.norm1.bias": "blocks.20.transformer_blocks.0.norm1.bias",
+            "control_model.input_blocks.8.1.transformer_blocks.0.norm2.weight": "blocks.20.transformer_blocks.0.norm2.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.norm2.bias": "blocks.20.transformer_blocks.0.norm2.bias",
+            "control_model.input_blocks.8.1.transformer_blocks.0.norm3.weight": "blocks.20.transformer_blocks.0.norm3.weight",
+            "control_model.input_blocks.8.1.transformer_blocks.0.norm3.bias": "blocks.20.transformer_blocks.0.norm3.bias",
+            "control_model.input_blocks.8.1.proj_out.weight": "blocks.20.proj_out.weight",
+            "control_model.input_blocks.8.1.proj_out.bias": "blocks.20.proj_out.bias",
+            "control_model.input_blocks.9.0.op.weight": "blocks.22.conv.weight",
+            "control_model.input_blocks.9.0.op.bias": "blocks.22.conv.bias",
+            "control_model.input_blocks.10.0.in_layers.0.weight": "blocks.24.norm1.weight",
+            "control_model.input_blocks.10.0.in_layers.0.bias": "blocks.24.norm1.bias",
+            "control_model.input_blocks.10.0.in_layers.2.weight": "blocks.24.conv1.weight",
+            "control_model.input_blocks.10.0.in_layers.2.bias": "blocks.24.conv1.bias",
+            "control_model.input_blocks.10.0.emb_layers.1.weight": "blocks.24.time_emb_proj.weight",
+            "control_model.input_blocks.10.0.emb_layers.1.bias": "blocks.24.time_emb_proj.bias",
+            "control_model.input_blocks.10.0.out_layers.0.weight": "blocks.24.norm2.weight",
+            "control_model.input_blocks.10.0.out_layers.0.bias": "blocks.24.norm2.bias",
+            "control_model.input_blocks.10.0.out_layers.3.weight": "blocks.24.conv2.weight",
+            "control_model.input_blocks.10.0.out_layers.3.bias": "blocks.24.conv2.bias",
+            "control_model.input_blocks.11.0.in_layers.0.weight": "blocks.26.norm1.weight",
+            "control_model.input_blocks.11.0.in_layers.0.bias": "blocks.26.norm1.bias",
+            "control_model.input_blocks.11.0.in_layers.2.weight": "blocks.26.conv1.weight",
+            "control_model.input_blocks.11.0.in_layers.2.bias": "blocks.26.conv1.bias",
+            "control_model.input_blocks.11.0.emb_layers.1.weight": "blocks.26.time_emb_proj.weight",
+            "control_model.input_blocks.11.0.emb_layers.1.bias": "blocks.26.time_emb_proj.bias",
+            "control_model.input_blocks.11.0.out_layers.0.weight": "blocks.26.norm2.weight",
+            "control_model.input_blocks.11.0.out_layers.0.bias": "blocks.26.norm2.bias",
+            "control_model.input_blocks.11.0.out_layers.3.weight": "blocks.26.conv2.weight",
+            "control_model.input_blocks.11.0.out_layers.3.bias": "blocks.26.conv2.bias",
+            "control_model.zero_convs.0.0.weight": "controlnet_blocks.0.weight",
+            "control_model.zero_convs.0.0.bias": "controlnet_blocks.0.bias",
+            "control_model.zero_convs.1.0.weight": "controlnet_blocks.1.weight",
+            "control_model.zero_convs.1.0.bias": "controlnet_blocks.0.bias",
+            "control_model.zero_convs.2.0.weight": "controlnet_blocks.2.weight",
+            "control_model.zero_convs.2.0.bias": "controlnet_blocks.0.bias",
+            "control_model.zero_convs.3.0.weight": "controlnet_blocks.3.weight",
+            "control_model.zero_convs.3.0.bias": "controlnet_blocks.0.bias",
+            "control_model.zero_convs.4.0.weight": "controlnet_blocks.4.weight",
+            "control_model.zero_convs.4.0.bias": "controlnet_blocks.4.bias",
+            "control_model.zero_convs.5.0.weight": "controlnet_blocks.5.weight",
+            "control_model.zero_convs.5.0.bias": "controlnet_blocks.4.bias",
+            "control_model.zero_convs.6.0.weight": "controlnet_blocks.6.weight",
+            "control_model.zero_convs.6.0.bias": "controlnet_blocks.4.bias",
+            "control_model.zero_convs.7.0.weight": "controlnet_blocks.7.weight",
+            "control_model.zero_convs.7.0.bias": "controlnet_blocks.7.bias",
+            "control_model.zero_convs.8.0.weight": "controlnet_blocks.8.weight",
+            "control_model.zero_convs.8.0.bias": "controlnet_blocks.7.bias",
+            "control_model.zero_convs.9.0.weight": "controlnet_blocks.9.weight",
+            "control_model.zero_convs.9.0.bias": "controlnet_blocks.7.bias",
+            "control_model.zero_convs.10.0.weight": "controlnet_blocks.10.weight",
+            "control_model.zero_convs.10.0.bias": "controlnet_blocks.7.bias",
+            "control_model.zero_convs.11.0.weight": "controlnet_blocks.11.weight",
+            "control_model.zero_convs.11.0.bias": "controlnet_blocks.7.bias",
+            "control_model.input_hint_block.0.weight": "controlnet_conv_in.blocks.0.weight",
+            "control_model.input_hint_block.0.bias": "controlnet_conv_in.blocks.0.bias",
+            "control_model.input_hint_block.2.weight": "controlnet_conv_in.blocks.2.weight",
+            "control_model.input_hint_block.2.bias": "controlnet_conv_in.blocks.2.bias",
+            "control_model.input_hint_block.4.weight": "controlnet_conv_in.blocks.4.weight",
+            "control_model.input_hint_block.4.bias": "controlnet_conv_in.blocks.4.bias",
+            "control_model.input_hint_block.6.weight": "controlnet_conv_in.blocks.6.weight",
+            "control_model.input_hint_block.6.bias": "controlnet_conv_in.blocks.6.bias",
+            "control_model.input_hint_block.8.weight": "controlnet_conv_in.blocks.8.weight",
+            "control_model.input_hint_block.8.bias": "controlnet_conv_in.blocks.8.bias",
+            "control_model.input_hint_block.10.weight": "controlnet_conv_in.blocks.10.weight",
+            "control_model.input_hint_block.10.bias": "controlnet_conv_in.blocks.10.bias",
+            "control_model.input_hint_block.12.weight": "controlnet_conv_in.blocks.12.weight",
+            "control_model.input_hint_block.12.bias": "controlnet_conv_in.blocks.12.bias",
+            "control_model.input_hint_block.14.weight": "controlnet_conv_in.blocks.14.weight",
+            "control_model.input_hint_block.14.bias": "controlnet_conv_in.blocks.14.bias",
+            "control_model.middle_block.0.in_layers.0.weight": "blocks.28.norm1.weight",
+            "control_model.middle_block.0.in_layers.0.bias": "blocks.28.norm1.bias",
+            "control_model.middle_block.0.in_layers.2.weight": "blocks.28.conv1.weight",
+            "control_model.middle_block.0.in_layers.2.bias": "blocks.28.conv1.bias",
+            "control_model.middle_block.0.emb_layers.1.weight": "blocks.28.time_emb_proj.weight",
+            "control_model.middle_block.0.emb_layers.1.bias": "blocks.28.time_emb_proj.bias",
+            "control_model.middle_block.0.out_layers.0.weight": "blocks.28.norm2.weight",
+            "control_model.middle_block.0.out_layers.0.bias": "blocks.28.norm2.bias",
+            "control_model.middle_block.0.out_layers.3.weight": "blocks.28.conv2.weight",
+            "control_model.middle_block.0.out_layers.3.bias": "blocks.28.conv2.bias",
+            "control_model.middle_block.1.norm.weight": "blocks.29.norm.weight",
+            "control_model.middle_block.1.norm.bias": "blocks.29.norm.bias",
+            "control_model.middle_block.1.proj_in.weight": "blocks.29.proj_in.weight",
+            "control_model.middle_block.1.proj_in.bias": "blocks.29.proj_in.bias",
+            "control_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight": "blocks.29.transformer_blocks.0.attn1.to_q.weight",
+            "control_model.middle_block.1.transformer_blocks.0.attn1.to_k.weight": "blocks.29.transformer_blocks.0.attn1.to_k.weight",
+            "control_model.middle_block.1.transformer_blocks.0.attn1.to_v.weight": "blocks.29.transformer_blocks.0.attn1.to_v.weight",
+            "control_model.middle_block.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.29.transformer_blocks.0.attn1.to_out.weight",
+            "control_model.middle_block.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.29.transformer_blocks.0.attn1.to_out.bias",
+            "control_model.middle_block.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.29.transformer_blocks.0.act_fn.proj.weight",
+            "control_model.middle_block.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.29.transformer_blocks.0.act_fn.proj.bias",
+            "control_model.middle_block.1.transformer_blocks.0.ff.net.2.weight": "blocks.29.transformer_blocks.0.ff.weight",
+            "control_model.middle_block.1.transformer_blocks.0.ff.net.2.bias": "blocks.29.transformer_blocks.0.ff.bias",
+            "control_model.middle_block.1.transformer_blocks.0.attn2.to_q.weight": "blocks.29.transformer_blocks.0.attn2.to_q.weight",
+            "control_model.middle_block.1.transformer_blocks.0.attn2.to_k.weight": "blocks.29.transformer_blocks.0.attn2.to_k.weight",
+            "control_model.middle_block.1.transformer_blocks.0.attn2.to_v.weight": "blocks.29.transformer_blocks.0.attn2.to_v.weight",
+            "control_model.middle_block.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.29.transformer_blocks.0.attn2.to_out.weight",
+            "control_model.middle_block.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.29.transformer_blocks.0.attn2.to_out.bias",
+            "control_model.middle_block.1.transformer_blocks.0.norm1.weight": "blocks.29.transformer_blocks.0.norm1.weight",
+            "control_model.middle_block.1.transformer_blocks.0.norm1.bias": "blocks.29.transformer_blocks.0.norm1.bias",
+            "control_model.middle_block.1.transformer_blocks.0.norm2.weight": "blocks.29.transformer_blocks.0.norm2.weight",
+            "control_model.middle_block.1.transformer_blocks.0.norm2.bias": "blocks.29.transformer_blocks.0.norm2.bias",
+            "control_model.middle_block.1.transformer_blocks.0.norm3.weight": "blocks.29.transformer_blocks.0.norm3.weight",
+            "control_model.middle_block.1.transformer_blocks.0.norm3.bias": "blocks.29.transformer_blocks.0.norm3.bias",
+            "control_model.middle_block.1.proj_out.weight": "blocks.29.proj_out.weight",
+            "control_model.middle_block.1.proj_out.bias": "blocks.29.proj_out.bias",
+            "control_model.middle_block.2.in_layers.0.weight": "blocks.30.norm1.weight",
+            "control_model.middle_block.2.in_layers.0.bias": "blocks.30.norm1.bias",
+            "control_model.middle_block.2.in_layers.2.weight": "blocks.30.conv1.weight",
+            "control_model.middle_block.2.in_layers.2.bias": "blocks.30.conv1.bias",
+            "control_model.middle_block.2.emb_layers.1.weight": "blocks.30.time_emb_proj.weight",
+            "control_model.middle_block.2.emb_layers.1.bias": "blocks.30.time_emb_proj.bias",
+            "control_model.middle_block.2.out_layers.0.weight": "blocks.30.norm2.weight",
+            "control_model.middle_block.2.out_layers.0.bias": "blocks.30.norm2.bias",
+            "control_model.middle_block.2.out_layers.3.weight": "blocks.30.conv2.weight",
+            "control_model.middle_block.2.out_layers.3.bias": "blocks.30.conv2.bias",
+            "control_model.middle_block_out.0.weight": "controlnet_blocks.12.weight",
+            "control_model.middle_block_out.0.bias": "controlnet_blocks.7.bias",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if ".proj_in." in name or ".proj_out." in name:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_

diffsynth/models/sd_ipadapter.py

@@ -0,0 +1,56 @@
+from .svd_image_encoder import SVDImageEncoder
+from .sdxl_ipadapter import IpAdapterImageProjModel, IpAdapterModule, SDXLIpAdapterStateDictConverter
+from transformers import CLIPImageProcessor
+import torch
+
+
+class IpAdapterCLIPImageEmbedder(SVDImageEncoder):
+    def __init__(self):
+        super().__init__()
+        self.image_processor = CLIPImageProcessor()
+
+    def forward(self, image):
+        pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values
+        pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype)
+        return super().forward(pixel_values)
+
+
+class SDIpAdapter(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        shape_list = [(768, 320)] * 2 + [(768, 640)] * 2 + [(768, 1280)] * 5 + [(768, 640)] * 3  + [(768, 320)] * 3 + [(768, 1280)] * 1
+        self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list])
+        self.image_proj = IpAdapterImageProjModel(cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4)
+        self.set_full_adapter()
+
+    def set_full_adapter(self):
+        block_ids = [1, 4, 9, 12, 17, 20, 40, 43, 46, 50, 53, 56, 60, 63, 66, 29]
+        self.call_block_id = {(i, 0): j for j, i in enumerate(block_ids)}
+
+    def set_less_adapter(self):
+        # IP-Adapter for SD v1.5 doesn't support this feature.
+        self.set_full_adapter(self)
+
+    def forward(self, hidden_states, scale=1.0):
+        hidden_states = self.image_proj(hidden_states)
+        hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1])
+        ip_kv_dict = {}
+        for (block_id, transformer_id) in self.call_block_id:
+            ipadapter_id = self.call_block_id[(block_id, transformer_id)]
+            ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
+            if block_id not in ip_kv_dict:
+                ip_kv_dict[block_id] = {}
+            ip_kv_dict[block_id][transformer_id] = {
+                "ip_k": ip_k,
+                "ip_v": ip_v,
+                "scale": scale
+            }
+        return ip_kv_dict
+
+    def state_dict_converter(self):
+        return SDIpAdapterStateDictConverter()
+
+
+class SDIpAdapterStateDictConverter(SDXLIpAdapterStateDictConverter):
+    def __init__(self):
+        pass

diffsynth/models/sd_lora.py

@@ -0,0 +1,60 @@
+import torch
+from .sd_unet import SDUNetStateDictConverter, SDUNet
+from .sd_text_encoder import SDTextEncoderStateDictConverter, SDTextEncoder
+
+
+class SDLoRA:
+    def __init__(self):
+        pass
+
+    def convert_state_dict(self, state_dict, lora_prefix="lora_unet_", alpha=1.0, device="cuda"):
+        special_keys = {
+            "down.blocks": "down_blocks",
+            "up.blocks": "up_blocks",
+            "mid.block": "mid_block",
+            "proj.in": "proj_in",
+            "proj.out": "proj_out",
+            "transformer.blocks": "transformer_blocks",
+            "to.q": "to_q",
+            "to.k": "to_k",
+            "to.v": "to_v",
+            "to.out": "to_out",
+        }
+        state_dict_ = {}
+        for key in state_dict:
+            if ".lora_up" not in key:
+                continue
+            if not key.startswith(lora_prefix):
+                continue
+            weight_up = state_dict[key].to(device="cuda", dtype=torch.float16)
+            weight_down = state_dict[key.replace(".lora_up", ".lora_down")].to(device="cuda", dtype=torch.float16)
+            if len(weight_up.shape) == 4:
+                weight_up = weight_up.squeeze(3).squeeze(2).to(torch.float32)
+                weight_down = weight_down.squeeze(3).squeeze(2).to(torch.float32)
+                lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
+            else:
+                lora_weight = alpha * torch.mm(weight_up, weight_down)
+            target_name = key.split(".")[0].replace("_", ".")[len(lora_prefix):] + ".weight"
+            for special_key in special_keys:
+                target_name = target_name.replace(special_key, special_keys[special_key])
+            state_dict_[target_name] = lora_weight.cpu()
+        return state_dict_
+    
+    def add_lora_to_unet(self, unet: SDUNet, state_dict_lora, alpha=1.0, device="cuda"):
+        state_dict_unet = unet.state_dict()
+        state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix="lora_unet_", alpha=alpha, device=device)
+        state_dict_lora = SDUNetStateDictConverter().from_diffusers(state_dict_lora)
+        if len(state_dict_lora) > 0:
+            for name in state_dict_lora:
+                state_dict_unet[name] += state_dict_lora[name].to(device=device)
+            unet.load_state_dict(state_dict_unet)
+
+    def add_lora_to_text_encoder(self, text_encoder: SDTextEncoder, state_dict_lora, alpha=1.0, device="cuda"):
+        state_dict_text_encoder = text_encoder.state_dict()
+        state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix="lora_te_", alpha=alpha, device=device)
+        state_dict_lora = SDTextEncoderStateDictConverter().from_diffusers(state_dict_lora)
+        if len(state_dict_lora) > 0:
+            for name in state_dict_lora:
+                state_dict_text_encoder[name] += state_dict_lora[name].to(device=device)
+            text_encoder.load_state_dict(state_dict_text_encoder)
+

diffsynth/models/sd_motion.py

@@ -0,0 +1,198 @@
+from .sd_unet import SDUNet, Attention, GEGLU
+import torch
+from einops import rearrange, repeat
+
+
+class TemporalTransformerBlock(torch.nn.Module):
+
+    def __init__(self, dim, num_attention_heads, attention_head_dim, max_position_embeddings=32):
+        super().__init__()
+
+        # 1. Self-Attn
+        self.pe1 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim))
+        self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
+
+        # 2. Cross-Attn
+        self.pe2 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim))
+        self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.attn2 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
+
+        # 3. Feed-forward
+        self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.act_fn = GEGLU(dim, dim * 4)
+        self.ff = torch.nn.Linear(dim * 4, dim)
+
+
+    def forward(self, hidden_states, batch_size=1):
+
+        # 1. Self-Attention
+        norm_hidden_states = self.norm1(hidden_states)
+        norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size)
+        attn_output = self.attn1(norm_hidden_states + self.pe1[:, :norm_hidden_states.shape[1]])
+        attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size)
+        hidden_states = attn_output + hidden_states
+
+        # 2. Cross-Attention
+        norm_hidden_states = self.norm2(hidden_states)
+        norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size)
+        attn_output = self.attn2(norm_hidden_states + self.pe2[:, :norm_hidden_states.shape[1]])
+        attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size)
+        hidden_states = attn_output + hidden_states
+
+        # 3. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+        ff_output = self.act_fn(norm_hidden_states)
+        ff_output = self.ff(ff_output)
+        hidden_states = ff_output + hidden_states
+
+        return hidden_states
+
+
+class TemporalBlock(torch.nn.Module):
+    
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
+        self.proj_in = torch.nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = torch.nn.ModuleList([
+            TemporalTransformerBlock(
+                inner_dim,
+                num_attention_heads,
+                attention_head_dim
+            )
+            for d in range(num_layers)
+        ])
+
+        self.proj_out = torch.nn.Linear(inner_dim, in_channels)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, batch_size=1):
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+        hidden_states = self.proj_in(hidden_states)
+
+        for block in self.transformer_blocks:
+            hidden_states = block(
+                hidden_states,
+                batch_size=batch_size
+            )
+
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+        hidden_states = hidden_states + residual
+
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class SDMotionModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.motion_modules = torch.nn.ModuleList([
+            TemporalBlock(8, 40, 320, eps=1e-6),
+            TemporalBlock(8, 40, 320, eps=1e-6),
+            TemporalBlock(8, 80, 640, eps=1e-6),
+            TemporalBlock(8, 80, 640, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 80, 640, eps=1e-6),
+            TemporalBlock(8, 80, 640, eps=1e-6),
+            TemporalBlock(8, 80, 640, eps=1e-6),
+            TemporalBlock(8, 40, 320, eps=1e-6),
+            TemporalBlock(8, 40, 320, eps=1e-6),
+            TemporalBlock(8, 40, 320, eps=1e-6),
+        ])
+        self.call_block_id = {
+            1: 0,
+            4: 1,
+            9: 2,
+            12: 3,
+            17: 4,
+            20: 5,
+            24: 6,
+            26: 7,
+            29: 8,
+            32: 9,
+            34: 10,
+            36: 11,
+            40: 12,
+            43: 13,
+            46: 14,
+            50: 15,
+            53: 16,
+            56: 17,
+            60: 18,
+            63: 19,
+            66: 20
+        }
+        
+    def forward(self):
+        pass
+
+    def state_dict_converter(self):
+        return SDMotionModelStateDictConverter()
+
+
+class SDMotionModelStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "norm": "norm",
+            "proj_in": "proj_in",
+            "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q",
+            "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k",
+            "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v",
+            "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out",
+            "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1",
+            "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q",
+            "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k",
+            "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v",
+            "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out",
+            "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2",
+            "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1",
+            "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2",
+            "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj",
+            "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff",
+            "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3",
+            "proj_out": "proj_out",
+        }
+        name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")])
+        name_list += sorted([i for i in state_dict if i.startswith("mid_block.")])
+        name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")])
+        state_dict_ = {}
+        last_prefix, module_id = "", -1
+        for name in name_list:
+            names = name.split(".")
+            prefix_index = names.index("temporal_transformer") + 1
+            prefix = ".".join(names[:prefix_index])
+            if prefix != last_prefix:
+                last_prefix = prefix
+                module_id += 1
+            middle_name = ".".join(names[prefix_index:-1])
+            suffix = names[-1]
+            if "pos_encoder" in names:
+                rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]])
+            else:
+                rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix])
+            state_dict_[rename] = state_dict[name]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/sd_text_encoder.py

@@ -0,0 +1,320 @@
+import torch
+from .attention import Attention
+
+
+class CLIPEncoderLayer(torch.nn.Module):
+    def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True):
+        super().__init__()
+        self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True)
+        self.layer_norm1 = torch.nn.LayerNorm(embed_dim)
+        self.layer_norm2 = torch.nn.LayerNorm(embed_dim)
+        self.fc1 = torch.nn.Linear(embed_dim, intermediate_size)
+        self.fc2 = torch.nn.Linear(intermediate_size, embed_dim)
+
+        self.use_quick_gelu = use_quick_gelu
+
+    def quickGELU(self, x):
+        return x * torch.sigmoid(1.702 * x)
+    
+    def forward(self, hidden_states, attn_mask=None):
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states = self.attn(hidden_states, attn_mask=attn_mask)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.fc1(hidden_states)
+        if self.use_quick_gelu:
+            hidden_states = self.quickGELU(hidden_states)
+        else:
+            hidden_states = torch.nn.functional.gelu(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+    
+
+class SDTextEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072):
+        super().__init__()
+
+        # token_embedding
+        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))
+
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)])
+
+        # attn_mask
+        self.attn_mask = self.attention_mask(max_position_embeddings)
+
+        # final_layer_norm
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+
+    def attention_mask(self, length):
+        mask = torch.empty(length, length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        return mask
+
+    def forward(self, input_ids, clip_skip=1):
+        embeds = self.token_embedding(input_ids) + self.position_embeds
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                break
+        embeds = self.final_layer_norm(embeds)
+        return embeds
+    
+    def state_dict_converter(self):
+        return SDTextEncoderStateDictConverter()
+
+
+class SDTextEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_model.embeddings.position_embedding.weight": "position_embeds",
+            "text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "text_model.final_layer_norm.bias": "final_layer_norm.bias"
+        }
+        attn_rename_dict = {
+            "self_attn.q_proj": "attn.to_q",
+            "self_attn.k_proj": "attn.to_k",
+            "self_attn.v_proj": "attn.to_v",
+            "self_attn.out_proj": "attn.to_out",
+            "layer_norm1": "layer_norm1",
+            "layer_norm2": "layer_norm2",
+            "mlp.fc1": "fc1",
+            "mlp.fc2": "fc2",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+            elif name.startswith("text_model.encoder.layers."):
+                param = state_dict[name]
+                names = name.split(".")
+                layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+                name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": "encoders.1.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.q_proj.bias": "encoders.1.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.q_proj.weight": "encoders.1.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.v_proj.bias": "encoders.1.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.v_proj.weight": "encoders.1.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm1.bias": "encoders.10.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm1.weight": "encoders.10.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm2.bias": "encoders.10.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm2.weight": "encoders.10.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc1.bias": "encoders.10.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc1.weight": "encoders.10.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc2.bias": "encoders.10.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc2.weight": "encoders.10.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.k_proj.bias": "encoders.10.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.k_proj.weight": "encoders.10.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.out_proj.bias": "encoders.10.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.out_proj.weight": "encoders.10.attn.to_out.weight",        
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.q_proj.bias": "encoders.10.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.q_proj.weight": "encoders.10.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.v_proj.bias": "encoders.10.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.v_proj.weight": "encoders.10.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm1.bias": "encoders.11.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm1.weight": "encoders.11.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm2.bias": "encoders.11.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm2.weight": "encoders.11.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1.bias": "encoders.11.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1.weight": "encoders.11.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.bias": "encoders.11.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.weight": "encoders.11.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj.bias": "encoders.11.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj.weight": "encoders.11.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.bias": "encoders.11.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.weight": "encoders.11.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.q_proj.bias": "encoders.11.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.q_proj.weight": "encoders.11.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.v_proj.bias": "encoders.11.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.v_proj.weight": "encoders.11.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm1.bias": "encoders.2.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm1.weight": "encoders.2.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm2.bias": "encoders.2.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm2.weight": "encoders.2.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc1.bias": "encoders.2.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc1.weight": "encoders.2.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2.bias": "encoders.2.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2.weight": "encoders.2.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj.bias": "encoders.2.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj.weight": "encoders.2.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj.bias": "encoders.2.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj.weight": "encoders.2.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.q_proj.bias": "encoders.2.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.q_proj.weight": "encoders.2.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.v_proj.bias": "encoders.2.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.v_proj.weight": "encoders.2.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm1.bias": "encoders.3.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm1.weight": "encoders.3.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm2.bias": "encoders.3.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm2.weight": "encoders.3.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc1.bias": "encoders.3.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc1.weight": "encoders.3.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2.bias": "encoders.3.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2.weight": "encoders.3.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj.bias": "encoders.3.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj.weight": "encoders.3.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj.bias": "encoders.3.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj.weight": "encoders.3.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.q_proj.bias": "encoders.3.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.q_proj.weight": "encoders.3.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.v_proj.bias": "encoders.3.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.v_proj.weight": "encoders.3.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm1.bias": "encoders.4.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm1.weight": "encoders.4.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm2.bias": "encoders.4.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm2.weight": "encoders.4.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.mlp.fc1.bias": "encoders.4.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.mlp.fc1.weight": "encoders.4.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.mlp.fc2.bias": "encoders.4.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.mlp.fc2.weight": "encoders.4.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.k_proj.bias": "encoders.4.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.k_proj.weight": "encoders.4.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.out_proj.bias": "encoders.4.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.out_proj.weight": "encoders.4.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.q_proj.bias": "encoders.4.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.q_proj.weight": "encoders.4.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.v_proj.bias": "encoders.4.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.v_proj.weight": "encoders.4.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.layer_norm1.bias": "encoders.5.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.layer_norm1.weight": "encoders.5.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.layer_norm2.bias": "encoders.5.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.layer_norm2.weight": "encoders.5.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.mlp.fc1.bias": "encoders.5.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.mlp.fc1.weight": "encoders.5.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.mlp.fc2.bias": "encoders.5.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.mlp.fc2.weight": "encoders.5.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.k_proj.bias": "encoders.5.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.k_proj.weight": "encoders.5.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.out_proj.bias": "encoders.5.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.out_proj.weight": "encoders.5.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.q_proj.bias": "encoders.5.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.q_proj.weight": "encoders.5.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.v_proj.bias": "encoders.5.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.v_proj.weight": "encoders.5.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.layer_norm1.bias": "encoders.6.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.layer_norm1.weight": "encoders.6.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.layer_norm2.bias": "encoders.6.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.layer_norm2.weight": "encoders.6.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.mlp.fc1.bias": "encoders.6.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.mlp.fc1.weight": "encoders.6.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.mlp.fc2.bias": "encoders.6.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.mlp.fc2.weight": "encoders.6.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.k_proj.bias": "encoders.6.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.k_proj.weight": "encoders.6.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.out_proj.bias": "encoders.6.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.out_proj.weight": "encoders.6.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.q_proj.bias": "encoders.6.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.q_proj.weight": "encoders.6.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.v_proj.bias": "encoders.6.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.v_proj.weight": "encoders.6.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.layer_norm1.bias": "encoders.7.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.layer_norm1.weight": "encoders.7.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.layer_norm2.bias": "encoders.7.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.layer_norm2.weight": "encoders.7.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.mlp.fc1.bias": "encoders.7.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.mlp.fc1.weight": "encoders.7.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.mlp.fc2.bias": "encoders.7.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.mlp.fc2.weight": "encoders.7.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.k_proj.bias": "encoders.7.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.k_proj.weight": "encoders.7.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.out_proj.bias": "encoders.7.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.out_proj.weight": "encoders.7.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.q_proj.bias": "encoders.7.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.q_proj.weight": "encoders.7.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.v_proj.bias": "encoders.7.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.v_proj.weight": "encoders.7.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm1.bias": "encoders.8.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm1.weight": "encoders.8.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm2.bias": "encoders.8.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm2.weight": "encoders.8.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc1.bias": "encoders.8.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc1.weight": "encoders.8.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc2.bias": "encoders.8.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc2.weight": "encoders.8.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.k_proj.bias": "encoders.8.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.k_proj.weight": "encoders.8.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.out_proj.bias": "encoders.8.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.out_proj.weight": "encoders.8.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.q_proj.bias": "encoders.8.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.q_proj.weight": "encoders.8.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.v_proj.bias": "encoders.8.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.v_proj.weight": "encoders.8.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm1.bias": "encoders.9.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias",
+            "cond_stage_model.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight": "position_embeds"
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+        return state_dict_

