Full Code of KwaiVGI/SynCamMaster for AI

gitextract_iigplzbn/

├── .gitignore
├── README.md
├── diffsynth/
│   ├── __init__.py
│   ├── configs/
│   │   ├── __init__.py
│   │   └── model_config.py
│   ├── controlnets/
│   │   ├── __init__.py
│   │   ├── controlnet_unit.py
│   │   └── processors.py
│   ├── data/
│   │   ├── __init__.py
│   │   ├── simple_text_image.py
│   │   └── video.py
│   ├── extensions/
│   │   ├── ESRGAN/
│   │   │   └── __init__.py
│   │   ├── FastBlend/
│   │   │   ├── __init__.py
│   │   │   ├── api.py
│   │   │   ├── cupy_kernels.py
│   │   │   ├── data.py
│   │   │   ├── patch_match.py
│   │   │   └── runners/
│   │   │       ├── __init__.py
│   │   │       ├── accurate.py
│   │   │       ├── balanced.py
│   │   │       ├── fast.py
│   │   │       └── interpolation.py
│   │   ├── ImageQualityMetric/
│   │   │   ├── BLIP/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── blip.py
│   │   │   │   ├── blip_pretrain.py
│   │   │   │   ├── med.py
│   │   │   │   └── vit.py
│   │   │   ├── __init__.py
│   │   │   ├── aesthetic.py
│   │   │   ├── clip.py
│   │   │   ├── config.py
│   │   │   ├── hps.py
│   │   │   ├── imagereward.py
│   │   │   ├── mps.py
│   │   │   ├── open_clip/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── coca_model.py
│   │   │   │   ├── constants.py
│   │   │   │   ├── factory.py
│   │   │   │   ├── generation_utils.py
│   │   │   │   ├── hf_configs.py
│   │   │   │   ├── hf_model.py
│   │   │   │   ├── loss.py
│   │   │   │   ├── model.py
│   │   │   │   ├── model_configs/
│   │   │   │   │   └── ViT-H-14.json
│   │   │   │   ├── modified_resnet.py
│   │   │   │   ├── openai.py
│   │   │   │   ├── pretrained.py
│   │   │   │   ├── push_to_hf_hub.py
│   │   │   │   ├── timm_model.py
│   │   │   │   ├── tokenizer.py
│   │   │   │   ├── transform.py
│   │   │   │   ├── transformer.py
│   │   │   │   ├── utils.py
│   │   │   │   └── version.py
│   │   │   ├── pickscore.py
│   │   │   └── trainer/
│   │   │       ├── __init__.py
│   │   │       └── models/
│   │   │           ├── __init__.py
│   │   │           ├── base_model.py
│   │   │           ├── clip_model.py
│   │   │           └── cross_modeling.py
│   │   ├── RIFE/
│   │   │   └── __init__.py
│   │   └── __init__.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── attention.py
│   │   ├── cog_dit.py
│   │   ├── cog_vae.py
│   │   ├── downloader.py
│   │   ├── flux_controlnet.py
│   │   ├── flux_dit.py
│   │   ├── flux_ipadapter.py
│   │   ├── flux_text_encoder.py
│   │   ├── flux_vae.py
│   │   ├── hunyuan_dit.py
│   │   ├── hunyuan_dit_text_encoder.py
│   │   ├── hunyuan_video_dit.py
│   │   ├── hunyuan_video_text_encoder.py
│   │   ├── hunyuan_video_vae_decoder.py
│   │   ├── hunyuan_video_vae_encoder.py
│   │   ├── kolors_text_encoder.py
│   │   ├── lora.py
│   │   ├── model_manager.py
│   │   ├── omnigen.py
│   │   ├── sd3_dit.py
│   │   ├── sd3_text_encoder.py
│   │   ├── sd3_vae_decoder.py
│   │   ├── sd3_vae_encoder.py
│   │   ├── sd_controlnet.py
│   │   ├── sd_ipadapter.py
│   │   ├── sd_motion.py
│   │   ├── sd_text_encoder.py
│   │   ├── sd_unet.py
│   │   ├── sd_vae_decoder.py
│   │   ├── sd_vae_encoder.py
│   │   ├── sdxl_controlnet.py
│   │   ├── sdxl_ipadapter.py
│   │   ├── sdxl_motion.py
│   │   ├── sdxl_text_encoder.py
│   │   ├── sdxl_unet.py
│   │   ├── sdxl_vae_decoder.py
│   │   ├── sdxl_vae_encoder.py
│   │   ├── stepvideo_dit.py
│   │   ├── stepvideo_text_encoder.py
│   │   ├── stepvideo_vae.py
│   │   ├── svd_image_encoder.py
│   │   ├── svd_unet.py
│   │   ├── svd_vae_decoder.py
│   │   ├── svd_vae_encoder.py
│   │   ├── tiler.py
│   │   ├── utils.py
│   │   ├── wan_video_dit.py
│   │   ├── wan_video_image_encoder.py
│   │   ├── wan_video_text_encoder.py
│   │   └── wan_video_vae.py
│   ├── pipelines/
│   │   ├── __init__.py
│   │   ├── base.py
│   │   ├── cog_video.py
│   │   ├── dancer.py
│   │   ├── flux_image.py
│   │   ├── hunyuan_image.py
│   │   ├── hunyuan_video.py
│   │   ├── omnigen_image.py
│   │   ├── pipeline_runner.py
│   │   ├── sd3_image.py
│   │   ├── sd_image.py
│   │   ├── sd_video.py
│   │   ├── sdxl_image.py
│   │   ├── sdxl_video.py
│   │   ├── step_video.py
│   │   ├── svd_video.py
│   │   ├── wan_video.py
│   │   └── wan_video_syncammaster.py
│   ├── processors/
│   │   ├── FastBlend.py
│   │   ├── PILEditor.py
│   │   ├── RIFE.py
│   │   ├── __init__.py
│   │   ├── base.py
│   │   └── sequencial_processor.py
│   ├── prompters/
│   │   ├── __init__.py
│   │   ├── base_prompter.py
│   │   ├── cog_prompter.py
│   │   ├── flux_prompter.py
│   │   ├── hunyuan_dit_prompter.py
│   │   ├── hunyuan_video_prompter.py
│   │   ├── kolors_prompter.py
│   │   ├── omnigen_prompter.py
│   │   ├── omost.py
│   │   ├── prompt_refiners.py
│   │   ├── sd3_prompter.py
│   │   ├── sd_prompter.py
│   │   ├── sdxl_prompter.py
│   │   ├── stepvideo_prompter.py
│   │   └── wan_prompter.py
│   ├── schedulers/
│   │   ├── __init__.py
│   │   ├── continuous_ode.py
│   │   ├── ddim.py
│   │   └── flow_match.py
│   ├── tokenizer_configs/
│   │   ├── __init__.py
│   │   ├── cog/
│   │   │   └── tokenizer/
│   │   │       ├── added_tokens.json
│   │   │       ├── special_tokens_map.json
│   │   │       ├── spiece.model
│   │   │       └── tokenizer_config.json
│   │   ├── flux/
│   │   │   ├── tokenizer_1/
│   │   │   │   ├── merges.txt
│   │   │   │   ├── special_tokens_map.json
│   │   │   │   ├── tokenizer_config.json
│   │   │   │   └── vocab.json
│   │   │   └── tokenizer_2/
│   │   │       ├── special_tokens_map.json
│   │   │       ├── spiece.model
│   │   │       ├── tokenizer.json
│   │   │       └── tokenizer_config.json
│   │   ├── hunyuan_dit/
│   │   │   ├── tokenizer/
│   │   │   │   ├── special_tokens_map.json
│   │   │   │   ├── tokenizer_config.json
│   │   │   │   ├── vocab.txt
│   │   │   │   └── vocab_org.txt
│   │   │   └── tokenizer_t5/
│   │   │       ├── config.json
│   │   │       ├── special_tokens_map.json
│   │   │       ├── spiece.model
│   │   │       └── tokenizer_config.json
│   │   ├── hunyuan_video/
│   │   │   ├── tokenizer_1/
│   │   │   │   ├── merges.txt
│   │   │   │   ├── special_tokens_map.json
│   │   │   │   ├── tokenizer_config.json
│   │   │   │   └── vocab.json
│   │   │   └── tokenizer_2/
│   │   │       ├── preprocessor_config.json
│   │   │       ├── special_tokens_map.json
│   │   │       ├── tokenizer.json
│   │   │       └── tokenizer_config.json
│   │   ├── kolors/
│   │   │   └── tokenizer/
│   │   │       ├── tokenizer.model
│   │   │       ├── tokenizer_config.json
│   │   │       └── vocab.txt
│   │   ├── stable_diffusion/
│   │   │   └── tokenizer/
│   │   │       ├── merges.txt
│   │   │       ├── special_tokens_map.json
│   │   │       ├── tokenizer_config.json
│   │   │       └── vocab.json
│   │   ├── stable_diffusion_3/
│   │   │   ├── tokenizer_1/
│   │   │   │   ├── merges.txt
│   │   │   │   ├── special_tokens_map.json
│   │   │   │   ├── tokenizer_config.json
│   │   │   │   └── vocab.json
│   │   │   ├── tokenizer_2/
│   │   │   │   ├── merges.txt
│   │   │   │   ├── special_tokens_map.json
│   │   │   │   ├── tokenizer_config.json
│   │   │   │   └── vocab.json
│   │   │   └── tokenizer_3/
│   │   │       ├── special_tokens_map.json
│   │   │       ├── spiece.model
│   │   │       ├── tokenizer.json
│   │   │       └── tokenizer_config.json
│   │   └── stable_diffusion_xl/
│   │       └── tokenizer_2/
│   │           ├── merges.txt
│   │           ├── special_tokens_map.json
│   │           ├── tokenizer_config.json
│   │           └── vocab.json
│   ├── trainers/
│   │   ├── __init__.py
│   │   └── text_to_image.py
│   └── vram_management/
│       ├── __init__.py
│       └── layers.py
├── download_wan2.1.py
├── example_test_data/
│   ├── cameras/
│   │   └── camera_extrinsics.json
│   └── metadata.csv
├── generate_sample_list.py
├── inference_syncammaster.py
├── models/
│   └── SynCamMaster/
│       └── checkpoints/
│           └── Put SynCamMaster ckpt file here.txt
├── requirements.txt
├── setup.py
├── train_syncammaster.py
└── vis_cam.py

SYMBOL INDEX (2289 symbols across 137 files)

FILE: diffsynth/controlnets/controlnet_unit.py
  class ControlNetConfigUnit (line 6) | class ControlNetConfigUnit:
    method __init__ (line 7) | def __init__(self, processor_id: Processor_id, model_path, scale=1.0, ...
  class ControlNetUnit (line 14) | class ControlNetUnit:
    method __init__ (line 15) | def __init__(self, processor, model, scale=1.0):
  class MultiControlNetManager (line 21) | class MultiControlNetManager:
    method __init__ (line 22) | def __init__(self, controlnet_units=[]):
    method cpu (line 27) | def cpu(self):
    method to (line 31) | def to(self, device):
    method process_image (line 37) | def process_image(self, image, processor_id=None):
    method __call__ (line 48) | def __call__(
  class FluxMultiControlNetManager (line 68) | class FluxMultiControlNetManager(MultiControlNetManager):
    method __init__ (line 69) | def __init__(self, controlnet_units=[]):
    method process_image (line 72) | def process_image(self, image, processor_id=None):
    method __call__ (line 79) | def __call__(self, conditionings, **kwargs):

FILE: diffsynth/controlnets/processors.py
  class Annotator (line 8) | class Annotator:
    method __init__ (line 9) | def __init__(self, processor_id: Processor_id, model_path="models/Anno...
    method to (line 42) | def to(self,device):
    method __call__ (line 47) | def __call__(self, image, mask=None):

FILE: diffsynth/data/simple_text_image.py
  class TextImageDataset (line 8) | class TextImageDataset(torch.utils.data.Dataset):
    method __init__ (line 9) | def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, w...
    method __getitem__ (line 26) | def __getitem__(self, index):
    method __len__ (line 40) | def __len__(self):

FILE: diffsynth/data/video.py
  class LowMemoryVideo (line 7) | class LowMemoryVideo:
    method __init__ (line 8) | def __init__(self, file_name):
    method __len__ (line 11) | def __len__(self):
    method __getitem__ (line 14) | def __getitem__(self, item):
    method __del__ (line 17) | def __del__(self):
  function split_file_name (line 21) | def split_file_name(file_name):
  function search_for_images (line 40) | def search_for_images(folder):
  class LowMemoryImageFolder (line 48) | class LowMemoryImageFolder:
    method __init__ (line 49) | def __init__(self, folder, file_list=None):
    method __len__ (line 55) | def __len__(self):
    method __getitem__ (line 58) | def __getitem__(self, item):
    method __del__ (line 61) | def __del__(self):
  function crop_and_resize (line 65) | def crop_and_resize(image, height, width):
  class VideoData (line 81) | class VideoData:
    method __init__ (line 82) | def __init__(self, video_file=None, image_folder=None, height=None, wi...
    method raw_data (line 94) | def raw_data(self):
    method set_length (line 100) | def set_length(self, length):
    method set_shape (line 103) | def set_shape(self, height, width):
    method __len__ (line 107) | def __len__(self):
    method shape (line 113) | def shape(self):
    method __getitem__ (line 120) | def __getitem__(self, item):
    method __del__ (line 128) | def __del__(self):
    method save_images (line 131) | def save_images(self, folder):
  function save_video (line 138) | def save_video(frames, save_path, fps, quality=9, ffmpeg_params=None):
  function save_frames (line 145) | def save_frames(frames, save_path):

FILE: diffsynth/extensions/ESRGAN/__init__.py
  class ResidualDenseBlock (line 7) | class ResidualDenseBlock(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, num_feat=64, num_grow_ch=32):
    method forward (line 18) | def forward(self, x):
  class RRDB (line 27) | class RRDB(torch.nn.Module):
    method __init__ (line 29) | def __init__(self, num_feat, num_grow_ch=32):
    method forward (line 35) | def forward(self, x):
  class RRDBNet (line 42) | class RRDBNet(torch.nn.Module):
    method __init__ (line 44) | def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=2...
    method forward (line 56) | def forward(self, x):
    method state_dict_converter (line 70) | def state_dict_converter():
  class RRDBNetStateDictConverter (line 74) | class RRDBNetStateDictConverter:
    method __init__ (line 75) | def __init__(self):
    method from_diffusers (line 78) | def from_diffusers(self, state_dict):
    method from_civitai (line 81) | def from_civitai(self, state_dict):
  class ESRGAN (line 85) | class ESRGAN(torch.nn.Module):
    method __init__ (line 86) | def __init__(self, model):
    method from_model_manager (line 91) | def from_model_manager(model_manager):
    method process_image (line 94) | def process_image(self, image):
    method process_images (line 98) | def process_images(self, images):
    method decode_images (line 103) | def decode_images(self, images):
    method upscale (line 109) | def upscale(self, images, batch_size=4, progress_bar=lambda x:x):

FILE: diffsynth/extensions/FastBlend/__init__.py
  class FastBlendSmoother (line 7) | class FastBlendSmoother:
    method __init__ (line 8) | def __init__(self):
    method from_model_manager (line 22) | def from_model_manager(model_manager):
    method run (line 26) | def run(self, frames_guide, frames_style, batch_size, window_size, ebs...
    method __call__ (line 54) | def __call__(self, rendered_frames, original_frames=None, **kwargs):

FILE: diffsynth/extensions/FastBlend/api.py
  function check_input_for_blending (line 7) | def check_input_for_blending(video_guide, video_guide_folder, video_styl...
  function smooth_video (line 25) | def smooth_video(
  class KeyFrameMatcher (line 92) | class KeyFrameMatcher:
    method __init__ (line 93) | def __init__(self):
    method extract_number_from_filename (line 96) | def extract_number_from_filename(self, file_name):
    method extract_number_from_filenames (line 113) | def extract_number_from_filenames(self, file_names):
    method match_using_filename (line 121) | def match_using_filename(self, file_names_a, file_names_b):
    method match_using_numbers (line 131) | def match_using_numbers(self, file_names_a, file_names_b):
    method match_filenames (line 143) | def match_filenames(self, file_names_a, file_names_b):
  function detect_frames (line 151) | def detect_frames(frames_path, keyframes_path):
  function check_input_for_interpolating (line 182) | def check_input_for_interpolating(frames_path, keyframes_path):
  function interpolate_video (line 202) | def interpolate_video(
  function on_ui_tabs (line 257) | def on_ui_tabs():

FILE: diffsynth/extensions/FastBlend/data.py
  function read_video (line 6) | def read_video(file_name):
  function get_video_fps (line 16) | def get_video_fps(file_name):
  function save_video (line 23) | def save_video(frames_path, video_path, num_frames, fps):
  class LowMemoryVideo (line 32) | class LowMemoryVideo:
    method __init__ (line 33) | def __init__(self, file_name):
    method __len__ (line 36) | def __len__(self):
    method __getitem__ (line 39) | def __getitem__(self, item):
    method __del__ (line 42) | def __del__(self):
  function split_file_name (line 46) | def split_file_name(file_name):
  function search_for_images (line 65) | def search_for_images(folder):
  function read_images (line 73) | def read_images(folder):
  class LowMemoryImageFolder (line 79) | class LowMemoryImageFolder:
    method __init__ (line 80) | def __init__(self, folder, file_list=None):
    method __len__ (line 86) | def __len__(self):
    method __getitem__ (line 89) | def __getitem__(self, item):
    method __del__ (line 92) | def __del__(self):
  class VideoData (line 96) | class VideoData:
    method __init__ (line 97) | def __init__(self, video_file, image_folder, **kwargs):
    method raw_data (line 110) | def raw_data(self):
    method set_length (line 116) | def set_length(self, length):
    method set_shape (line 119) | def set_shape(self, height, width):
    method __len__ (line 123) | def __len__(self):
    method shape (line 129) | def shape(self):
    method __getitem__ (line 136) | def __getitem__(self, item):
    method __del__ (line 145) | def __del__(self):

FILE: diffsynth/extensions/FastBlend/patch_match.py
  class PatchMatcher (line 7) | class PatchMatcher:
    method __init__ (line 8) | def __init__(
    method pad_image (line 37) | def pad_image(self, image):
    method unpad_image (line 40) | def unpad_image(self, image):
    method apply_nnf_to_image (line 43) | def apply_nnf_to_image(self, nnf, source):
    method get_patch_error (line 53) | def get_patch_error(self, source, nnf, target):
    method get_pairwise_patch_error (line 63) | def get_pairwise_patch_error(self, source, nnf):
    method get_error (line 76) | def get_error(self, source_guide, target_guide, source_style, target_s...
    method clamp_bound (line 89) | def clamp_bound(self, nnf):
    method random_step (line 94) | def random_step(self, nnf, r):
    method neighboor_step (line 100) | def neighboor_step(self, nnf, d):
    method shift_nnf (line 116) | def shift_nnf(self, nnf, d):
    method track_step (line 125) | def track_step(self, nnf, d):
    method C (line 134) | def C(self, n, m):
    method bezier_step (line 145) | def bezier_step(self, nnf, r):
    method update (line 158) | def update(self, source_guide, target_guide, source_style, target_styl...
    method propagation (line 165) | def propagation(self, source_guide, target_guide, source_style, target...
    method random_search (line 171) | def random_search(self, source_guide, target_guide, source_style, targ...
    method track (line 177) | def track(self, source_guide, target_guide, source_style, target_style...
    method iteration (line 185) | def iteration(self, source_guide, target_guide, source_style, target_s...
    method estimate_nnf (line 191) | def estimate_nnf(self, source_guide, target_guide, source_style, nnf):
  class PyramidPatchMatcher (line 205) | class PyramidPatchMatcher:
    method __init__ (line 206) | def __init__(
    method resample_image (line 234) | def resample_image(self, images, level):
    method initialize_nnf (line 244) | def initialize_nnf(self, batch_size):
    method update_nnf (line 262) | def update_nnf(self, nnf, level):
    method apply_nnf_to_image (line 276) | def apply_nnf_to_image(self, nnf, image):
    method estimate_nnf (line 282) | def estimate_nnf(self, source_guide, target_guide, source_style):

FILE: diffsynth/extensions/FastBlend/runners/accurate.py
  class AccurateModeRunner (line 8) | class AccurateModeRunner:
    method __init__ (line 9) | def __init__(self):
    method run (line 12) | def run(self, frames_guide, frames_style, batch_size, window_size, ebs...

FILE: diffsynth/extensions/FastBlend/runners/balanced.py
  class BalancedModeRunner (line 8) | class BalancedModeRunner:
    method __init__ (line 9) | def __init__(self):
    method run (line 12) | def run(self, frames_guide, frames_style, batch_size, window_size, ebs...

FILE: diffsynth/extensions/FastBlend/runners/fast.py
  class TableManager (line 8) | class TableManager:
    method __init__ (line 9) | def __init__(self):
    method task_list (line 12) | def task_list(self, n):
    method build_remapping_table (line 34) | def build_remapping_table(self, frames_guide, frames_style, patch_matc...
    method remapping_table_to_blending_table (line 56) | def remapping_table_to_blending_table(self, table):
    method tree_query (line 66) | def tree_query(self, leftbound, rightbound):
    method process_window_sum (line 77) | def process_window_sum(self, frames_guide, blending_table, patch_match...
  class FastModeRunner (line 109) | class FastModeRunner:
    method __init__ (line 110) | def __init__(self):
    method run (line 113) | def run(self, frames_guide, frames_style, batch_size, window_size, ebs...

FILE: diffsynth/extensions/FastBlend/runners/interpolation.py
  class InterpolationModeRunner (line 8) | class InterpolationModeRunner:
    method __init__ (line 9) | def __init__(self):
    method get_index_dict (line 12) | def get_index_dict(self, index_style):
    method get_weight (line 18) | def get_weight(self, l, m, r):
    method get_task_group (line 26) | def get_task_group(self, index_style, n):
    method run (line 48) | def run(self, frames_guide, frames_style, index_style, batch_size, ebs...
  class InterpolationModeSingleFrameRunner (line 87) | class InterpolationModeSingleFrameRunner:
    method __init__ (line 88) | def __init__(self):
    method run (line 91) | def run(self, frames_guide, frames_style, index_style, batch_size, ebs...

FILE: diffsynth/extensions/ImageQualityMetric/BLIP/blip.py
  function default_bert (line 16) | def default_bert():
  function init_tokenizer (line 23) | def init_tokenizer(bert_model_path):
  function create_vit (line 31) | def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer...
  function is_url (line 49) | def is_url(url_or_filename):
  function load_checkpoint (line 53) | def load_checkpoint(model,url_or_filename):

FILE: diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py
  class BLIP_Pretrain (line 13) | class BLIP_Pretrain(nn.Module):
    method __init__ (line 14) | def __init__(self,

FILE: diffsynth/extensions/ImageQualityMetric/BLIP/med.py
  class BertEmbeddings (line 44) | class BertEmbeddings(nn.Module):
    method __init__ (line 47) | def __init__(self, config):
    method forward (line 63) | def forward(
  class BertSelfAttention (line 89) | class BertSelfAttention(nn.Module):
    method __init__ (line 90) | def __init__(self, config, is_cross_attention):
    method save_attn_gradients (line 118) | def save_attn_gradients(self, attn_gradients):
    method get_attn_gradients (line 121) | def get_attn_gradients(self):
    method save_attention_map (line 124) | def save_attention_map(self, attention_map):
    method get_attention_map (line 127) | def get_attention_map(self):
    method transpose_for_scores (line 130) | def transpose_for_scores(self, x):
    method forward (line 135) | def forward(
  class BertSelfOutput (line 220) | class BertSelfOutput(nn.Module):
    method __init__ (line 221) | def __init__(self, config):
    method forward (line 227) | def forward(self, hidden_states, input_tensor):
  class BertAttention (line 234) | class BertAttention(nn.Module):
    method __init__ (line 235) | def __init__(self, config, is_cross_attention=False):
    method prune_heads (line 241) | def prune_heads(self, heads):
    method forward (line 259) | def forward(
  class BertIntermediate (line 283) | class BertIntermediate(nn.Module):
    method __init__ (line 284) | def __init__(self, config):
    method forward (line 292) | def forward(self, hidden_states):
  class BertOutput (line 298) | class BertOutput(nn.Module):
    method __init__ (line 299) | def __init__(self, config):
    method forward (line 305) | def forward(self, hidden_states, input_tensor):
  class BertLayer (line 312) | class BertLayer(nn.Module):
    method __init__ (line 313) | def __init__(self, config, layer_num):
    method forward (line 325) | def forward(
    method feed_forward_chunk (line 372) | def feed_forward_chunk(self, attention_output):
  class BertEncoder (line 378) | class BertEncoder(nn.Module):
    method __init__ (line 379) | def __init__(self, config):
    method forward (line 385) | def forward(
  class BertPooler (line 478) | class BertPooler(nn.Module):
    method __init__ (line 479) | def __init__(self, config):
    method forward (line 484) | def forward(self, hidden_states):
  class BertPredictionHeadTransform (line 493) | class BertPredictionHeadTransform(nn.Module):
    method __init__ (line 494) | def __init__(self, config):
    method forward (line 503) | def forward(self, hidden_states):
  class BertLMPredictionHead (line 510) | class BertLMPredictionHead(nn.Module):
    method __init__ (line 511) | def __init__(self, config):
    method forward (line 524) | def forward(self, hidden_states):
  class BertOnlyMLMHead (line 530) | class BertOnlyMLMHead(nn.Module):
    method __init__ (line 531) | def __init__(self, config):
    method forward (line 535) | def forward(self, sequence_output):
  class BertPreTrainedModel (line 540) | class BertPreTrainedModel(PreTrainedModel):
    method _init_weights (line 550) | def _init_weights(self, module):
  class BertModel (line 563) | class BertModel(BertPreTrainedModel):
    method __init__ (line 573) | def __init__(self, config, add_pooling_layer=True):
    method get_input_embeddings (line 586) | def get_input_embeddings(self):
    method set_input_embeddings (line 589) | def set_input_embeddings(self, value):
    method _prune_heads (line 592) | def _prune_heads(self, heads_to_prune):
    method get_extended_attention_mask (line 601) | def get_extended_attention_mask(self, attention_mask: Tensor, input_sh...
    method forward (line 662) | def forward(
  class BertLMHeadModel (line 803) | class BertLMHeadModel(BertPreTrainedModel):
    method __init__ (line 808) | def __init__(self, config):
    method get_output_embeddings (line 816) | def get_output_embeddings(self):
    method set_output_embeddings (line 819) | def set_output_embeddings(self, new_embeddings):
    method forward (line 822) | def forward(
    method prepare_inputs_for_generation (line 924) | def prepare_inputs_for_generation(self, input_ids, past=None, attentio...
    method _reorder_cache (line 943) | def _reorder_cache(self, past, beam_idx):

FILE: diffsynth/extensions/ImageQualityMetric/BLIP/vit.py
  class Mlp (line 19) | class Mlp(nn.Module):
    method __init__ (line 22) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 31) | def forward(self, x):
  class Attention (line 40) | class Attention(nn.Module):
    method __init__ (line 41) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at...
    method save_attn_gradients (line 54) | def save_attn_gradients(self, attn_gradients):
    method get_attn_gradients (line 57) | def get_attn_gradients(self):
    method save_attention_map (line 60) | def save_attention_map(self, attention_map):
    method get_attention_map (line 63) | def get_attention_map(self):
    method forward (line 66) | def forward(self, x, register_hook=False):
  class Block (line 85) | class Block(nn.Module):
    method __init__ (line 87) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 103) | def forward(self, x, register_hook=False):
  class VisionTransformer (line 109) | class VisionTransformer(nn.Module):
    method __init__ (line 114) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
    method _init_weights (line 163) | def _init_weights(self, m):
    method no_weight_decay (line 173) | def no_weight_decay(self):
    method forward (line 176) | def forward(self, x, register_blk=-1):
    method load_pretrained (line 193) | def load_pretrained(self, checkpoint_path, prefix=''):
  function _load_weights (line 198) | def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix...
  function interpolate_pos_embed (line 277) | def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):

FILE: diffsynth/extensions/ImageQualityMetric/__init__.py
  function download_preference_model (line 136) | def download_preference_model(model_name: preference_model_id, cache_dir...
  function load_preference_model (line 144) | def load_preference_model(model_name: preference_model_id, device = "cud...

FILE: diffsynth/extensions/ImageQualityMetric/aesthetic.py
  class MLP (line 10) | class MLP(torch.nn.Module):
    method __init__ (line 11) | def __init__(self, input_size: int, xcol: str = "emb", ycol: str = "av...
    method forward (line 31) | def forward(self, x: torch.Tensor) -> torch.Tensor:
    method training_step (line 34) | def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor:
    method validation_step (line 41) | def validation_step(self, batch: dict, batch_idx: int) -> torch.Tensor:
    method configure_optimizers (line 48) | def configure_optimizers(self) -> torch.optim.Optimizer:
  class AestheticScore (line 52) | class AestheticScore(torch.nn.Module):
    method __init__ (line 53) | def __init__(self, device: torch.device, path: str = MODEL_PATHS):
    method _calculate_score (line 76) | def _calculate_score(self, image: torch.Tensor) -> float:
    method score (line 96) | def score(self, images: Union[str, List[str], Image.Image, List[Image....

FILE: diffsynth/extensions/ImageQualityMetric/clip.py
  class CLIPScore (line 7) | class CLIPScore(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, device: torch.device, path: str = MODEL_PATHS):
    method _calculate_score (line 44) | def _calculate_score(self, image: torch.Tensor, prompt: str) -> float:
    method score (line 67) | def score(self, images: Union[str, List[str], Image.Image, List[Image....

FILE: diffsynth/extensions/ImageQualityMetric/config.py
  function get_model_path (line 8) | def get_model_path(model_name):

FILE: diffsynth/extensions/ImageQualityMetric/hps.py
  class HPScore_v2 (line 9) | class HPScore_v2(torch.nn.Module):
    method __init__ (line 10) | def __init__(self, device: torch.device, path: str = MODEL_PATHS, mode...
    method _calculate_score (line 62) | def _calculate_score(self, image: torch.Tensor, prompt: str) -> float:
    method score (line 85) | def score(self, images: Union[str, List[str], Image.Image, List[Image....

FILE: diffsynth/extensions/ImageQualityMetric/imagereward.py
  function _convert_image_to_rgb (line 12) | def _convert_image_to_rgb(image):
  function _transform (line 15) | def _transform(n_px):
  class MLP (line 24) | class MLP(torch.nn.Module):
    method __init__ (line 25) | def __init__(self, input_size):
    method forward (line 51) | def forward(self, input):
  class ImageReward (line 54) | class ImageReward(torch.nn.Module):
    method __init__ (line 55) | def __init__(self, med_config, device='cpu', bert_model_path=""):
    method score_grad (line 66) | def score_grad(self, prompt_ids, prompt_attention_mask, image):
    method score (line 91) | def score(self, images: Union[str, List[str], Image.Image, List[Image....
    method _calculate_score (line 125) | def _calculate_score(self, prompt: str, image: torch.Tensor) -> torch....
    method inference_rank (line 150) | def inference_rank(self, prompt: str, generations_list: List[Union[str...
  class ImageRewardScore (line 190) | class ImageRewardScore(torch.nn.Module):
    method __init__ (line 191) | def __init__(self, device: Union[str, torch.device], path: str = MODEL...
    method score (line 202) | def score(self, images: Union[str, List[str], Image.Image, List[Image....

FILE: diffsynth/extensions/ImageQualityMetric/mps.py
  class MPScore (line 27) | class MPScore(torch.nn.Module):
    method __init__ (line 28) | def __init__(self, device: Union[str, torch.device], path: str = MODEL...
    method _calculate_score (line 45) | def _calculate_score(self, image: torch.Tensor, prompt: str) -> float:
    method score (line 99) | def score(self, images: Union[str, List[str], Image.Image, List[Image....

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py
  class MultimodalCfg (line 45) | class MultimodalCfg(CLIPTextCfg):
  function _build_text_decoder_tower (line 53) | def _build_text_decoder_tower(
  class CoCa (line 79) | class CoCa(nn.Module):
    method __init__ (line 80) | def __init__(
    method set_grad_checkpointing (line 126) | def set_grad_checkpointing(self, enable=True):
    method _encode_image (line 131) | def _encode_image(self, images, normalize=True):
    method _encode_text (line 136) | def _encode_text(self, text, normalize=True, embed_cls=True):
    method encode_image (line 142) | def encode_image(self, images, normalize=True):
    method encode_text (line 146) | def encode_text(self, text, normalize=True, embed_cls=True):
    method forward (line 150) | def forward(self, image, text, embed_cls=True, image_latent=None, imag...
    method generate (line 167) | def generate(
    method _generate_beamsearch (line 290) | def _generate_beamsearch(
  function prepare_inputs_for_generation (line 439) | def prepare_inputs_for_generation(input_ids, image_inputs, past=None, **...