diffsynth/models/sd_vae_decoder.py

@@ -0,0 +1,332 @@
+import torch
+from .attention import Attention
+from .sd_unet import ResnetBlock, UpSampler
+from .tiler import TileWorker
+
+
+class VAEAttentionBlock(torch.nn.Module):
+
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
+
+        self.transformer_blocks = torch.nn.ModuleList([
+            Attention(
+                inner_dim,
+                num_attention_heads,
+                attention_head_dim,
+                bias_q=True,
+                bias_kv=True,
+                bias_out=True
+            )
+            for d in range(num_layers)
+        ])
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack):
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+
+        for block in self.transformer_blocks:
+            hidden_states = block(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+        hidden_states = hidden_states + residual
+
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class SDVAEDecoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.18215
+        self.post_quant_conv = torch.nn.Conv2d(4, 4, kernel_size=1)
+        self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            UpSampler(256),
+            # UpDecoderBlock2D
+            ResnetBlock(256, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1)
+    
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+
+        # 1. pre-process
+        sample = sample / self.scaling_factor
+        hidden_states = self.post_quant_conv(sample)
+        hidden_states = self.conv_in(hidden_states)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+    
+    def state_dict_converter(self):
+        return SDVAEDecoderStateDictConverter()
+    
+
+class SDVAEDecoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock'
+        ]
+
+        # Rename each parameter
+        local_rename_dict = {
+            "post_quant_conv": "post_quant_conv",
+            "decoder.conv_in": "conv_in",
+            "decoder.mid_block.attentions.0.group_norm": "blocks.1.norm",
+            "decoder.mid_block.attentions.0.to_q": "blocks.1.transformer_blocks.0.to_q",
+            "decoder.mid_block.attentions.0.to_k": "blocks.1.transformer_blocks.0.to_k",
+            "decoder.mid_block.attentions.0.to_v": "blocks.1.transformer_blocks.0.to_v",
+            "decoder.mid_block.attentions.0.to_out.0": "blocks.1.transformer_blocks.0.to_out",
+            "decoder.mid_block.resnets.0.norm1": "blocks.0.norm1",
+            "decoder.mid_block.resnets.0.conv1": "blocks.0.conv1",
+            "decoder.mid_block.resnets.0.norm2": "blocks.0.norm2",
+            "decoder.mid_block.resnets.0.conv2": "blocks.0.conv2",
+            "decoder.mid_block.resnets.1.norm1": "blocks.2.norm1",
+            "decoder.mid_block.resnets.1.conv1": "blocks.2.conv1",
+            "decoder.mid_block.resnets.1.norm2": "blocks.2.norm2",
+            "decoder.mid_block.resnets.1.conv2": "blocks.2.conv2",
+            "decoder.conv_norm_out": "conv_norm_out",
+            "decoder.conv_out": "conv_out",
+        }
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": 2, "DownSampler": 2, "UpSampler": 2}
+        last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            name_prefix = ".".join(names[:-1])
+            if name_prefix in local_rename_dict:
+                rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1]
+            elif name.startswith("decoder.up_blocks"):
+                block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]]
+                block_type_with_id = ".".join(names[:5])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:5])
+                names = ["blocks", str(block_id[block_type])] + names[5:]
+                rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "first_stage_model.decoder.conv_in.bias": "conv_in.bias",
+            "first_stage_model.decoder.conv_in.weight": "conv_in.weight",
+            "first_stage_model.decoder.conv_out.bias": "conv_out.bias",
+            "first_stage_model.decoder.conv_out.weight": "conv_out.weight",
+            "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias",
+            "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight",
+            "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias",
+            "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight",
+            "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias",    
+            "first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight",
+            "first_stage_model.decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias",
+            "first_stage_model.decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight",
+            "first_stage_model.decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias",
+            "first_stage_model.decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight",
+            "first_stage_model.decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias",
+            "first_stage_model.decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight",
+            "first_stage_model.decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias",
+            "first_stage_model.decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight",
+            "first_stage_model.decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias",
+            "first_stage_model.decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight",
+            "first_stage_model.decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias",
+            "first_stage_model.decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight",
+            "first_stage_model.decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias",
+            "first_stage_model.decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight",
+            "first_stage_model.decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias",
+            "first_stage_model.decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight",
+            "first_stage_model.decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias",
+            "first_stage_model.decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight",
+            "first_stage_model.decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias",
+            "first_stage_model.decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight",
+            "first_stage_model.decoder.norm_out.bias": "conv_norm_out.bias",
+            "first_stage_model.decoder.norm_out.weight": "conv_norm_out.weight",
+            "first_stage_model.decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias",
+            "first_stage_model.decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight",
+            "first_stage_model.decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias",
+            "first_stage_model.decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight",
+            "first_stage_model.decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias",
+            "first_stage_model.decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight",       
+            "first_stage_model.decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias",
+            "first_stage_model.decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight",
+            "first_stage_model.decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias",
+            "first_stage_model.decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight",
+            "first_stage_model.decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias",
+            "first_stage_model.decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight",
+            "first_stage_model.decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias",
+            "first_stage_model.decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight",
+            "first_stage_model.decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias",
+            "first_stage_model.decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight",
+            "first_stage_model.decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias",
+            "first_stage_model.decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight",
+            "first_stage_model.decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias",
+            "first_stage_model.decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight",
+            "first_stage_model.decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias",
+            "first_stage_model.decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight",
+            "first_stage_model.decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias",
+            "first_stage_model.decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight",
+            "first_stage_model.decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias",
+            "first_stage_model.decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight",
+            "first_stage_model.decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias",
+            "first_stage_model.decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight",
+            "first_stage_model.decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias",
+            "first_stage_model.decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight",
+            "first_stage_model.decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias",
+            "first_stage_model.decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight",       
+            "first_stage_model.decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias",
+            "first_stage_model.decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight",
+            "first_stage_model.decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias",
+            "first_stage_model.decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight",
+            "first_stage_model.decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias",
+            "first_stage_model.decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight",
+            "first_stage_model.decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias",
+            "first_stage_model.decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight",
+            "first_stage_model.decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias",
+            "first_stage_model.decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight",
+            "first_stage_model.decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias",
+            "first_stage_model.decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight",
+            "first_stage_model.decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias",
+            "first_stage_model.decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight",
+            "first_stage_model.decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias",
+            "first_stage_model.decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight",
+            "first_stage_model.decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias",
+            "first_stage_model.decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight",
+            "first_stage_model.decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias",
+            "first_stage_model.decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight",
+            "first_stage_model.decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias",
+            "first_stage_model.decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight",
+            "first_stage_model.decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias",
+            "first_stage_model.decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight",
+            "first_stage_model.decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias",
+            "first_stage_model.decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight",
+            "first_stage_model.decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias",
+            "first_stage_model.decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight",
+            "first_stage_model.decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias",
+            "first_stage_model.decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight",
+            "first_stage_model.decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias",
+            "first_stage_model.decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight",
+            "first_stage_model.decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias",
+            "first_stage_model.decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight",
+            "first_stage_model.decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias",
+            "first_stage_model.decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight",
+            "first_stage_model.decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias",
+            "first_stage_model.decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight",
+            "first_stage_model.decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias",
+            "first_stage_model.decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight",
+            "first_stage_model.decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias",
+            "first_stage_model.decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight",
+            "first_stage_model.decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias",
+            "first_stage_model.decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight",
+            "first_stage_model.decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias",
+            "first_stage_model.decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight",
+            "first_stage_model.decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias",
+            "first_stage_model.decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight",
+            "first_stage_model.decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "first_stage_model.decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "first_stage_model.decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "first_stage_model.decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "first_stage_model.decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "first_stage_model.decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "first_stage_model.decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "first_stage_model.decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "first_stage_model.decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "first_stage_model.decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "first_stage_model.decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "first_stage_model.decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "first_stage_model.decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "first_stage_model.decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "first_stage_model.decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "first_stage_model.decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias",
+            "first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight",
+            "first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias",
+            "first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight",
+            "first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias",
+            "first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight",
+            "first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias",
+            "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight",
+            "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias",
+            "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight",
+            "first_stage_model.post_quant_conv.bias": "post_quant_conv.bias",
+            "first_stage_model.post_quant_conv.weight": "post_quant_conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_

diffsynth/models/sd_vae_encoder.py

@@ -0,0 +1,278 @@
+import torch
+from .sd_unet import ResnetBlock, DownSampler
+from .sd_vae_decoder import VAEAttentionBlock
+from .tiler import TileWorker
+from einops import rearrange
+
+
+class SDVAEEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.18215
+        self.quant_conv = torch.nn.Conv2d(8, 8, kernel_size=1)
+        self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # DownEncoderBlock2D
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            DownSampler(128, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(128, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            DownSampler(256, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(256, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            DownSampler(512, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(512, 8, kernel_size=3, padding=1)
+
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+        
+        # 1. pre-process
+        hidden_states = self.conv_in(sample)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        hidden_states = self.quant_conv(hidden_states)
+        hidden_states = hidden_states[:, :4]
+        hidden_states *= self.scaling_factor
+
+        return hidden_states
+    
+    def encode_video(self, sample, batch_size=8):
+        B = sample.shape[0]
+        hidden_states = []
+
+        for i in range(0, sample.shape[2], batch_size):
+
+            j = min(i + batch_size, sample.shape[2])
+            sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W")
+
+            hidden_states_batch = self(sample_batch)
+            hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B)
+
+            hidden_states.append(hidden_states_batch)
+        
+        hidden_states = torch.concat(hidden_states, dim=2)
+        return hidden_states
+    
+    def state_dict_converter(self):
+        return SDVAEEncoderStateDictConverter()
+    
+
+class SDVAEEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            'ResnetBlock', 'ResnetBlock', 'DownSampler',
+            'ResnetBlock', 'ResnetBlock', 'DownSampler',
+            'ResnetBlock', 'ResnetBlock', 'DownSampler',
+            'ResnetBlock', 'ResnetBlock',
+            'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock'
+        ]
+
+        # Rename each parameter
+        local_rename_dict = {
+            "quant_conv": "quant_conv",
+            "encoder.conv_in": "conv_in",
+            "encoder.mid_block.attentions.0.group_norm": "blocks.12.norm",
+            "encoder.mid_block.attentions.0.to_q": "blocks.12.transformer_blocks.0.to_q",
+            "encoder.mid_block.attentions.0.to_k": "blocks.12.transformer_blocks.0.to_k",
+            "encoder.mid_block.attentions.0.to_v": "blocks.12.transformer_blocks.0.to_v",
+            "encoder.mid_block.attentions.0.to_out.0": "blocks.12.transformer_blocks.0.to_out",
+            "encoder.mid_block.resnets.0.norm1": "blocks.11.norm1",
+            "encoder.mid_block.resnets.0.conv1": "blocks.11.conv1",
+            "encoder.mid_block.resnets.0.norm2": "blocks.11.norm2",
+            "encoder.mid_block.resnets.0.conv2": "blocks.11.conv2",
+            "encoder.mid_block.resnets.1.norm1": "blocks.13.norm1",
+            "encoder.mid_block.resnets.1.conv1": "blocks.13.conv1",
+            "encoder.mid_block.resnets.1.norm2": "blocks.13.norm2",
+            "encoder.mid_block.resnets.1.conv2": "blocks.13.conv2",
+            "encoder.conv_norm_out": "conv_norm_out",
+            "encoder.conv_out": "conv_out",
+        }
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": -1, "DownSampler": -1, "UpSampler": -1}
+        last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            name_prefix = ".".join(names[:-1])
+            if name_prefix in local_rename_dict:
+                rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1]
+            elif name.startswith("encoder.down_blocks"):
+                block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]]
+                block_type_with_id = ".".join(names[:5])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:5])
+                names = ["blocks", str(block_id[block_type])] + names[5:]
+                rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "first_stage_model.encoder.conv_in.bias": "conv_in.bias",
+            "first_stage_model.encoder.conv_in.weight": "conv_in.weight",
+            "first_stage_model.encoder.conv_out.bias": "conv_out.bias",
+            "first_stage_model.encoder.conv_out.weight": "conv_out.weight",
+            "first_stage_model.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias",
+            "first_stage_model.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight",
+            "first_stage_model.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias",
+            "first_stage_model.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight",
+            "first_stage_model.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias",
+            "first_stage_model.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight",
+            "first_stage_model.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias",
+            "first_stage_model.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight",
+            "first_stage_model.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias",
+            "first_stage_model.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight",
+            "first_stage_model.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias",
+            "first_stage_model.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight",
+            "first_stage_model.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias",
+            "first_stage_model.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight",
+            "first_stage_model.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias",
+            "first_stage_model.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight",
+            "first_stage_model.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias",
+            "first_stage_model.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight",
+            "first_stage_model.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "first_stage_model.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "first_stage_model.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "first_stage_model.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "first_stage_model.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias",
+            "first_stage_model.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight",
+            "first_stage_model.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "first_stage_model.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "first_stage_model.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "first_stage_model.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "first_stage_model.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "first_stage_model.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "first_stage_model.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "first_stage_model.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "first_stage_model.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "first_stage_model.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "first_stage_model.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "first_stage_model.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "first_stage_model.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias",
+            "first_stage_model.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight",
+            "first_stage_model.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias",
+            "first_stage_model.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight",
+            "first_stage_model.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias",
+            "first_stage_model.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight",
+            "first_stage_model.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias",
+            "first_stage_model.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight",
+            "first_stage_model.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias",
+            "first_stage_model.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight",
+            "first_stage_model.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias",
+            "first_stage_model.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight",
+            "first_stage_model.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias",
+            "first_stage_model.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight",
+            "first_stage_model.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias",
+            "first_stage_model.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight",
+            "first_stage_model.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias",
+            "first_stage_model.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight",
+            "first_stage_model.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias",
+            "first_stage_model.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight",
+            "first_stage_model.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias",
+            "first_stage_model.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight",
+            "first_stage_model.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias",
+            "first_stage_model.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight",
+            "first_stage_model.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias",
+            "first_stage_model.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight",
+            "first_stage_model.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias",
+            "first_stage_model.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight",
+            "first_stage_model.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias",
+            "first_stage_model.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight",
+            "first_stage_model.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias",
+            "first_stage_model.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight",
+            "first_stage_model.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias",
+            "first_stage_model.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight",
+            "first_stage_model.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias",
+            "first_stage_model.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight",
+            "first_stage_model.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias",
+            "first_stage_model.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight",
+            "first_stage_model.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias",
+            "first_stage_model.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight",
+            "first_stage_model.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias",
+            "first_stage_model.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight",
+            "first_stage_model.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias",       
+            "first_stage_model.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight",   
+            "first_stage_model.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias",
+            "first_stage_model.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight",
+            "first_stage_model.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias",
+            "first_stage_model.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight",
+            "first_stage_model.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias",
+            "first_stage_model.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight",
+            "first_stage_model.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias",
+            "first_stage_model.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight",
+            "first_stage_model.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias",
+            "first_stage_model.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight",
+            "first_stage_model.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias",
+            "first_stage_model.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight",
+            "first_stage_model.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias",
+            "first_stage_model.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight",
+            "first_stage_model.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias",
+            "first_stage_model.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight",
+            "first_stage_model.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias",
+            "first_stage_model.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight",
+            "first_stage_model.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias",
+            "first_stage_model.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight",
+            "first_stage_model.encoder.norm_out.bias": "conv_norm_out.bias",
+            "first_stage_model.encoder.norm_out.weight": "conv_norm_out.weight",
+            "first_stage_model.quant_conv.bias": "quant_conv.bias",
+            "first_stage_model.quant_conv.weight": "quant_conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_

diffsynth/models/sdxl_ipadapter.py

@@ -0,0 +1,121 @@
+from .svd_image_encoder import SVDImageEncoder
+from transformers import CLIPImageProcessor
+import torch
+
+
+class IpAdapterXLCLIPImageEmbedder(SVDImageEncoder):
+    def __init__(self):
+        super().__init__(embed_dim=1664, encoder_intermediate_size=8192, projection_dim=1280, num_encoder_layers=48, num_heads=16, head_dim=104)
+        self.image_processor = CLIPImageProcessor()
+
+    def forward(self, image):
+        pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values
+        pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype)
+        return super().forward(pixel_values)
+
+
+class IpAdapterImageProjModel(torch.nn.Module):
+    def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280, clip_extra_context_tokens=4):
+        super().__init__()
+        self.cross_attention_dim = cross_attention_dim
+        self.clip_extra_context_tokens = clip_extra_context_tokens
+        self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
+        self.norm = torch.nn.LayerNorm(cross_attention_dim)
+
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
+        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
+        return clip_extra_context_tokens
+
+
+class IpAdapterModule(torch.nn.Module):
+    def __init__(self, input_dim, output_dim):
+        super().__init__()
+        self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+        self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+
+    def forward(self, hidden_states):
+        ip_k = self.to_k_ip(hidden_states)
+        ip_v = self.to_v_ip(hidden_states)
+        return ip_k, ip_v
+
+
+class SDXLIpAdapter(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        shape_list = [(2048, 640)] * 4 + [(2048, 1280)] * 50 + [(2048, 640)] * 6 + [(2048, 1280)] * 10
+        self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list])
+        self.image_proj = IpAdapterImageProjModel()
+        self.set_full_adapter()
+
+    def set_full_adapter(self):
+        map_list = sum([
+            [(7, i) for i in range(2)],
+            [(10, i) for i in range(2)],
+            [(15, i) for i in range(10)],
+            [(18, i) for i in range(10)],
+            [(25, i) for i in range(10)],
+            [(28, i) for i in range(10)],
+            [(31, i) for i in range(10)],
+            [(35, i) for i in range(2)],
+            [(38, i) for i in range(2)],
+            [(41, i) for i in range(2)],
+            [(21, i) for i in range(10)],
+        ], [])
+        self.call_block_id = {i: j for j, i in enumerate(map_list)}
+
+    def set_less_adapter(self):
+        map_list = sum([
+            [(7, i) for i in range(2)],
+            [(10, i) for i in range(2)],
+            [(15, i) for i in range(10)],
+            [(18, i) for i in range(10)],
+            [(25, i) for i in range(10)],
+            [(28, i) for i in range(10)],
+            [(31, i) for i in range(10)],
+            [(35, i) for i in range(2)],
+            [(38, i) for i in range(2)],
+            [(41, i) for i in range(2)],
+            [(21, i) for i in range(10)],
+        ], [])
+        self.call_block_id = {i: j for j, i in enumerate(map_list) if j>=34 and j<44}
+
+    def forward(self, hidden_states, scale=1.0):
+        hidden_states = self.image_proj(hidden_states)
+        hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1])
+        ip_kv_dict = {}
+        for (block_id, transformer_id) in self.call_block_id:
+            ipadapter_id = self.call_block_id[(block_id, transformer_id)]
+            ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
+            if block_id not in ip_kv_dict:
+                ip_kv_dict[block_id] = {}
+            ip_kv_dict[block_id][transformer_id] = {
+                "ip_k": ip_k,
+                "ip_v": ip_v,
+                "scale": scale
+            }
+        return ip_kv_dict
+
+    def state_dict_converter(self):
+        return SDXLIpAdapterStateDictConverter()
+
+
+class SDXLIpAdapterStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict["ip_adapter"]:
+            names = name.split(".")
+            layer_id = str(int(names[0]) // 2)
+            name_ = ".".join(["ipadapter_modules"] + [layer_id] + names[1:])
+            state_dict_[name_] = state_dict["ip_adapter"][name]
+        for name in state_dict["image_proj"]:
+            name_ = "image_proj." + name
+            state_dict_[name_] = state_dict["image_proj"][name]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+

diffsynth/models/sdxl_motion.py

@@ -0,0 +1,103 @@
+from .sd_motion import TemporalBlock
+import torch
+
+
+
+class SDXLMotionModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.motion_modules = torch.nn.ModuleList([
+            TemporalBlock(8, 320//8, 320, eps=1e-6),
+            TemporalBlock(8, 320//8, 320, eps=1e-6),
+
+            TemporalBlock(8, 640//8, 640, eps=1e-6),
+            TemporalBlock(8, 640//8, 640, eps=1e-6),
+
+            TemporalBlock(8, 1280//8, 1280, eps=1e-6),
+            TemporalBlock(8, 1280//8, 1280, eps=1e-6),
+
+            TemporalBlock(8, 1280//8, 1280, eps=1e-6),
+            TemporalBlock(8, 1280//8, 1280, eps=1e-6),
+            TemporalBlock(8, 1280//8, 1280, eps=1e-6),
+
+            TemporalBlock(8, 640//8, 640, eps=1e-6),
+            TemporalBlock(8, 640//8, 640, eps=1e-6),
+            TemporalBlock(8, 640//8, 640, eps=1e-6),
+
+            TemporalBlock(8, 320//8, 320, eps=1e-6),
+            TemporalBlock(8, 320//8, 320, eps=1e-6),
+            TemporalBlock(8, 320//8, 320, eps=1e-6),
+        ])
+        self.call_block_id = {
+            0: 0,
+            2: 1,
+            7: 2,
+            10: 3,
+            15: 4,
+            18: 5,
+            25: 6,
+            28: 7,
+            31: 8,
+            35: 9,
+            38: 10,
+            41: 11,
+            44: 12,
+            46: 13,
+            48: 14,
+        }
+        
+    def forward(self):
+        pass
+
+    def state_dict_converter(self):
+        return SDMotionModelStateDictConverter()
+
+
+class SDMotionModelStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "norm": "norm",
+            "proj_in": "proj_in",
+            "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q",
+            "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k",
+            "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v",
+            "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out",
+            "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1",
+            "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q",
+            "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k",
+            "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v",
+            "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out",
+            "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2",
+            "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1",
+            "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2",
+            "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj",
+            "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff",
+            "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3",
+            "proj_out": "proj_out",
+        }
+        name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")])
+        name_list += sorted([i for i in state_dict if i.startswith("mid_block.")])
+        name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")])
+        state_dict_ = {}
+        last_prefix, module_id = "", -1
+        for name in name_list:
+            names = name.split(".")
+            prefix_index = names.index("temporal_transformer") + 1
+            prefix = ".".join(names[:prefix_index])
+            if prefix != last_prefix:
+                last_prefix = prefix
+                module_id += 1
+            middle_name = ".".join(names[prefix_index:-1])
+            suffix = names[-1]
+            if "pos_encoder" in names:
+                rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]])
+            else:
+                rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix])
+            state_dict_[rename] = state_dict[name]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/sdxl_text_encoder.py

@@ -0,0 +1,757 @@
+import torch
+from .sd_text_encoder import CLIPEncoderLayer
+    
+
+class SDXLTextEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=11, encoder_intermediate_size=3072):
+        super().__init__()
+
+        # token_embedding
+        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))
+
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)])
+
+        # attn_mask
+        self.attn_mask = self.attention_mask(max_position_embeddings)
+
+        # The text encoder is different to that in Stable Diffusion 1.x.
+        # It does not include final_layer_norm.
+
+    def attention_mask(self, length):
+        mask = torch.empty(length, length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        return mask
+
+    def forward(self, input_ids, clip_skip=1):
+        embeds = self.token_embedding(input_ids) + self.position_embeds
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                break
+        return embeds
+    
+    def state_dict_converter(self):
+        return SDXLTextEncoderStateDictConverter()
+    
+
+class SDXLTextEncoder2(torch.nn.Module):
+    def __init__(self, embed_dim=1280, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=32, encoder_intermediate_size=5120):
+        super().__init__()
+
+        # token_embedding
+        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))
+
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=20, head_dim=64, use_quick_gelu=False) for _ in range(num_encoder_layers)])
+
+        # attn_mask
+        self.attn_mask = self.attention_mask(max_position_embeddings)
+
+        # final_layer_norm
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+
+        # text_projection
+        self.text_projection = torch.nn.Linear(embed_dim, embed_dim, bias=False)
+
+    def attention_mask(self, length):
+        mask = torch.empty(length, length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        return mask
+
+    def forward(self, input_ids, clip_skip=2):
+        embeds = self.token_embedding(input_ids) + self.position_embeds
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                hidden_states = embeds
+        embeds = self.final_layer_norm(embeds)
+        pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)]
+        pooled_embeds = self.text_projection(pooled_embeds)
+        return pooled_embeds, hidden_states
+    
+    def state_dict_converter(self):
+        return SDXLTextEncoder2StateDictConverter()
+
+
+class SDXLTextEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_model.embeddings.position_embedding.weight": "position_embeds",
+            "text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "text_model.final_layer_norm.bias": "final_layer_norm.bias"
+        }
+        attn_rename_dict = {
+            "self_attn.q_proj": "attn.to_q",
+            "self_attn.k_proj": "attn.to_k",
+            "self_attn.v_proj": "attn.to_v",
+            "self_attn.out_proj": "attn.to_out",
+            "layer_norm1": "layer_norm1",
+            "layer_norm2": "layer_norm2",
+            "mlp.fc1": "fc1",
+            "mlp.fc2": "fc2",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+            elif name.startswith("text_model.encoder.layers."):
+                param = state_dict[name]
+                names = name.split(".")
+                layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+                name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight": "position_embeds",
+            "conditioner.embedders.0.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": "encoders.1.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.q_proj.bias": "encoders.1.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.q_proj.weight": "encoders.1.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.v_proj.bias": "encoders.1.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.1.self_attn.v_proj.weight": "encoders.1.attn.to_v.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.layer_norm1.bias": "encoders.10.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.layer_norm1.weight": "encoders.10.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.layer_norm2.bias": "encoders.10.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.layer_norm2.weight": "encoders.10.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.mlp.fc1.bias": "encoders.10.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.mlp.fc1.weight": "encoders.10.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.mlp.fc2.bias": "encoders.10.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.mlp.fc2.weight": "encoders.10.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.k_proj.bias": "encoders.10.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.k_proj.weight": "encoders.10.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.out_proj.bias": "encoders.10.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.out_proj.weight": "encoders.10.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.q_proj.bias": "encoders.10.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.q_proj.weight": "encoders.10.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.v_proj.bias": "encoders.10.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.10.self_attn.v_proj.weight": "encoders.10.attn.to_v.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.layer_norm1.bias": "encoders.2.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.layer_norm1.weight": "encoders.2.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.layer_norm2.bias": "encoders.2.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.layer_norm2.weight": "encoders.2.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.mlp.fc1.bias": "encoders.2.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.mlp.fc1.weight": "encoders.2.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.mlp.fc2.bias": "encoders.2.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.mlp.fc2.weight": "encoders.2.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.k_proj.bias": "encoders.2.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.k_proj.weight": "encoders.2.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.out_proj.bias": "encoders.2.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.out_proj.weight": "encoders.2.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.q_proj.bias": "encoders.2.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.q_proj.weight": "encoders.2.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.v_proj.bias": "encoders.2.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.2.self_attn.v_proj.weight": "encoders.2.attn.to_v.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.layer_norm1.bias": "encoders.3.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.layer_norm1.weight": "encoders.3.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.layer_norm2.bias": "encoders.3.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.layer_norm2.weight": "encoders.3.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.mlp.fc1.bias": "encoders.3.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.mlp.fc1.weight": "encoders.3.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.mlp.fc2.bias": "encoders.3.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.mlp.fc2.weight": "encoders.3.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.self_attn.k_proj.bias": "encoders.3.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.self_attn.k_proj.weight": "encoders.3.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.self_attn.out_proj.bias": "encoders.3.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.self_attn.out_proj.weight": "encoders.3.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.self_attn.q_proj.bias": "encoders.3.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.self_attn.q_proj.weight": "encoders.3.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.self_attn.v_proj.bias": "encoders.3.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.3.self_attn.v_proj.weight": "encoders.3.attn.to_v.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.layer_norm1.bias": "encoders.4.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.layer_norm1.weight": "encoders.4.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.layer_norm2.bias": "encoders.4.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.layer_norm2.weight": "encoders.4.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.mlp.fc1.bias": "encoders.4.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.mlp.fc1.weight": "encoders.4.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.mlp.fc2.bias": "encoders.4.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.mlp.fc2.weight": "encoders.4.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.k_proj.bias": "encoders.4.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.k_proj.weight": "encoders.4.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.out_proj.bias": "encoders.4.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.out_proj.weight": "encoders.4.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.q_proj.bias": "encoders.4.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.q_proj.weight": "encoders.4.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.v_proj.bias": "encoders.4.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.4.self_attn.v_proj.weight": "encoders.4.attn.to_v.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.layer_norm1.bias": "encoders.5.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.layer_norm1.weight": "encoders.5.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.layer_norm2.bias": "encoders.5.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.layer_norm2.weight": "encoders.5.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.mlp.fc1.bias": "encoders.5.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.mlp.fc1.weight": "encoders.5.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.mlp.fc2.bias": "encoders.5.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.mlp.fc2.weight": "encoders.5.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.self_attn.k_proj.bias": "encoders.5.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.self_attn.k_proj.weight": "encoders.5.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.self_attn.out_proj.bias": "encoders.5.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.self_attn.out_proj.weight": "encoders.5.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.self_attn.q_proj.bias": "encoders.5.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.self_attn.q_proj.weight": "encoders.5.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.self_attn.v_proj.bias": "encoders.5.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.5.self_attn.v_proj.weight": "encoders.5.attn.to_v.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.layer_norm1.bias": "encoders.6.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.layer_norm1.weight": "encoders.6.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.layer_norm2.bias": "encoders.6.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.layer_norm2.weight": "encoders.6.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc1.bias": "encoders.6.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc1.weight": "encoders.6.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc2.bias": "encoders.6.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.mlp.fc2.weight": "encoders.6.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.k_proj.bias": "encoders.6.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.k_proj.weight": "encoders.6.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.out_proj.bias": "encoders.6.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.out_proj.weight": "encoders.6.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.q_proj.bias": "encoders.6.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.q_proj.weight": "encoders.6.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.v_proj.bias": "encoders.6.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.6.self_attn.v_proj.weight": "encoders.6.attn.to_v.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.layer_norm1.bias": "encoders.7.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.layer_norm1.weight": "encoders.7.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.layer_norm2.bias": "encoders.7.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.layer_norm2.weight": "encoders.7.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc1.bias": "encoders.7.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc1.weight": "encoders.7.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc2.bias": "encoders.7.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.mlp.fc2.weight": "encoders.7.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.k_proj.bias": "encoders.7.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.k_proj.weight": "encoders.7.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.out_proj.bias": "encoders.7.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.out_proj.weight": "encoders.7.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.q_proj.bias": "encoders.7.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.q_proj.weight": "encoders.7.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.v_proj.bias": "encoders.7.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.7.self_attn.v_proj.weight": "encoders.7.attn.to_v.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm1.bias": "encoders.8.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm1.weight": "encoders.8.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm2.bias": "encoders.8.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.layer_norm2.weight": "encoders.8.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc1.bias": "encoders.8.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc1.weight": "encoders.8.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc2.bias": "encoders.8.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.mlp.fc2.weight": "encoders.8.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.k_proj.bias": "encoders.8.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.k_proj.weight": "encoders.8.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.out_proj.bias": "encoders.8.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.out_proj.weight": "encoders.8.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.q_proj.bias": "encoders.8.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.q_proj.weight": "encoders.8.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.v_proj.bias": "encoders.8.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.8.self_attn.v_proj.weight": "encoders.8.attn.to_v.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm1.bias": "encoders.9.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
+
+
+class SDXLTextEncoder2StateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_model.embeddings.position_embedding.weight": "position_embeds",
+            "text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "text_model.final_layer_norm.bias": "final_layer_norm.bias",
+            "text_projection.weight": "text_projection.weight"
+        }
+        attn_rename_dict = {
+            "self_attn.q_proj": "attn.to_q",
+            "self_attn.k_proj": "attn.to_k",
+            "self_attn.v_proj": "attn.to_v",
+            "self_attn.out_proj": "attn.to_out",
+            "layer_norm1": "layer_norm1",
+            "layer_norm2": "layer_norm2",
+            "mlp.fc1": "fc1",
+            "mlp.fc2": "fc2",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+            elif name.startswith("text_model.encoder.layers."):
+                param = state_dict[name]
+                names = name.split(".")
+                layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+                name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "conditioner.embedders.1.model.ln_final.bias": "final_layer_norm.bias",
+            "conditioner.embedders.1.model.ln_final.weight": "final_layer_norm.weight",
+            "conditioner.embedders.1.model.positional_embedding": "position_embeds",
+            "conditioner.embedders.1.model.token_embedding.weight": "token_embedding.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.0.attn.in_proj_bias": ['encoders.0.attn.to_q.bias', 'encoders.0.attn.to_k.bias', 'encoders.0.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.0.attn.in_proj_weight": ['encoders.0.attn.to_q.weight', 'encoders.0.attn.to_k.weight', 'encoders.0.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.0.attn.out_proj.bias": "encoders.0.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.0.attn.out_proj.weight": "encoders.0.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.0.ln_1.bias": "encoders.0.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.0.ln_1.weight": "encoders.0.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.0.ln_2.bias": "encoders.0.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.0.ln_2.weight": "encoders.0.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_fc.bias": "encoders.0.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_fc.weight": "encoders.0.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_proj.bias": "encoders.0.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_proj.weight": "encoders.0.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.1.attn.in_proj_bias": ['encoders.1.attn.to_q.bias', 'encoders.1.attn.to_k.bias', 'encoders.1.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.1.attn.in_proj_weight": ['encoders.1.attn.to_q.weight', 'encoders.1.attn.to_k.weight', 'encoders.1.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.1.attn.out_proj.bias": "encoders.1.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.1.attn.out_proj.weight": "encoders.1.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.1.ln_1.bias": "encoders.1.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.1.ln_1.weight": "encoders.1.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.1.ln_2.bias": "encoders.1.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.1.ln_2.weight": "encoders.1.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_fc.bias": "encoders.1.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_fc.weight": "encoders.1.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_proj.bias": "encoders.1.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_proj.weight": "encoders.1.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.10.attn.in_proj_bias": ['encoders.10.attn.to_q.bias', 'encoders.10.attn.to_k.bias', 'encoders.10.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.10.attn.in_proj_weight": ['encoders.10.attn.to_q.weight', 'encoders.10.attn.to_k.weight', 'encoders.10.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.10.attn.out_proj.bias": "encoders.10.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.10.attn.out_proj.weight": "encoders.10.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.10.ln_1.bias": "encoders.10.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.10.ln_1.weight": "encoders.10.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.10.ln_2.bias": "encoders.10.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.10.ln_2.weight": "encoders.10.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_fc.bias": "encoders.10.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_fc.weight": "encoders.10.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_proj.bias": "encoders.10.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_proj.weight": "encoders.10.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.11.attn.in_proj_bias": ['encoders.11.attn.to_q.bias', 'encoders.11.attn.to_k.bias', 'encoders.11.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.11.attn.in_proj_weight": ['encoders.11.attn.to_q.weight', 'encoders.11.attn.to_k.weight', 'encoders.11.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.11.attn.out_proj.bias": "encoders.11.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.11.attn.out_proj.weight": "encoders.11.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.11.ln_1.bias": "encoders.11.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.11.ln_1.weight": "encoders.11.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.11.ln_2.bias": "encoders.11.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.11.ln_2.weight": "encoders.11.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_fc.bias": "encoders.11.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_fc.weight": "encoders.11.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_proj.bias": "encoders.11.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_proj.weight": "encoders.11.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.12.attn.in_proj_bias": ['encoders.12.attn.to_q.bias', 'encoders.12.attn.to_k.bias', 'encoders.12.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.12.attn.in_proj_weight": ['encoders.12.attn.to_q.weight', 'encoders.12.attn.to_k.weight', 'encoders.12.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.12.attn.out_proj.bias": "encoders.12.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.12.attn.out_proj.weight": "encoders.12.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.12.ln_1.bias": "encoders.12.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.12.ln_1.weight": "encoders.12.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.12.ln_2.bias": "encoders.12.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.12.ln_2.weight": "encoders.12.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_fc.bias": "encoders.12.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_fc.weight": "encoders.12.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_proj.bias": "encoders.12.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_proj.weight": "encoders.12.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.13.attn.in_proj_bias": ['encoders.13.attn.to_q.bias', 'encoders.13.attn.to_k.bias', 'encoders.13.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.13.attn.in_proj_weight": ['encoders.13.attn.to_q.weight', 'encoders.13.attn.to_k.weight', 'encoders.13.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.13.attn.out_proj.bias": "encoders.13.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.13.attn.out_proj.weight": "encoders.13.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.13.ln_1.bias": "encoders.13.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.13.ln_1.weight": "encoders.13.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.13.ln_2.bias": "encoders.13.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.13.ln_2.weight": "encoders.13.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_fc.bias": "encoders.13.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_fc.weight": "encoders.13.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_proj.bias": "encoders.13.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_proj.weight": "encoders.13.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.14.attn.in_proj_bias": ['encoders.14.attn.to_q.bias', 'encoders.14.attn.to_k.bias', 'encoders.14.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.14.attn.in_proj_weight": ['encoders.14.attn.to_q.weight', 'encoders.14.attn.to_k.weight', 'encoders.14.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.14.attn.out_proj.bias": "encoders.14.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.14.attn.out_proj.weight": "encoders.14.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.14.ln_1.bias": "encoders.14.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.14.ln_1.weight": "encoders.14.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.14.ln_2.bias": "encoders.14.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.14.ln_2.weight": "encoders.14.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_fc.bias": "encoders.14.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_fc.weight": "encoders.14.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_proj.bias": "encoders.14.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_proj.weight": "encoders.14.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.15.attn.in_proj_bias": ['encoders.15.attn.to_q.bias', 'encoders.15.attn.to_k.bias', 'encoders.15.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.15.attn.in_proj_weight": ['encoders.15.attn.to_q.weight', 'encoders.15.attn.to_k.weight', 'encoders.15.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.15.attn.out_proj.bias": "encoders.15.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.15.attn.out_proj.weight": "encoders.15.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.15.ln_1.bias": "encoders.15.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.15.ln_1.weight": "encoders.15.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.15.ln_2.bias": "encoders.15.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.15.ln_2.weight": "encoders.15.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_fc.bias": "encoders.15.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_fc.weight": "encoders.15.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_proj.bias": "encoders.15.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_proj.weight": "encoders.15.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.16.attn.in_proj_bias": ['encoders.16.attn.to_q.bias', 'encoders.16.attn.to_k.bias', 'encoders.16.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.16.attn.in_proj_weight": ['encoders.16.attn.to_q.weight', 'encoders.16.attn.to_k.weight', 'encoders.16.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.16.attn.out_proj.bias": "encoders.16.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.16.attn.out_proj.weight": "encoders.16.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.16.ln_1.bias": "encoders.16.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.16.ln_1.weight": "encoders.16.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.16.ln_2.bias": "encoders.16.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.16.ln_2.weight": "encoders.16.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_fc.bias": "encoders.16.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_fc.weight": "encoders.16.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_proj.bias": "encoders.16.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_proj.weight": "encoders.16.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.17.attn.in_proj_bias": ['encoders.17.attn.to_q.bias', 'encoders.17.attn.to_k.bias', 'encoders.17.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.17.attn.in_proj_weight": ['encoders.17.attn.to_q.weight', 'encoders.17.attn.to_k.weight', 'encoders.17.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.17.attn.out_proj.bias": "encoders.17.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.17.attn.out_proj.weight": "encoders.17.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.17.ln_1.bias": "encoders.17.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.17.ln_1.weight": "encoders.17.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.17.ln_2.bias": "encoders.17.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.17.ln_2.weight": "encoders.17.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_fc.bias": "encoders.17.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_fc.weight": "encoders.17.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_proj.bias": "encoders.17.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_proj.weight": "encoders.17.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.18.attn.in_proj_bias": ['encoders.18.attn.to_q.bias', 'encoders.18.attn.to_k.bias', 'encoders.18.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.18.attn.in_proj_weight": ['encoders.18.attn.to_q.weight', 'encoders.18.attn.to_k.weight', 'encoders.18.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.18.attn.out_proj.bias": "encoders.18.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.18.attn.out_proj.weight": "encoders.18.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.18.ln_1.bias": "encoders.18.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.18.ln_1.weight": "encoders.18.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.18.ln_2.bias": "encoders.18.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.18.ln_2.weight": "encoders.18.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.18.mlp.c_fc.bias": "encoders.18.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.18.mlp.c_fc.weight": "encoders.18.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.18.mlp.c_proj.bias": "encoders.18.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.18.mlp.c_proj.weight": "encoders.18.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.19.attn.in_proj_bias": ['encoders.19.attn.to_q.bias', 'encoders.19.attn.to_k.bias', 'encoders.19.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.19.attn.in_proj_weight": ['encoders.19.attn.to_q.weight', 'encoders.19.attn.to_k.weight', 'encoders.19.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.19.attn.out_proj.bias": "encoders.19.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.19.attn.out_proj.weight": "encoders.19.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.19.ln_1.bias": "encoders.19.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.19.ln_1.weight": "encoders.19.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.19.ln_2.bias": "encoders.19.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.19.ln_2.weight": "encoders.19.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.19.mlp.c_fc.bias": "encoders.19.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.19.mlp.c_fc.weight": "encoders.19.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.19.mlp.c_proj.bias": "encoders.19.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.19.mlp.c_proj.weight": "encoders.19.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.2.attn.in_proj_bias": ['encoders.2.attn.to_q.bias', 'encoders.2.attn.to_k.bias', 'encoders.2.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.2.attn.in_proj_weight": ['encoders.2.attn.to_q.weight', 'encoders.2.attn.to_k.weight', 'encoders.2.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.2.attn.out_proj.bias": "encoders.2.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.2.attn.out_proj.weight": "encoders.2.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.2.ln_1.bias": "encoders.2.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.2.ln_1.weight": "encoders.2.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.2.ln_2.bias": "encoders.2.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.2.ln_2.weight": "encoders.2.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.2.mlp.c_fc.bias": "encoders.2.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.2.mlp.c_fc.weight": "encoders.2.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.2.mlp.c_proj.bias": "encoders.2.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.2.mlp.c_proj.weight": "encoders.2.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.20.attn.in_proj_bias": ['encoders.20.attn.to_q.bias', 'encoders.20.attn.to_k.bias', 'encoders.20.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.20.attn.in_proj_weight": ['encoders.20.attn.to_q.weight', 'encoders.20.attn.to_k.weight', 'encoders.20.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.20.attn.out_proj.bias": "encoders.20.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.20.attn.out_proj.weight": "encoders.20.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.20.ln_1.bias": "encoders.20.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.20.ln_1.weight": "encoders.20.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.20.ln_2.bias": "encoders.20.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.20.ln_2.weight": "encoders.20.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.20.mlp.c_fc.bias": "encoders.20.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.20.mlp.c_fc.weight": "encoders.20.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.20.mlp.c_proj.bias": "encoders.20.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.20.mlp.c_proj.weight": "encoders.20.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.21.attn.in_proj_bias": ['encoders.21.attn.to_q.bias', 'encoders.21.attn.to_k.bias', 'encoders.21.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.21.attn.in_proj_weight": ['encoders.21.attn.to_q.weight', 'encoders.21.attn.to_k.weight', 'encoders.21.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.21.attn.out_proj.bias": "encoders.21.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.21.attn.out_proj.weight": "encoders.21.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.21.ln_1.bias": "encoders.21.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.21.ln_1.weight": "encoders.21.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.21.ln_2.bias": "encoders.21.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.21.ln_2.weight": "encoders.21.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.21.mlp.c_fc.bias": "encoders.21.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.21.mlp.c_fc.weight": "encoders.21.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.21.mlp.c_proj.bias": "encoders.21.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.21.mlp.c_proj.weight": "encoders.21.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.22.attn.in_proj_bias": ['encoders.22.attn.to_q.bias', 'encoders.22.attn.to_k.bias', 'encoders.22.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.22.attn.in_proj_weight": ['encoders.22.attn.to_q.weight', 'encoders.22.attn.to_k.weight', 'encoders.22.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.22.attn.out_proj.bias": "encoders.22.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.22.attn.out_proj.weight": "encoders.22.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.22.ln_1.bias": "encoders.22.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.22.ln_1.weight": "encoders.22.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.22.ln_2.bias": "encoders.22.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.22.ln_2.weight": "encoders.22.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.22.mlp.c_fc.bias": "encoders.22.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.22.mlp.c_fc.weight": "encoders.22.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.22.mlp.c_proj.bias": "encoders.22.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.22.mlp.c_proj.weight": "encoders.22.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.23.attn.in_proj_bias": ['encoders.23.attn.to_q.bias', 'encoders.23.attn.to_k.bias', 'encoders.23.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.23.attn.in_proj_weight": ['encoders.23.attn.to_q.weight', 'encoders.23.attn.to_k.weight', 'encoders.23.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.23.attn.out_proj.bias": "encoders.23.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.23.attn.out_proj.weight": "encoders.23.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.23.ln_1.bias": "encoders.23.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.23.ln_1.weight": "encoders.23.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.23.ln_2.bias": "encoders.23.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.23.ln_2.weight": "encoders.23.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.23.mlp.c_fc.bias": "encoders.23.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.23.mlp.c_fc.weight": "encoders.23.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.23.mlp.c_proj.bias": "encoders.23.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.23.mlp.c_proj.weight": "encoders.23.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.24.attn.in_proj_bias": ['encoders.24.attn.to_q.bias', 'encoders.24.attn.to_k.bias', 'encoders.24.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.24.attn.in_proj_weight": ['encoders.24.attn.to_q.weight', 'encoders.24.attn.to_k.weight', 'encoders.24.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.24.attn.out_proj.bias": "encoders.24.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.24.attn.out_proj.weight": "encoders.24.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.24.ln_1.bias": "encoders.24.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.24.ln_1.weight": "encoders.24.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.24.ln_2.bias": "encoders.24.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.24.ln_2.weight": "encoders.24.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.24.mlp.c_fc.bias": "encoders.24.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.24.mlp.c_fc.weight": "encoders.24.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.24.mlp.c_proj.bias": "encoders.24.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.24.mlp.c_proj.weight": "encoders.24.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.25.attn.in_proj_bias": ['encoders.25.attn.to_q.bias', 'encoders.25.attn.to_k.bias', 'encoders.25.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.25.attn.in_proj_weight": ['encoders.25.attn.to_q.weight', 'encoders.25.attn.to_k.weight', 'encoders.25.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.25.attn.out_proj.bias": "encoders.25.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.25.attn.out_proj.weight": "encoders.25.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.25.ln_1.bias": "encoders.25.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.25.ln_1.weight": "encoders.25.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.25.ln_2.bias": "encoders.25.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.25.ln_2.weight": "encoders.25.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.25.mlp.c_fc.bias": "encoders.25.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.25.mlp.c_fc.weight": "encoders.25.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.25.mlp.c_proj.bias": "encoders.25.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.25.mlp.c_proj.weight": "encoders.25.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.26.attn.in_proj_bias": ['encoders.26.attn.to_q.bias', 'encoders.26.attn.to_k.bias', 'encoders.26.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.26.attn.in_proj_weight": ['encoders.26.attn.to_q.weight', 'encoders.26.attn.to_k.weight', 'encoders.26.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.26.attn.out_proj.bias": "encoders.26.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.26.attn.out_proj.weight": "encoders.26.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.26.ln_1.bias": "encoders.26.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.26.ln_1.weight": "encoders.26.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.26.ln_2.bias": "encoders.26.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.26.ln_2.weight": "encoders.26.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.26.mlp.c_fc.bias": "encoders.26.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.26.mlp.c_fc.weight": "encoders.26.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.26.mlp.c_proj.bias": "encoders.26.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.26.mlp.c_proj.weight": "encoders.26.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.27.attn.in_proj_bias": ['encoders.27.attn.to_q.bias', 'encoders.27.attn.to_k.bias', 'encoders.27.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.27.attn.in_proj_weight": ['encoders.27.attn.to_q.weight', 'encoders.27.attn.to_k.weight', 'encoders.27.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.27.attn.out_proj.bias": "encoders.27.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.27.attn.out_proj.weight": "encoders.27.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.27.ln_1.bias": "encoders.27.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.27.ln_1.weight": "encoders.27.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.27.ln_2.bias": "encoders.27.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.27.ln_2.weight": "encoders.27.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.27.mlp.c_fc.bias": "encoders.27.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.27.mlp.c_fc.weight": "encoders.27.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.27.mlp.c_proj.bias": "encoders.27.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.27.mlp.c_proj.weight": "encoders.27.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.28.attn.in_proj_bias": ['encoders.28.attn.to_q.bias', 'encoders.28.attn.to_k.bias', 'encoders.28.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.28.attn.in_proj_weight": ['encoders.28.attn.to_q.weight', 'encoders.28.attn.to_k.weight', 'encoders.28.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.28.attn.out_proj.bias": "encoders.28.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.28.attn.out_proj.weight": "encoders.28.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.28.ln_1.bias": "encoders.28.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.28.ln_1.weight": "encoders.28.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.28.ln_2.bias": "encoders.28.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.28.ln_2.weight": "encoders.28.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.28.mlp.c_fc.bias": "encoders.28.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.28.mlp.c_fc.weight": "encoders.28.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.28.mlp.c_proj.bias": "encoders.28.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.28.mlp.c_proj.weight": "encoders.28.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.29.attn.in_proj_bias": ['encoders.29.attn.to_q.bias', 'encoders.29.attn.to_k.bias', 'encoders.29.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.29.attn.in_proj_weight": ['encoders.29.attn.to_q.weight', 'encoders.29.attn.to_k.weight', 'encoders.29.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.29.attn.out_proj.bias": "encoders.29.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.29.attn.out_proj.weight": "encoders.29.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.29.ln_1.bias": "encoders.29.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.29.ln_1.weight": "encoders.29.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.29.ln_2.bias": "encoders.29.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.29.ln_2.weight": "encoders.29.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.29.mlp.c_fc.bias": "encoders.29.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.29.mlp.c_fc.weight": "encoders.29.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.29.mlp.c_proj.bias": "encoders.29.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.29.mlp.c_proj.weight": "encoders.29.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.3.attn.in_proj_bias": ['encoders.3.attn.to_q.bias', 'encoders.3.attn.to_k.bias', 'encoders.3.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.3.attn.in_proj_weight": ['encoders.3.attn.to_q.weight', 'encoders.3.attn.to_k.weight', 'encoders.3.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.3.attn.out_proj.bias": "encoders.3.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.3.attn.out_proj.weight": "encoders.3.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.3.ln_1.bias": "encoders.3.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.3.ln_1.weight": "encoders.3.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.3.ln_2.bias": "encoders.3.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.3.ln_2.weight": "encoders.3.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.3.mlp.c_fc.bias": "encoders.3.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.3.mlp.c_fc.weight": "encoders.3.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.3.mlp.c_proj.bias": "encoders.3.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.3.mlp.c_proj.weight": "encoders.3.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.30.attn.in_proj_bias": ['encoders.30.attn.to_q.bias', 'encoders.30.attn.to_k.bias', 'encoders.30.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.30.attn.in_proj_weight": ['encoders.30.attn.to_q.weight', 'encoders.30.attn.to_k.weight', 'encoders.30.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.30.attn.out_proj.bias": "encoders.30.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.30.attn.out_proj.weight": "encoders.30.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.30.ln_1.bias": "encoders.30.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.30.ln_1.weight": "encoders.30.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.30.ln_2.bias": "encoders.30.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.30.ln_2.weight": "encoders.30.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.30.mlp.c_fc.bias": "encoders.30.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.30.mlp.c_fc.weight": "encoders.30.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.30.mlp.c_proj.bias": "encoders.30.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.30.mlp.c_proj.weight": "encoders.30.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.31.attn.in_proj_bias": ['encoders.31.attn.to_q.bias', 'encoders.31.attn.to_k.bias', 'encoders.31.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.31.attn.in_proj_weight": ['encoders.31.attn.to_q.weight', 'encoders.31.attn.to_k.weight', 'encoders.31.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.31.attn.out_proj.bias": "encoders.31.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.31.attn.out_proj.weight": "encoders.31.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.31.ln_1.bias": "encoders.31.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.31.ln_1.weight": "encoders.31.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.31.ln_2.bias": "encoders.31.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.31.ln_2.weight": "encoders.31.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.31.mlp.c_fc.bias": "encoders.31.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.31.mlp.c_fc.weight": "encoders.31.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.31.mlp.c_proj.bias": "encoders.31.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.31.mlp.c_proj.weight": "encoders.31.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.4.attn.in_proj_bias": ['encoders.4.attn.to_q.bias', 'encoders.4.attn.to_k.bias', 'encoders.4.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.4.attn.in_proj_weight": ['encoders.4.attn.to_q.weight', 'encoders.4.attn.to_k.weight', 'encoders.4.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.4.attn.out_proj.bias": "encoders.4.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.4.attn.out_proj.weight": "encoders.4.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.4.ln_1.bias": "encoders.4.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.4.ln_1.weight": "encoders.4.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.4.ln_2.bias": "encoders.4.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.4.ln_2.weight": "encoders.4.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.4.mlp.c_fc.bias": "encoders.4.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.4.mlp.c_fc.weight": "encoders.4.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.4.mlp.c_proj.bias": "encoders.4.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.4.mlp.c_proj.weight": "encoders.4.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.5.attn.in_proj_bias": ['encoders.5.attn.to_q.bias', 'encoders.5.attn.to_k.bias', 'encoders.5.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.5.attn.in_proj_weight": ['encoders.5.attn.to_q.weight', 'encoders.5.attn.to_k.weight', 'encoders.5.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.5.attn.out_proj.bias": "encoders.5.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.5.attn.out_proj.weight": "encoders.5.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.5.ln_1.bias": "encoders.5.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.5.ln_1.weight": "encoders.5.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.5.ln_2.bias": "encoders.5.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.5.ln_2.weight": "encoders.5.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.5.mlp.c_fc.bias": "encoders.5.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.5.mlp.c_fc.weight": "encoders.5.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.5.mlp.c_proj.bias": "encoders.5.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.5.mlp.c_proj.weight": "encoders.5.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.6.attn.in_proj_bias": ['encoders.6.attn.to_q.bias', 'encoders.6.attn.to_k.bias', 'encoders.6.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.6.attn.in_proj_weight": ['encoders.6.attn.to_q.weight', 'encoders.6.attn.to_k.weight', 'encoders.6.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.6.attn.out_proj.bias": "encoders.6.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.6.attn.out_proj.weight": "encoders.6.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.6.ln_1.bias": "encoders.6.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.6.ln_1.weight": "encoders.6.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.6.ln_2.bias": "encoders.6.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.6.ln_2.weight": "encoders.6.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.6.mlp.c_fc.bias": "encoders.6.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.6.mlp.c_fc.weight": "encoders.6.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.6.mlp.c_proj.bias": "encoders.6.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.6.mlp.c_proj.weight": "encoders.6.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.7.attn.in_proj_bias": ['encoders.7.attn.to_q.bias', 'encoders.7.attn.to_k.bias', 'encoders.7.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.7.attn.in_proj_weight": ['encoders.7.attn.to_q.weight', 'encoders.7.attn.to_k.weight', 'encoders.7.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.7.attn.out_proj.bias": "encoders.7.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.7.attn.out_proj.weight": "encoders.7.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.7.ln_1.bias": "encoders.7.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.7.ln_1.weight": "encoders.7.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.7.ln_2.bias": "encoders.7.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.7.ln_2.weight": "encoders.7.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.7.mlp.c_fc.bias": "encoders.7.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.7.mlp.c_fc.weight": "encoders.7.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.7.mlp.c_proj.bias": "encoders.7.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.7.mlp.c_proj.weight": "encoders.7.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.8.attn.in_proj_bias": ['encoders.8.attn.to_q.bias', 'encoders.8.attn.to_k.bias', 'encoders.8.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.8.attn.in_proj_weight": ['encoders.8.attn.to_q.weight', 'encoders.8.attn.to_k.weight', 'encoders.8.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.8.attn.out_proj.bias": "encoders.8.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.8.attn.out_proj.weight": "encoders.8.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.8.ln_1.bias": "encoders.8.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.8.ln_1.weight": "encoders.8.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.8.ln_2.bias": "encoders.8.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.8.ln_2.weight": "encoders.8.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.8.mlp.c_fc.bias": "encoders.8.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.8.mlp.c_fc.weight": "encoders.8.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.8.mlp.c_proj.bias": "encoders.8.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.8.mlp.c_proj.weight": "encoders.8.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.9.attn.in_proj_bias": ['encoders.9.attn.to_q.bias', 'encoders.9.attn.to_k.bias', 'encoders.9.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.9.attn.in_proj_weight": ['encoders.9.attn.to_q.weight', 'encoders.9.attn.to_k.weight', 'encoders.9.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.9.attn.out_proj.bias": "encoders.9.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.9.attn.out_proj.weight": "encoders.9.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.9.ln_1.bias": "encoders.9.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.9.ln_1.weight": "encoders.9.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.9.ln_2.bias": "encoders.9.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.9.ln_2.weight": "encoders.9.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_fc.bias": "encoders.9.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_fc.weight": "encoders.9.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias": "encoders.9.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.weight": "encoders.9.fc2.weight",
+            "conditioner.embedders.1.model.text_projection": "text_projection.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "conditioner.embedders.1.model.positional_embedding":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                elif name == "conditioner.embedders.1.model.text_projection":
+                    param = param.T
+                if isinstance(rename_dict[name], str):
+                    state_dict_[rename_dict[name]] = param
+                else:
+                    length = param.shape[0] // 3
+                    for i, rename in enumerate(rename_dict[name]):
+                        state_dict_[rename] = param[i*length: i*length+length]
+        return state_dict_