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/factory.py
  function _natural_key (line 29) | def _natural_key(string_):
  function _rescan_model_configs (line 33) | def _rescan_model_configs():
  function list_models (line 57) | def list_models():
  function add_model_config (line 62) | def add_model_config(path):
  function get_model_config (line 70) | def get_model_config(model_name):
  function get_tokenizer (line 77) | def get_tokenizer(model_name, open_clip_bpe_path=None):
  function load_state_dict (line 87) | def load_state_dict(checkpoint_path: str, map_location='cpu'):
  function load_checkpoint (line 98) | def load_checkpoint(model, checkpoint_path, strict=True):
  function create_model (line 108) | def create_model(
  function create_loss (line 244) | def create_loss(args):
  class MLP (line 274) | class MLP(torch.nn.Module):
    method __init__ (line 275) | def __init__(self, input_size):
    method forward (line 289) | def forward(self, x):
  function create_model_and_transforms (line 309) | def create_model_and_transforms(
  function create_model_from_pretrained (line 394) | def create_model_from_pretrained(

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py
  class BaseModelOutput (line 21) | class BaseModelOutput:
  class PretrainedConfig (line 25) | class PretrainedConfig:
  function _camel2snake (line 32) | def _camel2snake(s):
  function register_pooler (line 40) | def register_pooler(cls):
  class MeanPooler (line 47) | class MeanPooler(nn.Module):
    method forward (line 50) | def forward(self, x: BaseModelOutput, attention_mask: TensorType):
  class MaxPooler (line 56) | class MaxPooler(nn.Module):
    method forward (line 59) | def forward(self, x: BaseModelOutput, attention_mask: TensorType):
  class ClsPooler (line 65) | class ClsPooler(nn.Module):
    method __init__ (line 68) | def __init__(self, use_pooler_output=True):
    method forward (line 73) | def forward(self, x: BaseModelOutput, attention_mask: TensorType):
  class HFTextEncoder (line 83) | class HFTextEncoder(nn.Module):
    method __init__ (line 87) | def __init__(
    method forward (line 137) | def forward(self, x: TensorType):
    method lock (line 154) | def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
    method set_grad_checkpointing (line 172) | def set_grad_checkpointing(self, enable=True):
    method init_parameters (line 175) | def init_parameters(self):

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/loss.py
  function gather_features (line 20) | def gather_features(
  class ClipLoss (line 67) | class ClipLoss(nn.Module):
    method __init__ (line 69) | def __init__(
    method get_ground_truth (line 90) | def get_ground_truth(self, device, num_logits) -> torch.Tensor:
    method get_logits (line 103) | def get_logits(self, image_features, text_features, logit_scale):
    method forward (line 121) | def forward(self, image_features, text_features, logit_scale, output_d...
  class PreferenceLoss (line 133) | class PreferenceLoss(nn.Module):
    method forward (line 135) | def forward(self, logits_per_image, num_images, labels):
  class HPSLoss (line 143) | class HPSLoss(nn.Module):
    method forward (line 145) | def forward(self, text_logits, labels):
  class RankingLoss (line 171) | class RankingLoss(nn.Module):
    method forward (line 173) | def forward(self, logits_per_image, num_images, labels, margin = 1.0):
  class CoCaLoss (line 192) | class CoCaLoss(ClipLoss):
    method __init__ (line 193) | def __init__(
    method forward (line 218) | def forward(self, image_features, text_features, logits, labels, logit...
  class DistillClipLoss (line 234) | class DistillClipLoss(ClipLoss):
    method dist_loss (line 236) | def dist_loss(self, teacher_logits, student_logits):
    method forward (line 239) | def forward(

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/model.py
  class CLIPVisionCfg (line 24) | class CLIPVisionCfg:
  class CLIPTextCfg (line 49) | class CLIPTextCfg:
  function get_cast_dtype (line 66) | def get_cast_dtype(precision: str):
  function _build_vision_tower (line 75) | def _build_vision_tower(
  function _build_text_tower (line 137) | def _build_text_tower(
  class CLIP (line 176) | class CLIP(nn.Module):
    method __init__ (line 179) | def __init__(
    method lock_image_tower (line 203) | def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
    method lock_text_tower (line 207) | def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm:...
    method set_grad_checkpointing (line 224) | def set_grad_checkpointing(self, enable=True):
    method encode_image (line 228) | def encode_image(self, image, normalize: bool = False):
    method encode_text (line 232) | def encode_text(self, text, normalize: bool = False):
    method forward (line 246) | def forward(self, image, text):
  class CustomTextCLIP (line 258) | class CustomTextCLIP(nn.Module):
    method __init__ (line 261) | def __init__(
    method lock_image_tower (line 276) | def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
    method lock_text_tower (line 280) | def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm:...
    method set_grad_checkpointing (line 284) | def set_grad_checkpointing(self, enable=True):
    method encode_image (line 288) | def encode_image(self, image, normalize: bool = False):
    method encode_text (line 292) | def encode_text(self, text, normalize: bool = False):
    method forward (line 296) | def forward(self, image, text):
  function convert_weights_to_lp (line 308) | def convert_weights_to_lp(model: nn.Module, dtype=torch.float16):
  function convert_to_custom_text_state_dict (line 336) | def convert_to_custom_text_state_dict(state_dict: dict):
  function build_model_from_openai_state_dict (line 354) | def build_model_from_openai_state_dict(
  function trace_model (line 414) | def trace_model(model, batch_size=256, device=torch.device('cpu')):
  function resize_pos_embed (line 430) | def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', ...

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py
  class Bottleneck (line 10) | class Bottleneck(nn.Module):
    method __init__ (line 13) | def __init__(self, inplanes, planes, stride=1):
    method forward (line 42) | def forward(self, x: torch.Tensor):
  class AttentionPool2d (line 58) | class AttentionPool2d(nn.Module):
    method __init__ (line 59) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 68) | def forward(self, x):
  class ModifiedResNet (line 95) | class ModifiedResNet(nn.Module):
    method __init__ (line 103) | def __init__(self, layers, output_dim, heads, image_size=224, width=64):
    method _make_layer (line 132) | def _make_layer(self, planes, blocks, stride=1):
    method init_parameters (line 141) | def init_parameters(self):
    method lock (line 154) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 162) | def set_grad_checkpointing(self, enable=True):
    method stem (line 166) | def stem(self, x):
    method forward (line 173) | def forward(self, x):

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/openai.py
  function list_openai_models (line 18) | def list_openai_models() -> List[str]:
  function load_openai_model (line 23) | def load_openai_model(

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py
  function _pcfg (line 21) | def _pcfg(url='', hf_hub='', mean=None, std=None):
  function _clean_tag (line 235) | def _clean_tag(tag: str):
  function list_pretrained (line 240) | def list_pretrained(as_str: bool = False):
  function list_pretrained_models_by_tag (line 247) | def list_pretrained_models_by_tag(tag: str):
  function list_pretrained_tags_by_model (line 257) | def list_pretrained_tags_by_model(model: str):
  function is_pretrained_cfg (line 265) | def is_pretrained_cfg(model: str, tag: str):
  function get_pretrained_cfg (line 271) | def get_pretrained_cfg(model: str, tag: str):
  function get_pretrained_url (line 278) | def get_pretrained_url(model: str, tag: str):
  function download_pretrained_from_url (line 283) | def download_pretrained_from_url(
  function has_hf_hub (line 329) | def has_hf_hub(necessary=False):
  function download_pretrained_from_hf (line 337) | def download_pretrained_from_hf(
  function download_pretrained (line 348) | def download_pretrained(

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py
  function save_config_for_hf (line 27) | def save_config_for_hf(
  function save_for_hf (line 45) | def save_for_hf(
  function push_to_hf_hub (line 65) | def push_to_hf_hub(
  function push_pretrained_to_hf_hub (line 124) | def push_pretrained_to_hf_hub(
  function generate_readme (line 163) | def generate_readme(model_card: dict, model_name: str):

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py
  class TimmModel (line 28) | class TimmModel(nn.Module):
    method __init__ (line 33) | def __init__(
    method lock (line 85) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 118) | def set_grad_checkpointing(self, enable=True):
    method forward (line 124) | def forward(self, x):

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py
  function default_bpe (line 21) | def default_bpe():
  function bytes_to_unicode (line 29) | def bytes_to_unicode():
  function get_pairs (line 51) | def get_pairs(word):
  function basic_clean (line 63) | def basic_clean(text):
  function whitespace_clean (line 69) | def whitespace_clean(text):
  class SimpleTokenizer (line 75) | class SimpleTokenizer(object):
    method __init__ (line 76) | def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
    method bpe (line 101) | def bpe(self, token):
    method encode (line 142) | def encode(self, text):
    method decode (line 150) | def decode(self, tokens):
    method __call__ (line 155) | def __call__(self, texts: Union[str, List[str]], context_length: int =...
  class HFTokenizer (line 188) | class HFTokenizer:
    method __init__ (line 191) | def __init__(self, tokenizer_name: str):
    method save_pretrained (line 195) | def save_pretrained(self, dest):
    method __call__ (line 198) | def __call__(self, texts: Union[str, List[str]], context_length: int =...

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/transform.py
  class AugmentationCfg (line 16) | class AugmentationCfg:
  class ResizeMaxSize (line 26) | class ResizeMaxSize(nn.Module):
    method __init__ (line 28) | def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, ...
    method forward (line 37) | def forward(self, img):
  function _convert_to_rgb_or_rgba (line 52) | def _convert_to_rgb_or_rgba(image):
  class MaskAwareNormalize (line 66) | class MaskAwareNormalize(nn.Module):
    method __init__ (line 67) | def __init__(self, mean, std):
    method forward (line 71) | def forward(self, tensor):
  function image_transform (line 77) | def image_transform(

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py
  class LayerNormFp32 (line 13) | class LayerNormFp32(nn.LayerNorm):
    method forward (line 16) | def forward(self, x: torch.Tensor):
  class LayerNorm (line 22) | class LayerNorm(nn.LayerNorm):
    method forward (line 25) | def forward(self, x: torch.Tensor):
  class QuickGELU (line 31) | class QuickGELU(nn.Module):
    method forward (line 33) | def forward(self, x: torch.Tensor):
  class LayerScale (line 37) | class LayerScale(nn.Module):
    method __init__ (line 38) | def __init__(self, dim, init_values=1e-5, inplace=False):
    method forward (line 43) | def forward(self, x):
  class PatchDropout (line 47) | class PatchDropout(nn.Module):
    method __init__ (line 52) | def __init__(self, prob, exclude_first_token=True):
    method forward (line 58) | def forward(self, x):
  class Attention (line 87) | class Attention(nn.Module):
    method __init__ (line 88) | def __init__(
    method forward (line 127) | def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
  class AttentionalPooler (line 163) | class AttentionalPooler(nn.Module):
    method __init__ (line 164) | def __init__(
    method forward (line 178) | def forward(self, x: torch.Tensor):
    method _repeat (line 185) | def _repeat(self, query, N: int):
  class ResidualAttentionBlock (line 189) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 190) | def __init__(
    method attention (line 217) | def attention(
    method forward (line 232) | def forward(
  class CustomResidualAttentionBlock (line 247) | class CustomResidualAttentionBlock(nn.Module):
    method __init__ (line 248) | def __init__(
    method forward (line 282) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
  class Transformer (line 288) | class Transformer(nn.Module):
    method __init__ (line 289) | def __init__(
    method get_cast_dtype (line 310) | def get_cast_dtype(self) -> torch.dtype:
    method forward (line 313) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
  class VisionTransformer (line 323) | class VisionTransformer(nn.Module):
    method __init__ (line 326) | def __init__(
    method lock (line 395) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method init_parameters (line 428) | def init_parameters(self):
    method set_grad_checkpointing (line 449) | def set_grad_checkpointing(self, enable=True):
    method _global_pool (line 452) | def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.T...
    method forward (line 458) | def forward(self, x: torch.Tensor, skip_pool: bool = False):
  class TextTransformer (line 507) | class TextTransformer(nn.Module):
    method __init__ (line 510) | def __init__(
    method init_parameters (line 558) | def init_parameters(self):
    method set_grad_checkpointing (line 577) | def set_grad_checkpointing(self, enable=True):
    method build_attention_mask (line 580) | def build_attention_mask(self):
    method build_cls_mask (line 588) | def build_cls_mask(self, text, cast_dtype: torch.dtype):
    method _repeat (line 597) | def _repeat(self, t, N: int):
    method forward (line 600) | def forward(self, text):
  class MultimodalTransformer (line 635) | class MultimodalTransformer(Transformer):
    method __init__ (line 636) | def __init__(
    method init_parameters (line 677) | def init_parameters(self):
    method build_attention_mask (line 695) | def build_attention_mask(self):
    method forward (line 703) | def forward(self, image_embs, text_embs):
    method set_grad_checkpointing (line 726) | def set_grad_checkpointing(self, enable=True):

FILE: diffsynth/extensions/ImageQualityMetric/open_clip/utils.py
  function freeze_batch_norm_2d (line 8) | def freeze_batch_norm_2d(module, module_match={}, name=''):
  function _ntuple (line 48) | def _ntuple(n):

FILE: diffsynth/extensions/ImageQualityMetric/pickscore.py
  class PickScore (line 8) | class PickScore(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, device: Union[str, torch.device], path: str = MODEL...
    method _calculate_score (line 22) | def _calculate_score(self, image: torch.Tensor, prompt: str, softmax: ...
    method score (line 58) | def score(self, images: Union[str, List[str], Image.Image, List[Image....

FILE: diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py
  class BaseModelConfig (line 6) | class BaseModelConfig:

FILE: diffsynth/extensions/ImageQualityMetric/trainer/models/clip_model.py
  class XCLIPModel (line 17) | class XCLIPModel(HFCLIPModel):
    method __init__ (line 18) | def __init__(self, config: CLIPConfig):
    method get_text_features (line 21) | def get_text_features(
    method get_image_features (line 61) | def get_image_features(
  class ClipModelConfig (line 93) | class ClipModelConfig(BaseModelConfig):
  class CLIPModel (line 98) | class CLIPModel(nn.Module):
    method __init__ (line 99) | def __init__(self, ckpt, config_file=False):
    method get_text_features (line 110) | def get_text_features(self, *args, **kwargs):
    method get_image_features (line 113) | def get_image_features(self, *args, **kwargs):
    method forward (line 116) | def forward(self, text_inputs=None, image_inputs=None, condition_input...
    method logit_scale (line 141) | def logit_scale(self):
    method save (line 144) | def save(self, path):

FILE: diffsynth/extensions/ImageQualityMetric/trainer/models/cross_modeling.py
  function exists (line 8) | def exists(val):
  function default (line 11) | def default(val, d):
  class LayerNorm (line 18) | class LayerNorm(nn.Module):
    method __init__ (line 19) | def __init__(self, dim):
    method forward (line 24) | def forward(self, x):
  class Residual (line 30) | class Residual(nn.Module):
    method __init__ (line 31) | def __init__(self, fn):
    method forward (line 35) | def forward(self, x, *args, **kwargs):
  class RotaryEmbedding (line 43) | class RotaryEmbedding(nn.Module):
    method __init__ (line 44) | def __init__(self, dim):
    method forward (line 49) | def forward(self, max_seq_len, *, device):
  function rotate_half (line 55) | def rotate_half(x):
  function apply_rotary_pos_emb (line 61) | def apply_rotary_pos_emb(pos, t):
  class SwiGLU (line 69) | class SwiGLU(nn.Module):
    method forward (line 70) | def forward(self, x):
  class ParallelTransformerBlock (line 78) | class ParallelTransformerBlock(nn.Module):
    method __init__ (line 79) | def __init__(self, dim, dim_head=64, heads=8, ff_mult=4):
    method get_rotary_embedding (line 102) | def get_rotary_embedding(self, n, device):
    method forward (line 110) | def forward(self, x, attn_mask=None):
  class CrossAttention (line 172) | class CrossAttention(nn.Module):
    method __init__ (line 173) | def __init__(
    method forward (line 207) | def forward(self, x, context, mask):
  class Cross_model (line 261) | class Cross_model(nn.Module):
    method __init__ (line 262) | def __init__(
    method forward (line 281) | def forward(

FILE: diffsynth/extensions/RIFE/__init__.py
  function warp (line 8) | def warp(tenInput, tenFlow, device):
  function conv (line 26) | def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dila...
  class IFBlock (line 34) | class IFBlock(nn.Module):
    method __init__ (line 35) | def __init__(self, in_planes, c=64):
    method forward (line 45) | def forward(self, x, flow, scale=1):
  class IFNet (line 60) | class IFNet(nn.Module):
    method __init__ (line 61) | def __init__(self, **kwargs):
    method forward (line 68) | def forward(self, x, scale_list=[4, 2, 1], training=False):
    method state_dict_converter (line 103) | def state_dict_converter():
  class IFNetStateDictConverter (line 107) | class IFNetStateDictConverter:
    method __init__ (line 108) | def __init__(self):
    method from_diffusers (line 111) | def from_diffusers(self, state_dict):
    method from_civitai (line 115) | def from_civitai(self, state_dict):
  class RIFEInterpolater (line 119) | class RIFEInterpolater:
    method __init__ (line 120) | def __init__(self, model, device="cuda"):
    method from_model_manager (line 127) | def from_model_manager(model_manager):
    method process_image (line 130) | def process_image(self, image):
    method process_images (line 139) | def process_images(self, images):
    method decode_images (line 144) | def decode_images(self, images):
    method add_interpolated_images (line 149) | def add_interpolated_images(self, images, interpolated_images):
    method interpolate_ (line 159) | def interpolate_(self, images, scale=1.0):
    method interpolate (line 171) | def interpolate(self, images, scale=1.0, batch_size=4, num_iter=1, pro...
  class RIFESmoother (line 200) | class RIFESmoother(RIFEInterpolater):
    method __init__ (line 201) | def __init__(self, model, device="cuda"):
    method from_model_manager (line 205) | def from_model_manager(model_manager):
    method process_tensors (line 208) | def process_tensors(self, input_tensor, scale=1.0, batch_size=4):
    method __call__ (line 220) | def __call__(self, rendered_frames, scale=1.0, batch_size=4, num_iter=...