diffsynth/models/sdxl_vae_decoder.py

@@ -0,0 +1,15 @@
+from .sd_vae_decoder import SDVAEDecoder, SDVAEDecoderStateDictConverter
+
+
+class SDXLVAEDecoder(SDVAEDecoder):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.13025
+
+    def state_dict_converter(self):
+        return SDXLVAEDecoderStateDictConverter()
+    
+
+class SDXLVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter):
+    def __init__(self):
+        super().__init__()

diffsynth/models/sdxl_vae_encoder.py

@@ -0,0 +1,15 @@
+from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder
+
+
+class SDXLVAEEncoder(SDVAEEncoder):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.13025
+    
+    def state_dict_converter(self):
+        return SDXLVAEEncoderStateDictConverter()
+
+
+class SDXLVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
+    def __init__(self):
+        super().__init__()

diffsynth/models/svd_image_encoder.py

@@ -0,0 +1,504 @@
+import torch
+from .sd_text_encoder import CLIPEncoderLayer
+
+
+class CLIPVisionEmbeddings(torch.nn.Module):
+    def __init__(self, embed_dim=1280, image_size=224, patch_size=14, num_channels=3):
+        super().__init__()
+
+        # class_embeds (This is a fixed tensor)
+        self.class_embedding = torch.nn.Parameter(torch.randn(1, 1, embed_dim))
+
+        # position_embeds
+        self.patch_embedding = torch.nn.Conv2d(in_channels=num_channels, out_channels=embed_dim, kernel_size=patch_size, stride=patch_size, bias=False)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, (image_size // patch_size) ** 2 + 1, embed_dim))
+
+    def forward(self, pixel_values):
+        batch_size = pixel_values.shape[0]
+        patch_embeds = self.patch_embedding(pixel_values)
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+        class_embeds = self.class_embedding.repeat(batch_size, 1, 1)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + self.position_embeds
+        return embeddings
+
+
+class SVDImageEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=1280, layer_norm_eps=1e-5, num_encoder_layers=32, encoder_intermediate_size=5120, projection_dim=1024, num_heads=16, head_dim=80):
+        super().__init__()
+        self.embeddings = CLIPVisionEmbeddings(embed_dim=embed_dim)
+        self.pre_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps)
+        self.encoders = torch.nn.ModuleList([
+            CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=num_heads, head_dim=head_dim, use_quick_gelu=False)
+            for _ in range(num_encoder_layers)])
+        self.post_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps)
+        self.visual_projection = torch.nn.Linear(embed_dim, projection_dim, bias=False)
+
+    def forward(self, pixel_values):
+        embeds = self.embeddings(pixel_values)
+        embeds = self.pre_layernorm(embeds)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds)
+        embeds = self.post_layernorm(embeds[:, 0, :])
+        embeds = self.visual_projection(embeds)
+        return embeds
+
+    def state_dict_converter(self):
+        return SVDImageEncoderStateDictConverter()
+
+
+class SVDImageEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "vision_model.embeddings.patch_embedding.weight": "embeddings.patch_embedding.weight",
+            "vision_model.embeddings.class_embedding": "embeddings.class_embedding",
+            "vision_model.embeddings.position_embedding.weight": "embeddings.position_embeds",
+            "vision_model.pre_layrnorm.weight": "pre_layernorm.weight",
+            "vision_model.pre_layrnorm.bias": "pre_layernorm.bias",
+            "vision_model.post_layernorm.weight": "post_layernorm.weight",
+            "vision_model.post_layernorm.bias": "post_layernorm.bias",
+            "visual_projection.weight": "visual_projection.weight"
+        }
+        attn_rename_dict = {
+            "self_attn.q_proj": "attn.to_q",
+            "self_attn.k_proj": "attn.to_k",
+            "self_attn.v_proj": "attn.to_v",
+            "self_attn.out_proj": "attn.to_out",
+            "layer_norm1": "layer_norm1",
+            "layer_norm2": "layer_norm2",
+            "mlp.fc1": "fc1",
+            "mlp.fc2": "fc2",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "vision_model.embeddings.class_embedding":
+                    param = state_dict[name].view(1, 1, -1)
+                elif name == "vision_model.embeddings.position_embedding.weight":
+                    param = state_dict[name].unsqueeze(0)
+                state_dict_[rename_dict[name]] = param
+            elif name.startswith("vision_model.encoder.layers."):
+                param = state_dict[name]
+                names = name.split(".")
+                layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+                name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "conditioner.embedders.0.open_clip.model.visual.class_embedding": "embeddings.class_embedding",
+            "conditioner.embedders.0.open_clip.model.visual.conv1.weight": "embeddings.patch_embedding.weight",
+            "conditioner.embedders.0.open_clip.model.visual.ln_post.bias": "post_layernorm.bias",
+            "conditioner.embedders.0.open_clip.model.visual.ln_post.weight": "post_layernorm.weight",
+            "conditioner.embedders.0.open_clip.model.visual.ln_pre.bias": "pre_layernorm.bias",
+            "conditioner.embedders.0.open_clip.model.visual.ln_pre.weight": "pre_layernorm.weight",
+            "conditioner.embedders.0.open_clip.model.visual.positional_embedding": "embeddings.position_embeds",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.in_proj_bias": ['encoders.0.attn.to_q.bias', 'encoders.0.attn.to_k.bias', 'encoders.0.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.in_proj_weight": ['encoders.0.attn.to_q.weight', 'encoders.0.attn.to_k.weight', 'encoders.0.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.out_proj.bias": "encoders.0.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.out_proj.weight": "encoders.0.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_1.bias": "encoders.0.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_1.weight": "encoders.0.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_2.bias": "encoders.0.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_2.weight": "encoders.0.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_fc.bias": "encoders.0.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_fc.weight": "encoders.0.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_proj.bias": "encoders.0.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_proj.weight": "encoders.0.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.in_proj_bias": ['encoders.1.attn.to_q.bias', 'encoders.1.attn.to_k.bias', 'encoders.1.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.in_proj_weight": ['encoders.1.attn.to_q.weight', 'encoders.1.attn.to_k.weight', 'encoders.1.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.out_proj.bias": "encoders.1.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.out_proj.weight": "encoders.1.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_1.bias": "encoders.1.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_1.weight": "encoders.1.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_2.bias": "encoders.1.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_2.weight": "encoders.1.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_fc.bias": "encoders.1.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_fc.weight": "encoders.1.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_proj.bias": "encoders.1.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_proj.weight": "encoders.1.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.in_proj_bias": ['encoders.10.attn.to_q.bias', 'encoders.10.attn.to_k.bias', 'encoders.10.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.in_proj_weight": ['encoders.10.attn.to_q.weight', 'encoders.10.attn.to_k.weight', 'encoders.10.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.out_proj.bias": "encoders.10.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.out_proj.weight": "encoders.10.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.ln_1.bias": "encoders.10.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.ln_1.weight": "encoders.10.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.ln_2.bias": "encoders.10.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.ln_2.weight": "encoders.10.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.mlp.c_fc.bias": "encoders.10.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.mlp.c_fc.weight": "encoders.10.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.mlp.c_proj.bias": "encoders.10.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.mlp.c_proj.weight": "encoders.10.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.attn.in_proj_bias": ['encoders.11.attn.to_q.bias', 'encoders.11.attn.to_k.bias', 'encoders.11.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.attn.in_proj_weight": ['encoders.11.attn.to_q.weight', 'encoders.11.attn.to_k.weight', 'encoders.11.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.attn.out_proj.bias": "encoders.11.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.attn.out_proj.weight": "encoders.11.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.ln_1.bias": "encoders.11.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.ln_1.weight": "encoders.11.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.ln_2.bias": "encoders.11.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.ln_2.weight": "encoders.11.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.mlp.c_fc.bias": "encoders.11.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.mlp.c_fc.weight": "encoders.11.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.mlp.c_proj.bias": "encoders.11.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.11.mlp.c_proj.weight": "encoders.11.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.attn.in_proj_bias": ['encoders.12.attn.to_q.bias', 'encoders.12.attn.to_k.bias', 'encoders.12.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.attn.in_proj_weight": ['encoders.12.attn.to_q.weight', 'encoders.12.attn.to_k.weight', 'encoders.12.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.attn.out_proj.bias": "encoders.12.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.attn.out_proj.weight": "encoders.12.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.ln_1.bias": "encoders.12.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.ln_1.weight": "encoders.12.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.ln_2.bias": "encoders.12.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.ln_2.weight": "encoders.12.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.mlp.c_fc.bias": "encoders.12.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.mlp.c_fc.weight": "encoders.12.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.mlp.c_proj.bias": "encoders.12.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.12.mlp.c_proj.weight": "encoders.12.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.attn.in_proj_bias": ['encoders.13.attn.to_q.bias', 'encoders.13.attn.to_k.bias', 'encoders.13.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.attn.in_proj_weight": ['encoders.13.attn.to_q.weight', 'encoders.13.attn.to_k.weight', 'encoders.13.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.attn.out_proj.bias": "encoders.13.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.attn.out_proj.weight": "encoders.13.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.ln_1.bias": "encoders.13.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.ln_1.weight": "encoders.13.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.ln_2.bias": "encoders.13.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.ln_2.weight": "encoders.13.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.mlp.c_fc.bias": "encoders.13.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.mlp.c_fc.weight": "encoders.13.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.mlp.c_proj.bias": "encoders.13.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.13.mlp.c_proj.weight": "encoders.13.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.attn.in_proj_bias": ['encoders.14.attn.to_q.bias', 'encoders.14.attn.to_k.bias', 'encoders.14.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.attn.in_proj_weight": ['encoders.14.attn.to_q.weight', 'encoders.14.attn.to_k.weight', 'encoders.14.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.attn.out_proj.bias": "encoders.14.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.attn.out_proj.weight": "encoders.14.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.ln_1.bias": "encoders.14.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.ln_1.weight": "encoders.14.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.ln_2.bias": "encoders.14.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.ln_2.weight": "encoders.14.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.mlp.c_fc.bias": "encoders.14.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.mlp.c_fc.weight": "encoders.14.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.mlp.c_proj.bias": "encoders.14.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.14.mlp.c_proj.weight": "encoders.14.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.attn.in_proj_bias": ['encoders.15.attn.to_q.bias', 'encoders.15.attn.to_k.bias', 'encoders.15.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.attn.in_proj_weight": ['encoders.15.attn.to_q.weight', 'encoders.15.attn.to_k.weight', 'encoders.15.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.attn.out_proj.bias": "encoders.15.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.attn.out_proj.weight": "encoders.15.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.ln_1.bias": "encoders.15.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.ln_1.weight": "encoders.15.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.ln_2.bias": "encoders.15.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.ln_2.weight": "encoders.15.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.mlp.c_fc.bias": "encoders.15.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.mlp.c_fc.weight": "encoders.15.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.mlp.c_proj.bias": "encoders.15.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.15.mlp.c_proj.weight": "encoders.15.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.attn.in_proj_bias": ['encoders.16.attn.to_q.bias', 'encoders.16.attn.to_k.bias', 'encoders.16.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.attn.in_proj_weight": ['encoders.16.attn.to_q.weight', 'encoders.16.attn.to_k.weight', 'encoders.16.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.attn.out_proj.bias": "encoders.16.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.attn.out_proj.weight": "encoders.16.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.ln_1.bias": "encoders.16.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.ln_1.weight": "encoders.16.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.ln_2.bias": "encoders.16.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.ln_2.weight": "encoders.16.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.mlp.c_fc.bias": "encoders.16.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.mlp.c_fc.weight": "encoders.16.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.mlp.c_proj.bias": "encoders.16.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.16.mlp.c_proj.weight": "encoders.16.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.attn.in_proj_bias": ['encoders.17.attn.to_q.bias', 'encoders.17.attn.to_k.bias', 'encoders.17.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.attn.in_proj_weight": ['encoders.17.attn.to_q.weight', 'encoders.17.attn.to_k.weight', 'encoders.17.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.attn.out_proj.bias": "encoders.17.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.attn.out_proj.weight": "encoders.17.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.ln_1.bias": "encoders.17.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.ln_1.weight": "encoders.17.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.ln_2.bias": "encoders.17.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.ln_2.weight": "encoders.17.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.mlp.c_fc.bias": "encoders.17.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.mlp.c_fc.weight": "encoders.17.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.mlp.c_proj.bias": "encoders.17.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.17.mlp.c_proj.weight": "encoders.17.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.attn.in_proj_bias": ['encoders.18.attn.to_q.bias', 'encoders.18.attn.to_k.bias', 'encoders.18.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.attn.in_proj_weight": ['encoders.18.attn.to_q.weight', 'encoders.18.attn.to_k.weight', 'encoders.18.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.attn.out_proj.bias": "encoders.18.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.attn.out_proj.weight": "encoders.18.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.ln_1.bias": "encoders.18.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.ln_1.weight": "encoders.18.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.ln_2.bias": "encoders.18.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.ln_2.weight": "encoders.18.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.mlp.c_fc.bias": "encoders.18.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.mlp.c_fc.weight": "encoders.18.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.mlp.c_proj.bias": "encoders.18.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.18.mlp.c_proj.weight": "encoders.18.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.attn.in_proj_bias": ['encoders.19.attn.to_q.bias', 'encoders.19.attn.to_k.bias', 'encoders.19.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.attn.in_proj_weight": ['encoders.19.attn.to_q.weight', 'encoders.19.attn.to_k.weight', 'encoders.19.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.attn.out_proj.bias": "encoders.19.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.attn.out_proj.weight": "encoders.19.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.ln_1.bias": "encoders.19.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.ln_1.weight": "encoders.19.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.ln_2.bias": "encoders.19.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.ln_2.weight": "encoders.19.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.mlp.c_fc.bias": "encoders.19.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.mlp.c_fc.weight": "encoders.19.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.mlp.c_proj.bias": "encoders.19.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.19.mlp.c_proj.weight": "encoders.19.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.attn.in_proj_bias": ['encoders.2.attn.to_q.bias', 'encoders.2.attn.to_k.bias', 'encoders.2.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.attn.in_proj_weight": ['encoders.2.attn.to_q.weight', 'encoders.2.attn.to_k.weight', 'encoders.2.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.attn.out_proj.bias": "encoders.2.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.attn.out_proj.weight": "encoders.2.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.ln_1.bias": "encoders.2.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.ln_1.weight": "encoders.2.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.ln_2.bias": "encoders.2.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.ln_2.weight": "encoders.2.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.mlp.c_fc.bias": "encoders.2.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.mlp.c_fc.weight": "encoders.2.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.mlp.c_proj.bias": "encoders.2.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.2.mlp.c_proj.weight": "encoders.2.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.attn.in_proj_bias": ['encoders.20.attn.to_q.bias', 'encoders.20.attn.to_k.bias', 'encoders.20.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.attn.in_proj_weight": ['encoders.20.attn.to_q.weight', 'encoders.20.attn.to_k.weight', 'encoders.20.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.attn.out_proj.bias": "encoders.20.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.attn.out_proj.weight": "encoders.20.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.ln_1.bias": "encoders.20.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.ln_1.weight": "encoders.20.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.ln_2.bias": "encoders.20.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.ln_2.weight": "encoders.20.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.mlp.c_fc.bias": "encoders.20.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.mlp.c_fc.weight": "encoders.20.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.mlp.c_proj.bias": "encoders.20.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.20.mlp.c_proj.weight": "encoders.20.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.attn.in_proj_bias": ['encoders.21.attn.to_q.bias', 'encoders.21.attn.to_k.bias', 'encoders.21.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.attn.in_proj_weight": ['encoders.21.attn.to_q.weight', 'encoders.21.attn.to_k.weight', 'encoders.21.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.attn.out_proj.bias": "encoders.21.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.attn.out_proj.weight": "encoders.21.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.ln_1.bias": "encoders.21.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.ln_1.weight": "encoders.21.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.ln_2.bias": "encoders.21.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.ln_2.weight": "encoders.21.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.mlp.c_fc.bias": "encoders.21.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.mlp.c_fc.weight": "encoders.21.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.mlp.c_proj.bias": "encoders.21.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.21.mlp.c_proj.weight": "encoders.21.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.attn.in_proj_bias": ['encoders.22.attn.to_q.bias', 'encoders.22.attn.to_k.bias', 'encoders.22.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.attn.in_proj_weight": ['encoders.22.attn.to_q.weight', 'encoders.22.attn.to_k.weight', 'encoders.22.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.attn.out_proj.bias": "encoders.22.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.attn.out_proj.weight": "encoders.22.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.ln_1.bias": "encoders.22.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.ln_1.weight": "encoders.22.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.ln_2.bias": "encoders.22.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.ln_2.weight": "encoders.22.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.mlp.c_fc.bias": "encoders.22.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.mlp.c_fc.weight": "encoders.22.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.mlp.c_proj.bias": "encoders.22.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.22.mlp.c_proj.weight": "encoders.22.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.attn.in_proj_bias": ['encoders.23.attn.to_q.bias', 'encoders.23.attn.to_k.bias', 'encoders.23.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.attn.in_proj_weight": ['encoders.23.attn.to_q.weight', 'encoders.23.attn.to_k.weight', 'encoders.23.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.attn.out_proj.bias": "encoders.23.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.attn.out_proj.weight": "encoders.23.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.ln_1.bias": "encoders.23.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.ln_1.weight": "encoders.23.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.ln_2.bias": "encoders.23.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.ln_2.weight": "encoders.23.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.mlp.c_fc.bias": "encoders.23.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.mlp.c_fc.weight": "encoders.23.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.mlp.c_proj.bias": "encoders.23.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.23.mlp.c_proj.weight": "encoders.23.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.attn.in_proj_bias": ['encoders.24.attn.to_q.bias', 'encoders.24.attn.to_k.bias', 'encoders.24.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.attn.in_proj_weight": ['encoders.24.attn.to_q.weight', 'encoders.24.attn.to_k.weight', 'encoders.24.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.attn.out_proj.bias": "encoders.24.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.attn.out_proj.weight": "encoders.24.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.ln_1.bias": "encoders.24.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.ln_1.weight": "encoders.24.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.ln_2.bias": "encoders.24.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.ln_2.weight": "encoders.24.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.mlp.c_fc.bias": "encoders.24.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.mlp.c_fc.weight": "encoders.24.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.mlp.c_proj.bias": "encoders.24.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.24.mlp.c_proj.weight": "encoders.24.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.attn.in_proj_bias": ['encoders.25.attn.to_q.bias', 'encoders.25.attn.to_k.bias', 'encoders.25.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.attn.in_proj_weight": ['encoders.25.attn.to_q.weight', 'encoders.25.attn.to_k.weight', 'encoders.25.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.attn.out_proj.bias": "encoders.25.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.attn.out_proj.weight": "encoders.25.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.ln_1.bias": "encoders.25.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.ln_1.weight": "encoders.25.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.ln_2.bias": "encoders.25.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.ln_2.weight": "encoders.25.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.mlp.c_fc.bias": "encoders.25.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.mlp.c_fc.weight": "encoders.25.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.mlp.c_proj.bias": "encoders.25.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.25.mlp.c_proj.weight": "encoders.25.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.attn.in_proj_bias": ['encoders.26.attn.to_q.bias', 'encoders.26.attn.to_k.bias', 'encoders.26.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.attn.in_proj_weight": ['encoders.26.attn.to_q.weight', 'encoders.26.attn.to_k.weight', 'encoders.26.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.attn.out_proj.bias": "encoders.26.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.attn.out_proj.weight": "encoders.26.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.ln_1.bias": "encoders.26.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.ln_1.weight": "encoders.26.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.ln_2.bias": "encoders.26.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.ln_2.weight": "encoders.26.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.mlp.c_fc.bias": "encoders.26.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.mlp.c_fc.weight": "encoders.26.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.mlp.c_proj.bias": "encoders.26.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.26.mlp.c_proj.weight": "encoders.26.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.attn.in_proj_bias": ['encoders.27.attn.to_q.bias', 'encoders.27.attn.to_k.bias', 'encoders.27.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.attn.in_proj_weight": ['encoders.27.attn.to_q.weight', 'encoders.27.attn.to_k.weight', 'encoders.27.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.attn.out_proj.bias": "encoders.27.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.attn.out_proj.weight": "encoders.27.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.ln_1.bias": "encoders.27.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.ln_1.weight": "encoders.27.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.ln_2.bias": "encoders.27.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.ln_2.weight": "encoders.27.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.mlp.c_fc.bias": "encoders.27.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.mlp.c_fc.weight": "encoders.27.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.mlp.c_proj.bias": "encoders.27.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.27.mlp.c_proj.weight": "encoders.27.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.attn.in_proj_bias": ['encoders.28.attn.to_q.bias', 'encoders.28.attn.to_k.bias', 'encoders.28.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.attn.in_proj_weight": ['encoders.28.attn.to_q.weight', 'encoders.28.attn.to_k.weight', 'encoders.28.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.attn.out_proj.bias": "encoders.28.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.attn.out_proj.weight": "encoders.28.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.ln_1.bias": "encoders.28.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.ln_1.weight": "encoders.28.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.ln_2.bias": "encoders.28.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.ln_2.weight": "encoders.28.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.mlp.c_fc.bias": "encoders.28.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.mlp.c_fc.weight": "encoders.28.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.mlp.c_proj.bias": "encoders.28.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.28.mlp.c_proj.weight": "encoders.28.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.attn.in_proj_bias": ['encoders.29.attn.to_q.bias', 'encoders.29.attn.to_k.bias', 'encoders.29.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.attn.in_proj_weight": ['encoders.29.attn.to_q.weight', 'encoders.29.attn.to_k.weight', 'encoders.29.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.attn.out_proj.bias": "encoders.29.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.attn.out_proj.weight": "encoders.29.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.ln_1.bias": "encoders.29.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.ln_1.weight": "encoders.29.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.ln_2.bias": "encoders.29.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.ln_2.weight": "encoders.29.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.mlp.c_fc.bias": "encoders.29.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.mlp.c_fc.weight": "encoders.29.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.mlp.c_proj.bias": "encoders.29.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.29.mlp.c_proj.weight": "encoders.29.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.attn.in_proj_bias": ['encoders.3.attn.to_q.bias', 'encoders.3.attn.to_k.bias', 'encoders.3.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.attn.in_proj_weight": ['encoders.3.attn.to_q.weight', 'encoders.3.attn.to_k.weight', 'encoders.3.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.attn.out_proj.bias": "encoders.3.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.attn.out_proj.weight": "encoders.3.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.ln_1.bias": "encoders.3.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.ln_1.weight": "encoders.3.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.ln_2.bias": "encoders.3.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.ln_2.weight": "encoders.3.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.mlp.c_fc.bias": "encoders.3.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.mlp.c_fc.weight": "encoders.3.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.mlp.c_proj.bias": "encoders.3.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.3.mlp.c_proj.weight": "encoders.3.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.attn.in_proj_bias": ['encoders.30.attn.to_q.bias', 'encoders.30.attn.to_k.bias', 'encoders.30.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.attn.in_proj_weight": ['encoders.30.attn.to_q.weight', 'encoders.30.attn.to_k.weight', 'encoders.30.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.attn.out_proj.bias": "encoders.30.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.attn.out_proj.weight": "encoders.30.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.ln_1.bias": "encoders.30.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.ln_1.weight": "encoders.30.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.ln_2.bias": "encoders.30.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.ln_2.weight": "encoders.30.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.mlp.c_fc.bias": "encoders.30.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.mlp.c_fc.weight": "encoders.30.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.mlp.c_proj.bias": "encoders.30.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.30.mlp.c_proj.weight": "encoders.30.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.attn.in_proj_bias": ['encoders.31.attn.to_q.bias', 'encoders.31.attn.to_k.bias', 'encoders.31.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.attn.in_proj_weight": ['encoders.31.attn.to_q.weight', 'encoders.31.attn.to_k.weight', 'encoders.31.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.attn.out_proj.bias": "encoders.31.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.attn.out_proj.weight": "encoders.31.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.ln_1.bias": "encoders.31.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.ln_1.weight": "encoders.31.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.ln_2.bias": "encoders.31.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.ln_2.weight": "encoders.31.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.mlp.c_fc.bias": "encoders.31.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.mlp.c_fc.weight": "encoders.31.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.mlp.c_proj.bias": "encoders.31.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.31.mlp.c_proj.weight": "encoders.31.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.attn.in_proj_bias": ['encoders.4.attn.to_q.bias', 'encoders.4.attn.to_k.bias', 'encoders.4.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.attn.in_proj_weight": ['encoders.4.attn.to_q.weight', 'encoders.4.attn.to_k.weight', 'encoders.4.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.attn.out_proj.bias": "encoders.4.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.attn.out_proj.weight": "encoders.4.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.ln_1.bias": "encoders.4.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.ln_1.weight": "encoders.4.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.ln_2.bias": "encoders.4.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.ln_2.weight": "encoders.4.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.mlp.c_fc.bias": "encoders.4.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.mlp.c_fc.weight": "encoders.4.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.mlp.c_proj.bias": "encoders.4.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.mlp.c_proj.weight": "encoders.4.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.attn.in_proj_bias": ['encoders.5.attn.to_q.bias', 'encoders.5.attn.to_k.bias', 'encoders.5.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.attn.in_proj_weight": ['encoders.5.attn.to_q.weight', 'encoders.5.attn.to_k.weight', 'encoders.5.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.attn.out_proj.bias": "encoders.5.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.attn.out_proj.weight": "encoders.5.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.ln_1.bias": "encoders.5.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.ln_1.weight": "encoders.5.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.ln_2.bias": "encoders.5.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.ln_2.weight": "encoders.5.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.mlp.c_fc.bias": "encoders.5.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.mlp.c_fc.weight": "encoders.5.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.mlp.c_proj.bias": "encoders.5.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.mlp.c_proj.weight": "encoders.5.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.attn.in_proj_bias": ['encoders.6.attn.to_q.bias', 'encoders.6.attn.to_k.bias', 'encoders.6.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.attn.in_proj_weight": ['encoders.6.attn.to_q.weight', 'encoders.6.attn.to_k.weight', 'encoders.6.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.attn.out_proj.bias": "encoders.6.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.attn.out_proj.weight": "encoders.6.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.ln_1.bias": "encoders.6.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.ln_1.weight": "encoders.6.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.ln_2.bias": "encoders.6.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.ln_2.weight": "encoders.6.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.mlp.c_fc.bias": "encoders.6.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.mlp.c_fc.weight": "encoders.6.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.mlp.c_proj.bias": "encoders.6.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.mlp.c_proj.weight": "encoders.6.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.attn.in_proj_bias": ['encoders.7.attn.to_q.bias', 'encoders.7.attn.to_k.bias', 'encoders.7.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.attn.in_proj_weight": ['encoders.7.attn.to_q.weight', 'encoders.7.attn.to_k.weight', 'encoders.7.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.attn.out_proj.bias": "encoders.7.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.attn.out_proj.weight": "encoders.7.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.ln_1.bias": "encoders.7.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.ln_1.weight": "encoders.7.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.ln_2.bias": "encoders.7.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.ln_2.weight": "encoders.7.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.mlp.c_fc.bias": "encoders.7.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.mlp.c_fc.weight": "encoders.7.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.mlp.c_proj.bias": "encoders.7.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.mlp.c_proj.weight": "encoders.7.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.attn.in_proj_bias": ['encoders.8.attn.to_q.bias', 'encoders.8.attn.to_k.bias', 'encoders.8.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.attn.in_proj_weight": ['encoders.8.attn.to_q.weight', 'encoders.8.attn.to_k.weight', 'encoders.8.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.attn.out_proj.bias": "encoders.8.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.attn.out_proj.weight": "encoders.8.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.ln_1.bias": "encoders.8.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.ln_1.weight": "encoders.8.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.ln_2.bias": "encoders.8.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.ln_2.weight": "encoders.8.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.mlp.c_fc.bias": "encoders.8.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.mlp.c_fc.weight": "encoders.8.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.mlp.c_proj.bias": "encoders.8.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.mlp.c_proj.weight": "encoders.8.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.attn.in_proj_bias": ['encoders.9.attn.to_q.bias', 'encoders.9.attn.to_k.bias', 'encoders.9.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.attn.in_proj_weight": ['encoders.9.attn.to_q.weight', 'encoders.9.attn.to_k.weight', 'encoders.9.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.attn.out_proj.bias": "encoders.9.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.attn.out_proj.weight": "encoders.9.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_1.bias": "encoders.9.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_1.weight": "encoders.9.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_2.bias": "encoders.9.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_2.weight": "encoders.9.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_fc.bias": "encoders.9.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_fc.weight": "encoders.9.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_proj.bias": "encoders.9.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_proj.weight": "encoders.9.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.proj": "visual_projection.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "conditioner.embedders.0.open_clip.model.visual.class_embedding":
+                    param = param.reshape((1, 1, param.shape[0]))
+                elif name == "conditioner.embedders.0.open_clip.model.visual.positional_embedding":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                elif name == "conditioner.embedders.0.open_clip.model.visual.proj":
+                    param = param.T
+                if isinstance(rename_dict[name], str):
+                    state_dict_[rename_dict[name]] = param
+                else:
+                    length = param.shape[0] // 3
+                    for i, rename in enumerate(rename_dict[name]):
+                        state_dict_[rename] = param[i*length: i*length+length]
+        return state_dict_

diffsynth/models/svd_vae_decoder.py

@@ -0,0 +1,577 @@
+import torch
+from .attention import Attention
+from .sd_unet import ResnetBlock, UpSampler
+from .tiler import TileWorker
+from einops import rearrange, repeat
+
+
+class VAEAttentionBlock(torch.nn.Module):
+
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
+
+        self.transformer_blocks = torch.nn.ModuleList([
+            Attention(
+                inner_dim,
+                num_attention_heads,
+                attention_head_dim,
+                bias_q=True,
+                bias_kv=True,
+                bias_out=True
+            )
+            for d in range(num_layers)
+        ])
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack):
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+
+        for block in self.transformer_blocks:
+            hidden_states = block(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+        hidden_states = hidden_states + residual
+
+        return hidden_states, time_emb, text_emb, res_stack
+    
+
+class TemporalResnetBlock(torch.nn.Module):
+
+    def __init__(self, in_channels, out_channels, groups=32, eps=1e-5):
+        super().__init__()
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0))
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
+        self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0))
+        self.nonlinearity = torch.nn.SiLU()
+        self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5]))
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        x_spatial = hidden_states
+        x = rearrange(hidden_states, "T C H W -> 1 C T H W")
+        x = self.norm1(x)
+        x = self.nonlinearity(x)
+        x = self.conv1(x)
+        x = self.norm2(x)
+        x = self.nonlinearity(x)
+        x = self.conv2(x)
+        x_temporal = hidden_states + x[0].permute(1, 0, 2, 3)
+        alpha = torch.sigmoid(self.mix_factor)
+        hidden_states = alpha * x_temporal + (1 - alpha) * x_spatial
+        return hidden_states, time_emb, text_emb, res_stack
+    
+
+class SVDVAEDecoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.18215
+        self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # UNetMidBlock
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            # UpDecoderBlock
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock
+            ResnetBlock(512, 256, eps=1e-6),
+            TemporalResnetBlock(256, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            TemporalResnetBlock(256, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            TemporalResnetBlock(256, 256, eps=1e-6),
+            UpSampler(256),
+            # UpDecoderBlock
+            ResnetBlock(256, 128, eps=1e-6),
+            TemporalResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            TemporalResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            TemporalResnetBlock(128, 128, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1)
+        self.time_conv_out = torch.nn.Conv3d(3, 3, kernel_size=(3, 1, 1), padding=(1, 0, 0))
+
+
+    def forward(self, sample):
+        # 1. pre-process
+        hidden_states = rearrange(sample, "C T H W -> T C H W")
+        hidden_states = hidden_states / self.scaling_factor
+        hidden_states = self.conv_in(hidden_states)
+        time_emb, text_emb, res_stack = None, None, None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        hidden_states = rearrange(hidden_states, "T C H W -> C T H W")
+        hidden_states = self.time_conv_out(hidden_states)
+
+        return hidden_states
+    
+    
+    def build_mask(self, data, is_bound):
+        _, T, H, W = data.shape
+        t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W)
+        h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W)
+        w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W)
+        border_width = (T + H + W) // 6
+        pad = torch.ones_like(t) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else t + 1,
+            pad if is_bound[1] else T - t,
+            pad if is_bound[2] else h + 1,
+            pad if is_bound[3] else H - h,
+            pad if is_bound[4] else w + 1,
+            pad if is_bound[5] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=data.dtype, device=data.device)
+        mask = rearrange(mask, "T H W -> 1 T H W")
+        return mask
+    
+
+    def decode_video(
+        self, sample,
+        batch_time=8, batch_height=128, batch_width=128,
+        stride_time=4, stride_height=32, stride_width=32,
+        progress_bar=lambda x:x
+    ):
+        sample = sample.permute(1, 0, 2, 3)
+        data_device = sample.device
+        computation_device = self.conv_in.weight.device
+        torch_dtype = sample.dtype
+        _, T, H, W = sample.shape
+
+        weight = torch.zeros((1, T, H*8, W*8), dtype=torch_dtype, device=data_device)
+        values = torch.zeros((3, T, H*8, W*8), dtype=torch_dtype, device=data_device)
+
+        # Split tasks
+        tasks = []
+        for t in range(0, T, stride_time):
+            for h in range(0, H, stride_height):
+                for w in range(0, W, stride_width):
+                    if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\
+                        or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\
+                        or (w-stride_width >= 0 and w-stride_width+batch_width >= W):
+                        continue
+                    tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width))
+        
+        # Run
+        for tl, tr, hl, hr, wl, wr in progress_bar(tasks):
+            sample_batch = sample[:, tl:tr, hl:hr, wl:wr].to(computation_device)
+            sample_batch = self.forward(sample_batch).to(data_device)
+            mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W))
+            values[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += sample_batch * mask
+            weight[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += mask
+        values /= weight
+        return values
+    
+    
+    def state_dict_converter(self):
+        return SVDVAEDecoderStateDictConverter()
+    
+
+class SVDVAEDecoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        static_rename_dict = {
+            "decoder.conv_in":  "conv_in",
+            "decoder.mid_block.attentions.0.group_norm": "blocks.2.norm",
+            "decoder.mid_block.attentions.0.to_q": "blocks.2.transformer_blocks.0.to_q",
+            "decoder.mid_block.attentions.0.to_k": "blocks.2.transformer_blocks.0.to_k",
+            "decoder.mid_block.attentions.0.to_v": "blocks.2.transformer_blocks.0.to_v",
+            "decoder.mid_block.attentions.0.to_out.0": "blocks.2.transformer_blocks.0.to_out",
+            "decoder.up_blocks.0.upsamplers.0.conv": "blocks.11.conv",
+            "decoder.up_blocks.1.upsamplers.0.conv": "blocks.18.conv",
+            "decoder.up_blocks.2.upsamplers.0.conv": "blocks.25.conv",
+            "decoder.conv_norm_out": "conv_norm_out",
+            "decoder.conv_out": "conv_out",
+            "decoder.time_conv_out": "time_conv_out"
+        }
+        prefix_rename_dict = {
+            "decoder.mid_block.resnets.0.spatial_res_block": "blocks.0",
+            "decoder.mid_block.resnets.0.temporal_res_block": "blocks.1",
+            "decoder.mid_block.resnets.0.time_mixer": "blocks.1",
+            "decoder.mid_block.resnets.1.spatial_res_block": "blocks.3",
+            "decoder.mid_block.resnets.1.temporal_res_block": "blocks.4",
+            "decoder.mid_block.resnets.1.time_mixer": "blocks.4",
+
+            "decoder.up_blocks.0.resnets.0.spatial_res_block": "blocks.5",
+            "decoder.up_blocks.0.resnets.0.temporal_res_block": "blocks.6",
+            "decoder.up_blocks.0.resnets.0.time_mixer": "blocks.6",
+            "decoder.up_blocks.0.resnets.1.spatial_res_block": "blocks.7",
+            "decoder.up_blocks.0.resnets.1.temporal_res_block": "blocks.8",
+            "decoder.up_blocks.0.resnets.1.time_mixer": "blocks.8",
+            "decoder.up_blocks.0.resnets.2.spatial_res_block": "blocks.9",
+            "decoder.up_blocks.0.resnets.2.temporal_res_block": "blocks.10",
+            "decoder.up_blocks.0.resnets.2.time_mixer": "blocks.10",
+
+            "decoder.up_blocks.1.resnets.0.spatial_res_block": "blocks.12",
+            "decoder.up_blocks.1.resnets.0.temporal_res_block": "blocks.13",
+            "decoder.up_blocks.1.resnets.0.time_mixer": "blocks.13",
+            "decoder.up_blocks.1.resnets.1.spatial_res_block": "blocks.14",
+            "decoder.up_blocks.1.resnets.1.temporal_res_block": "blocks.15",
+            "decoder.up_blocks.1.resnets.1.time_mixer": "blocks.15",
+            "decoder.up_blocks.1.resnets.2.spatial_res_block": "blocks.16",
+            "decoder.up_blocks.1.resnets.2.temporal_res_block": "blocks.17",
+            "decoder.up_blocks.1.resnets.2.time_mixer": "blocks.17",
+
+            "decoder.up_blocks.2.resnets.0.spatial_res_block": "blocks.19",
+            "decoder.up_blocks.2.resnets.0.temporal_res_block": "blocks.20",
+            "decoder.up_blocks.2.resnets.0.time_mixer": "blocks.20",
+            "decoder.up_blocks.2.resnets.1.spatial_res_block": "blocks.21",
+            "decoder.up_blocks.2.resnets.1.temporal_res_block": "blocks.22",
+            "decoder.up_blocks.2.resnets.1.time_mixer": "blocks.22",
+            "decoder.up_blocks.2.resnets.2.spatial_res_block": "blocks.23",
+            "decoder.up_blocks.2.resnets.2.temporal_res_block": "blocks.24",
+            "decoder.up_blocks.2.resnets.2.time_mixer": "blocks.24",
+
+            "decoder.up_blocks.3.resnets.0.spatial_res_block": "blocks.26",
+            "decoder.up_blocks.3.resnets.0.temporal_res_block": "blocks.27",
+            "decoder.up_blocks.3.resnets.0.time_mixer": "blocks.27",
+            "decoder.up_blocks.3.resnets.1.spatial_res_block": "blocks.28",
+            "decoder.up_blocks.3.resnets.1.temporal_res_block": "blocks.29",
+            "decoder.up_blocks.3.resnets.1.time_mixer": "blocks.29",
+            "decoder.up_blocks.3.resnets.2.spatial_res_block": "blocks.30",
+            "decoder.up_blocks.3.resnets.2.temporal_res_block": "blocks.31",
+            "decoder.up_blocks.3.resnets.2.time_mixer": "blocks.31",
+        }
+        suffix_rename_dict = {
+            "norm1.weight": "norm1.weight",
+            "conv1.weight": "conv1.weight",
+            "norm2.weight": "norm2.weight",
+            "conv2.weight": "conv2.weight",
+            "conv_shortcut.weight": "conv_shortcut.weight",
+            "norm1.bias": "norm1.bias",
+            "conv1.bias": "conv1.bias",
+            "norm2.bias": "norm2.bias",
+            "conv2.bias": "conv2.bias",
+            "conv_shortcut.bias": "conv_shortcut.bias",
+            "mix_factor": "mix_factor",
+        }
+
+        state_dict_ = {}
+        for name in static_rename_dict:
+            state_dict_[static_rename_dict[name] + ".weight"] = state_dict[name + ".weight"]
+            state_dict_[static_rename_dict[name] + ".bias"] = state_dict[name + ".bias"]
+        for prefix_name in prefix_rename_dict:
+            for suffix_name in suffix_rename_dict:
+                name = prefix_name + "." + suffix_name
+                name_ = prefix_rename_dict[prefix_name] + "." + suffix_rename_dict[suffix_name]
+                if name in state_dict:
+                    state_dict_[name_] = state_dict[name]
+        
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "first_stage_model.decoder.conv_in.bias": "conv_in.bias",
+            "first_stage_model.decoder.conv_in.weight": "conv_in.weight",
+            "first_stage_model.decoder.conv_out.bias": "conv_out.bias",
+            "first_stage_model.decoder.conv_out.time_mix_conv.bias": "time_conv_out.bias",
+            "first_stage_model.decoder.conv_out.time_mix_conv.weight": "time_conv_out.weight",
+            "first_stage_model.decoder.conv_out.weight": "conv_out.weight",
+            "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.2.transformer_blocks.0.to_k.bias",
+            "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.2.transformer_blocks.0.to_k.weight",
+            "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.2.norm.bias",
+            "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.2.norm.weight",
+            "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.2.transformer_blocks.0.to_out.bias",
+            "first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.2.transformer_blocks.0.to_out.weight",
+            "first_stage_model.decoder.mid.attn_1.q.bias": "blocks.2.transformer_blocks.0.to_q.bias",
+            "first_stage_model.decoder.mid.attn_1.q.weight": "blocks.2.transformer_blocks.0.to_q.weight",
+            "first_stage_model.decoder.mid.attn_1.v.bias": "blocks.2.transformer_blocks.0.to_v.bias",
+            "first_stage_model.decoder.mid.attn_1.v.weight": "blocks.2.transformer_blocks.0.to_v.weight",
+            "first_stage_model.decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias",
+            "first_stage_model.decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight",
+            "first_stage_model.decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias",
+            "first_stage_model.decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight",
+            "first_stage_model.decoder.mid.block_1.mix_factor": "blocks.1.mix_factor",
+            "first_stage_model.decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias",
+            "first_stage_model.decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight",
+            "first_stage_model.decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias",
+            "first_stage_model.decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight",
+            "first_stage_model.decoder.mid.block_1.time_stack.in_layers.0.bias": "blocks.1.norm1.bias",
+            "first_stage_model.decoder.mid.block_1.time_stack.in_layers.0.weight": "blocks.1.norm1.weight",
+            "first_stage_model.decoder.mid.block_1.time_stack.in_layers.2.bias": "blocks.1.conv1.bias",
+            "first_stage_model.decoder.mid.block_1.time_stack.in_layers.2.weight": "blocks.1.conv1.weight",
+            "first_stage_model.decoder.mid.block_1.time_stack.out_layers.0.bias": "blocks.1.norm2.bias",
+            "first_stage_model.decoder.mid.block_1.time_stack.out_layers.0.weight": "blocks.1.norm2.weight",
+            "first_stage_model.decoder.mid.block_1.time_stack.out_layers.3.bias": "blocks.1.conv2.bias",
+            "first_stage_model.decoder.mid.block_1.time_stack.out_layers.3.weight": "blocks.1.conv2.weight",
+            "first_stage_model.decoder.mid.block_2.conv1.bias": "blocks.3.conv1.bias",
+            "first_stage_model.decoder.mid.block_2.conv1.weight": "blocks.3.conv1.weight",
+            "first_stage_model.decoder.mid.block_2.conv2.bias": "blocks.3.conv2.bias",
+            "first_stage_model.decoder.mid.block_2.conv2.weight": "blocks.3.conv2.weight",
+            "first_stage_model.decoder.mid.block_2.mix_factor": "blocks.4.mix_factor",
+            "first_stage_model.decoder.mid.block_2.norm1.bias": "blocks.3.norm1.bias",
+            "first_stage_model.decoder.mid.block_2.norm1.weight": "blocks.3.norm1.weight",
+            "first_stage_model.decoder.mid.block_2.norm2.bias": "blocks.3.norm2.bias",
+            "first_stage_model.decoder.mid.block_2.norm2.weight": "blocks.3.norm2.weight",
+            "first_stage_model.decoder.mid.block_2.time_stack.in_layers.0.bias": "blocks.4.norm1.bias",
+            "first_stage_model.decoder.mid.block_2.time_stack.in_layers.0.weight": "blocks.4.norm1.weight",
+            "first_stage_model.decoder.mid.block_2.time_stack.in_layers.2.bias": "blocks.4.conv1.bias",
+            "first_stage_model.decoder.mid.block_2.time_stack.in_layers.2.weight": "blocks.4.conv1.weight",
+            "first_stage_model.decoder.mid.block_2.time_stack.out_layers.0.bias": "blocks.4.norm2.bias",
+            "first_stage_model.decoder.mid.block_2.time_stack.out_layers.0.weight": "blocks.4.norm2.weight",
+            "first_stage_model.decoder.mid.block_2.time_stack.out_layers.3.bias": "blocks.4.conv2.bias",
+            "first_stage_model.decoder.mid.block_2.time_stack.out_layers.3.weight": "blocks.4.conv2.weight",
+            "first_stage_model.decoder.norm_out.bias": "conv_norm_out.bias",
+            "first_stage_model.decoder.norm_out.weight": "conv_norm_out.weight",
+            "first_stage_model.decoder.up.0.block.0.conv1.bias": "blocks.26.conv1.bias",
+            "first_stage_model.decoder.up.0.block.0.conv1.weight": "blocks.26.conv1.weight",
+            "first_stage_model.decoder.up.0.block.0.conv2.bias": "blocks.26.conv2.bias",
+            "first_stage_model.decoder.up.0.block.0.conv2.weight": "blocks.26.conv2.weight",
+            "first_stage_model.decoder.up.0.block.0.mix_factor": "blocks.27.mix_factor",
+            "first_stage_model.decoder.up.0.block.0.nin_shortcut.bias": "blocks.26.conv_shortcut.bias",
+            "first_stage_model.decoder.up.0.block.0.nin_shortcut.weight": "blocks.26.conv_shortcut.weight",
+            "first_stage_model.decoder.up.0.block.0.norm1.bias": "blocks.26.norm1.bias",
+            "first_stage_model.decoder.up.0.block.0.norm1.weight": "blocks.26.norm1.weight",
+            "first_stage_model.decoder.up.0.block.0.norm2.bias": "blocks.26.norm2.bias",
+            "first_stage_model.decoder.up.0.block.0.norm2.weight": "blocks.26.norm2.weight",
+            "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.0.bias": "blocks.27.norm1.bias",
+            "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.0.weight": "blocks.27.norm1.weight",
+            "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.2.bias": "blocks.27.conv1.bias",
+            "first_stage_model.decoder.up.0.block.0.time_stack.in_layers.2.weight": "blocks.27.conv1.weight",
+            "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.0.bias": "blocks.27.norm2.bias",
+            "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.0.weight": "blocks.27.norm2.weight",
+            "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.3.bias": "blocks.27.conv2.bias",
+            "first_stage_model.decoder.up.0.block.0.time_stack.out_layers.3.weight": "blocks.27.conv2.weight",
+            "first_stage_model.decoder.up.0.block.1.conv1.bias": "blocks.28.conv1.bias",
+            "first_stage_model.decoder.up.0.block.1.conv1.weight": "blocks.28.conv1.weight",
+            "first_stage_model.decoder.up.0.block.1.conv2.bias": "blocks.28.conv2.bias",
+            "first_stage_model.decoder.up.0.block.1.conv2.weight": "blocks.28.conv2.weight",
+            "first_stage_model.decoder.up.0.block.1.mix_factor": "blocks.29.mix_factor",
+            "first_stage_model.decoder.up.0.block.1.norm1.bias": "blocks.28.norm1.bias",
+            "first_stage_model.decoder.up.0.block.1.norm1.weight": "blocks.28.norm1.weight",
+            "first_stage_model.decoder.up.0.block.1.norm2.bias": "blocks.28.norm2.bias",
+            "first_stage_model.decoder.up.0.block.1.norm2.weight": "blocks.28.norm2.weight",
+            "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.0.bias": "blocks.29.norm1.bias",
+            "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.0.weight": "blocks.29.norm1.weight",
+            "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.2.bias": "blocks.29.conv1.bias",
+            "first_stage_model.decoder.up.0.block.1.time_stack.in_layers.2.weight": "blocks.29.conv1.weight",
+            "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.0.bias": "blocks.29.norm2.bias",
+            "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.0.weight": "blocks.29.norm2.weight",
+            "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.3.bias": "blocks.29.conv2.bias",
+            "first_stage_model.decoder.up.0.block.1.time_stack.out_layers.3.weight": "blocks.29.conv2.weight",
+            "first_stage_model.decoder.up.0.block.2.conv1.bias": "blocks.30.conv1.bias",
+            "first_stage_model.decoder.up.0.block.2.conv1.weight": "blocks.30.conv1.weight",
+            "first_stage_model.decoder.up.0.block.2.conv2.bias": "blocks.30.conv2.bias",
+            "first_stage_model.decoder.up.0.block.2.conv2.weight": "blocks.30.conv2.weight",
+            "first_stage_model.decoder.up.0.block.2.mix_factor": "blocks.31.mix_factor",
+            "first_stage_model.decoder.up.0.block.2.norm1.bias": "blocks.30.norm1.bias",
+            "first_stage_model.decoder.up.0.block.2.norm1.weight": "blocks.30.norm1.weight",
+            "first_stage_model.decoder.up.0.block.2.norm2.bias": "blocks.30.norm2.bias",
+            "first_stage_model.decoder.up.0.block.2.norm2.weight": "blocks.30.norm2.weight",
+            "first_stage_model.decoder.up.0.block.2.time_stack.in_layers.0.bias": "blocks.31.norm1.bias",
+            "first_stage_model.decoder.up.0.block.2.time_stack.in_layers.0.weight": "blocks.31.norm1.weight",
+            "first_stage_model.decoder.up.0.block.2.time_stack.in_layers.2.bias": "blocks.31.conv1.bias",
+            "first_stage_model.decoder.up.0.block.2.time_stack.in_layers.2.weight": "blocks.31.conv1.weight",
+            "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.0.bias": "blocks.31.norm2.bias",
+            "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.0.weight": "blocks.31.norm2.weight",
+            "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.3.bias": "blocks.31.conv2.bias",
+            "first_stage_model.decoder.up.0.block.2.time_stack.out_layers.3.weight": "blocks.31.conv2.weight",
+            "first_stage_model.decoder.up.1.block.0.conv1.bias": "blocks.19.conv1.bias",
+            "first_stage_model.decoder.up.1.block.0.conv1.weight": "blocks.19.conv1.weight",
+            "first_stage_model.decoder.up.1.block.0.conv2.bias": "blocks.19.conv2.bias",
+            "first_stage_model.decoder.up.1.block.0.conv2.weight": "blocks.19.conv2.weight",
+            "first_stage_model.decoder.up.1.block.0.mix_factor": "blocks.20.mix_factor",
+            "first_stage_model.decoder.up.1.block.0.nin_shortcut.bias": "blocks.19.conv_shortcut.bias",
+            "first_stage_model.decoder.up.1.block.0.nin_shortcut.weight": "blocks.19.conv_shortcut.weight",
+            "first_stage_model.decoder.up.1.block.0.norm1.bias": "blocks.19.norm1.bias",
+            "first_stage_model.decoder.up.1.block.0.norm1.weight": "blocks.19.norm1.weight",
+            "first_stage_model.decoder.up.1.block.0.norm2.bias": "blocks.19.norm2.bias",
+            "first_stage_model.decoder.up.1.block.0.norm2.weight": "blocks.19.norm2.weight",
+            "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.0.bias": "blocks.20.norm1.bias",
+            "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.0.weight": "blocks.20.norm1.weight",
+            "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.2.bias": "blocks.20.conv1.bias",
+            "first_stage_model.decoder.up.1.block.0.time_stack.in_layers.2.weight": "blocks.20.conv1.weight",
+            "first_stage_model.decoder.up.1.block.0.time_stack.out_layers.0.bias": "blocks.20.norm2.bias",
+            "first_stage_model.decoder.up.1.block.0.time_stack.out_layers.0.weight": "blocks.20.norm2.weight",
+            "first_stage_model.decoder.up.1.block.0.time_stack.out_layers.3.bias": "blocks.20.conv2.bias",
+            "first_stage_model.decoder.up.1.block.0.time_stack.out_layers.3.weight": "blocks.20.conv2.weight",
+            "first_stage_model.decoder.up.1.block.1.conv1.bias": "blocks.21.conv1.bias",
+            "first_stage_model.decoder.up.1.block.1.conv1.weight": "blocks.21.conv1.weight",
+            "first_stage_model.decoder.up.1.block.1.conv2.bias": "blocks.21.conv2.bias",
+            "first_stage_model.decoder.up.1.block.1.conv2.weight": "blocks.21.conv2.weight",
+            "first_stage_model.decoder.up.1.block.1.mix_factor": "blocks.22.mix_factor",
+            "first_stage_model.decoder.up.1.block.1.norm1.bias": "blocks.21.norm1.bias",
+            "first_stage_model.decoder.up.1.block.1.norm1.weight": "blocks.21.norm1.weight",
+            "first_stage_model.decoder.up.1.block.1.norm2.bias": "blocks.21.norm2.bias",
+            "first_stage_model.decoder.up.1.block.1.norm2.weight": "blocks.21.norm2.weight",
+            "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.0.bias": "blocks.22.norm1.bias",
+            "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.0.weight": "blocks.22.norm1.weight",
+            "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.2.bias": "blocks.22.conv1.bias",
+            "first_stage_model.decoder.up.1.block.1.time_stack.in_layers.2.weight": "blocks.22.conv1.weight",
+            "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.0.bias": "blocks.22.norm2.bias",
+            "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.0.weight": "blocks.22.norm2.weight",
+            "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.3.bias": "blocks.22.conv2.bias",
+            "first_stage_model.decoder.up.1.block.1.time_stack.out_layers.3.weight": "blocks.22.conv2.weight",
+            "first_stage_model.decoder.up.1.block.2.conv1.bias": "blocks.23.conv1.bias",
+            "first_stage_model.decoder.up.1.block.2.conv1.weight": "blocks.23.conv1.weight",
+            "first_stage_model.decoder.up.1.block.2.conv2.bias": "blocks.23.conv2.bias",
+            "first_stage_model.decoder.up.1.block.2.conv2.weight": "blocks.23.conv2.weight",
+            "first_stage_model.decoder.up.1.block.2.mix_factor": "blocks.24.mix_factor",
+            "first_stage_model.decoder.up.1.block.2.norm1.bias": "blocks.23.norm1.bias",
+            "first_stage_model.decoder.up.1.block.2.norm1.weight": "blocks.23.norm1.weight",
+            "first_stage_model.decoder.up.1.block.2.norm2.bias": "blocks.23.norm2.bias",
+            "first_stage_model.decoder.up.1.block.2.norm2.weight": "blocks.23.norm2.weight",
+            "first_stage_model.decoder.up.1.block.2.time_stack.in_layers.0.bias": "blocks.24.norm1.bias",
+            "first_stage_model.decoder.up.1.block.2.time_stack.in_layers.0.weight": "blocks.24.norm1.weight",
+            "first_stage_model.decoder.up.1.block.2.time_stack.in_layers.2.bias": "blocks.24.conv1.bias",
+            "first_stage_model.decoder.up.1.block.2.time_stack.in_layers.2.weight": "blocks.24.conv1.weight",
+            "first_stage_model.decoder.up.1.block.2.time_stack.out_layers.0.bias": "blocks.24.norm2.bias",
+            "first_stage_model.decoder.up.1.block.2.time_stack.out_layers.0.weight": "blocks.24.norm2.weight",
+            "first_stage_model.decoder.up.1.block.2.time_stack.out_layers.3.bias": "blocks.24.conv2.bias",
+            "first_stage_model.decoder.up.1.block.2.time_stack.out_layers.3.weight": "blocks.24.conv2.weight",
+            "first_stage_model.decoder.up.1.upsample.conv.bias": "blocks.25.conv.bias",
+            "first_stage_model.decoder.up.1.upsample.conv.weight": "blocks.25.conv.weight",
+            "first_stage_model.decoder.up.2.block.0.conv1.bias": "blocks.12.conv1.bias",
+            "first_stage_model.decoder.up.2.block.0.conv1.weight": "blocks.12.conv1.weight",
+            "first_stage_model.decoder.up.2.block.0.conv2.bias": "blocks.12.conv2.bias",
+            "first_stage_model.decoder.up.2.block.0.conv2.weight": "blocks.12.conv2.weight",
+            "first_stage_model.decoder.up.2.block.0.mix_factor": "blocks.13.mix_factor",
+            "first_stage_model.decoder.up.2.block.0.norm1.bias": "blocks.12.norm1.bias",
+            "first_stage_model.decoder.up.2.block.0.norm1.weight": "blocks.12.norm1.weight",
+            "first_stage_model.decoder.up.2.block.0.norm2.bias": "blocks.12.norm2.bias",
+            "first_stage_model.decoder.up.2.block.0.norm2.weight": "blocks.12.norm2.weight",
+            "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.0.bias": "blocks.13.norm1.bias",
+            "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.0.weight": "blocks.13.norm1.weight",
+            "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.2.bias": "blocks.13.conv1.bias",
+            "first_stage_model.decoder.up.2.block.0.time_stack.in_layers.2.weight": "blocks.13.conv1.weight",
+            "first_stage_model.decoder.up.2.block.0.time_stack.out_layers.0.bias": "blocks.13.norm2.bias",
+            "first_stage_model.decoder.up.2.block.0.time_stack.out_layers.0.weight": "blocks.13.norm2.weight",
+            "first_stage_model.decoder.up.2.block.0.time_stack.out_layers.3.bias": "blocks.13.conv2.bias",
+            "first_stage_model.decoder.up.2.block.0.time_stack.out_layers.3.weight": "blocks.13.conv2.weight",
+            "first_stage_model.decoder.up.2.block.1.conv1.bias": "blocks.14.conv1.bias",
+            "first_stage_model.decoder.up.2.block.1.conv1.weight": "blocks.14.conv1.weight",
+            "first_stage_model.decoder.up.2.block.1.conv2.bias": "blocks.14.conv2.bias",
+            "first_stage_model.decoder.up.2.block.1.conv2.weight": "blocks.14.conv2.weight",
+            "first_stage_model.decoder.up.2.block.1.mix_factor": "blocks.15.mix_factor",
+            "first_stage_model.decoder.up.2.block.1.norm1.bias": "blocks.14.norm1.bias",
+            "first_stage_model.decoder.up.2.block.1.norm1.weight": "blocks.14.norm1.weight",
+            "first_stage_model.decoder.up.2.block.1.norm2.bias": "blocks.14.norm2.bias",
+            "first_stage_model.decoder.up.2.block.1.norm2.weight": "blocks.14.norm2.weight",
+            "first_stage_model.decoder.up.2.block.1.time_stack.in_layers.0.bias": "blocks.15.norm1.bias",
+            "first_stage_model.decoder.up.2.block.1.time_stack.in_layers.0.weight": "blocks.15.norm1.weight",
+            "first_stage_model.decoder.up.2.block.1.time_stack.in_layers.2.bias": "blocks.15.conv1.bias",
+            "first_stage_model.decoder.up.2.block.1.time_stack.in_layers.2.weight": "blocks.15.conv1.weight",
+            "first_stage_model.decoder.up.2.block.1.time_stack.out_layers.0.bias": "blocks.15.norm2.bias",
+            "first_stage_model.decoder.up.2.block.1.time_stack.out_layers.0.weight": "blocks.15.norm2.weight",
+            "first_stage_model.decoder.up.2.block.1.time_stack.out_layers.3.bias": "blocks.15.conv2.bias",
+            "first_stage_model.decoder.up.2.block.1.time_stack.out_layers.3.weight": "blocks.15.conv2.weight",
+            "first_stage_model.decoder.up.2.block.2.conv1.bias": "blocks.16.conv1.bias",
+            "first_stage_model.decoder.up.2.block.2.conv1.weight": "blocks.16.conv1.weight",
+            "first_stage_model.decoder.up.2.block.2.conv2.bias": "blocks.16.conv2.bias",
+            "first_stage_model.decoder.up.2.block.2.conv2.weight": "blocks.16.conv2.weight",
+            "first_stage_model.decoder.up.2.block.2.mix_factor": "blocks.17.mix_factor",
+            "first_stage_model.decoder.up.2.block.2.norm1.bias": "blocks.16.norm1.bias",
+            "first_stage_model.decoder.up.2.block.2.norm1.weight": "blocks.16.norm1.weight",
+            "first_stage_model.decoder.up.2.block.2.norm2.bias": "blocks.16.norm2.bias",
+            "first_stage_model.decoder.up.2.block.2.norm2.weight": "blocks.16.norm2.weight",
+            "first_stage_model.decoder.up.2.block.2.time_stack.in_layers.0.bias": "blocks.17.norm1.bias",
+            "first_stage_model.decoder.up.2.block.2.time_stack.in_layers.0.weight": "blocks.17.norm1.weight",
+            "first_stage_model.decoder.up.2.block.2.time_stack.in_layers.2.bias": "blocks.17.conv1.bias",
+            "first_stage_model.decoder.up.2.block.2.time_stack.in_layers.2.weight": "blocks.17.conv1.weight",
+            "first_stage_model.decoder.up.2.block.2.time_stack.out_layers.0.bias": "blocks.17.norm2.bias",
+            "first_stage_model.decoder.up.2.block.2.time_stack.out_layers.0.weight": "blocks.17.norm2.weight",
+            "first_stage_model.decoder.up.2.block.2.time_stack.out_layers.3.bias": "blocks.17.conv2.bias",
+            "first_stage_model.decoder.up.2.block.2.time_stack.out_layers.3.weight": "blocks.17.conv2.weight",
+            "first_stage_model.decoder.up.2.upsample.conv.bias": "blocks.18.conv.bias",
+            "first_stage_model.decoder.up.2.upsample.conv.weight": "blocks.18.conv.weight",
+            "first_stage_model.decoder.up.3.block.0.conv1.bias": "blocks.5.conv1.bias",
+            "first_stage_model.decoder.up.3.block.0.conv1.weight": "blocks.5.conv1.weight",
+            "first_stage_model.decoder.up.3.block.0.conv2.bias": "blocks.5.conv2.bias",
+            "first_stage_model.decoder.up.3.block.0.conv2.weight": "blocks.5.conv2.weight",
+            "first_stage_model.decoder.up.3.block.0.mix_factor": "blocks.6.mix_factor",
+            "first_stage_model.decoder.up.3.block.0.norm1.bias": "blocks.5.norm1.bias",
+            "first_stage_model.decoder.up.3.block.0.norm1.weight": "blocks.5.norm1.weight",
+            "first_stage_model.decoder.up.3.block.0.norm2.bias": "blocks.5.norm2.bias",
+            "first_stage_model.decoder.up.3.block.0.norm2.weight": "blocks.5.norm2.weight",
+            "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.0.bias": "blocks.6.norm1.bias",
+            "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.0.weight": "blocks.6.norm1.weight",
+            "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.2.bias": "blocks.6.conv1.bias",
+            "first_stage_model.decoder.up.3.block.0.time_stack.in_layers.2.weight": "blocks.6.conv1.weight",
+            "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.0.bias": "blocks.6.norm2.bias",
+            "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.0.weight": "blocks.6.norm2.weight",
+            "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.3.bias": "blocks.6.conv2.bias",
+            "first_stage_model.decoder.up.3.block.0.time_stack.out_layers.3.weight": "blocks.6.conv2.weight",
+            "first_stage_model.decoder.up.3.block.1.conv1.bias": "blocks.7.conv1.bias",
+            "first_stage_model.decoder.up.3.block.1.conv1.weight": "blocks.7.conv1.weight",
+            "first_stage_model.decoder.up.3.block.1.conv2.bias": "blocks.7.conv2.bias",
+            "first_stage_model.decoder.up.3.block.1.conv2.weight": "blocks.7.conv2.weight",
+            "first_stage_model.decoder.up.3.block.1.mix_factor": "blocks.8.mix_factor",
+            "first_stage_model.decoder.up.3.block.1.norm1.bias": "blocks.7.norm1.bias",
+            "first_stage_model.decoder.up.3.block.1.norm1.weight": "blocks.7.norm1.weight",
+            "first_stage_model.decoder.up.3.block.1.norm2.bias": "blocks.7.norm2.bias",
+            "first_stage_model.decoder.up.3.block.1.norm2.weight": "blocks.7.norm2.weight",
+            "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.0.bias": "blocks.8.norm1.bias",
+            "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.0.weight": "blocks.8.norm1.weight",
+            "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.2.bias": "blocks.8.conv1.bias",
+            "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.2.weight": "blocks.8.conv1.weight",
+            "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.0.bias": "blocks.8.norm2.bias",
+            "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.0.weight": "blocks.8.norm2.weight",
+            "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.3.bias": "blocks.8.conv2.bias",
+            "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.3.weight": "blocks.8.conv2.weight",
+            "first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.9.conv1.bias",
+            "first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.9.conv1.weight",
+            "first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.9.conv2.bias",
+            "first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.9.conv2.weight",
+            "first_stage_model.decoder.up.3.block.2.mix_factor": "blocks.10.mix_factor",
+            "first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.9.norm1.bias",
+            "first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.9.norm1.weight",
+            "first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.9.norm2.bias",
+            "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.9.norm2.weight",
+            "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.bias": "blocks.10.norm1.bias",
+            "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.weight": "blocks.10.norm1.weight",
+            "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.bias": "blocks.10.conv1.bias",
+            "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.weight": "blocks.10.conv1.weight",
+            "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.bias": "blocks.10.norm2.bias",
+            "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.weight": "blocks.10.norm2.weight",
+            "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.bias": "blocks.10.conv2.bias",
+            "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.weight": "blocks.10.conv2.weight",
+            "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.11.conv.bias",
+            "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.11.conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "blocks.2.transformer_blocks.0" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_