FILE: diffsynth/models/attention.py
  function low_version_attention (line 5) | def low_version_attention(query, key, value, attn_bias=None):
  class Attention (line 15) | class Attention(torch.nn.Module):
    method __init__ (line 17) | def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=Fal...
    method interact_with_ipadapter (line 29) | def interact_with_ipadapter(self, hidden_states, q, ip_k, ip_v, scale=...
    method torch_forward (line 37) | def torch_forward(self, hidden_states, encoder_hidden_states=None, att...
    method xformers_forward (line 64) | def xformers_forward(self, hidden_states, encoder_hidden_states=None, ...
    method forward (line 88) | def forward(self, hidden_states, encoder_hidden_states=None, attn_mask...

FILE: diffsynth/models/cog_dit.py
  class CogPatchify (line 11) | class CogPatchify(torch.nn.Module):
    method __init__ (line 12) | def __init__(self, dim_in, dim_out, patch_size) -> None:
    method forward (line 16) | def forward(self, hidden_states):
  class CogAdaLayerNorm (line 23) | class CogAdaLayerNorm(torch.nn.Module):
    method __init__ (line 24) | def __init__(self, dim, dim_cond, single=False):
    method forward (line 31) | def forward(self, hidden_states, prompt_emb, emb):
  class CogDiTBlock (line 45) | class CogDiTBlock(torch.nn.Module):
    method __init__ (line 46) | def __init__(self, dim, dim_cond, num_heads):
    method apply_rotary_emb (line 61) | def apply_rotary_emb(self, x, freqs_cis):
    method process_qkv (line 72) | def process_qkv(self, q, k, v, image_rotary_emb, text_seq_length):
    method forward (line 80) | def forward(self, hidden_states, prompt_emb, time_emb, image_rotary_emb):
  class CogDiT (line 108) | class CogDiT(torch.nn.Module):
    method __init__ (line 109) | def __init__(self):
    method get_resize_crop_region_for_grid (line 120) | def get_resize_crop_region_for_grid(self, src, tgt_width, tgt_height):
    method get_3d_rotary_pos_embed (line 138) | def get_3d_rotary_pos_embed(
    method prepare_rotary_positional_embeddings (line 202) | def prepare_rotary_positional_embeddings(
    method unpatchify (line 230) | def unpatchify(self, hidden_states, height, width):
    method build_mask (line 235) | def build_mask(self, T, H, W, dtype, device, is_bound):
    method tiled_forward (line 255) | def tiled_forward(self, hidden_states, timestep, prompt_emb, tile_size...
    method forward (line 286) | def forward(self, hidden_states, timestep, prompt_emb, image_rotary_em...
    method state_dict_converter (line 328) | def state_dict_converter():
    method from_pretrained (line 333) | def from_pretrained(file_path, torch_dtype=torch.bfloat16):
  class CogDiTStateDictConverter (line 342) | class CogDiTStateDictConverter:
    method __init__ (line 343) | def __init__(self):
    method from_diffusers (line 347) | def from_diffusers(self, state_dict):
    method from_civitai (line 407) | def from_civitai(self, state_dict):

FILE: diffsynth/models/cog_vae.py
  class Downsample3D (line 7) | class Downsample3D(torch.nn.Module):
    method __init__ (line 8) | def __init__(
    method forward (line 22) | def forward(self, x: torch.Tensor, xq: torch.Tensor) -> torch.Tensor:
  class Upsample3D (line 57) | class Upsample3D(torch.nn.Module):
    method __init__ (line 58) | def __init__(
    method forward (line 71) | def forward(self, inputs: torch.Tensor, xq: torch.Tensor) -> torch.Ten...
  class CogVideoXSpatialNorm3D (line 103) | class CogVideoXSpatialNorm3D(torch.nn.Module):
    method __init__ (line 104) | def __init__(self, f_channels, zq_channels, groups):
    method forward (line 111) | def forward(self, f: torch.Tensor, zq: torch.Tensor) -> torch.Tensor:
  class Resnet3DBlock (line 128) | class Resnet3DBlock(torch.nn.Module):
    method __init__ (line 129) | def __init__(self, in_channels, out_channels, spatial_norm_dim, groups...
    method forward (line 152) | def forward(self, hidden_states, zq):
  class CachedConv3d (line 169) | class CachedConv3d(torch.nn.Conv3d):
    method __init__ (line 170) | def __init__(self, in_channels, out_channels, kernel_size, stride=1, p...
    method clear_cache (line 175) | def clear_cache(self):
    method forward (line 179) | def forward(self, input: torch.Tensor, use_cache = True) -> torch.Tensor:
  class CogVAEDecoder (line 189) | class CogVAEDecoder(torch.nn.Module):
    method __init__ (line 190) | def __init__(self):
    method forward (line 224) | def forward(self, sample):
    method decode_video (line 238) | def decode_video(self, sample, tiled=True, tile_size=(60, 90), tile_st...
    method decode_small_video (line 254) | def decode_small_video(self, sample):
    method state_dict_converter (line 273) | def state_dict_converter():
  class CogVAEEncoder (line 278) | class CogVAEEncoder(torch.nn.Module):
    method __init__ (line 279) | def __init__(self):
    method forward (line 309) | def forward(self, sample):
    method encode_video (line 323) | def encode_video(self, sample, tiled=True, tile_size=(60, 90), tile_st...
    method encode_small_video (line 339) | def encode_small_video(self, sample):
    method state_dict_converter (line 358) | def state_dict_converter():
  class CogVAEEncoderStateDictConverter (line 363) | class CogVAEEncoderStateDictConverter:
    method __init__ (line 364) | def __init__(self):
    method from_diffusers (line 368) | def from_diffusers(self, state_dict):
    method from_civitai (line 435) | def from_civitai(self, state_dict):
  class CogVAEDecoderStateDictConverter (line 440) | class CogVAEDecoderStateDictConverter:
    method __init__ (line 441) | def __init__(self):
    method from_diffusers (line 445) | def from_diffusers(self, state_dict):
    method from_civitai (line 516) | def from_civitai(self, state_dict):

FILE: diffsynth/models/downloader.py
  function download_from_modelscope (line 9) | def download_from_modelscope(model_id, origin_file_path, local_dir):
  function download_from_huggingface (line 24) | def download_from_huggingface(model_id, origin_file_path, local_dir):
  function download_customized_models (line 53) | def download_customized_models(
  function download_models (line 72) | def download_models(

FILE: diffsynth/models/flux_controlnet.py
  class FluxControlNet (line 8) | class FluxControlNet(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, disable_guidance_embedder=False, num_joint_blocks=5...
    method prepare_image_ids (line 29) | def prepare_image_ids(self, latents):
    method patchify (line 46) | def patchify(self, hidden_states):
    method align_res_stack_to_original_blocks (line 51) | def align_res_stack_to_original_blocks(self, res_stack, num_blocks, hi...
    method forward (line 59) | def forward(
    method state_dict_converter (line 106) | def state_dict_converter():
    method quantize (line 109) | def quantize(self):
  class FluxControlNetStateDictConverter (line 212) | class FluxControlNetStateDictConverter:
    method __init__ (line 213) | def __init__(self):
    method from_diffusers (line 216) | def from_diffusers(self, state_dict):
    method from_civitai (line 326) | def from_civitai(self, state_dict):

FILE: diffsynth/models/flux_dit.py
  function interact_with_ipadapter (line 7) | def interact_with_ipadapter(hidden_states, q, ip_k, ip_v, scale=1.0):
  class RoPEEmbedding (line 15) | class RoPEEmbedding(torch.nn.Module):
    method __init__ (line 16) | def __init__(self, dim, theta, axes_dim):
    method rope (line 23) | def rope(self, pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
    method forward (line 39) | def forward(self, ids):
  class FluxJointAttention (line 46) | class FluxJointAttention(torch.nn.Module):
    method __init__ (line 47) | def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False):
    method apply_rope (line 66) | def apply_rope(self, xq, xk, freqs_cis):
    method forward (line 73) | def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb, ...
  class FluxJointTransformerBlock (line 109) | class FluxJointTransformerBlock(torch.nn.Module):
    method __init__ (line 110) | def __init__(self, dim, num_attention_heads):
    method forward (line 132) | def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary...
  class FluxSingleAttention (line 153) | class FluxSingleAttention(torch.nn.Module):
    method __init__ (line 154) | def __init__(self, dim_a, dim_b, num_heads, head_dim):
    method apply_rope (line 165) | def apply_rope(self, xq, xk, freqs_cis):
    method forward (line 173) | def forward(self, hidden_states, image_rotary_emb):
  class AdaLayerNormSingle (line 190) | class AdaLayerNormSingle(torch.nn.Module):
    method __init__ (line 191) | def __init__(self, dim):
    method forward (line 198) | def forward(self, x, emb):
  class FluxSingleTransformerBlock (line 206) | class FluxSingleTransformerBlock(torch.nn.Module):
    method __init__ (line 207) | def __init__(self, dim, num_attention_heads):
    method apply_rope (line 221) | def apply_rope(self, xq, xk, freqs_cis):
    method process_attention (line 229) | def process_attention(self, hidden_states, image_rotary_emb, attn_mask...
    method forward (line 246) | def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary...
  class AdaLayerNormContinuous (line 263) | class AdaLayerNormContinuous(torch.nn.Module):
    method __init__ (line 264) | def __init__(self, dim):
    method forward (line 270) | def forward(self, x, conditioning):
  class FluxDiT (line 278) | class FluxDiT(torch.nn.Module):
    method __init__ (line 279) | def __init__(self, disable_guidance_embedder=False):
    method patchify (line 295) | def patchify(self, hidden_states):
    method unpatchify (line 300) | def unpatchify(self, hidden_states, height, width):
    method prepare_image_ids (line 305) | def prepare_image_ids(self, latents):
    method tiled_forward (line 322) | def tiled_forward(
    method construct_mask (line 341) | def construct_mask(self, entity_masks, prompt_seq_len, image_seq_len):
    method process_entity_masks (line 376) | def process_entity_masks(self, hidden_states, prompt_emb, entity_promp...
    method forward (line 405) | def forward(
    method quantize (line 474) | def quantize(self):
    method state_dict_converter (line 553) | def state_dict_converter():
  class FluxDiTStateDictConverter (line 557) | class FluxDiTStateDictConverter:
    method __init__ (line 558) | def __init__(self):
    method from_diffusers (line 561) | def from_diffusers(self, state_dict):
    method from_civitai (line 662) | def from_civitai(self, state_dict):

FILE: diffsynth/models/flux_ipadapter.py
  class MLPProjModel (line 7) | class MLPProjModel(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num...
    method forward (line 21) | def forward(self, id_embeds):
  class IpAdapterModule (line 27) | class IpAdapterModule(torch.nn.Module):
    method __init__ (line 28) | def __init__(self, num_attention_heads, attention_head_dim, input_dim):
    method forward (line 38) | def forward(self, hidden_states):
  class FluxIpAdapter (line 50) | class FluxIpAdapter(torch.nn.Module):
    method __init__ (line 51) | def __init__(self, num_attention_heads=24, attention_head_dim=128, cro...
    method set_adapter (line 57) | def set_adapter(self):
    method forward (line 60) | def forward(self, hidden_states, scale=1.0):
    method state_dict_converter (line 75) | def state_dict_converter():
  class FluxIpAdapterStateDictConverter (line 79) | class FluxIpAdapterStateDictConverter:
    method __init__ (line 80) | def __init__(self):
    method from_diffusers (line 83) | def from_diffusers(self, state_dict):
    method from_civitai (line 93) | def from_civitai(self, state_dict):

FILE: diffsynth/models/flux_text_encoder.py
  class FluxTextEncoder2 (line 7) | class FluxTextEncoder2(T5EncoderModel):
    method __init__ (line 8) | def __init__(self, config):
    method forward (line 12) | def forward(self, input_ids):
    method state_dict_converter (line 18) | def state_dict_converter():
  class FluxTextEncoder2StateDictConverter (line 23) | class FluxTextEncoder2StateDictConverter():
    method __init__ (line 24) | def __init__(self):
    method from_diffusers (line 27) | def from_diffusers(self, state_dict):
    method from_civitai (line 31) | def from_civitai(self, state_dict):

FILE: diffsynth/models/flux_vae.py
  class FluxVAEEncoder (line 5) | class FluxVAEEncoder(SD3VAEEncoder):
    method __init__ (line 6) | def __init__(self):
    method state_dict_converter (line 12) | def state_dict_converter():
  class FluxVAEDecoder (line 16) | class FluxVAEDecoder(SD3VAEDecoder):
    method __init__ (line 17) | def __init__(self):
    method state_dict_converter (line 23) | def state_dict_converter():
  class FluxVAEEncoderStateDictConverter (line 27) | class FluxVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
    method __init__ (line 28) | def __init__(self):
    method from_civitai (line 31) | def from_civitai(self, state_dict):
  class FluxVAEDecoderStateDictConverter (line 151) | class FluxVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter):
    method __init__ (line 152) | def __init__(self):
    method from_civitai (line 155) | def from_civitai(self, state_dict):

FILE: diffsynth/models/hunyuan_dit.py
  class HunyuanDiTRotaryEmbedding (line 7) | class HunyuanDiTRotaryEmbedding(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, q_norm_shape=88, k_norm_shape=88, rotary_emb_on_k=T...
    method reshape_for_broadcast (line 16) | def reshape_for_broadcast(self, freqs_cis, x):
    method rotate_half (line 21) | def rotate_half(self, x):
    method apply_rotary_emb (line 25) | def apply_rotary_emb(self, xq, xk, freqs_cis):
    method forward (line 34) | def forward(self, q, k, v, freqs_cis_img, to_cache=False):
  class FP32_Layernorm (line 55) | class FP32_Layernorm(torch.nn.LayerNorm):
    method forward (line 56) | def forward(self, inputs):
  class FP32_SiLU (line 61) | class FP32_SiLU(torch.nn.SiLU):
    method forward (line 62) | def forward(self, inputs):
  class HunyuanDiTFinalLayer (line 67) | class HunyuanDiTFinalLayer(torch.nn.Module):
    method __init__ (line 68) | def __init__(self, final_hidden_size=1408, condition_dim=1408, patch_s...
    method modulate (line 77) | def modulate(self, x, shift, scale):
    method forward (line 80) | def forward(self, hidden_states, condition_emb):
  class HunyuanDiTBlock (line 87) | class HunyuanDiTBlock(torch.nn.Module):
    method __init__ (line 89) | def __init__(
    method forward (line 118) | def forward(self, hidden_states, condition_emb, text_emb, freq_cis_img...
  class AttentionPool (line 140) | class AttentionPool(torch.nn.Module):
    method __init__ (line 141) | def __init__(self, spacial_dim, embed_dim, num_heads, output_dim = None):
    method forward (line 150) | def forward(self, x):
  class PatchEmbed (line 176) | class PatchEmbed(torch.nn.Module):
    method __init__ (line 177) | def __init__(
    method forward (line 187) | def forward(self, x):
  function timestep_embedding (line 193) | def timestep_embedding(t, dim, max_period=10000, repeat_only=False):
  class TimestepEmbedder (line 213) | class TimestepEmbedder(torch.nn.Module):
    method __init__ (line 214) | def __init__(self, hidden_size=1408, frequency_embedding_size=256):
    method forward (line 223) | def forward(self, t):
  class HunyuanDiT (line 229) | class HunyuanDiT(torch.nn.Module):
    method __init__ (line 230) | def __init__(self, num_layers_down=21, num_layers_up=19, in_channels=4...
    method prepare_text_emb (line 262) | def prepare_text_emb(self, text_emb, text_emb_t5, text_emb_mask, text_...
    method prepare_extra_emb (line 271) | def prepare_extra_emb(self, text_emb_t5, timestep, size_emb, dtype, ba...
    method unpatchify (line 291) | def unpatchify(self, x, h, w):
    method build_mask (line 294) | def build_mask(self, data, is_bound):
    method tiled_block_forward (line 311) | def tiled_block_forward(self, block, hidden_states, condition_emb, tex...
    method forward (line 348) | def forward(
    method state_dict_converter (line 402) | def state_dict_converter():
  class HunyuanDiTStateDictConverter (line 407) | class HunyuanDiTStateDictConverter():
    method __init__ (line 408) | def __init__(self):
    method from_diffusers (line 411) | def from_diffusers(self, state_dict):
    method from_civitai (line 450) | def from_civitai(self, state_dict):