diffsynth/models/svd_vae_encoder.py

@@ -0,0 +1,138 @@
+from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder
+
+
+class SVDVAEEncoder(SDVAEEncoder):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.13025
+    
+    def state_dict_converter(self):
+        return SVDVAEEncoderStateDictConverter()
+
+
+class SVDVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
+    def __init__(self):
+        super().__init__()
+
+    def from_diffusers(self, state_dict):
+        return super().from_diffusers(state_dict)
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "conditioner.embedders.3.encoder.encoder.conv_in.bias": "conv_in.bias",
+            "conditioner.embedders.3.encoder.encoder.conv_in.weight": "conv_in.weight",
+            "conditioner.embedders.3.encoder.encoder.conv_out.bias": "conv_out.bias",
+            "conditioner.embedders.3.encoder.encoder.conv_out.weight": "conv_out.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.norm_out.bias": "conv_norm_out.bias",
+            "conditioner.embedders.3.encoder.encoder.norm_out.weight": "conv_norm_out.weight",
+            "conditioner.embedders.3.encoder.quant_conv.bias": "quant_conv.bias",
+            "conditioner.embedders.3.encoder.quant_conv.weight": "quant_conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_

diffsynth/models/tiler.py

@@ -0,0 +1,106 @@
+import torch
+from einops import rearrange, repeat
+
+
+class TileWorker:
+    def __init__(self):
+        pass
+
+
+    def mask(self, height, width, border_width):
+        # Create a mask with shape (height, width).
+        # The centre area is filled with 1, and the border line is filled with values in range (0, 1].
+        x = torch.arange(height).repeat(width, 1).T
+        y = torch.arange(width).repeat(height, 1)
+        mask = torch.stack([x + 1, height - x, y + 1, width - y]).min(dim=0).values
+        mask = (mask / border_width).clip(0, 1)
+        return mask
+
+
+    def tile(self, model_input, tile_size, tile_stride, tile_device, tile_dtype):
+        # Convert a tensor (b, c, h, w) to (b, c, tile_size, tile_size, tile_num)
+        batch_size, channel, _, _ = model_input.shape
+        model_input = model_input.to(device=tile_device, dtype=tile_dtype)
+        unfold_operator = torch.nn.Unfold(
+            kernel_size=(tile_size, tile_size),
+            stride=(tile_stride, tile_stride)
+        )
+        model_input = unfold_operator(model_input)
+        model_input = model_input.view((batch_size, channel, tile_size, tile_size, -1))
+
+        return model_input
+
+
+    def tiled_inference(self, forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype):
+        # Call y=forward_fn(x) for each tile
+        tile_num = model_input.shape[-1]
+        model_output_stack = []
+
+        for tile_id in range(0, tile_num, tile_batch_size):
+
+            # process input
+            tile_id_ = min(tile_id + tile_batch_size, tile_num)
+            x = model_input[:, :, :, :, tile_id: tile_id_]
+            x = x.to(device=inference_device, dtype=inference_dtype)
+            x = rearrange(x, "b c h w n -> (n b) c h w")
+
+            # process output
+            y = forward_fn(x)
+            y = rearrange(y, "(n b) c h w -> b c h w n", n=tile_id_-tile_id)
+            y = y.to(device=tile_device, dtype=tile_dtype)
+            model_output_stack.append(y)
+
+        model_output = torch.concat(model_output_stack, dim=-1)
+        return model_output
+
+
+    def io_scale(self, model_output, tile_size):
+        # Determine the size modification happend in forward_fn
+        # We only consider the same scale on height and width.
+        io_scale = model_output.shape[2] / tile_size
+        return io_scale
+    
+
+    def untile(self, model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype):
+        # The reversed function of tile
+        mask = self.mask(tile_size, tile_size, border_width)
+        mask = mask.to(device=tile_device, dtype=tile_dtype)
+        mask = rearrange(mask, "h w -> 1 1 h w 1")
+        model_output = model_output * mask
+
+        fold_operator = torch.nn.Fold(
+            output_size=(height, width),
+            kernel_size=(tile_size, tile_size),
+            stride=(tile_stride, tile_stride)
+        )
+        mask = repeat(mask[0, 0, :, :, 0], "h w -> 1 (h w) n", n=model_output.shape[-1])
+        model_output = rearrange(model_output, "b c h w n -> b (c h w) n")
+        model_output = fold_operator(model_output) / fold_operator(mask)
+
+        return model_output
+
+
+    def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_batch_size=1, tile_device="cpu", tile_dtype=torch.float32, border_width=None):
+        # Prepare
+        inference_device, inference_dtype = model_input.device, model_input.dtype
+        height, width = model_input.shape[2], model_input.shape[3]
+        border_width = int(tile_stride*0.5) if border_width is None else border_width
+
+        # tile
+        model_input = self.tile(model_input, tile_size, tile_stride, tile_device, tile_dtype)
+
+        # inference
+        model_output = self.tiled_inference(forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype)
+
+        # resize
+        io_scale = self.io_scale(model_output, tile_size)
+        height, width = int(height*io_scale), int(width*io_scale)
+        tile_size, tile_stride = int(tile_size*io_scale), int(tile_stride*io_scale)
+        border_width = int(border_width*io_scale)
+
+        # untile
+        model_output = self.untile(model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype)
+        
+        # Done!
+        model_output = model_output.to(device=inference_device, dtype=inference_dtype)
+        return model_output

diffsynth/pipelines/__init__.py

@@ -0,0 +1,6 @@
+from .stable_diffusion import SDImagePipeline
+from .stable_diffusion_xl import SDXLImagePipeline
+from .stable_diffusion_video import SDVideoPipeline, SDVideoPipelineRunner
+from .stable_diffusion_xl_video import SDXLVideoPipeline
+from .stable_video_diffusion import SVDVideoPipeline
+from .hunyuan_dit import HunyuanDiTImagePipeline

diffsynth/pipelines/dancer.py

@@ -0,0 +1,174 @@
+import torch
+from ..models import SDUNet, SDMotionModel, SDXLUNet, SDXLMotionModel
+from ..models.sd_unet import PushBlock, PopBlock
+from ..controlnets import MultiControlNetManager
+
+
+def lets_dance(
+    unet: SDUNet,
+    motion_modules: SDMotionModel = None,
+    controlnet: MultiControlNetManager = None,
+    sample = None,
+    timestep = None,
+    encoder_hidden_states = None,
+    ipadapter_kwargs_list = {},
+    controlnet_frames = None,
+    unet_batch_size = 1,
+    controlnet_batch_size = 1,
+    cross_frame_attention = False,
+    tiled=False,
+    tile_size=64,
+    tile_stride=32,
+    device = "cuda",
+    vram_limit_level = 0,
+):
+    # 1. ControlNet
+    #     This part will be repeated on overlapping frames if animatediff_batch_size > animatediff_stride.
+    #     I leave it here because I intend to do something interesting on the ControlNets.
+    controlnet_insert_block_id = 30
+    if controlnet is not None and controlnet_frames is not None:
+        res_stacks = []
+        # process controlnet frames with batch
+        for batch_id in range(0, sample.shape[0], controlnet_batch_size):
+            batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0])
+            res_stack = controlnet(
+                sample[batch_id: batch_id_],
+                timestep,
+                encoder_hidden_states[batch_id: batch_id_],
+                controlnet_frames[:, batch_id: batch_id_],
+                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
+            )
+            if vram_limit_level >= 1:
+                res_stack = [res.cpu() for res in res_stack]
+            res_stacks.append(res_stack)
+        # concat the residual
+        additional_res_stack = []
+        for i in range(len(res_stacks[0])):
+            res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0)
+            additional_res_stack.append(res)
+    else:
+        additional_res_stack = None
+
+    # 2. time
+    time_emb = unet.time_proj(timestep[None]).to(sample.dtype)
+    time_emb = unet.time_embedding(time_emb)
+
+    # 3. pre-process
+    height, width = sample.shape[2], sample.shape[3]
+    hidden_states = unet.conv_in(sample)
+    text_emb = encoder_hidden_states
+    res_stack = [hidden_states.cpu() if vram_limit_level>=1 else hidden_states]
+
+    # 4. blocks
+    for block_id, block in enumerate(unet.blocks):
+        # 4.1 UNet
+        if isinstance(block, PushBlock):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+            if vram_limit_level>=1:
+                res_stack[-1] = res_stack[-1].cpu()
+        elif isinstance(block, PopBlock):
+            if vram_limit_level>=1:
+                res_stack[-1] = res_stack[-1].to(device)
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        else:
+            hidden_states_input = hidden_states
+            hidden_states_output = []
+            for batch_id in range(0, sample.shape[0], unet_batch_size):
+                batch_id_ = min(batch_id + unet_batch_size, sample.shape[0])
+                hidden_states, _, _, _ = block(
+                    hidden_states_input[batch_id: batch_id_],
+                    time_emb,
+                    text_emb[batch_id: batch_id_],
+                    res_stack,
+                    cross_frame_attention=cross_frame_attention,
+                    ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}),
+                    tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
+                )
+                hidden_states_output.append(hidden_states)
+            hidden_states = torch.concat(hidden_states_output, dim=0)
+        # 4.2 AnimateDiff
+        if motion_modules is not None:
+            if block_id in motion_modules.call_block_id:
+                motion_module_id = motion_modules.call_block_id[block_id]
+                hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id](
+                    hidden_states, time_emb, text_emb, res_stack,
+                    batch_size=1
+                )
+        # 4.3 ControlNet
+        if block_id == controlnet_insert_block_id and additional_res_stack is not None:
+            hidden_states += additional_res_stack.pop().to(device)
+            if vram_limit_level>=1:
+                res_stack = [(res.to(device) + additional_res.to(device)).cpu() for res, additional_res in zip(res_stack, additional_res_stack)]
+            else:
+                res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)]
+    
+    # 5. output
+    hidden_states = unet.conv_norm_out(hidden_states)
+    hidden_states = unet.conv_act(hidden_states)
+    hidden_states = unet.conv_out(hidden_states)
+
+    return hidden_states
+
+
+
+
+def lets_dance_xl(
+    unet: SDXLUNet,
+    motion_modules: SDXLMotionModel = None,
+    controlnet: MultiControlNetManager = None,
+    sample = None,
+    add_time_id = None,
+    add_text_embeds = None,
+    timestep = None,
+    encoder_hidden_states = None,
+    ipadapter_kwargs_list = {},
+    controlnet_frames = None,
+    unet_batch_size = 1,
+    controlnet_batch_size = 1,
+    cross_frame_attention = False,
+    tiled=False,
+    tile_size=64,
+    tile_stride=32,
+    device = "cuda",
+    vram_limit_level = 0,
+):
+    # 2. time
+    t_emb = unet.time_proj(timestep[None]).to(sample.dtype)
+    t_emb = unet.time_embedding(t_emb)
+
+    time_embeds = unet.add_time_proj(add_time_id)
+    time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1))
+    add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1)
+    add_embeds = add_embeds.to(sample.dtype)
+    add_embeds = unet.add_time_embedding(add_embeds)
+
+    time_emb = t_emb + add_embeds
+
+    # 3. pre-process
+    height, width = sample.shape[2], sample.shape[3]
+    hidden_states = unet.conv_in(sample)
+    text_emb = encoder_hidden_states
+    res_stack = [hidden_states]
+
+    # 4. blocks
+    for block_id, block in enumerate(unet.blocks):
+        hidden_states, time_emb, text_emb, res_stack = block(
+            hidden_states, time_emb, text_emb, res_stack,
+            tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
+            ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {})
+        )
+        # 4.2 AnimateDiff
+        if motion_modules is not None:
+            if block_id in motion_modules.call_block_id:
+                motion_module_id = motion_modules.call_block_id[block_id]
+                hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id](
+                    hidden_states, time_emb, text_emb, res_stack,
+                    batch_size=1
+                )
+
+    # 5. output
+    hidden_states = unet.conv_norm_out(hidden_states)
+    hidden_states = unet.conv_act(hidden_states)
+    hidden_states = unet.conv_out(hidden_states)
+
+    return hidden_states

diffsynth/pipelines/hunyuan_dit.py

@@ -0,0 +1,298 @@
+from ..models.hunyuan_dit import HunyuanDiT
+from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
+from ..models.sdxl_vae_encoder import SDXLVAEEncoder
+from ..models.sdxl_vae_decoder import SDXLVAEDecoder
+from ..models import ModelManager
+from ..prompts import HunyuanDiTPrompter
+from ..schedulers import EnhancedDDIMScheduler
+import torch
+from tqdm import tqdm
+from PIL import Image
+import numpy as np
+
+
+
+class ImageSizeManager:
+    def __init__(self):
+        pass
+
+
+    def _to_tuple(self, x):
+        if isinstance(x, int):
+            return x, x
+        else:
+            return x
+
+
+    def get_fill_resize_and_crop(self, src, tgt):
+        th, tw = self._to_tuple(tgt)
+        h, w = self._to_tuple(src)
+
+        tr = th / tw        # base 分辨率
+        r = h / w           # 目标分辨率
+
+        # resize
+        if r > tr:
+            resize_height = th
+            resize_width = int(round(th / h * w))
+        else:
+            resize_width = tw
+            resize_height = int(round(tw / w * h))    # 根据base分辨率，将目标分辨率resize下来
+
+        crop_top = int(round((th - resize_height) / 2.0))
+        crop_left = int(round((tw - resize_width) / 2.0))
+
+        return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+    def get_meshgrid(self, start, *args):
+        if len(args) == 0:
+            # start is grid_size
+            num = self._to_tuple(start)
+            start = (0, 0)
+            stop = num
+        elif len(args) == 1:
+            # start is start, args[0] is stop, step is 1
+            start = self._to_tuple(start)
+            stop = self._to_tuple(args[0])
+            num = (stop[0] - start[0], stop[1] - start[1])
+        elif len(args) == 2:
+            # start is start, args[0] is stop, args[1] is num
+            start = self._to_tuple(start)       # 左上角   eg: 12,0
+            stop = self._to_tuple(args[0])      # 右下角   eg: 20,32
+            num = self._to_tuple(args[1])       # 目标大小  eg: 32,124
+        else:
+            raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}")
+
+        grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) # 12-20 中间差值32份   0-32 中间差值124份
+        grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32)
+        grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+        grid = np.stack(grid, axis=0)   # [2, W, H]
+        return grid
+
+
+    def get_2d_rotary_pos_embed(self, embed_dim, start, *args, use_real=True):
+        grid = self.get_meshgrid(start, *args)   # [2, H, w]
+        grid = grid.reshape([2, 1, *grid.shape[1:]])   # 返回一个采样矩阵  分辨率与目标分辨率一致
+        pos_embed = self.get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
+        return pos_embed
+
+
+    def get_2d_rotary_pos_embed_from_grid(self, embed_dim, grid, use_real=False):
+        assert embed_dim % 4 == 0
+
+        # use half of dimensions to encode grid_h
+        emb_h = self.get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real)  # (H*W, D/4)
+        emb_w = self.get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real)  # (H*W, D/4)
+
+        if use_real:
+            cos = torch.cat([emb_h[0], emb_w[0]], dim=1)    # (H*W, D/2)
+            sin = torch.cat([emb_h[1], emb_w[1]], dim=1)    # (H*W, D/2)
+            return cos, sin
+        else:
+            emb = torch.cat([emb_h, emb_w], dim=1)    # (H*W, D/2)
+            return emb
+
+
+    def get_1d_rotary_pos_embed(self, dim: int, pos, theta: float = 10000.0, use_real=False):
+        if isinstance(pos, int):
+            pos = np.arange(pos)
+        freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))  # [D/2]
+        t = torch.from_numpy(pos).to(freqs.device)  # type: ignore  # [S]
+        freqs = torch.outer(t, freqs).float()  # type: ignore   # [S, D/2]
+        if use_real:
+            freqs_cos = freqs.cos().repeat_interleave(2, dim=1)  # [S, D]
+            freqs_sin = freqs.sin().repeat_interleave(2, dim=1)  # [S, D]
+            return freqs_cos, freqs_sin
+        else:
+            freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
+            return freqs_cis
+        
+
+    def calc_rope(self, height, width):
+        patch_size = 2
+        head_size = 88
+        th = height // 8 // patch_size
+        tw = width // 8 // patch_size
+        base_size = 512 // 8 // patch_size
+        start, stop = self.get_fill_resize_and_crop((th, tw), base_size)
+        sub_args = [start, stop, (th, tw)]
+        rope = self.get_2d_rotary_pos_embed(head_size, *sub_args)
+        return rope
+
+
+
+class HunyuanDiTImagePipeline(torch.nn.Module):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16):
+        super().__init__()
+        self.scheduler = EnhancedDDIMScheduler(prediction_type="v_prediction", beta_start=0.00085, beta_end=0.03)
+        self.prompter = HunyuanDiTPrompter()
+        self.device = device
+        self.torch_dtype = torch_dtype
+        self.image_size_manager = ImageSizeManager()
+        # models
+        self.text_encoder: HunyuanDiTCLIPTextEncoder = None
+        self.text_encoder_t5: HunyuanDiTT5TextEncoder = None
+        self.dit: HunyuanDiT = None
+        self.vae_decoder: SDXLVAEDecoder = None
+        self.vae_encoder: SDXLVAEEncoder = None
+
+
+    def fetch_main_models(self, model_manager: ModelManager):
+        self.text_encoder = model_manager.hunyuan_dit_clip_text_encoder
+        self.text_encoder_t5 = model_manager.hunyuan_dit_t5_text_encoder
+        self.dit = model_manager.hunyuan_dit
+        self.vae_decoder = model_manager.vae_decoder
+        self.vae_encoder = model_manager.vae_encoder
+
+
+    def fetch_prompter(self, model_manager: ModelManager):
+        self.prompter.load_from_model_manager(model_manager)
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager):
+        pipe = HunyuanDiTImagePipeline(
+            device=model_manager.device,
+            torch_dtype=model_manager.torch_dtype,
+        )
+        pipe.fetch_main_models(model_manager)
+        pipe.fetch_prompter(model_manager)
+        return pipe
+    
+
+    def preprocess_image(self, image):
+        image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
+        return image
+    
+
+    def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
+        image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+        image = image.cpu().permute(1, 2, 0).numpy()
+        image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
+        return image
+    
+
+    def prepare_extra_input(self, height=1024, width=1024, tiled=False, tile_size=64, tile_stride=32, batch_size=1):
+        if tiled:
+            height, width = tile_size * 16, tile_size * 16
+        image_meta_size = torch.as_tensor([width, height, width, height, 0, 0]).to(device=self.device)
+        freqs_cis_img = self.image_size_manager.calc_rope(height, width)
+        image_meta_size = torch.stack([image_meta_size] * batch_size)
+        return {
+            "size_emb": image_meta_size,
+            "freq_cis_img": (freqs_cis_img[0].to(dtype=self.torch_dtype, device=self.device), freqs_cis_img[1].to(dtype=self.torch_dtype, device=self.device)),
+            "tiled": tiled,
+            "tile_size": tile_size,
+            "tile_stride": tile_stride
+        }
+    
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt,
+        negative_prompt="",
+        cfg_scale=7.5,
+        clip_skip=1,
+        clip_skip_2=1,
+        input_image=None,
+        reference_images=[],
+        reference_strengths=[0.4],
+        denoising_strength=1.0,
+        height=1024,
+        width=1024,
+        num_inference_steps=20,
+        tiled=False,
+        tile_size=64,
+        tile_stride=32,
+        progress_bar_cmd=tqdm,
+        progress_bar_st=None,
+    ):
+        # Prepare scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
+
+        # Prepare latent tensors
+        noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+        if input_image is not None:
+            image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
+            latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype)
+            latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
+        else:
+            latents = noise.clone()
+
+        # Prepare reference latents
+        reference_latents = []
+        for reference_image in reference_images:
+            reference_image = self.preprocess_image(reference_image).to(device=self.device, dtype=self.torch_dtype)
+            reference_latents.append(self.vae_encoder(reference_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype))
+
+        # Encode prompts
+        prompt_emb_posi, attention_mask_posi, prompt_emb_t5_posi, attention_mask_t5_posi = self.prompter.encode_prompt(
+            self.text_encoder,
+            self.text_encoder_t5,
+            prompt,
+            clip_skip=clip_skip,
+            clip_skip_2=clip_skip_2,
+            positive=True,
+            device=self.device
+        )
+        if cfg_scale != 1.0:
+            prompt_emb_nega, attention_mask_nega, prompt_emb_t5_nega, attention_mask_t5_nega = self.prompter.encode_prompt(
+                self.text_encoder,
+                self.text_encoder_t5,
+                negative_prompt,
+                clip_skip=clip_skip,
+                clip_skip_2=clip_skip_2,
+                positive=False,
+                device=self.device
+            )
+
+        # Prepare positional id
+        extra_input = self.prepare_extra_input(height, width, tiled, tile_size)
+
+        # Denoise
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = torch.tensor([timestep]).to(dtype=self.torch_dtype, device=self.device)
+
+            # In-context reference
+            for reference_latents_, reference_strength in zip(reference_latents, reference_strengths):
+                if progress_id < num_inference_steps * reference_strength:
+                    noisy_reference_latents = self.scheduler.add_noise(reference_latents_, noise, self.scheduler.timesteps[progress_id])
+                    self.dit(
+                        noisy_reference_latents,
+                        prompt_emb_posi, prompt_emb_t5_posi, attention_mask_posi, attention_mask_t5_posi,
+                        timestep,
+                        **extra_input,
+                        to_cache=True
+                    )
+            # Positive side
+            noise_pred_posi = self.dit(
+                latents,
+                prompt_emb_posi, prompt_emb_t5_posi, attention_mask_posi, attention_mask_t5_posi,
+                timestep,
+                **extra_input,
+            )
+            if cfg_scale != 1.0:
+                # Negative side
+                noise_pred_nega = self.dit(
+                    latents,
+                    prompt_emb_nega, prompt_emb_t5_nega, attention_mask_nega, attention_mask_t5_nega,
+                    timestep,
+                    **extra_input
+                )
+                # Classifier-free guidance
+                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+            else:
+                noise_pred = noise_pred_posi
+
+            latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents)
+
+            if progress_bar_st is not None:
+                progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
+        
+        # Decode image
+        image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+
+        return image

diffsynth/pipelines/stable_diffusion.py

@@ -0,0 +1,167 @@
+from ..models import ModelManager, SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder
+from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator
+from ..prompts import SDPrompter
+from ..schedulers import EnhancedDDIMScheduler
+from .dancer import lets_dance
+from typing import List
+import torch
+from tqdm import tqdm
+from PIL import Image
+import numpy as np
+
+
+class SDImagePipeline(torch.nn.Module):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16):
+        super().__init__()
+        self.scheduler = EnhancedDDIMScheduler()
+        self.prompter = SDPrompter()
+        self.device = device
+        self.torch_dtype = torch_dtype
+        # models
+        self.text_encoder: SDTextEncoder = None
+        self.unet: SDUNet = None
+        self.vae_decoder: SDVAEDecoder = None
+        self.vae_encoder: SDVAEEncoder = None
+        self.controlnet: MultiControlNetManager = None
+        self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None
+        self.ipadapter: SDIpAdapter = None
+
+
+    def fetch_main_models(self, model_manager: ModelManager):
+        self.text_encoder = model_manager.text_encoder
+        self.unet = model_manager.unet
+        self.vae_decoder = model_manager.vae_decoder
+        self.vae_encoder = model_manager.vae_encoder
+
+
+    def fetch_controlnet_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]):
+        controlnet_units = []
+        for config in controlnet_config_units:
+            controlnet_unit = ControlNetUnit(
+                Annotator(config.processor_id),
+                model_manager.get_model_with_model_path(config.model_path),
+                config.scale
+            )
+            controlnet_units.append(controlnet_unit)
+        self.controlnet = MultiControlNetManager(controlnet_units)
+
+    
+    def fetch_ipadapter(self, model_manager: ModelManager):
+        if "ipadapter" in model_manager.model:
+            self.ipadapter = model_manager.ipadapter
+        if "ipadapter_image_encoder" in model_manager.model:
+            self.ipadapter_image_encoder = model_manager.ipadapter_image_encoder
+
+
+    def fetch_prompter(self, model_manager: ModelManager):
+        self.prompter.load_from_model_manager(model_manager)
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]):
+        pipe = SDImagePipeline(
+            device=model_manager.device,
+            torch_dtype=model_manager.torch_dtype,
+        )
+        pipe.fetch_main_models(model_manager)
+        pipe.fetch_prompter(model_manager)
+        pipe.fetch_controlnet_models(model_manager, controlnet_config_units)
+        pipe.fetch_ipadapter(model_manager)
+        return pipe
+    
+
+    def preprocess_image(self, image):
+        image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
+        return image
+    
+
+    def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
+        image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+        image = image.cpu().permute(1, 2, 0).numpy()
+        image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
+        return image
+    
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt,
+        negative_prompt="",
+        cfg_scale=7.5,
+        clip_skip=1,
+        input_image=None,
+        ipadapter_images=None,
+        ipadapter_scale=1.0,
+        controlnet_image=None,
+        denoising_strength=1.0,
+        height=512,
+        width=512,
+        num_inference_steps=20,
+        tiled=False,
+        tile_size=64,
+        tile_stride=32,
+        progress_bar_cmd=tqdm,
+        progress_bar_st=None,
+    ):
+        # Prepare scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
+
+        # Prepare latent tensors
+        if input_image is not None:
+            image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
+            latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+            noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+            latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
+        else:
+            latents = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+
+        # Encode prompts
+        prompt_emb_posi = self.prompter.encode_prompt(self.text_encoder, prompt, clip_skip=clip_skip, device=self.device, positive=True)
+        prompt_emb_nega = self.prompter.encode_prompt(self.text_encoder, negative_prompt, clip_skip=clip_skip, device=self.device, positive=False)
+
+        # IP-Adapter
+        if ipadapter_images is not None:
+            ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images)
+            ipadapter_kwargs_list_posi = self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)
+            ipadapter_kwargs_list_nega = self.ipadapter(torch.zeros_like(ipadapter_image_encoding))
+        else:
+            ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {}, {}
+
+        # Prepare ControlNets
+        if controlnet_image is not None:
+            controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype)
+            controlnet_image = controlnet_image.unsqueeze(1)
+        
+        # Denoise
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = torch.IntTensor((timestep,))[0].to(self.device)
+
+            # Classifier-free guidance
+            noise_pred_posi = lets_dance(
+                self.unet, motion_modules=None, controlnet=self.controlnet,
+                sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_posi, controlnet_frames=controlnet_image,
+                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
+                ipadapter_kwargs_list=ipadapter_kwargs_list_posi,
+                device=self.device, vram_limit_level=0
+            )
+            noise_pred_nega = lets_dance(
+                self.unet, motion_modules=None, controlnet=self.controlnet,
+                sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega, controlnet_frames=controlnet_image,
+                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
+                ipadapter_kwargs_list=ipadapter_kwargs_list_nega,
+                device=self.device, vram_limit_level=0
+            )
+            noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+
+            # DDIM
+            latents = self.scheduler.step(noise_pred, timestep, latents)
+
+            # UI
+            if progress_bar_st is not None:
+                progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
+        
+        # Decode image
+        image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+
+        return image

diffsynth/pipelines/stable_diffusion_video.py

@@ -0,0 +1,356 @@
+from ..models import ModelManager, SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDMotionModel
+from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator
+from ..prompts import SDPrompter
+from ..schedulers import EnhancedDDIMScheduler
+from ..data import VideoData, save_frames, save_video
+from .dancer import lets_dance
+from ..processors.sequencial_processor import SequencialProcessor
+from typing import List
+import torch, os, json
+from tqdm import tqdm
+from PIL import Image
+import numpy as np
+
+
+def lets_dance_with_long_video(
+    unet: SDUNet,
+    motion_modules: SDMotionModel = None,
+    controlnet: MultiControlNetManager = None,
+    sample = None,
+    timestep = None,
+    encoder_hidden_states = None,
+    controlnet_frames = None,
+    animatediff_batch_size = 16,
+    animatediff_stride = 8,
+    unet_batch_size = 1,
+    controlnet_batch_size = 1,
+    cross_frame_attention = False,
+    device = "cuda",
+    vram_limit_level = 0,
+):
+    num_frames = sample.shape[0]
+    hidden_states_output = [(torch.zeros(sample[0].shape, dtype=sample[0].dtype), 0) for i in range(num_frames)]
+
+    for batch_id in range(0, num_frames, animatediff_stride):
+        batch_id_ = min(batch_id + animatediff_batch_size, num_frames)
+
+        # process this batch
+        hidden_states_batch = lets_dance(
+            unet, motion_modules, controlnet,
+            sample[batch_id: batch_id_].to(device),
+            timestep,
+            encoder_hidden_states[batch_id: batch_id_].to(device),
+            controlnet_frames=controlnet_frames[:, batch_id: batch_id_].to(device) if controlnet_frames is not None else None,
+            unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size,
+            cross_frame_attention=cross_frame_attention,
+            device=device, vram_limit_level=vram_limit_level
+        ).cpu()
+
+        # update hidden_states
+        for i, hidden_states_updated in zip(range(batch_id, batch_id_), hidden_states_batch):
+            bias = max(1 - abs(i - (batch_id + batch_id_ - 1) / 2) / ((batch_id_ - batch_id - 1 + 1e-2) / 2), 1e-2)
+            hidden_states, num = hidden_states_output[i]
+            hidden_states = hidden_states * (num / (num + bias)) + hidden_states_updated * (bias / (num + bias))
+            hidden_states_output[i] = (hidden_states, num + bias)
+
+        if batch_id_ == num_frames:
+            break
+
+    # output
+    hidden_states = torch.stack([h for h, _ in hidden_states_output])
+    return hidden_states
+
+
+class SDVideoPipeline(torch.nn.Module):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16, use_animatediff=True):
+        super().__init__()
+        self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_animatediff else "scaled_linear")
+        self.prompter = SDPrompter()
+        self.device = device
+        self.torch_dtype = torch_dtype
+        # models
+        self.text_encoder: SDTextEncoder = None
+        self.unet: SDUNet = None
+        self.vae_decoder: SDVAEDecoder = None
+        self.vae_encoder: SDVAEEncoder = None
+        self.controlnet: MultiControlNetManager = None
+        self.motion_modules: SDMotionModel = None
+
+
+    def fetch_main_models(self, model_manager: ModelManager):
+        self.text_encoder = model_manager.text_encoder
+        self.unet = model_manager.unet
+        self.vae_decoder = model_manager.vae_decoder
+        self.vae_encoder = model_manager.vae_encoder
+
+
+    def fetch_controlnet_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]):
+        controlnet_units = []
+        for config in controlnet_config_units:
+            controlnet_unit = ControlNetUnit(
+                Annotator(config.processor_id),
+                model_manager.get_model_with_model_path(config.model_path),
+                config.scale
+            )
+            controlnet_units.append(controlnet_unit)
+        self.controlnet = MultiControlNetManager(controlnet_units)
+
+
+    def fetch_motion_modules(self, model_manager: ModelManager):
+        if "motion_modules" in model_manager.model:
+            self.motion_modules = model_manager.motion_modules
+
+
+    def fetch_prompter(self, model_manager: ModelManager):
+        self.prompter.load_from_model_manager(model_manager)
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]):
+        pipe = SDVideoPipeline(
+            device=model_manager.device,
+            torch_dtype=model_manager.torch_dtype,
+            use_animatediff="motion_modules" in model_manager.model
+        )
+        pipe.fetch_main_models(model_manager)
+        pipe.fetch_motion_modules(model_manager)
+        pipe.fetch_prompter(model_manager)
+        pipe.fetch_controlnet_models(model_manager, controlnet_config_units)
+        return pipe
+    
+
+    def preprocess_image(self, image):
+        image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
+        return image
+    
+
+    def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
+        image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+        image = image.cpu().permute(1, 2, 0).numpy()
+        image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
+        return image
+    
+
+    def decode_images(self, latents, tiled=False, tile_size=64, tile_stride=32):
+        images = [
+            self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+            for frame_id in range(latents.shape[0])
+        ]
+        return images
+    
+
+    def encode_images(self, processed_images, tiled=False, tile_size=64, tile_stride=32):
+        latents = []
+        for image in processed_images:
+            image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype)
+            latent = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).cpu()
+            latents.append(latent)
+        latents = torch.concat(latents, dim=0)
+        return latents
+    
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt,
+        negative_prompt="",
+        cfg_scale=7.5,
+        clip_skip=1,
+        num_frames=None,
+        input_frames=None,
+        controlnet_frames=None,
+        denoising_strength=1.0,
+        height=512,
+        width=512,
+        num_inference_steps=20,
+        animatediff_batch_size = 16,
+        animatediff_stride = 8,
+        unet_batch_size = 1,
+        controlnet_batch_size = 1,
+        cross_frame_attention = False,
+        smoother=None,
+        smoother_progress_ids=[],
+        vram_limit_level=0,
+        progress_bar_cmd=tqdm,
+        progress_bar_st=None,
+    ):
+        # Prepare scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
+
+        # Prepare latent tensors
+        if self.motion_modules is None:
+            noise = torch.randn((1, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1)
+        else:
+            noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype)
+        if input_frames is None or denoising_strength == 1.0:
+            latents = noise
+        else:
+            latents = self.encode_images(input_frames)
+            latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
+
+        # Encode prompts
+        prompt_emb_posi = self.prompter.encode_prompt(self.text_encoder, prompt, clip_skip=clip_skip, device=self.device, positive=True).cpu()
+        prompt_emb_nega = self.prompter.encode_prompt(self.text_encoder, negative_prompt, clip_skip=clip_skip, device=self.device, positive=False).cpu()
+        prompt_emb_posi = prompt_emb_posi.repeat(num_frames, 1, 1)
+        prompt_emb_nega = prompt_emb_nega.repeat(num_frames, 1, 1)
+
+        # Prepare ControlNets
+        if controlnet_frames is not None:
+            if isinstance(controlnet_frames[0], list):
+                controlnet_frames_ = []
+                for processor_id in range(len(controlnet_frames)):
+                    controlnet_frames_.append(
+                        torch.stack([
+                            self.controlnet.process_image(controlnet_frame, processor_id=processor_id).to(self.torch_dtype)
+                            for controlnet_frame in progress_bar_cmd(controlnet_frames[processor_id])
+                        ], dim=1)
+                    )
+                controlnet_frames = torch.concat(controlnet_frames_, dim=0)
+            else:
+                controlnet_frames = torch.stack([
+                    self.controlnet.process_image(controlnet_frame).to(self.torch_dtype)
+                    for controlnet_frame in progress_bar_cmd(controlnet_frames)
+                ], dim=1)
+        
+        # Denoise
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = torch.IntTensor((timestep,))[0].to(self.device)
+
+            # Classifier-free guidance
+            noise_pred_posi = lets_dance_with_long_video(
+                self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet,
+                sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_posi, controlnet_frames=controlnet_frames,
+                animatediff_batch_size=animatediff_batch_size, animatediff_stride=animatediff_stride,
+                unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size,
+                cross_frame_attention=cross_frame_attention,
+                device=self.device, vram_limit_level=vram_limit_level
+            )
+            noise_pred_nega = lets_dance_with_long_video(
+                self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet,
+                sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega, controlnet_frames=controlnet_frames,
+                animatediff_batch_size=animatediff_batch_size, animatediff_stride=animatediff_stride,
+                unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size,
+                cross_frame_attention=cross_frame_attention,
+                device=self.device, vram_limit_level=vram_limit_level
+            )
+            noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+
+            # DDIM and smoother
+            if smoother is not None and progress_id in smoother_progress_ids:
+                rendered_frames = self.scheduler.step(noise_pred, timestep, latents, to_final=True)
+                rendered_frames = self.decode_images(rendered_frames)
+                rendered_frames = smoother(rendered_frames, original_frames=input_frames)
+                target_latents = self.encode_images(rendered_frames)
+                noise_pred = self.scheduler.return_to_timestep(timestep, latents, target_latents)
+            latents = self.scheduler.step(noise_pred, timestep, latents)
+
+            # UI
+            if progress_bar_st is not None:
+                progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
+        
+        # Decode image
+        output_frames = self.decode_images(latents)
+
+        # Post-process
+        if smoother is not None and (num_inference_steps in smoother_progress_ids or -1 in smoother_progress_ids):
+            output_frames = smoother(output_frames, original_frames=input_frames)
+
+        return output_frames
+
+
+
+class SDVideoPipelineRunner:
+    def __init__(self, in_streamlit=False):
+        self.in_streamlit = in_streamlit
+
+
+    def load_pipeline(self, model_list, textual_inversion_folder, device, lora_alphas, controlnet_units):
+        # Load models
+        model_manager = ModelManager(torch_dtype=torch.float16, device=device)
+        model_manager.load_textual_inversions(textual_inversion_folder)
+        model_manager.load_models(model_list, lora_alphas=lora_alphas)
+        pipe = SDVideoPipeline.from_model_manager(
+            model_manager,
+            [
+                ControlNetConfigUnit(
+                    processor_id=unit["processor_id"],
+                    model_path=unit["model_path"],
+                    scale=unit["scale"]
+                ) for unit in controlnet_units
+            ]
+        )
+        return model_manager, pipe
+    
+
+    def load_smoother(self, model_manager, smoother_configs):
+        smoother = SequencialProcessor.from_model_manager(model_manager, smoother_configs)
+        return smoother
+
+
+    def synthesize_video(self, model_manager, pipe, seed, smoother, **pipeline_inputs):
+        torch.manual_seed(seed)
+        if self.in_streamlit:
+            import streamlit as st
+            progress_bar_st = st.progress(0.0)
+            output_video = pipe(**pipeline_inputs, smoother=smoother, progress_bar_st=progress_bar_st)
+            progress_bar_st.progress(1.0)
+        else:
+            output_video = pipe(**pipeline_inputs, smoother=smoother)
+        model_manager.to("cpu")
+        return output_video
+
+
+    def load_video(self, video_file, image_folder, height, width, start_frame_id, end_frame_id):
+        video = VideoData(video_file=video_file, image_folder=image_folder, height=height, width=width)
+        if start_frame_id is None:
+            start_frame_id = 0
+        if end_frame_id is None:
+            end_frame_id = len(video)
+        frames = [video[i] for i in range(start_frame_id, end_frame_id)]
+        return frames
+
+
+    def add_data_to_pipeline_inputs(self, data, pipeline_inputs):
+        pipeline_inputs["input_frames"] = self.load_video(**data["input_frames"])
+        pipeline_inputs["num_frames"] = len(pipeline_inputs["input_frames"])
+        pipeline_inputs["width"], pipeline_inputs["height"] = pipeline_inputs["input_frames"][0].size
+        if len(data["controlnet_frames"]) > 0:
+            pipeline_inputs["controlnet_frames"] = [self.load_video(**unit) for unit in data["controlnet_frames"]]
+        return pipeline_inputs
+
+
+    def save_output(self, video, output_folder, fps, config):
+        os.makedirs(output_folder, exist_ok=True)
+        save_frames(video, os.path.join(output_folder, "frames"))
+        save_video(video, os.path.join(output_folder, "video.mp4"), fps=fps)
+        config["pipeline"]["pipeline_inputs"]["input_frames"] = []
+        config["pipeline"]["pipeline_inputs"]["controlnet_frames"] = []
+        with open(os.path.join(output_folder, "config.json"), 'w') as file:
+            json.dump(config, file, indent=4)
+
+
+    def run(self, config):
+        if self.in_streamlit:
+            import streamlit as st
+        if self.in_streamlit: st.markdown("Loading videos ...")
+        config["pipeline"]["pipeline_inputs"] = self.add_data_to_pipeline_inputs(config["data"], config["pipeline"]["pipeline_inputs"])
+        if self.in_streamlit: st.markdown("Loading videos ... done!")
+        if self.in_streamlit: st.markdown("Loading models ...")
+        model_manager, pipe = self.load_pipeline(**config["models"])
+        if self.in_streamlit: st.markdown("Loading models ... done!")
+        if "smoother_configs" in config:
+            if self.in_streamlit: st.markdown("Loading smoother ...")
+            smoother = self.load_smoother(model_manager, config["smoother_configs"])
+            if self.in_streamlit: st.markdown("Loading smoother ... done!")
+        else:
+            smoother = None
+        if self.in_streamlit: st.markdown("Synthesizing videos ...")
+        output_video = self.synthesize_video(model_manager, pipe, config["pipeline"]["seed"], smoother, **config["pipeline"]["pipeline_inputs"])
+        if self.in_streamlit: st.markdown("Synthesizing videos ... done!")
+        if self.in_streamlit: st.markdown("Saving videos ...")
+        self.save_output(output_video, config["data"]["output_folder"], config["data"]["fps"], config)
+        if self.in_streamlit: st.markdown("Saving videos ... done!")
+        if self.in_streamlit: st.markdown("Finished!")
+        video_file = open(os.path.join(os.path.join(config["data"]["output_folder"], "video.mp4")), 'rb')
+        if self.in_streamlit: st.video(video_file.read())

diffsynth/pipelines/stable_diffusion_xl.py

@@ -0,0 +1,175 @@
+from ..models import ModelManager, SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
+# TODO: SDXL ControlNet
+from ..prompts import SDXLPrompter
+from ..schedulers import EnhancedDDIMScheduler
+from .dancer import lets_dance_xl
+import torch
+from tqdm import tqdm
+from PIL import Image
+import numpy as np
+
+
+class SDXLImagePipeline(torch.nn.Module):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16):
+        super().__init__()
+        self.scheduler = EnhancedDDIMScheduler()
+        self.prompter = SDXLPrompter()
+        self.device = device
+        self.torch_dtype = torch_dtype
+        # models
+        self.text_encoder: SDXLTextEncoder = None
+        self.text_encoder_2: SDXLTextEncoder2 = None
+        self.unet: SDXLUNet = None
+        self.vae_decoder: SDXLVAEDecoder = None
+        self.vae_encoder: SDXLVAEEncoder = None
+        self.ipadapter_image_encoder: IpAdapterXLCLIPImageEmbedder = None
+        self.ipadapter: SDXLIpAdapter = None
+        # TODO: SDXL ControlNet
+    
+    def fetch_main_models(self, model_manager: ModelManager):
+        self.text_encoder = model_manager.text_encoder
+        self.text_encoder_2 = model_manager.text_encoder_2
+        self.unet = model_manager.unet
+        self.vae_decoder = model_manager.vae_decoder
+        self.vae_encoder = model_manager.vae_encoder
+
+
+    def fetch_controlnet_models(self, model_manager: ModelManager, **kwargs):
+        # TODO: SDXL ControlNet
+        pass
+    
+
+    def fetch_ipadapter(self, model_manager: ModelManager):
+        if "ipadapter_xl" in model_manager.model:
+            self.ipadapter = model_manager.ipadapter_xl
+        if "ipadapter_xl_image_encoder" in model_manager.model:
+            self.ipadapter_image_encoder = model_manager.ipadapter_xl_image_encoder
+
+
+    def fetch_prompter(self, model_manager: ModelManager):
+        self.prompter.load_from_model_manager(model_manager)
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager, controlnet_config_units = [], **kwargs):
+        pipe = SDXLImagePipeline(
+            device=model_manager.device,
+            torch_dtype=model_manager.torch_dtype,
+        )
+        pipe.fetch_main_models(model_manager)
+        pipe.fetch_prompter(model_manager)
+        pipe.fetch_controlnet_models(model_manager, controlnet_config_units=controlnet_config_units)
+        pipe.fetch_ipadapter(model_manager)
+        return pipe
+    
+
+    def preprocess_image(self, image):
+        image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
+        return image
+    
+
+    def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
+        image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+        image = image.cpu().permute(1, 2, 0).numpy()
+        image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
+        return image
+    
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt,
+        negative_prompt="",
+        cfg_scale=7.5,
+        clip_skip=1,
+        clip_skip_2=2,
+        input_image=None,
+        ipadapter_images=None,
+        ipadapter_scale=1.0,
+        controlnet_image=None,
+        denoising_strength=1.0,
+        height=1024,
+        width=1024,
+        num_inference_steps=20,
+        tiled=False,
+        tile_size=64,
+        tile_stride=32,
+        progress_bar_cmd=tqdm,
+        progress_bar_st=None,
+    ):
+        # Prepare scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
+
+        # Prepare latent tensors
+        if input_image is not None:
+            image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
+            latents = self.vae_encoder(image.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype)
+            noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+            latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
+        else:
+            latents = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+
+        # Encode prompts
+        add_prompt_emb_posi, prompt_emb_posi = self.prompter.encode_prompt(
+            self.text_encoder,
+            self.text_encoder_2,
+            prompt,
+            clip_skip=clip_skip, clip_skip_2=clip_skip_2,
+            device=self.device,
+            positive=True,
+        )
+        if cfg_scale != 1.0:
+            add_prompt_emb_nega, prompt_emb_nega = self.prompter.encode_prompt(
+                self.text_encoder,
+                self.text_encoder_2,
+                negative_prompt,
+                clip_skip=clip_skip, clip_skip_2=clip_skip_2,
+                device=self.device,
+                positive=False,
+            )
+
+        # Prepare positional id
+        add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device)
+
+        # IP-Adapter
+        if ipadapter_images is not None:
+            ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images)
+            ipadapter_kwargs_list_posi = self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)
+            ipadapter_kwargs_list_nega = self.ipadapter(torch.zeros_like(ipadapter_image_encoding))
+        else:
+            ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {}, {}
+        
+        # Denoise
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = torch.IntTensor((timestep,))[0].to(self.device)
+
+            # Classifier-free guidance
+            noise_pred_posi = lets_dance_xl(
+                self.unet,
+                sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_posi,
+                add_time_id=add_time_id, add_text_embeds=add_prompt_emb_posi,
+                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
+                ipadapter_kwargs_list=ipadapter_kwargs_list_posi,
+            )
+            if cfg_scale != 1.0:
+                noise_pred_nega = lets_dance_xl(
+                    self.unet,
+                    sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega,
+                    add_time_id=add_time_id, add_text_embeds=add_prompt_emb_nega,
+                    tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
+                    ipadapter_kwargs_list=ipadapter_kwargs_list_nega,
+                )
+                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+            else:
+                noise_pred = noise_pred_posi
+
+            latents = self.scheduler.step(noise_pred, timestep, latents)
+            
+            if progress_bar_st is not None:
+                progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
+        
+        # Decode image
+        image = self.decode_image(latents.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+
+        return image

diffsynth/pipelines/stable_diffusion_xl_video.py

@@ -0,0 +1,190 @@
+from ..models import ModelManager, SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLMotionModel
+from .dancer import lets_dance_xl
+# TODO: SDXL ControlNet
+from ..prompts import SDXLPrompter
+from ..schedulers import EnhancedDDIMScheduler
+import torch
+from tqdm import tqdm
+from PIL import Image
+import numpy as np
+
+
+class SDXLVideoPipeline(torch.nn.Module):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16, use_animatediff=True):
+        super().__init__()
+        self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_animatediff else "scaled_linear")
+        self.prompter = SDXLPrompter()
+        self.device = device
+        self.torch_dtype = torch_dtype
+        # models
+        self.text_encoder: SDXLTextEncoder = None
+        self.text_encoder_2: SDXLTextEncoder2 = None
+        self.unet: SDXLUNet = None
+        self.vae_decoder: SDXLVAEDecoder = None
+        self.vae_encoder: SDXLVAEEncoder = None
+        # TODO: SDXL ControlNet
+        self.motion_modules: SDXLMotionModel = None
+    
+    
+    def fetch_main_models(self, model_manager: ModelManager):
+        self.text_encoder = model_manager.text_encoder
+        self.text_encoder_2 = model_manager.text_encoder_2
+        self.unet = model_manager.unet
+        self.vae_decoder = model_manager.vae_decoder
+        self.vae_encoder = model_manager.vae_encoder
+
+
+    def fetch_controlnet_models(self, model_manager: ModelManager, **kwargs):
+        # TODO: SDXL ControlNet
+        pass
+
+
+    def fetch_motion_modules(self, model_manager: ModelManager):
+        if "motion_modules_xl" in model_manager.model:
+            self.motion_modules = model_manager.motion_modules_xl
+
+
+    def fetch_prompter(self, model_manager: ModelManager):
+        self.prompter.load_from_model_manager(model_manager)
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager, controlnet_config_units = [], **kwargs):
+        pipe = SDXLVideoPipeline(
+            device=model_manager.device,
+            torch_dtype=model_manager.torch_dtype,
+            use_animatediff="motion_modules_xl" in model_manager.model
+        )
+        pipe.fetch_main_models(model_manager)
+        pipe.fetch_motion_modules(model_manager)
+        pipe.fetch_prompter(model_manager)
+        pipe.fetch_controlnet_models(model_manager, controlnet_config_units=controlnet_config_units)
+        return pipe
+    
+
+    def preprocess_image(self, image):
+        image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
+        return image
+    
+
+    def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
+        image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+        image = image.cpu().permute(1, 2, 0).numpy()
+        image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
+        return image
+    
+
+    def decode_images(self, latents, tiled=False, tile_size=64, tile_stride=32):
+        images = [
+            self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+            for frame_id in range(latents.shape[0])
+        ]
+        return images
+    
+
+    def encode_images(self, processed_images, tiled=False, tile_size=64, tile_stride=32):
+        latents = []
+        for image in processed_images:
+            image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype)
+            latent = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).cpu()
+            latents.append(latent)
+        latents = torch.concat(latents, dim=0)
+        return latents
+    
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt,
+        negative_prompt="",
+        cfg_scale=7.5,
+        clip_skip=1,
+        clip_skip_2=2,
+        num_frames=None,
+        input_frames=None,
+        controlnet_frames=None,
+        denoising_strength=1.0,
+        height=512,
+        width=512,
+        num_inference_steps=20,
+        animatediff_batch_size = 16,
+        animatediff_stride = 8,
+        unet_batch_size = 1,
+        controlnet_batch_size = 1,
+        cross_frame_attention = False,
+        smoother=None,
+        smoother_progress_ids=[],
+        vram_limit_level=0,
+        progress_bar_cmd=tqdm,
+        progress_bar_st=None,
+    ):
+        # Prepare scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
+
+        # Prepare latent tensors
+        if self.motion_modules is None:
+            noise = torch.randn((1, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1)
+        else:
+            noise = torch.randn((num_frames, 4, height//8, width//8), device="cuda", dtype=self.torch_dtype)
+        if input_frames is None or denoising_strength == 1.0:
+            latents = noise
+        else:
+            latents = self.encode_images(input_frames)
+            latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
+
+        # Encode prompts
+        add_prompt_emb_posi, prompt_emb_posi = self.prompter.encode_prompt(
+            self.text_encoder,
+            self.text_encoder_2,
+            prompt,
+            clip_skip=clip_skip, clip_skip_2=clip_skip_2,
+            device=self.device,
+            positive=True,
+        )
+        if cfg_scale != 1.0:
+            add_prompt_emb_nega, prompt_emb_nega = self.prompter.encode_prompt(
+                self.text_encoder,
+                self.text_encoder_2,
+                negative_prompt,
+                clip_skip=clip_skip, clip_skip_2=clip_skip_2,
+                device=self.device,
+                positive=False,
+            )
+
+        # Prepare positional id
+        add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device)
+        
+        # Denoise
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = torch.IntTensor((timestep,))[0].to(self.device)
+
+            # Classifier-free guidance
+            noise_pred_posi = lets_dance_xl(
+                self.unet, motion_modules=self.motion_modules, controlnet=None,
+                sample=latents, add_time_id=add_time_id, add_text_embeds=add_prompt_emb_posi,
+                timestep=timestep, encoder_hidden_states=prompt_emb_posi, controlnet_frames=controlnet_frames,
+                cross_frame_attention=cross_frame_attention,
+                device=self.device, vram_limit_level=vram_limit_level
+            )
+            if cfg_scale != 1.0:
+                noise_pred_nega = lets_dance_xl(
+                    self.unet, motion_modules=self.motion_modules, controlnet=None,
+                    sample=latents, add_time_id=add_time_id, add_text_embeds=add_prompt_emb_nega,
+                    timestep=timestep, encoder_hidden_states=prompt_emb_nega, controlnet_frames=controlnet_frames,
+                    cross_frame_attention=cross_frame_attention,
+                    device=self.device, vram_limit_level=vram_limit_level
+                )
+                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+            else:
+                noise_pred = noise_pred_posi
+
+            latents = self.scheduler.step(noise_pred, timestep, latents)
+            
+            if progress_bar_st is not None:
+                progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
+        
+        # Decode image
+        image = self.decode_images(latents.to(torch.float32))
+
+        return image