FILE: diffsynth/models/hunyuan_dit_text_encoder.py
  class HunyuanDiTCLIPTextEncoder (line 6) | class HunyuanDiTCLIPTextEncoder(BertModel):
    method __init__ (line 7) | def __init__(self):
    method forward (line 43) | def forward(self, input_ids, attention_mask, clip_skip=1):
    method state_dict_converter (line 83) | def state_dict_converter():
  class HunyuanDiTT5TextEncoder (line 88) | class HunyuanDiTT5TextEncoder(T5EncoderModel):
    method __init__ (line 89) | def __init__(self):
    method forward (line 123) | def forward(self, input_ids, attention_mask, clip_skip=1):
    method state_dict_converter (line 136) | def state_dict_converter():
  class HunyuanDiTCLIPTextEncoderStateDictConverter (line 141) | class HunyuanDiTCLIPTextEncoderStateDictConverter():
    method __init__ (line 142) | def __init__(self):
    method from_diffusers (line 145) | def from_diffusers(self, state_dict):
    method from_civitai (line 149) | def from_civitai(self, state_dict):
  class HunyuanDiTT5TextEncoderStateDictConverter (line 153) | class HunyuanDiTT5TextEncoderStateDictConverter():
    method __init__ (line 154) | def __init__(self):
    method from_diffusers (line 157) | def from_diffusers(self, state_dict):
    method from_civitai (line 162) | def from_civitai(self, state_dict):

FILE: diffsynth/models/hunyuan_video_dit.py
  function HunyuanVideoRope (line 10) | def HunyuanVideoRope(latents):
  class PatchEmbed (line 196) | class PatchEmbed(torch.nn.Module):
    method __init__ (line 197) | def __init__(self, patch_size=(1, 2, 2), in_channels=16, embed_dim=3072):
    method forward (line 201) | def forward(self, x):
  class IndividualTokenRefinerBlock (line 207) | class IndividualTokenRefinerBlock(torch.nn.Module):
    method __init__ (line 208) | def __init__(self, hidden_size=3072, num_heads=24):
    method forward (line 226) | def forward(self, x, c, attn_mask=None):
  class SingleTokenRefiner (line 242) | class SingleTokenRefiner(torch.nn.Module):
    method __init__ (line 243) | def __init__(self, in_channels=4096, hidden_size=3072, depth=2):
    method forward (line 254) | def forward(self, x, t, mask=None):
  class ModulateDiT (line 275) | class ModulateDiT(torch.nn.Module):
    method __init__ (line 276) | def __init__(self, hidden_size, factor=6):
    method forward (line 281) | def forward(self, x):
  function modulate (line 285) | def modulate(x, shift=None, scale=None, tr_shift=None, tr_scale=None, tr...
  function reshape_for_broadcast (line 301) | def reshape_for_broadcast(
  function rotate_half (line 347) | def rotate_half(x):
  function apply_rotary_emb (line 354) | def apply_rotary_emb(
  function attention (line 387) | def attention(q, k, v):
  function apply_gate (line 394) | def apply_gate(x, gate, tr_gate=None, tr_token=None):
  class MMDoubleStreamBlockComponent (line 403) | class MMDoubleStreamBlockComponent(torch.nn.Module):
    method __init__ (line 404) | def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
    method forward (line 423) | def forward(self, hidden_states, conditioning, freqs_cis=None, token_r...
    method process_ff (line 444) | def process_ff(self, hidden_states, attn_output, mod, mod_tr=None, tr_...
  class MMDoubleStreamBlock (line 456) | class MMDoubleStreamBlock(torch.nn.Module):
    method __init__ (line 457) | def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
    method forward (line 462) | def forward(self, hidden_states_a, hidden_states_b, conditioning, freq...
  class MMSingleStreamBlockOriginal (line 478) | class MMSingleStreamBlockOriginal(torch.nn.Module):
    method __init__ (line 479) | def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
    method forward (line 496) | def forward(self, x, vec, freqs_cis=None, txt_len=256):
  class MMSingleStreamBlock (line 518) | class MMSingleStreamBlock(torch.nn.Module):
    method __init__ (line 519) | def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
    method forward (line 537) | def forward(self, hidden_states, conditioning, freqs_cis=None, txt_len...
  class FinalLayer (line 573) | class FinalLayer(torch.nn.Module):
    method __init__ (line 574) | def __init__(self, hidden_size=3072, patch_size=(1, 2, 2), out_channel...
    method forward (line 582) | def forward(self, x, c):
  class HunyuanVideoDiT (line 589) | class HunyuanVideoDiT(torch.nn.Module):
    method __init__ (line 590) | def __init__(self, in_channels=16, hidden_size=3072, text_dim=4096, nu...
    method unpatchify (line 612) | def unpatchify(self, x, T, H, W):
    method enable_block_wise_offload (line 616) | def enable_block_wise_offload(self, warm_device="cuda", cold_device="c...
    method load_models_to_device (line 621) | def load_models_to_device(self, loadmodel_names=[], device="cpu"):
    method prepare_freqs (line 628) | def prepare_freqs(self, latents):
    method forward (line 631) | def forward(
    method enable_auto_offload (line 664) | def enable_auto_offload(self, dtype=torch.bfloat16, device="cuda"):
    method state_dict_converter (line 810) | def state_dict_converter():
  class HunyuanVideoDiTStateDictConverter (line 814) | class HunyuanVideoDiTStateDictConverter:
    method __init__ (line 815) | def __init__(self):
    method from_civitai (line 818) | def from_civitai(self, state_dict):

FILE: diffsynth/models/hunyuan_video_text_encoder.py
  class HunyuanVideoLLMEncoder (line 6) | class HunyuanVideoLLMEncoder(LlamaModel):
    method __init__ (line 8) | def __init__(self, config: LlamaConfig):
    method enable_auto_offload (line 12) | def enable_auto_offload(self, **kwargs):
    method forward (line 15) | def forward(self, input_ids, attention_mask, hidden_state_skip_layer=2):
  class HunyuanVideoMLLMEncoder (line 52) | class HunyuanVideoMLLMEncoder(LlavaForConditionalGeneration):
    method __init__ (line 54) | def __init__(self, config):
    method enable_auto_offload (line 58) | def enable_auto_offload(self, **kwargs):
    method forward (line 62) | def forward(self, input_ids, pixel_values, attention_mask, hidden_stat...

FILE: diffsynth/models/hunyuan_video_vae_decoder.py
  class CausalConv3d (line 10) | class CausalConv3d(nn.Module):
    method __init__ (line 12) | def __init__(self, in_channel, out_channel, kernel_size, stride=1, dil...
    method forward (line 19) | def forward(self, x):
  class UpsampleCausal3D (line 24) | class UpsampleCausal3D(nn.Module):
    method __init__ (line 26) | def __init__(self, channels, use_conv=False, out_channels=None, kernel...
    method forward (line 36) | def forward(self, hidden_states):
  class ResnetBlockCausal3D (line 64) | class ResnetBlockCausal3D(nn.Module):
    method __init__ (line 66) | def __init__(self, in_channels, out_channels=None, dropout=0.0, groups...
    method forward (line 86) | def forward(self, input_tensor):
  function prepare_causal_attention_mask (line 107) | def prepare_causal_attention_mask(n_frame, n_hw, dtype, device, batch_si...
  class Attention (line 118) | class Attention(nn.Module):
    method __init__ (line 120) | def __init__(self,
    method forward (line 140) | def forward(self, input_tensor, attn_mask=None):
  class UNetMidBlockCausal3D (line 162) | class UNetMidBlockCausal3D(nn.Module):
    method __init__ (line 164) | def __init__(self, in_channels, dropout=0.0, num_layers=1, eps=1e-6, n...
    method forward (line 203) | def forward(self, hidden_states):
  class UpDecoderBlockCausal3D (line 216) | class UpDecoderBlockCausal3D(nn.Module):
    method __init__ (line 218) | def __init__(
    method forward (line 254) | def forward(self, hidden_states):
  class DecoderCausal3D (line 263) | class DecoderCausal3D(nn.Module):
    method __init__ (line 265) | def __init__(
    method forward (line 331) | def forward(self, hidden_states):
  class HunyuanVideoVAEDecoder (line 369) | class HunyuanVideoVAEDecoder(nn.Module):
    method __init__ (line 371) | def __init__(
    method forward (line 401) | def forward(self, latents):
    method build_1d_mask (line 408) | def build_1d_mask(self, length, left_bound, right_bound, border_width):
    method build_mask (line 417) | def build_mask(self, data, is_bound, border_width):
    method tile_forward (line 432) | def tile_forward(self, hidden_states, tile_size, tile_stride):
    method decode_video (line 488) | def decode_video(self, latents, tile_size=(17, 32, 32), tile_stride=(1...
    method state_dict_converter (line 493) | def state_dict_converter():
  class HunyuanVideoVAEDecoderStateDictConverter (line 497) | class HunyuanVideoVAEDecoderStateDictConverter:
    method __init__ (line 499) | def __init__(self):
    method from_diffusers (line 502) | def from_diffusers(self, state_dict):

FILE: diffsynth/models/hunyuan_video_vae_encoder.py
  class DownsampleCausal3D (line 10) | class DownsampleCausal3D(nn.Module):
    method __init__ (line 12) | def __init__(self, channels, out_channels, kernel_size=3, bias=True, s...
    method forward (line 16) | def forward(self, hidden_states):
  class DownEncoderBlockCausal3D (line 21) | class DownEncoderBlockCausal3D(nn.Module):
    method __init__ (line 23) | def __init__(
    method forward (line 57) | def forward(self, hidden_states):
  class EncoderCausal3D (line 68) | class EncoderCausal3D(nn.Module):
    method __init__ (line 70) | def __init__(
    method forward (line 129) | def forward(self, hidden_states):
  class HunyuanVideoVAEEncoder (line 167) | class HunyuanVideoVAEEncoder(nn.Module):
    method __init__ (line 169) | def __init__(
    method forward (line 199) | def forward(self, images):
    method build_1d_mask (line 207) | def build_1d_mask(self, length, left_bound, right_bound, border_width):
    method build_mask (line 216) | def build_mask(self, data, is_bound, border_width):
    method tile_forward (line 231) | def tile_forward(self, hidden_states, tile_size, tile_stride):
    method encode_video (line 287) | def encode_video(self, latents, tile_size=(65, 256, 256), tile_stride=...
    method state_dict_converter (line 293) | def state_dict_converter():
  class HunyuanVideoVAEEncoderStateDictConverter (line 297) | class HunyuanVideoVAEEncoderStateDictConverter:
    method __init__ (line 299) | def __init__(self):
    method from_diffusers (line 302) | def from_diffusers(self, state_dict):

FILE: diffsynth/models/kolors_text_encoder.py
  class Kernel (line 52) | class Kernel:
    method __init__ (line 53) | def __init__(self, code: bytes, function_names: List[str]):
  class W8A16Linear (line 78) | class W8A16Linear(torch.autograd.Function):
    method forward (line 80) | def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: to...
    method backward (line 92) | def backward(ctx, grad_output: torch.Tensor):
  function compress_int4_weight (line 101) | def compress_int4_weight(weight: torch.Tensor):  # (n, m)
  function extract_weight_to_half (line 122) | def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tenso...
  class QuantizedLinear (line 158) | class QuantizedLinear(torch.nn.Module):
    method __init__ (line 159) | def __init__(self, weight_bit_width: int, weight, bias=None, device="c...
    method forward (line 180) | def forward(self, input):
  function quantize (line 187) | def quantize(model, weight_bit_width, empty_init=False, device=None):
  class ChatGLMConfig (line 227) | class ChatGLMConfig(PretrainedConfig):
    method __init__ (line 229) | def __init__(
  function default_init (line 307) | def default_init(cls, *args, **kwargs):
  class InvalidScoreLogitsProcessor (line 311) | class InvalidScoreLogitsProcessor(LogitsProcessor):
    method __call__ (line 312) | def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTen...
  class PrefixEncoder (line 319) | class PrefixEncoder(torch.nn.Module):
    method __init__ (line 326) | def __init__(self, config: ChatGLMConfig):
    method forward (line 342) | def forward(self, prefix: torch.Tensor):
  function split_tensor_along_last_dim (line 351) | def split_tensor_along_last_dim(
  class RotaryEmbedding (line 379) | class RotaryEmbedding(nn.Module):
    method __init__ (line 380) | def __init__(self, dim, original_impl=False, device=None, dtype=None):
    method forward_impl (line 387) | def forward_impl(
    method forward (line 412) | def forward(self, max_seq_len, offset=0):
  function apply_rotary_pos_emb (line 419) | def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> t...
  class RMSNorm (line 439) | class RMSNorm(torch.nn.Module):
    method __init__ (line 440) | def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None...
    method forward (line 445) | def forward(self, hidden_states: torch.Tensor):
  class CoreAttention (line 453) | class CoreAttention(torch.nn.Module):
    method __init__ (line 454) | def __init__(self, config: ChatGLMConfig, layer_number):
    method forward (line 479) | def forward(self, query_layer, key_layer, value_layer, attention_mask):
  class SelfAttention (line 571) | class SelfAttention(torch.nn.Module):
    method __init__ (line 578) | def __init__(self, config: ChatGLMConfig, layer_number, device=None):
    method _allocate_memory (line 607) | def _allocate_memory(self, inference_max_sequence_len, batch_size, dev...
    method forward (line 621) | def forward(
  function _config_to_kwargs (line 710) | def _config_to_kwargs(args):
  class MLP (line 717) | class MLP(torch.nn.Module):
    method __init__ (line 725) | def __init__(self, config: ChatGLMConfig, device=None):
    method forward (line 754) | def forward(self, hidden_states):
  class GLMBlock (line 763) | class GLMBlock(torch.nn.Module):
    method __init__ (line 770) | def __init__(self, config: ChatGLMConfig, layer_number, device=None):
    method forward (line 794) | def forward(
  class GLMTransformer (line 837) | class GLMTransformer(torch.nn.Module):
    method __init__ (line 840) | def __init__(self, config: ChatGLMConfig, device=None):
    method _get_layer (line 863) | def _get_layer(self, layer_number):
    method forward (line 866) | def forward(
  class ChatGLMPreTrainedModel (line 919) | class ChatGLMPreTrainedModel(PreTrainedModel):
    method _init_weights (line 931) | def _init_weights(self, module: nn.Module):
    method get_masks (line 935) | def get_masks(self, input_ids, past_key_values, padding_mask=None):
    method get_position_ids (line 953) | def get_position_ids(self, input_ids, device):
    method _set_gradient_checkpointing (line 958) | def _set_gradient_checkpointing(self, module, value=False):
  class Embedding (line 963) | class Embedding(torch.nn.Module):
    method __init__ (line 966) | def __init__(self, config: ChatGLMConfig, device=None):
    method forward (line 979) | def forward(self, input_ids):
  class ChatGLMModel (line 991) | class ChatGLMModel(ChatGLMPreTrainedModel):
    method __init__ (line 992) | def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
    method get_input_embeddings (line 1026) | def get_input_embeddings(self):
    method get_prompt (line 1029) | def get_prompt(self, batch_size, device, dtype=torch.half):
    method forward (line 1044) | def forward(
    method quantize (line 1103) | def quantize(self, weight_bit_width: int):
  class ChatGLMForConditionalGeneration (line 1109) | class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
    method __init__ (line 1110) | def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
    method _update_model_kwargs_for_generation (line 1121) | def _update_model_kwargs_for_generation(
    method prepare_inputs_for_generation (line 1152) | def prepare_inputs_for_generation(
    method forward (line 1178) | def forward(
    method _reorder_cache (line 1239) | def _reorder_cache(
    method process_response (line 1257) | def process_response(self, output, history):
    method chat (line 1279) | def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] =...
    method stream_chat (line 1301) | def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, ...
    method stream_generate (line 1342) | def stream_generate(
    method quantize (line 1449) | def quantize(self, bits: int, empty_init=False, device=None, **kwargs):
  class ChatGLMForSequenceClassification (line 1468) | class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel):
    method __init__ (line 1469) | def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
    method forward (line 1485) | def forward(

FILE: diffsynth/models/lora.py
  class LoRAFromCivitai (line 15) | class LoRAFromCivitai:
    method __init__ (line 16) | def __init__(self):
    method convert_state_dict (line 23) | def convert_state_dict(self, state_dict, lora_prefix="lora_unet_", alp...
    method convert_state_dict_up_down (line 30) | def convert_state_dict_up_down(self, state_dict, lora_prefix="lora_une...
    method convert_state_dict_AB (line 53) | def convert_state_dict_AB(self, state_dict, lora_prefix="", alpha=1.0,...
    method load (line 76) | def load(self, model, state_dict_lora, lora_prefix, alpha=1.0, model_r...
    method match (line 99) | def match(self, model, state_dict_lora):
  class SDLoRAFromCivitai (line 125) | class SDLoRAFromCivitai(LoRAFromCivitai):
    method __init__ (line 126) | def __init__(self):
  class SDXLLoRAFromCivitai (line 152) | class SDXLLoRAFromCivitai(LoRAFromCivitai):
    method __init__ (line 153) | def __init__(self):
  class FluxLoRAFromCivitai (line 181) | class FluxLoRAFromCivitai(LoRAFromCivitai):
    method __init__ (line 182) | def __init__(self):
  class GeneralLoRAFromPeft (line 200) | class GeneralLoRAFromPeft:
    method __init__ (line 201) | def __init__(self):
    method get_name_dict (line 205) | def get_name_dict(self, lora_state_dict):
    method match (line 221) | def match(self, model: torch.nn.Module, state_dict_lora):
    method fetch_device_and_dtype (line 231) | def fetch_device_and_dtype(self, state_dict):
    method load (line 246) | def load(self, model, state_dict_lora, lora_prefix="", alpha=1.0, mode...
  class HunyuanVideoLoRAFromCivitai (line 267) | class HunyuanVideoLoRAFromCivitai(LoRAFromCivitai):
    method __init__ (line 268) | def __init__(self):
  class FluxLoRAConverter (line 275) | class FluxLoRAConverter:
    method __init__ (line 276) | def __init__(self):
    method align_to_opensource_format (line 280) | def align_to_opensource_format(state_dict, alpha=1.0):
    method align_to_diffsynth_format (line 323) | def align_to_diffsynth_format(state_dict):
  function get_lora_loaders (line 370) | def get_lora_loaders():

FILE: diffsynth/models/model_manager.py
  function load_model_from_single_file (line 56) | def load_model_from_single_file(state_dict, model_names, model_classes, ...
  function load_model_from_huggingface_folder (line 82) | def load_model_from_huggingface_folder(file_path, model_names, model_cla...
  function load_single_patch_model_from_single_file (line 100) | def load_single_patch_model_from_single_file(state_dict, model_name, mod...
  function load_patch_model_from_single_file (line 112) | def load_patch_model_from_single_file(state_dict, model_names, model_cla...
  class ModelDetectorTemplate (line 136) | class ModelDetectorTemplate:
    method __init__ (line 137) | def __init__(self):
    method match (line 140) | def match(self, file_path="", state_dict={}):
    method load (line 143) | def load(self, file_path="", state_dict={}, device="cuda", torch_dtype...
  class ModelDetectorFromSingleFile (line 148) | class ModelDetectorFromSingleFile:
    method __init__ (line 149) | def __init__(self, model_loader_configs=[]):
    method add_model_metadata (line 156) | def add_model_metadata(self, keys_hash, keys_hash_with_shape, model_na...
    method match (line 162) | def match(self, file_path="", state_dict={}):
    method load (line 176) | def load(self, file_path="", state_dict={}, device="cuda", torch_dtype...
  class ModelDetectorFromSplitedSingleFile (line 199) | class ModelDetectorFromSplitedSingleFile(ModelDetectorFromSingleFile):
    method __init__ (line 200) | def __init__(self, model_loader_configs=[]):
    method match (line 204) | def match(self, file_path="", state_dict={}):
    method load (line 216) | def load(self, file_path="", state_dict={}, device="cuda", torch_dtype...
  class ModelDetectorFromHuggingfaceFolder (line 236) | class ModelDetectorFromHuggingfaceFolder:
    method __init__ (line 237) | def __init__(self, model_loader_configs=[]):
    method add_model_metadata (line 243) | def add_model_metadata(self, architecture, huggingface_lib, model_name...
    method match (line 247) | def match(self, file_path="", state_dict={}):
    method load (line 260) | def load(self, file_path="", state_dict={}, device="cuda", torch_dtype...
  class ModelDetectorFromPatchedSingleFile (line 277) | class ModelDetectorFromPatchedSingleFile:
    method __init__ (line 278) | def __init__(self, model_loader_configs=[]):
    method add_model_metadata (line 284) | def add_model_metadata(self, keys_hash_with_shape, model_name, model_c...
    method match (line 288) | def match(self, file_path="", state_dict={}):
    method load (line 299) | def load(self, file_path="", state_dict={}, device="cuda", torch_dtype...
  class ModelManager (line 316) | class ModelManager:
    method __init__ (line 317) | def __init__(
    method load_model_from_single_file (line 340) | def load_model_from_single_file(self, file_path="", state_dict={}, mod...
    method load_model_from_huggingface_folder (line 352) | def load_model_from_huggingface_folder(self, file_path="", model_names...
    method load_patch_model_from_single_file (line 362) | def load_patch_model_from_single_file(self, file_path="", state_dict={...
    method load_lora (line 373) | def load_lora(self, file_path="", state_dict={}, lora_alpha=1.0):
    method load_model (line 395) | def load_model(self, file_path, model_names=None, device=None, torch_d...
    method load_models (line 424) | def load_models(self, file_path_list, model_names=None, device=None, t...
    method fetch_model (line 429) | def fetch_model(self, model_name, file_path=None, require_model_path=F...
    method to (line 451) | def to(self, device):

FILE: diffsynth/models/omnigen.py
  class Phi3Transformer (line 20) | class Phi3Transformer(Phi3Model):
    method prefetch_layer (line 27) | def prefetch_layer(self, layer_idx: int, device: torch.device):
    method evict_previous_layer (line 34) | def evict_previous_layer(self, layer_idx: int):
    method get_offlaod_layer (line 40) | def get_offlaod_layer(self, layer_idx: int, device: torch.device):
    method forward (line 56) | def forward(
  function modulate (line 191) | def modulate(x, shift, scale):
  class TimestepEmbedder (line 195) | class TimestepEmbedder(nn.Module):
    method __init__ (line 199) | def __init__(self, hidden_size, frequency_embedding_size=256):
    method timestep_embedding (line 209) | def timestep_embedding(t, dim, max_period=10000):
    method forward (line 229) | def forward(self, t, dtype=torch.float32):
  class FinalLayer (line 235) | class FinalLayer(nn.Module):
    method __init__ (line 239) | def __init__(self, hidden_size, patch_size, out_channels):
    method forward (line 248) | def forward(self, x, c):
  function get_2d_sincos_pos_embed (line 255) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra...
  function get_2d_sincos_pos_embed_from_grid (line 275) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
  function get_1d_sincos_pos_embed_from_grid (line 286) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
  class PatchEmbedMR (line 307) | class PatchEmbedMR(nn.Module):
    method __init__ (line 310) | def __init__(
    method forward (line 320) | def forward(self, x):
  class OmniGenOriginalModel (line 326) | class OmniGenOriginalModel(nn.Module):
    method __init__ (line 330) | def __init__(
    method from_pretrained (line 364) | def from_pretrained(cls, model_name):
    method initialize_weights (line 380) | def initialize_weights(self):
    method unpatchify (line 413) | def unpatchify(self, x, h, w):
    method cropped_pos_embed (line 426) | def cropped_pos_embed(self, height, width):
    method patch_multiple_resolutions (line 451) | def patch_multiple_resolutions(self, latents, padding_latent=None, is_...
    method forward (line 489) | def forward(self, x, timestep, input_ids, input_img_latents, input_ima...
    method forward_with_cfg (line 534) | def forward_with_cfg(self, x, timestep, input_ids, input_img_latents, ...
    method forward_with_separate_cfg (line 550) | def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_...
  class OmniGenTransformer (line 580) | class OmniGenTransformer(OmniGenOriginalModel):
    method __init__ (line 581) | def __init__(self):
    method forward (line 717) | def forward(self, x, timestep, input_ids, input_img_latents, input_ima...
    method forward_with_separate_cfg (line 760) | def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_...
    method state_dict_converter (line 790) | def state_dict_converter():
  class OmniGenTransformerStateDictConverter (line 795) | class OmniGenTransformerStateDictConverter:
    method __init__ (line 796) | def __init__(self):
    method from_diffusers (line 799) | def from_diffusers(self, state_dict):
    method from_civitai (line 802) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sd3_dit.py
  class RMSNorm (line 8) | class RMSNorm(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, dim, eps, elementwise_affine=True):
    method forward (line 17) | def forward(self, hidden_states):
  class PatchEmbed (line 28) | class PatchEmbed(torch.nn.Module):
    method __init__ (line 29) | def __init__(self, patch_size=2, in_channels=16, embed_dim=1536, pos_e...
    method cropped_pos_embed (line 37) | def cropped_pos_embed(self, height, width):
    method forward (line 45) | def forward(self, latent):
  class TimestepEmbeddings (line 54) | class TimestepEmbeddings(torch.nn.Module):
    method __init__ (line 55) | def __init__(self, dim_in, dim_out, computation_device=None):
    method forward (line 62) | def forward(self, timestep, dtype):
  class AdaLayerNorm (line 69) | class AdaLayerNorm(torch.nn.Module):
    method __init__ (line 70) | def __init__(self, dim, single=False, dual=False):
    method forward (line 77) | def forward(self, x, emb):
  class JointAttention (line 96) | class JointAttention(torch.nn.Module):
    method __init__ (line 97) | def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False...
    method process_qkv (line 122) | def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k):
    method forward (line 134) | def forward(self, hidden_states_a, hidden_states_b):
  class SingleAttention (line 156) | class SingleAttention(torch.nn.Module):
    method __init__ (line 157) | def __init__(self, dim_a, num_heads, head_dim, use_rms_norm=False):
    method process_qkv (line 173) | def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k):
    method forward (line 185) | def forward(self, hidden_states_a):
  class DualTransformerBlock (line 197) | class DualTransformerBlock(torch.nn.Module):
    method __init__ (line 198) | def __init__(self, dim, num_attention_heads, use_rms_norm=False):
    method forward (line 221) | def forward(self, hidden_states_a, hidden_states_b, temb):
  class JointTransformerBlock (line 243) | class JointTransformerBlock(torch.nn.Module):
    method __init__ (line 244) | def __init__(self, dim, num_attention_heads, use_rms_norm=False, dual=...
    method forward (line 268) | def forward(self, hidden_states_a, hidden_states_b, temb):
  class JointTransformerFinalBlock (line 294) | class JointTransformerFinalBlock(torch.nn.Module):
    method __init__ (line 295) | def __init__(self, dim, num_attention_heads, use_rms_norm=False):
    method forward (line 310) | def forward(self, hidden_states_a, hidden_states_b, temb):
  class SD3DiT (line 326) | class SD3DiT(torch.nn.Module):
    method __init__ (line 327) | def __init__(self, embed_dim=1536, num_layers=24, use_rms_norm=False, ...
    method tiled_forward (line 339) | def tiled_forward(self, hidden_states, timestep, prompt_emb, pooled_pr...
    method forward (line 351) | def forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_e...
    method state_dict_converter (line 381) | def state_dict_converter():
  class SD3DiTStateDictConverter (line 386) | class SD3DiTStateDictConverter:
    method __init__ (line 387) | def __init__(self):
    method infer_architecture (line 390) | def infer_architecture(self, state_dict):
    method from_diffusers (line 408) | def from_diffusers(self, state_dict):
    method from_civitai (line 472) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sd3_text_encoder.py
  class SD3TextEncoder1 (line 7) | class SD3TextEncoder1(SDTextEncoder):
    method __init__ (line 8) | def __init__(self, vocab_size=49408):
    method forward (line 11) | def forward(self, input_ids, clip_skip=2, extra_mask=None):
    method state_dict_converter (line 26) | def state_dict_converter():
  class SD3TextEncoder2 (line 31) | class SD3TextEncoder2(SDXLTextEncoder2):
    method __init__ (line 32) | def __init__(self):
    method state_dict_converter (line 36) | def state_dict_converter():
  class SD3TextEncoder3 (line 40) | class SD3TextEncoder3(T5EncoderModel):
    method __init__ (line 41) | def __init__(self):
    method forward (line 75) | def forward(self, input_ids):
    method state_dict_converter (line 81) | def state_dict_converter():
  class SD3TextEncoder1StateDictConverter (line 86) | class SD3TextEncoder1StateDictConverter:
    method __init__ (line 87) | def __init__(self):
    method from_diffusers (line 90) | def from_diffusers(self, state_dict):
    method from_civitai (line 122) | def from_civitai(self, state_dict):
  class SD3TextEncoder2StateDictConverter (line 337) | class SD3TextEncoder2StateDictConverter(SDXLTextEncoder2StateDictConvert...
    method __init__ (line 338) | def __init__(self):
    method from_diffusers (line 341) | def from_diffusers(self, state_dict):
    method from_civitai (line 344) | def from_civitai(self, state_dict):
  class SD3TextEncoder3StateDictConverter (line 880) | class SD3TextEncoder3StateDictConverter():
    method __init__ (line 881) | def __init__(self):
    method from_diffusers (line 884) | def from_diffusers(self, state_dict):
    method from_civitai (line 888) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sd3_vae_decoder.py
  class SD3VAEDecoder (line 8) | class SD3VAEDecoder(torch.nn.Module):
    method __init__ (line 9) | def __init__(self):
    method tiled_forward (line 45) | def tiled_forward(self, sample, tile_size=64, tile_stride=32):
    method forward (line 56) | def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, *...
    method state_dict_converter (line 80) | def state_dict_converter():

FILE: diffsynth/models/sd3_vae_encoder.py
  class SD3VAEEncoder (line 8) | class SD3VAEEncoder(torch.nn.Module):
    method __init__ (line 9) | def __init__(self):
    method tiled_forward (line 41) | def tiled_forward(self, sample, tile_size=64, tile_stride=32):
    method forward (line 52) | def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, *...
    method encode_video (line 76) | def encode_video(self, sample, batch_size=8):
    method state_dict_converter (line 94) | def state_dict_converter():

FILE: diffsynth/models/sd_controlnet.py
  class ControlNetConditioningLayer (line 6) | class ControlNetConditioningLayer(torch.nn.Module):
    method __init__ (line 7) | def __init__(self, channels = (3, 16, 32, 96, 256, 320)):
    method forward (line 19) | def forward(self, conditioning):
  class SDControlNet (line 25) | class SDControlNet(torch.nn.Module):
    method __init__ (line 26) | def __init__(self, global_pool=False):
    method forward (line 96) | def forward(
    method state_dict_converter (line 139) | def state_dict_converter():
  class SDControlNetStateDictConverter (line 143) | class SDControlNetStateDictConverter:
    method __init__ (line 144) | def __init__(self):
    method from_diffusers (line 147) | def from_diffusers(self, state_dict):
    method from_civitai (line 236) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sd_ipadapter.py
  class IpAdapterCLIPImageEmbedder (line 7) | class IpAdapterCLIPImageEmbedder(SVDImageEncoder):
    method __init__ (line 8) | def __init__(self):
    method forward (line 12) | def forward(self, image):
  class SDIpAdapter (line 18) | class SDIpAdapter(torch.nn.Module):
    method __init__ (line 19) | def __init__(self):
    method set_full_adapter (line 26) | def set_full_adapter(self):
    method set_less_adapter (line 30) | def set_less_adapter(self):
    method forward (line 34) | def forward(self, hidden_states, scale=1.0):
    method state_dict_converter (line 51) | def state_dict_converter():
  class SDIpAdapterStateDictConverter (line 55) | class SDIpAdapterStateDictConverter(SDXLIpAdapterStateDictConverter):
    method __init__ (line 56) | def __init__(self):