diffsynth/pipelines/stable_video_diffusion.py

@@ -0,0 +1,307 @@
+from ..models import ModelManager, SVDImageEncoder, SVDUNet, SVDVAEEncoder, SVDVAEDecoder
+from ..schedulers import ContinuousODEScheduler
+import torch
+from tqdm import tqdm
+from PIL import Image
+import numpy as np
+from einops import rearrange, repeat
+
+
+
+class SVDVideoPipeline(torch.nn.Module):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16):
+        super().__init__()
+        self.scheduler = ContinuousODEScheduler()
+        self.device = device
+        self.torch_dtype = torch_dtype
+        # models
+        self.image_encoder: SVDImageEncoder = None
+        self.unet: SVDUNet = None
+        self.vae_encoder: SVDVAEEncoder = None
+        self.vae_decoder: SVDVAEDecoder = None
+    
+
+    def fetch_main_models(self, model_manager: ModelManager):
+        self.image_encoder = model_manager.image_encoder
+        self.unet = model_manager.unet
+        self.vae_encoder = model_manager.vae_encoder
+        self.vae_decoder = model_manager.vae_decoder
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager, **kwargs):
+        pipe = SVDVideoPipeline(device=model_manager.device, torch_dtype=model_manager.torch_dtype)
+        pipe.fetch_main_models(model_manager)
+        return pipe
+    
+
+    def preprocess_image(self, image):
+        image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
+        return image
+    
+
+    def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
+        image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+        image = image.cpu().permute(1, 2, 0).numpy()
+        image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
+        return image
+    
+
+    def encode_image_with_clip(self, image):
+        image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype)
+        image = SVDCLIPImageProcessor().resize_with_antialiasing(image, (224, 224))
+        image = (image + 1.0) / 2.0
+        mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.torch_dtype)
+        std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.torch_dtype)
+        image = (image - mean) / std
+        image_emb = self.image_encoder(image)
+        return image_emb
+    
+
+    def encode_image_with_vae(self, image, noise_aug_strength):
+        image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype)
+        noise = torch.randn(image.shape, device="cpu", dtype=self.torch_dtype).to(self.device)
+        image = image + noise_aug_strength * noise
+        image_emb = self.vae_encoder(image) / self.vae_encoder.scaling_factor
+        return image_emb
+    
+
+    def encode_video_with_vae(self, video):
+        video = torch.concat([self.preprocess_image(frame) for frame in video], dim=0)
+        video = rearrange(video, "T C H W -> 1 C T H W")
+        video = video.to(device=self.device, dtype=self.torch_dtype)
+        latents = self.vae_encoder.encode_video(video)
+        latents = rearrange(latents[0], "C T H W -> T C H W")
+        return latents
+    
+
+    def tensor2video(self, frames):
+        frames = rearrange(frames, "C T H W -> T H W C")
+        frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)
+        frames = [Image.fromarray(frame) for frame in frames]
+        return frames
+    
+
+    def calculate_noise_pred(
+        self,
+        latents,
+        timestep,
+        add_time_id,
+        cfg_scales,
+        image_emb_vae_posi, image_emb_clip_posi,
+        image_emb_vae_nega, image_emb_clip_nega
+    ):
+        # Positive side
+        noise_pred_posi = self.unet(
+            torch.cat([latents, image_emb_vae_posi], dim=1),
+            timestep, image_emb_clip_posi, add_time_id
+        )
+        # Negative side
+        noise_pred_nega = self.unet(
+            torch.cat([latents, image_emb_vae_nega], dim=1),
+            timestep, image_emb_clip_nega, add_time_id
+        )
+
+        # Classifier-free guidance
+        noise_pred = noise_pred_nega + cfg_scales * (noise_pred_posi - noise_pred_nega)
+
+        return noise_pred
+    
+
+    def post_process_latents(self, latents, post_normalize=True, contrast_enhance_scale=1.0):
+        if post_normalize:
+            mean, std = latents.mean(), latents.std()
+            latents = (latents - latents.mean(dim=[1, 2, 3], keepdim=True)) / latents.std(dim=[1, 2, 3], keepdim=True) * std + mean
+        latents = latents * contrast_enhance_scale
+        return latents
+
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        input_image=None,
+        input_video=None,
+        mask_frames=[],
+        mask_frame_ids=[],
+        min_cfg_scale=1.0,
+        max_cfg_scale=3.0,
+        denoising_strength=1.0,
+        num_frames=25,
+        height=576,
+        width=1024,
+        fps=7,
+        motion_bucket_id=127,
+        noise_aug_strength=0.02,
+        num_inference_steps=20,
+        post_normalize=True,
+        contrast_enhance_scale=1.2,
+        progress_bar_cmd=tqdm,
+        progress_bar_st=None,
+    ):
+        # Prepare scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength)
+
+        # Prepare latent tensors
+        noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).to(self.device)
+        if denoising_strength == 1.0:
+            latents = noise.clone()
+        else:
+            latents = self.encode_video_with_vae(input_video)
+            latents = self.scheduler.add_noise(latents, noise, self.scheduler.timesteps[0])
+
+        # Prepare mask frames
+        if len(mask_frames) > 0:
+            mask_latents = self.encode_video_with_vae(mask_frames)
+
+        # Encode image
+        image_emb_clip_posi = self.encode_image_with_clip(input_image)
+        image_emb_clip_nega = torch.zeros_like(image_emb_clip_posi)
+        image_emb_vae_posi = repeat(self.encode_image_with_vae(input_image, noise_aug_strength), "B C H W -> (B T) C H W", T=num_frames)
+        image_emb_vae_nega = torch.zeros_like(image_emb_vae_posi)
+
+        # Prepare classifier-free guidance
+        cfg_scales = torch.linspace(min_cfg_scale, max_cfg_scale, num_frames)
+        cfg_scales = cfg_scales.reshape(num_frames, 1, 1, 1).to(device=self.device, dtype=self.torch_dtype)
+        
+        # Prepare positional id
+        add_time_id = torch.tensor([[fps-1, motion_bucket_id, noise_aug_strength]], device=self.device)
+
+        # Denoise
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+
+            # Mask frames
+            for frame_id, mask_frame_id in enumerate(mask_frame_ids):
+                latents[mask_frame_id] = self.scheduler.add_noise(mask_latents[frame_id], noise[mask_frame_id], timestep)
+
+            # Fetch model output
+            noise_pred = self.calculate_noise_pred(
+                latents, timestep, add_time_id, cfg_scales,
+                image_emb_vae_posi, image_emb_clip_posi, image_emb_vae_nega, image_emb_clip_nega
+            )
+
+            # Forward Euler
+            latents = self.scheduler.step(noise_pred, timestep, latents)
+            
+            # Update progress bar
+            if progress_bar_st is not None:
+                progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
+
+        # Decode image
+        latents = self.post_process_latents(latents, post_normalize=post_normalize, contrast_enhance_scale=contrast_enhance_scale)
+        video = self.vae_decoder.decode_video(latents, progress_bar=progress_bar_cmd)
+        video = self.tensor2video(video)
+
+        return video
+
+
+
+class SVDCLIPImageProcessor:
+    def __init__(self):
+        pass
+
+    def resize_with_antialiasing(self, input, size, interpolation="bicubic", align_corners=True):
+        h, w = input.shape[-2:]
+        factors = (h / size[0], w / size[1])
+
+        # First, we have to determine sigma
+        # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171
+        sigmas = (
+            max((factors[0] - 1.0) / 2.0, 0.001),
+            max((factors[1] - 1.0) / 2.0, 0.001),
+        )
+
+        # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma
+        # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206
+        # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now
+        ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3))
+
+        # Make sure it is odd
+        if (ks[0] % 2) == 0:
+            ks = ks[0] + 1, ks[1]
+
+        if (ks[1] % 2) == 0:
+            ks = ks[0], ks[1] + 1
+
+        input = self._gaussian_blur2d(input, ks, sigmas)
+
+        output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners)
+        return output
+
+
+    def _compute_padding(self, kernel_size):
+        """Compute padding tuple."""
+        # 4 or 6 ints:  (padding_left, padding_right,padding_top,padding_bottom)
+        # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad
+        if len(kernel_size) < 2:
+            raise AssertionError(kernel_size)
+        computed = [k - 1 for k in kernel_size]
+
+        # for even kernels we need to do asymmetric padding :(
+        out_padding = 2 * len(kernel_size) * [0]
+
+        for i in range(len(kernel_size)):
+            computed_tmp = computed[-(i + 1)]
+
+            pad_front = computed_tmp // 2
+            pad_rear = computed_tmp - pad_front
+
+            out_padding[2 * i + 0] = pad_front
+            out_padding[2 * i + 1] = pad_rear
+
+        return out_padding
+
+
+    def _filter2d(self, input, kernel):
+        # prepare kernel
+        b, c, h, w = input.shape
+        tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype)
+
+        tmp_kernel = tmp_kernel.expand(-1, c, -1, -1)
+
+        height, width = tmp_kernel.shape[-2:]
+
+        padding_shape: list[int] = self._compute_padding([height, width])
+        input = torch.nn.functional.pad(input, padding_shape, mode="reflect")
+
+        # kernel and input tensor reshape to align element-wise or batch-wise params
+        tmp_kernel = tmp_kernel.reshape(-1, 1, height, width)
+        input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1))
+
+        # convolve the tensor with the kernel.
+        output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1)
+
+        out = output.view(b, c, h, w)
+        return out
+
+
+    def _gaussian(self, window_size: int, sigma):
+        if isinstance(sigma, float):
+            sigma = torch.tensor([[sigma]])
+
+        batch_size = sigma.shape[0]
+
+        x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)
+
+        if window_size % 2 == 0:
+            x = x + 0.5
+
+        gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))
+
+        return gauss / gauss.sum(-1, keepdim=True)
+
+
+    def _gaussian_blur2d(self, input, kernel_size, sigma):
+        if isinstance(sigma, tuple):
+            sigma = torch.tensor([sigma], dtype=input.dtype)
+        else:
+            sigma = sigma.to(dtype=input.dtype)
+
+        ky, kx = int(kernel_size[0]), int(kernel_size[1])
+        bs = sigma.shape[0]
+        kernel_x = self._gaussian(kx, sigma[:, 1].view(bs, 1))
+        kernel_y = self._gaussian(ky, sigma[:, 0].view(bs, 1))
+        out_x = self._filter2d(input, kernel_x[..., None, :])
+        out = self._filter2d(out_x, kernel_y[..., None])
+
+        return out

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 +1,41 @@
+from .base import VideoProcessor
+
+
+class AutoVideoProcessor(VideoProcessor):
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def from_model_manager(model_manager, processor_type, **kwargs):
+        if processor_type == "FastBlend":
+            from .FastBlend import FastBlendSmoother
+            return FastBlendSmoother.from_model_manager(model_manager, **kwargs)
+        elif processor_type == "Contrast":
+            from .PILEditor import ContrastEditor
+            return ContrastEditor.from_model_manager(model_manager, **kwargs)
+        elif processor_type == "Sharpness":
+            from .PILEditor import SharpnessEditor
+            return SharpnessEditor.from_model_manager(model_manager, **kwargs)
+        elif processor_type == "RIFE":
+            from .RIFE import RIFESmoother
+            return RIFESmoother.from_model_manager(model_manager, **kwargs)
+        else:
+            raise ValueError(f"invalid processor_type: {processor_type}")
+
+
+class SequencialProcessor(VideoProcessor):
+    def __init__(self, processors=[]):
+        self.processors = processors
+
+    @staticmethod
+    def from_model_manager(model_manager, configs):
+        processors = [
+            AutoVideoProcessor.from_model_manager(model_manager, config["processor_type"], **config["config"])
+            for config in configs
+        ]
+        return SequencialProcessor(processors)
+    
+    def __call__(self, rendered_frames, **kwargs):
+        for processor in self.processors:
+            rendered_frames = processor(rendered_frames, **kwargs)
+        return rendered_frames

diffsynth/prompts/__init__.py

@@ -0,0 +1,3 @@
+from .sd_prompter import SDPrompter
+from .sdxl_prompter import SDXLPrompter
+from .hunyuan_dit_prompter import HunyuanDiTPrompter

diffsynth/prompts/hunyuan_dit_prompter.py

@@ -0,0 +1,62 @@
+from .utils import Prompter
+from transformers import BertModel, T5EncoderModel, BertTokenizer, AutoTokenizer
+import warnings, os
+
+
+class HunyuanDiTPrompter(Prompter):
+    def __init__(
+        self,
+        tokenizer_path=None,
+        tokenizer_t5_path=None
+    ):
+        if tokenizer_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer")
+        if tokenizer_t5_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_t5_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer_t5")
+        super().__init__()
+        self.tokenizer = BertTokenizer.from_pretrained(tokenizer_path)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            self.tokenizer_t5 = AutoTokenizer.from_pretrained(tokenizer_t5_path)
+
+
+    def encode_prompt_using_signle_model(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device):
+        text_inputs = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_attention_mask=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        attention_mask = text_inputs.attention_mask.to(device)
+        prompt_embeds = text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+            clip_skip=clip_skip
+        )
+        return prompt_embeds, attention_mask
+    
+
+    def encode_prompt(
+        self,
+        text_encoder: BertModel,
+        text_encoder_t5: T5EncoderModel,
+        prompt,
+        clip_skip=1,
+        clip_skip_2=1,
+        positive=True,
+        device="cuda"
+    ):
+        prompt = self.process_prompt(prompt, positive=positive)
+        
+        # CLIP
+        prompt_emb, attention_mask = self.encode_prompt_using_signle_model(prompt, text_encoder, self.tokenizer, self.tokenizer.model_max_length, clip_skip, device)
+
+        # T5
+        prompt_emb_t5, attention_mask_t5 = self.encode_prompt_using_signle_model(prompt, text_encoder_t5, self.tokenizer_t5, self.tokenizer_t5.model_max_length, clip_skip_2, device)
+        
+        return prompt_emb, attention_mask, prompt_emb_t5, attention_mask_t5

diffsynth/prompts/sd_prompter.py

@@ -0,0 +1,21 @@
+from .utils import Prompter, tokenize_long_prompt
+from transformers import CLIPTokenizer
+from ..models import SDTextEncoder
+import os
+
+
+class SDPrompter(Prompter):
+    def __init__(self, tokenizer_path=None):
+        if tokenizer_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion/tokenizer")
+        super().__init__()
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
+
+    def encode_prompt(self, text_encoder: SDTextEncoder, prompt, clip_skip=1, device="cuda", positive=True):
+        prompt = self.process_prompt(prompt, positive=positive)
+        input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device)
+        prompt_emb = text_encoder(input_ids, clip_skip=clip_skip)
+        prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1))
+
+        return prompt_emb

diffsynth/prompts/sdxl_prompter.py

@@ -0,0 +1,49 @@
+from .utils import Prompter, tokenize_long_prompt
+from transformers import CLIPTokenizer
+from ..models import SDXLTextEncoder, SDXLTextEncoder2
+import torch, os
+
+
+class SDXLPrompter(Prompter):
+    def __init__(
+        self,
+        tokenizer_path=None,
+        tokenizer_2_path=None
+    ):
+        if tokenizer_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion/tokenizer")
+        if tokenizer_2_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_xl/tokenizer_2")
+        super().__init__()
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
+        self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path)
+    
+    def encode_prompt(
+        self,
+        text_encoder: SDXLTextEncoder,
+        text_encoder_2: SDXLTextEncoder2,
+        prompt,
+        clip_skip=1,
+        clip_skip_2=2,
+        positive=True,
+        device="cuda"
+    ):
+        prompt = self.process_prompt(prompt, positive=positive)
+        
+        # 1
+        input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device)
+        prompt_emb_1 = text_encoder(input_ids, clip_skip=clip_skip)
+
+        # 2
+        input_ids_2 = tokenize_long_prompt(self.tokenizer_2, prompt).to(device)
+        add_text_embeds, prompt_emb_2 = text_encoder_2(input_ids_2, clip_skip=clip_skip_2)
+
+        # Merge
+        prompt_emb = torch.concatenate([prompt_emb_1, prompt_emb_2], dim=-1)
+
+        # For very long prompt, we only use the first 77 tokens to compute `add_text_embeds`.
+        add_text_embeds = add_text_embeds[0:1]
+        prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1))
+        return add_text_embeds, prompt_emb

diffsynth/prompts/utils.py

@@ -0,0 +1,123 @@
+from transformers import CLIPTokenizer, AutoTokenizer
+from ..models import ModelManager
+import os
+
+
+def tokenize_long_prompt(tokenizer, prompt):
+    # Get model_max_length from self.tokenizer
+    length = tokenizer.model_max_length
+
+    # To avoid the warning. set self.tokenizer.model_max_length to +oo.
+    tokenizer.model_max_length = 99999999
+
+    # Tokenize it!
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+    # Determine the real length.
+    max_length = (input_ids.shape[1] + length - 1) // length * length
+
+    # Restore tokenizer.model_max_length
+    tokenizer.model_max_length = length
+    
+    # Tokenize it again with fixed length.
+    input_ids = tokenizer(
+        prompt,
+        return_tensors="pt",
+        padding="max_length",
+        max_length=max_length,
+        truncation=True
+    ).input_ids
+
+    # Reshape input_ids to fit the text encoder.
+    num_sentence = input_ids.shape[1] // length
+    input_ids = input_ids.reshape((num_sentence, length))
+    
+    return input_ids
+
+
+class BeautifulPrompt:
+    def __init__(self, tokenizer_path=None, model=None):
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        self.model = model
+        self.template = 'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:'
+    
+    def __call__(self, raw_prompt):
+        model_input = self.template.format(raw_prompt=raw_prompt)
+        input_ids = self.tokenizer.encode(model_input, return_tensors='pt').to(self.model.device)
+        outputs = self.model.generate(
+            input_ids,
+            max_new_tokens=384,
+            do_sample=True,
+            temperature=0.9,
+            top_k=50,
+            top_p=0.95,
+            repetition_penalty=1.1,
+            num_return_sequences=1
+        )
+        prompt = raw_prompt + ", " + self.tokenizer.batch_decode(
+            outputs[:, input_ids.size(1):],
+            skip_special_tokens=True
+        )[0].strip()
+        return prompt
+    
+
+class Translator:
+    def __init__(self, tokenizer_path=None, model=None):
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        self.model = model
+
+    def __call__(self, prompt):
+        input_ids = self.tokenizer.encode(prompt, return_tensors='pt').to(self.model.device)
+        output_ids = self.model.generate(input_ids)
+        prompt = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
+        return prompt
+    
+
+class Prompter:
+    def __init__(self):
+        self.tokenizer: CLIPTokenizer = None
+        self.keyword_dict = {}
+        self.translator: Translator = None
+        self.beautiful_prompt: BeautifulPrompt = None
+
+    def load_textual_inversion(self, textual_inversion_dict):
+        self.keyword_dict = {}
+        additional_tokens = []
+        for keyword in textual_inversion_dict:
+            tokens, _ = textual_inversion_dict[keyword]
+            additional_tokens += tokens
+            self.keyword_dict[keyword] = " " + " ".join(tokens) + " "
+        self.tokenizer.add_tokens(additional_tokens)
+
+    def load_beautiful_prompt(self, model, model_path):
+        model_folder = os.path.dirname(model_path)
+        self.beautiful_prompt = BeautifulPrompt(tokenizer_path=model_folder, model=model)
+        if model_folder.endswith("v2"):
+            self.beautiful_prompt.template = """Converts a simple image description into a prompt. \
+Prompts are formatted as multiple related tags separated by commas, plus you can use () to increase the weight, [] to decrease the weight, \
+or use a number to specify the weight. You should add appropriate words to make the images described in the prompt more aesthetically pleasing, \
+but make sure there is a correlation between the input and output.\n\
+### Input: {raw_prompt}\n### Output:"""
+
+    def load_translator(self, model, model_path):
+        model_folder = os.path.dirname(model_path)
+        self.translator = Translator(tokenizer_path=model_folder, model=model)
+
+    def load_from_model_manager(self, model_manager: ModelManager):
+        self.load_textual_inversion(model_manager.textual_inversion_dict)
+        if "translator" in model_manager.model:
+            self.load_translator(model_manager.model["translator"], model_manager.model_path["translator"])
+        if "beautiful_prompt" in model_manager.model:
+            self.load_beautiful_prompt(model_manager.model["beautiful_prompt"], model_manager.model_path["beautiful_prompt"])
+
+    def process_prompt(self, prompt, positive=True):
+        for keyword in self.keyword_dict:
+            if keyword in prompt:
+                prompt = prompt.replace(keyword, self.keyword_dict[keyword])
+        if positive and self.translator is not None:
+            prompt = self.translator(prompt)
+            print(f"Your prompt is translated: \"{prompt}\"")
+        if positive and self.beautiful_prompt is not None:
+            prompt = self.beautiful_prompt(prompt)
+            print(f"Your prompt is refined by BeautifulPrompt: \"{prompt}\"")
+        return prompt

diffsynth/schedulers/__init__.py

@@ -0,0 +1,2 @@
+from .ddim import EnhancedDDIMScheduler
+from .continuous_ode import ContinuousODEScheduler

diffsynth/schedulers/continuous_ode.py

@@ -0,0 +1,59 @@
+import torch
+
+
+class ContinuousODEScheduler():
+
+    def __init__(self, num_inference_steps=100, sigma_max=700.0, sigma_min=0.002, rho=7.0):
+        self.sigma_max = sigma_max
+        self.sigma_min = sigma_min
+        self.rho = rho
+        self.set_timesteps(num_inference_steps)
+
+
+    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0):
+        ramp = torch.linspace(1-denoising_strength, 1, num_inference_steps)
+        min_inv_rho = torch.pow(torch.tensor((self.sigma_min,)), (1 / self.rho))
+        max_inv_rho = torch.pow(torch.tensor((self.sigma_max,)), (1 / self.rho))
+        self.sigmas = torch.pow(max_inv_rho + ramp * (min_inv_rho - max_inv_rho), self.rho)
+        self.timesteps = torch.log(self.sigmas) * 0.25
+
+
+    def step(self, model_output, timestep, sample, to_final=False):
+        timestep_id = torch.argmin((self.timesteps - timestep).abs())
+        sigma = self.sigmas[timestep_id]
+        sample *= (sigma*sigma + 1).sqrt()
+        estimated_sample = -sigma / (sigma*sigma + 1).sqrt() * model_output + 1 / (sigma*sigma + 1) * sample
+        if to_final or timestep_id + 1 >= len(self.timesteps):
+            prev_sample = estimated_sample
+        else:
+            sigma_ = self.sigmas[timestep_id + 1]
+            derivative = 1 / sigma * (sample - estimated_sample)
+            prev_sample = sample + derivative * (sigma_ - sigma)
+            prev_sample /= (sigma_*sigma_ + 1).sqrt()
+        return prev_sample
+    
+
+    def return_to_timestep(self, timestep, sample, sample_stablized):
+        # This scheduler doesn't support this function.
+        pass
+    
+    
+    def add_noise(self, original_samples, noise, timestep):
+        timestep_id = torch.argmin((self.timesteps - timestep).abs())
+        sigma = self.sigmas[timestep_id]
+        sample = (original_samples + noise * sigma) / (sigma*sigma + 1).sqrt()
+        return sample
+    
+
+    def training_target(self, sample, noise, timestep):
+        timestep_id = torch.argmin((self.timesteps - timestep).abs())
+        sigma = self.sigmas[timestep_id]
+        target = (-(sigma*sigma + 1).sqrt() / sigma + 1 / (sigma*sigma + 1).sqrt() / sigma) * sample + 1 / (sigma*sigma + 1).sqrt() * noise
+        return target
+    
+
+    def training_weight(self, timestep):
+        timestep_id = torch.argmin((self.timesteps - timestep).abs())
+        sigma = self.sigmas[timestep_id]
+        weight = (1 + sigma*sigma).sqrt() / sigma
+        return weight

diffsynth/schedulers/ddim.py

@@ -0,0 +1,73 @@
+import torch, math
+
+
+class EnhancedDDIMScheduler():
+
+    def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="epsilon"):
+        self.num_train_timesteps = num_train_timesteps
+        if beta_schedule == "scaled_linear":
+            betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32))
+        elif beta_schedule == "linear":
+            betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
+        else:
+            raise NotImplementedError(f"{beta_schedule} is not implemented")
+        self.alphas_cumprod = torch.cumprod(1.0 - betas, dim=0).tolist()
+        self.set_timesteps(10)
+        self.prediction_type = prediction_type
+
+
+    def set_timesteps(self, num_inference_steps, denoising_strength=1.0):
+        # The timesteps are aligned to 999...0, which is different from other implementations,
+        # but I think this implementation is more reasonable in theory.
+        max_timestep = max(round(self.num_train_timesteps * denoising_strength) - 1, 0)
+        num_inference_steps = min(num_inference_steps, max_timestep + 1)
+        if num_inference_steps == 1:
+            self.timesteps = [max_timestep]
+        else:
+            step_length = max_timestep / (num_inference_steps - 1)
+            self.timesteps = [round(max_timestep - i*step_length) for i in range(num_inference_steps)]
+
+
+    def denoise(self, model_output, sample, alpha_prod_t, alpha_prod_t_prev):
+        if self.prediction_type == "epsilon":
+            weight_e = math.sqrt(1 - alpha_prod_t_prev) - math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t) / alpha_prod_t)
+            weight_x = math.sqrt(alpha_prod_t_prev / alpha_prod_t)
+            prev_sample = sample * weight_x + model_output * weight_e
+        elif self.prediction_type == "v_prediction":
+            weight_e = -math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t)) + math.sqrt(alpha_prod_t * (1 - alpha_prod_t_prev))
+            weight_x = math.sqrt(alpha_prod_t * alpha_prod_t_prev) + math.sqrt((1 - alpha_prod_t) * (1 - alpha_prod_t_prev))
+            prev_sample = sample * weight_x + model_output * weight_e
+        else:
+            raise NotImplementedError(f"{self.prediction_type} is not implemented")
+        return prev_sample
+
+
+    def step(self, model_output, timestep, sample, to_final=False):
+        alpha_prod_t = self.alphas_cumprod[timestep]
+        timestep_id = self.timesteps.index(timestep)
+        if to_final or timestep_id + 1 >= len(self.timesteps):
+            alpha_prod_t_prev = 1.0
+        else:
+            timestep_prev = self.timesteps[timestep_id + 1]
+            alpha_prod_t_prev = self.alphas_cumprod[timestep_prev]
+
+        return self.denoise(model_output, sample, alpha_prod_t, alpha_prod_t_prev)
+
+
+    def return_to_timestep(self, timestep, sample, sample_stablized):
+        alpha_prod_t = self.alphas_cumprod[timestep]
+        noise_pred = (sample - math.sqrt(alpha_prod_t) * sample_stablized) / math.sqrt(1 - alpha_prod_t)
+        return noise_pred
+    
+    
+    def add_noise(self, original_samples, noise, timestep):
+        sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[timestep])
+        sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[timestep])
+        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
+        return noisy_samples
+    
+    def training_target(self, sample, noise, timestep):
+        sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[timestep])
+        sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[timestep])
+        target = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
+        return target

diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

diffsynth/tokenizer_configs/hunyuan_dit/tokenizer/tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "name_or_path": "hfl/chinese-roberta-wwm-ext",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]",
+  "model_max_length": 77
+}

diffsynth/tokenizer_configs/hunyuan_dit/tokenizer_t5/config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "/home/patrick/t5/mt5-xl",
+  "architectures": [
+    "MT5ForConditionalGeneration"
+  ],
+  "d_ff": 5120,
+  "d_kv": 64,
+  "d_model": 2048,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "mt5",
+  "num_decoder_layers": 24,
+  "num_heads": 32,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
+  "transformers_version": "4.10.0.dev0",
+  "use_cache": true,
+  "vocab_size": 250112
+}

diffsynth/tokenizer_configs/hunyuan_dit/tokenizer_t5/special_tokens_map.json

@@ -0,0 +1 @@
+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

diffsynth/tokenizer_configs/hunyuan_dit/tokenizer_t5/tokenizer_config.json

@@ -0,0 +1 @@
+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "extra_ids": 0, "additional_special_tokens": null, "special_tokens_map_file": "", "tokenizer_file": null, "name_or_path": "google/mt5-small", "model_max_length": 256, "legacy": true}

diffsynth/tokenizer_configs/stable_diffusion/tokenizer/special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

diffsynth/tokenizer_configs/stable_diffusion/tokenizer/tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "do_lower_case": true,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "name_or_path": "openai/clip-vit-large-patch14",
+  "pad_token": "<|endoftext|>",
+  "special_tokens_map_file": "./special_tokens_map.json",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

diffsynth/tokenizer_configs/stable_diffusion_xl/tokenizer_2/special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+    "bos_token": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false
+    },
+    "eos_token": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false
+    },
+    "pad_token": "!",
+    "unk_token": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false
+    }
+}

diffsynth/tokenizer_configs/stable_diffusion_xl/tokenizer_2/tokenizer_config.json

@@ -0,0 +1,38 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "!",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49406": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49407": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "!",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}