FILE: diffsynth/models/sd_motion.py
  class TemporalTransformerBlock (line 6) | class TemporalTransformerBlock(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, dim, num_attention_heads, attention_head_dim, max_p...
    method forward (line 27) | def forward(self, hidden_states, batch_size=1):
  class TemporalBlock (line 52) | class TemporalBlock(torch.nn.Module):
    method __init__ (line 54) | def __init__(self, num_attention_heads, attention_head_dim, in_channel...
    method forward (line 72) | def forward(self, hidden_states, time_emb, text_emb, res_stack, batch_...
  class SDMotionModel (line 94) | class SDMotionModel(torch.nn.Module):
    method __init__ (line 95) | def __init__(self):
    method forward (line 144) | def forward(self):
    method state_dict_converter (line 148) | def state_dict_converter():
  class SDMotionModelStateDictConverter (line 152) | class SDMotionModelStateDictConverter:
    method __init__ (line 153) | def __init__(self):
    method from_diffusers (line 156) | def from_diffusers(self, state_dict):
    method from_civitai (line 198) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sd_text_encoder.py
  class CLIPEncoderLayer (line 5) | class CLIPEncoderLayer(torch.nn.Module):
    method __init__ (line 6) | def __init__(self, embed_dim, intermediate_size, num_heads=12, head_di...
    method quickGELU (line 16) | def quickGELU(self, x):
    method forward (line 19) | def forward(self, hidden_states, attn_mask=None):
  class SDTextEncoder (line 39) | class SDTextEncoder(torch.nn.Module):
    method __init__ (line 40) | def __init__(self, embed_dim=768, vocab_size=49408, max_position_embed...
    method attention_mask (line 58) | def attention_mask(self, length):
    method forward (line 64) | def forward(self, input_ids, clip_skip=1):
    method state_dict_converter (line 75) | def state_dict_converter():
  class SDTextEncoderStateDictConverter (line 79) | class SDTextEncoderStateDictConverter:
    method __init__ (line 80) | def __init__(self):
    method from_diffusers (line 83) | def from_diffusers(self, state_dict):
    method from_civitai (line 115) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sd_unet.py
  class Timesteps (line 6) | class Timesteps(torch.nn.Module):
    method __init__ (line 7) | def __init__(self, num_channels):
    method forward (line 11) | def forward(self, timesteps):
  class GEGLU (line 20) | class GEGLU(torch.nn.Module):
    method __init__ (line 22) | def __init__(self, dim_in, dim_out):
    method forward (line 26) | def forward(self, hidden_states):
  class BasicTransformerBlock (line 31) | class BasicTransformerBlock(torch.nn.Module):
    method __init__ (line 33) | def __init__(self, dim, num_attention_heads, attention_head_dim, cross...
    method forward (line 50) | def forward(self, hidden_states, encoder_hidden_states, ipadapter_kwar...
  class DownSampler (line 70) | class DownSampler(torch.nn.Module):
    method __init__ (line 71) | def __init__(self, channels, padding=1, extra_padding=False):
    method forward (line 76) | def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwar...
  class UpSampler (line 83) | class UpSampler(torch.nn.Module):
    method __init__ (line 84) | def __init__(self, channels):
    method forward (line 88) | def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwar...
  class ResnetBlock (line 94) | class ResnetBlock(torch.nn.Module):
    method __init__ (line 95) | def __init__(self, in_channels, out_channels, temb_channels=None, grou...
    method forward (line 108) | def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwar...
  class AttentionBlock (line 126) | class AttentionBlock(torch.nn.Module):
    method __init__ (line 128) | def __init__(self, num_attention_heads, attention_head_dim, in_channel...
    method forward (line 148) | def forward(
  class PushBlock (line 211) | class PushBlock(torch.nn.Module):
    method __init__ (line 212) | def __init__(self):
    method forward (line 215) | def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwar...
  class PopBlock (line 220) | class PopBlock(torch.nn.Module):
    method __init__ (line 221) | def __init__(self):
    method forward (line 224) | def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwar...
  class SDUNet (line 230) | class SDUNet(torch.nn.Module):
    method __init__ (line 231) | def __init__(self):
    method forward (line 324) | def forward(self, sample, timestep, encoder_hidden_states, **kwargs):
    method state_dict_converter (line 346) | def state_dict_converter():
  class SDUNetStateDictConverter (line 350) | class SDUNetStateDictConverter:
    method __init__ (line 351) | def __init__(self):
    method from_diffusers (line 354) | def from_diffusers(self, state_dict):
    method from_civitai (line 412) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sd_vae_decoder.py
  class VAEAttentionBlock (line 7) | class VAEAttentionBlock(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, num_attention_heads, attention_head_dim, in_channel...
    method forward (line 27) | def forward(self, hidden_states, time_emb, text_emb, res_stack):
  class SDVAEDecoder (line 44) | class SDVAEDecoder(torch.nn.Module):
    method __init__ (line 45) | def __init__(self):
    method tiled_forward (line 81) | def tiled_forward(self, sample, tile_size=64, tile_stride=32):
    method forward (line 92) | def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, *...
    method state_dict_converter (line 120) | def state_dict_converter():
  class SDVAEDecoderStateDictConverter (line 124) | class SDVAEDecoderStateDictConverter:
    method __init__ (line 125) | def __init__(self):
    method from_diffusers (line 128) | def from_diffusers(self, state_dict):
    method from_civitai (line 186) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sd_vae_encoder.py
  class SDVAEEncoder (line 8) | class SDVAEEncoder(torch.nn.Module):
    method __init__ (line 9) | def __init__(self):
    method tiled_forward (line 41) | def tiled_forward(self, sample, tile_size=64, tile_stride=32):
    method forward (line 52) | def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, *...
    method encode_video (line 80) | def encode_video(self, sample, batch_size=8):
    method state_dict_converter (line 98) | def state_dict_converter():
  class SDVAEEncoderStateDictConverter (line 102) | class SDVAEEncoderStateDictConverter:
    method __init__ (line 103) | def __init__(self):
    method from_diffusers (line 106) | def from_diffusers(self, state_dict):
    method from_civitai (line 164) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sdxl_controlnet.py
  class QuickGELU (line 10) | class QuickGELU(torch.nn.Module):
    method forward (line 12) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 17) | class ResidualAttentionBlock(torch.nn.Module):
    method __init__ (line 19) | def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor ...
    method attention (line 32) | def attention(self, x: torch.Tensor):
    method forward (line 36) | def forward(self, x: torch.Tensor):
  class SDXLControlNetUnion (line 43) | class SDXLControlNetUnion(torch.nn.Module):
    method __init__ (line 44) | def __init__(self, global_pool=False):
    method fuse_condition_to_input (line 137) | def fuse_condition_to_input(self, hidden_states, task_id, conditioning):
    method forward (line 151) | def forward(
    method state_dict_converter (line 217) | def state_dict_converter():
  class SDXLControlNetUnionStateDictConverter (line 222) | class SDXLControlNetUnionStateDictConverter:
    method __init__ (line 223) | def __init__(self):
    method from_diffusers (line 226) | def from_diffusers(self, state_dict):
    method from_civitai (line 317) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sdxl_ipadapter.py
  class IpAdapterXLCLIPImageEmbedder (line 6) | class IpAdapterXLCLIPImageEmbedder(SVDImageEncoder):
    method __init__ (line 7) | def __init__(self):
    method forward (line 11) | def forward(self, image):
  class IpAdapterImageProjModel (line 17) | class IpAdapterImageProjModel(torch.nn.Module):
    method __init__ (line 18) | def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280,...
    method forward (line 25) | def forward(self, image_embeds):
  class IpAdapterModule (line 31) | class IpAdapterModule(torch.nn.Module):
    method __init__ (line 32) | def __init__(self, input_dim, output_dim):
    method forward (line 37) | def forward(self, hidden_states):
  class SDXLIpAdapter (line 43) | class SDXLIpAdapter(torch.nn.Module):
    method __init__ (line 44) | def __init__(self):
    method set_full_adapter (line 51) | def set_full_adapter(self):
    method set_less_adapter (line 67) | def set_less_adapter(self):
    method forward (line 83) | def forward(self, hidden_states, scale=1.0):
    method state_dict_converter (line 100) | def state_dict_converter():
  class SDXLIpAdapterStateDictConverter (line 104) | class SDXLIpAdapterStateDictConverter:
    method __init__ (line 105) | def __init__(self):
    method from_diffusers (line 108) | def from_diffusers(self, state_dict):
    method from_civitai (line 120) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sdxl_motion.py
  class SDXLMotionModel (line 6) | class SDXLMotionModel(torch.nn.Module):
    method __init__ (line 7) | def __init__(self):
    method forward (line 49) | def forward(self):
    method state_dict_converter (line 53) | def state_dict_converter():
  class SDMotionModelStateDictConverter (line 57) | class SDMotionModelStateDictConverter:
    method __init__ (line 58) | def __init__(self):
    method from_diffusers (line 61) | def from_diffusers(self, state_dict):
    method from_civitai (line 103) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sdxl_text_encoder.py
  class SDXLTextEncoder (line 5) | class SDXLTextEncoder(torch.nn.Module):
    method __init__ (line 6) | def __init__(self, embed_dim=768, vocab_size=49408, max_position_embed...
    method attention_mask (line 24) | def attention_mask(self, length):
    method forward (line 30) | def forward(self, input_ids, clip_skip=1):
    method state_dict_converter (line 40) | def state_dict_converter():
  class SDXLTextEncoder2 (line 44) | class SDXLTextEncoder2(torch.nn.Module):
    method __init__ (line 45) | def __init__(self, embed_dim=1280, vocab_size=49408, max_position_embe...
    method attention_mask (line 66) | def attention_mask(self, length):
    method forward (line 72) | def forward(self, input_ids, clip_skip=2):
    method state_dict_converter (line 85) | def state_dict_converter():
  class SDXLTextEncoderStateDictConverter (line 89) | class SDXLTextEncoderStateDictConverter:
    method __init__ (line 90) | def __init__(self):
    method from_diffusers (line 93) | def from_diffusers(self, state_dict):
    method from_civitai (line 125) | def from_civitai(self, state_dict):
  class SDXLTextEncoder2StateDictConverter (line 316) | class SDXLTextEncoder2StateDictConverter:
    method __init__ (line 317) | def __init__(self):
    method from_diffusers (line 320) | def from_diffusers(self, state_dict):
    method from_civitai (line 353) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sdxl_unet.py
  class SDXLUNet (line 5) | class SDXLUNet(torch.nn.Module):
    method __init__ (line 6) | def __init__(self, is_kolors=False):
    method forward (line 88) | def forward(
    method state_dict_converter (line 139) | def state_dict_converter():
  class SDXLUNetStateDictConverter (line 143) | class SDXLUNetStateDictConverter:
    method __init__ (line 144) | def __init__(self):
    method from_diffusers (line 147) | def from_diffusers(self, state_dict):
    method from_civitai (line 208) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sdxl_vae_decoder.py
  class SDXLVAEDecoder (line 4) | class SDXLVAEDecoder(SDVAEDecoder):
    method __init__ (line 5) | def __init__(self, upcast_to_float32=True):
    method state_dict_converter (line 10) | def state_dict_converter():
  class SDXLVAEDecoderStateDictConverter (line 14) | class SDXLVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter):
    method __init__ (line 15) | def __init__(self):
    method from_diffusers (line 18) | def from_diffusers(self, state_dict):
    method from_civitai (line 22) | def from_civitai(self, state_dict):

FILE: diffsynth/models/sdxl_vae_encoder.py
  class SDXLVAEEncoder (line 4) | class SDXLVAEEncoder(SDVAEEncoder):
    method __init__ (line 5) | def __init__(self, upcast_to_float32=True):
    method state_dict_converter (line 10) | def state_dict_converter():
  class SDXLVAEEncoderStateDictConverter (line 14) | class SDXLVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
    method __init__ (line 15) | def __init__(self):
    method from_diffusers (line 18) | def from_diffusers(self, state_dict):
    method from_civitai (line 22) | def from_civitai(self, state_dict):

FILE: diffsynth/models/stepvideo_dit.py
  class RMSNorm (line 20) | class RMSNorm(nn.Module):
    method __init__ (line 21) | def __init__(
    method _norm (line 47) | def _norm(self, x):
    method forward (line 60) | def forward(self, x):
  function get_activation (line 86) | def get_activation(act_fn: str) -> nn.Module:
  function get_timestep_embedding (line 103) | def get_timestep_embedding(
  class Timesteps (line 146) | class Timesteps(nn.Module):
    method __init__ (line 147) | def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale...
    method forward (line 153) | def forward(self, timesteps):
  class TimestepEmbedding (line 163) | class TimestepEmbedding(nn.Module):
    method __init__ (line 164) | def __init__(
    method forward (line 210) | def forward(self, sample, condition=None):
  class PixArtAlphaCombinedTimestepSizeEmbeddings (line 225) | class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
    method __init__ (line 226) | def __init__(self, embedding_dim, size_emb_dim, use_additional_conditi...
    method forward (line 240) | def forward(self, timestep, resolution=None, nframe=None, fps=None):
  class AdaLayerNormSingle (line 264) | class AdaLayerNormSingle(nn.Module):
    method __init__ (line 274) | def __init__(self, embedding_dim: int, use_additional_conditions: bool...
    method forward (line 286) | def forward(
  class PixArtAlphaTextProjection (line 298) | class PixArtAlphaTextProjection(nn.Module):
    method __init__ (line 305) | def __init__(self, in_features, hidden_size):
    method forward (line 319) | def forward(self, caption):
  class Attention (line 326) | class Attention(nn.Module):
    method __init__ (line 327) | def __init__(self):
    method attn_processor (line 330) | def attn_processor(self, attn_type):
    method torch_attn_func (line 338) | def torch_attn_func(
  class RoPE1D (line 366) | class RoPE1D:
    method __init__ (line 367) | def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0):
    method get_cos_sin (line 373) | def get_cos_sin(self, D, seq_len, device, dtype):
    method rotate_half (line 385) | def rotate_half(x):
    method apply_rope1d (line 389) | def apply_rope1d(self, tokens, pos1d, cos, sin):
    method __call__ (line 395) | def __call__(self, tokens, positions):
  class RoPE3D (line 410) | class RoPE3D(RoPE1D):
    method __init__ (line 411) | def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0):
    method get_mesh_3d (line 415) | def get_mesh_3d(self, rope_positions, bsz):
    method __call__ (line 425) | def __call__(self, tokens, rope_positions, ch_split, parallel=False):
  class SelfAttention (line 451) | class SelfAttention(Attention):
    method __init__ (line 452) | def __init__(self, hidden_dim, head_dim, bias=False, with_rope=True, w...
    method apply_rope3d (line 473) | def apply_rope3d(self, x, fhw_positions, rope_ch_split, parallel=True):
    method forward (line 477) | def forward(
  class CrossAttention (line 512) | class CrossAttention(Attention):
    method __init__ (line 513) | def __init__(self, hidden_dim, head_dim, bias=False, with_qk_norm=True...
    method forward (line 529) | def forward(
  class GELU (line 560) | class GELU(nn.Module):
    method __init__ (line 571) | def __init__(self, dim_in: int, dim_out: int, approximate: str = "none...
    method gelu (line 576) | def gelu(self, gate: torch.Tensor) -> torch.Tensor:
    method forward (line 579) | def forward(self, hidden_states):
  class FeedForward (line 585) | class FeedForward(nn.Module):
    method __init__ (line 586) | def __init__(
    method forward (line 604) | def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> tor...
  function modulate (line 610) | def modulate(x, scale, shift):
  function gate (line 615) | def gate(x, gate):
  class StepVideoTransformerBlock (line 620) | class StepVideoTransformerBlock(nn.Module):
    method __init__ (line 655) | def __init__(
    method forward (line 677) | def forward(
  class PatchEmbed (line 715) | class PatchEmbed(nn.Module):
    method __init__ (line 718) | def __init__(
    method forward (line 736) | def forward(self, latent):
  class StepVideoModel (line 746) | class StepVideoModel(torch.nn.Module):
    method __init__ (line 747) | def __init__(
    method patchfy (line 812) | def patchfy(self, hidden_states):
    method prepare_attn_mask (line 817) | def prepare_attn_mask(self, encoder_attention_mask, encoder_hidden_sta...
    method block_forward (line 826) | def block_forward(
    method forward (line 848) | def forward(
    method state_dict_converter (line 925) | def state_dict_converter():
  class StepVideoDiTStateDictConverter (line 929) | class StepVideoDiTStateDictConverter:
    method __init__ (line 930) | def __init__(self):
    method from_diffusers (line 933) | def from_diffusers(self, state_dict):
    method from_civitai (line 936) | def from_civitai(self, state_dict):

FILE: diffsynth/models/stepvideo_text_encoder.py
  class EmptyInitOnDevice (line 30) | class EmptyInitOnDevice(torch.overrides.TorchFunctionMode):
    method __init__ (line 31) | def __init__(self, device=None):
    method __torch_function__ (line 34) | def __torch_function__(self, func, types, args=(), kwargs=None):
  function with_empty_init (line 46) | def with_empty_init(func):
  class LLaMaEmbedding (line 55) | class LLaMaEmbedding(nn.Module):
    method __init__ (line 69) | def __init__(self,
    method forward (line 82) | def forward(self, input_ids):
  class StepChatTokenizer (line 105) | class StepChatTokenizer:
    method __init__ (line 108) | def __init__(
    method vocab (line 166) | def vocab(self):
    method inv_vocab (line 170) | def inv_vocab(self):
    method vocab_size (line 174) | def vocab_size(self):
    method tokenize (line 177) | def tokenize(self, text: str) -> List[int]:
    method detokenize (line 180) | def detokenize(self, token_ids: List[int]) -> str:
  class Tokens (line 184) | class Tokens:
    method __init__ (line 185) | def __init__(self, input_ids, cu_input_ids, attention_mask, cu_seqlens...
    method to (line 191) | def to(self, device):
  class Wrapped_StepChatTokenizer (line 198) | class Wrapped_StepChatTokenizer(StepChatTokenizer):
    method __call__ (line 199) | def __call__(self, text, max_length=320, padding="max_length", truncat...
  function flash_attn_func (line 245) | def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=T...
  class FlashSelfAttention (line 256) | class FlashSelfAttention(torch.nn.Module):
    method __init__ (line 257) | def __init__(
    method forward (line 265) | def forward(self, q, k, v, cu_seqlens=None, max_seq_len=None):
  function safediv (line 275) | def safediv(n, d):
  class MultiQueryAttention (line 281) | class MultiQueryAttention(nn.Module):
    method __init__ (line 282) | def __init__(self, cfg, layer_id=None):
    method forward (line 311) | def forward(
  class FeedForward (line 373) | class FeedForward(nn.Module):
    method __init__ (line 374) | def __init__(
    method forward (line 401) | def forward(self, x):
  class TransformerBlock (line 408) | class TransformerBlock(nn.Module):
    method __init__ (line 409) | def __init__(
    method forward (line 438) | def forward(
  class Transformer (line 455) | class Transformer(nn.Module):
    method __init__ (line 456) | def __init__(
    method _build_layers (line 465) | def _build_layers(self, config):
    method forward (line 476) | def forward(
  class Step1Model (line 497) | class Step1Model(PreTrainedModel):
    method __init__ (line 500) | def __init__(
    method forward (line 508) | def forward(
  class STEP1TextEncoder (line 524) | class STEP1TextEncoder(torch.nn.Module):
    method __init__ (line 525) | def __init__(self, model_dir, max_length=320):
    method from_pretrained (line 533) | def from_pretrained(path, torch_dtype=torch.bfloat16):
    method forward (line 538) | def forward(self, prompts, with_mask=True, max_length=None, device="cu...

FILE: diffsynth/models/stepvideo_vae.py
  class BaseGroupNorm (line 21) | class BaseGroupNorm(nn.GroupNorm):
    method __init__ (line 22) | def __init__(self, num_groups, num_channels):
    method forward (line 25) | def forward(self, x, zero_pad=False, **kwargs):
  function base_group_norm (line 32) | def base_group_norm(x, norm_layer, act_silu=False, channel_last=False):
  function base_conv2d (line 62) | def base_conv2d(x, conv_layer, channel_last=False, residual=None):
  function base_conv3d (line 74) | def base_conv3d(x, conv_layer, channel_last=False, residual=None, only_r...
  function cal_outsize (line 90) | def cal_outsize(input_sizes, kernel_sizes, stride, padding):
  function calc_out_ (line 115) | def calc_out_(in_size, padding, dilation, kernel, stride):
  function base_conv3d_channel_last (line 120) | def base_conv3d_channel_last(x, conv_layer, residual=None):
  class Upsample2D (line 160) | class Upsample2D(nn.Module):
    method __init__ (line 161) | def __init__(self,
    method forward (line 178) | def forward(self, x, output_size=None):
  class Downsample2D (line 198) | class Downsample2D(nn.Module):
    method __init__ (line 199) | def __init__(self, channels, use_conv=False, out_channels=None, paddin...
    method forward (line 213) | def forward(self, x):
  class CausalConv (line 226) | class CausalConv(nn.Module):
    method __init__ (line 227) | def __init__(self,
    method forward (line 252) | def forward(self, x, is_init=True, residual=None):
  class ChannelDuplicatingPixelUnshuffleUpSampleLayer3D (line 262) | class ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(nn.Module):
    method __init__ (line 263) | def __init__(
    method forward (line 276) | def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor:
  class ConvPixelShuffleUpSampleLayer3D (line 284) | class ConvPixelShuffleUpSampleLayer3D(nn.Module):
    method __init__ (line 285) | def __init__(
    method forward (line 301) | def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor:
    method pixel_shuffle_3d (line 307) | def pixel_shuffle_3d(x: torch.Tensor, factor: int) -> torch.Tensor:
  class ConvPixelUnshuffleDownSampleLayer3D (line 320) | class ConvPixelUnshuffleDownSampleLayer3D(nn.Module):
    method __init__ (line 321) | def __init__(
    method forward (line 338) | def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor:
    method pixel_unshuffle_3d (line 344) | def pixel_unshuffle_3d(x: torch.Tensor, factor: int) -> torch.Tensor:
  class PixelUnshuffleChannelAveragingDownSampleLayer3D (line 353) | class PixelUnshuffleChannelAveragingDownSampleLayer3D(nn.Module):
    method __init__ (line 354) | def __init__(
    method forward (line 367) | def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor:
    method __init__ (line 378) | def __init__(
    method forward (line 391) | def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor:
  function base_group_norm_with_zero_pad (line 405) | def base_group_norm_with_zero_pad(x, norm_layer, act_silu=True, pad_size...
  class CausalConvChannelLast (line 414) | class CausalConvChannelLast(CausalConv):
    method __init__ (line 415) | def __init__(self,
    method forward (line 427) | def forward(self, x, is_init=True, residual=None):
  class CausalConvAfterNorm (line 438) | class CausalConvAfterNorm(CausalConv):
    method __init__ (line 439) | def __init__(self,
    method forward (line 454) | def forward(self, x, is_init=True, residual=None):
  class AttnBlock (line 466) | class AttnBlock(nn.Module):
    method __init__ (line 467) | def __init__(self,
    method attention (line 478) | def attention(self, x, is_init=True):
    method forward (line 491) | def forward(self, x):
  class Resnet3DBlock (line 498) | class Resnet3DBlock(nn.Module):
    method __init__ (line 499) | def __init__(self,
    method forward (line 527) | def forward(self, x, temb=None, is_init=True):
  class Downsample3D (line 544) | class Downsample3D(nn.Module):
    method __init__ (line 545) | def __init__(self,
    method forward (line 556) | def forward(self, x, is_init=True):
  class VideoEncoder (line 563) | class VideoEncoder(nn.Module):
    method __init__ (line 564) | def __init__(self,
    method forward (line 626) | def forward(self, x, video_frame_num, is_init=True):
  class Res3DBlockUpsample (line 676) | class Res3DBlockUpsample(nn.Module):
    method __init__ (line 677) | def __init__(self,
    method forward (line 706) | def forward(self, x, is_init=False):
  class Upsample3D (line 729) | class Upsample3D(nn.Module):
    method __init__ (line 730) | def __init__(self,
    method forward (line 742) | def forward(self, x, is_init=True, is_split=True):
  class VideoDecoder (line 757) | class VideoDecoder(nn.Module):
    method __init__ (line 758) | def __init__(self,
    method forward (line 823) | def forward(self, z, is_init=True):
  function rms_norm (line 871) | def rms_norm(input, normalized_shape, eps=1e-6):
  class DiagonalGaussianDistribution (line 878) | class DiagonalGaussianDistribution(object):
    method __init__ (line 879) | def __init__(self, parameters, deterministic=False, rms_norm_mean=Fals...
    method sample (line 895) | def sample(self, generator=None):
  class StepVideoVAE (line 908) | class StepVideoVAE(nn.Module):
    method __init__ (line 909) | def __init__(self,
    method init_from_ckpt (line 948) | def init_from_ckpt(self, model_path):
    method load_from_dict (line 959) | def load_from_dict(self, p):
    method convert_channel_last (line 962) | def convert_channel_last(self):
    method naive_encode (line 966) | def naive_encode(self, x, is_init_image=True):
    method encode (line 973) | def encode(self, x):
    method decode_naive (line 983) | def decode_naive(self, z, is_init=True):
    method decode_original (line 989) | def decode_original(self, z):
    method mix (line 1014) | def mix(self, x, smooth_scale = 0.6):
    method single_decode (line 1025) | def single_decode(self, hidden_states, device):
    method build_1d_mask (line 1032) | def build_1d_mask(self, length, left_bound, right_bound, border_width):
    method build_mask (line 1040) | def build_mask(self, data, is_bound, border_width):
    method tiled_decode (line 1052) | def tiled_decode(self, hidden_states, device, tile_size=(34, 34), tile...
    method decode (line 1103) | def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34...
    method state_dict_converter (line 1113) | def state_dict_converter():
  class StepVideoVAEStateDictConverter (line 1117) | class StepVideoVAEStateDictConverter:
    method __init__ (line 1118) | def __init__(self):
    method from_diffusers (line 1121) | def from_diffusers(self, state_dict):
    method from_civitai (line 1124) | def from_civitai(self, state_dict):

FILE: diffsynth/models/svd_image_encoder.py
  class CLIPVisionEmbeddings (line 5) | class CLIPVisionEmbeddings(torch.nn.Module):
    method __init__ (line 6) | def __init__(self, embed_dim=1280, image_size=224, patch_size=14, num_...
    method forward (line 18) | def forward(self, pixel_values):
  class SVDImageEncoder (line 27) | class SVDImageEncoder(torch.nn.Module):
    method __init__ (line 28) | def __init__(self, embed_dim=1280, layer_norm_eps=1e-5, num_encoder_la...
    method forward (line 38) | def forward(self, pixel_values):
    method state_dict_converter (line 48) | def state_dict_converter():
  class SVDImageEncoderStateDictConverter (line 52) | class SVDImageEncoderStateDictConverter:
    method __init__ (line 53) | def __init__(self):
    method from_diffusers (line 56) | def from_diffusers(self, state_dict):
    method from_civitai (line 94) | def from_civitai(self, state_dict):

FILE: diffsynth/models/svd_unet.py
  class TemporalResnetBlock (line 6) | class TemporalResnetBlock(torch.nn.Module):
    method __init__ (line 7) | def __init__(self, in_channels, out_channels, temb_channels=None, grou...
    method forward (line 20) | def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwar...
  function get_timestep_embedding (line 40) | def get_timestep_embedding(
  class TemporalTimesteps (line 84) | class TemporalTimesteps(torch.nn.Module):
    method __init__ (line 85) | def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale...
    method forward (line 92) | def forward(self, timesteps):
  class TrainableTemporalTimesteps (line 103) | class TrainableTemporalTimesteps(torch.nn.Module):
    method __init__ (line 104) | def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale...
    method forward (line 110) | def forward(self, timesteps):
  class PositionalID (line 115) | class PositionalID(torch.nn.Module):
    method __init__ (line 116) | def __init__(self, max_id=25, repeat_length=20):
    method frame_id_to_position_id (line 121) | def frame_id_to_position_id(self, frame_id):
    method forward (line 132) | def forward(self, num_frames, pivot_frame_id=0):
  class TemporalAttentionBlock (line 138) | class TemporalAttentionBlock(torch.nn.Module):
    method __init__ (line 140) | def __init__(self, num_attention_heads, attention_head_dim, in_channel...
    method forward (line 180) | def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwar...
  class PopMixBlock (line 217) | class PopMixBlock(torch.nn.Module):
    method __init__ (line 218) | def __init__(self, in_channels=None):
    method forward (line 225) | def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwar...
  class SVDUNet (line 238) | class SVDUNet(torch.nn.Module):
    method __init__ (line 239) | def __init__(self, add_positional_conv=None):
    method build_mask (line 316) | def build_mask(self, data, is_bound):
    method tiled_forward (line 337) | def tiled_forward(
    method forward (line 373) | def forward(self, sample, timestep, encoder_hidden_states, add_time_id...
    method state_dict_converter (line 414) | def state_dict_converter():
  class SVDUNetStateDictConverter (line 419) | class SVDUNetStateDictConverter:
    method __init__ (line 420) | def __init__(self):
    method get_block_name (line 423) | def get_block_name(self, names):
    method from_diffusers (line 435) | def from_diffusers(self, state_dict):
    method from_civitai (line 555) | def from_civitai(self, state_dict, add_positional_conv=None):

FILE: diffsynth/models/svd_vae_decoder.py
  class VAEAttentionBlock (line 8) | class VAEAttentionBlock(torch.nn.Module):
    method __init__ (line 10) | def __init__(self, num_attention_heads, attention_head_dim, in_channel...
    method forward (line 28) | def forward(self, hidden_states, time_emb, text_emb, res_stack):
  class TemporalResnetBlock (line 45) | class TemporalResnetBlock(torch.nn.Module):
    method __init__ (line 47) | def __init__(self, in_channels, out_channels, groups=32, eps=1e-5):
    method forward (line 56) | def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwar...
  class SVDVAEDecoder (line 71) | class SVDVAEDecoder(torch.nn.Module):
    method __init__ (line 72) | def __init__(self):
    method forward (line 123) | def forward(self, sample):
    method build_mask (line 144) | def build_mask(self, data, is_bound):
    method decode_video (line 165) | def decode_video(
    method state_dict_converter (line 203) | def state_dict_converter():
  class SVDVAEDecoderStateDictConverter (line 207) | class SVDVAEDecoderStateDictConverter:
    method __init__ (line 208) | def __init__(self):
    method from_diffusers (line 211) | def from_diffusers(self, state_dict):
    method from_civitai (line 302) | def from_civitai(self, state_dict):

FILE: diffsynth/models/svd_vae_encoder.py
  class SVDVAEEncoder (line 4) | class SVDVAEEncoder(SDVAEEncoder):
    method __init__ (line 5) | def __init__(self):
    method state_dict_converter (line 10) | def state_dict_converter():
  class SVDVAEEncoderStateDictConverter (line 14) | class SVDVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
    method __init__ (line 15) | def __init__(self):
    method from_diffusers (line 18) | def from_diffusers(self, state_dict):
    method from_civitai (line 21) | def from_civitai(self, state_dict):

FILE: diffsynth/models/tiler.py
  class TileWorker (line 5) | class TileWorker:
    method __init__ (line 6) | def __init__(self):
    method mask (line 10) | def mask(self, height, width, border_width):
    method tile (line 20) | def tile(self, model_input, tile_size, tile_stride, tile_device, tile_...
    method tiled_inference (line 34) | def tiled_inference(self, forward_fn, model_input, tile_batch_size, in...
    method io_scale (line 57) | def io_scale(self, model_output, tile_size):
    method untile (line 64) | def untile(self, model_output, height, width, tile_size, tile_stride, ...
    method tiled_forward (line 83) | def tiled_forward(self, forward_fn, model_input, tile_size, tile_strid...
  class FastTileWorker (line 110) | class FastTileWorker:
    method __init__ (line 111) | def __init__(self):
    method build_mask (line 115) | def build_mask(self, data, is_bound):
    method tiled_forward (line 133) | def tiled_forward(self, forward_fn, model_input, tile_size, tile_strid...
  class TileWorker2Dto3D (line 164) | class TileWorker2Dto3D:
    method __init__ (line 168) | def __init__(self):
    method build_mask (line 172) | def build_mask(self, T, H, W, dtype, device, is_bound, border_width):
    method tiled_forward (line 192) | def tiled_forward(

FILE: diffsynth/models/utils.py
  function init_weights_on_device (line 7) | def init_weights_on_device(device = torch.device("meta"), include_buffer...
  function load_state_dict_from_folder (line 55) | def load_state_dict_from_folder(file_path, torch_dtype=None):
  function load_state_dict (line 65) | def load_state_dict(file_path, torch_dtype=None):
  function load_state_dict_from_safetensors (line 72) | def load_state_dict_from_safetensors(file_path, torch_dtype=None):
  function load_state_dict_from_bin (line 82) | def load_state_dict_from_bin(file_path, torch_dtype=None):
  function search_for_embeddings (line 91) | def search_for_embeddings(state_dict):
  function search_parameter (line 101) | def search_parameter(param, state_dict):
  function build_rename_dict (line 113) | def build_rename_dict(source_state_dict, target_state_dict, split_qkv=Fa...
  function search_for_files (line 135) | def search_for_files(folder, extensions):
  function convert_state_dict_keys_to_single_str (line 148) | def convert_state_dict_keys_to_single_str(state_dict, with_shape=True):
  function split_state_dict_with_prefix (line 164) | def split_state_dict_with_prefix(state_dict):
  function hash_state_dict_keys (line 179) | def hash_state_dict_keys(state_dict, with_shape=True):

FILE: diffsynth/models/wan_video_dit.py
  function flash_attention (line 28) | def flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, n...
  function modulate (line 62) | def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
  function sinusoidal_embedding_1d (line 66) | def sinusoidal_embedding_1d(dim, position):
  function precompute_freqs_cis_3d (line 73) | def precompute_freqs_cis_3d(dim: int, end: int = 1024, theta: float = 10...
  function precompute_freqs_cis (line 81) | def precompute_freqs_cis(dim: int, end: int = 1024, theta: float = 10000...
  function rope_apply (line 90) | def rope_apply(x, freqs, num_heads):
  class RMSNorm (line 98) | class RMSNorm(nn.Module):
    method __init__ (line 99) | def __init__(self, dim, eps=1e-5):
    method norm (line 104) | def norm(self, x):
    method forward (line 107) | def forward(self, x):
  class AttentionModule (line 112) | class AttentionModule(nn.Module):
    method __init__ (line 113) | def __init__(self, num_heads):
    method forward (line 117) | def forward(self, q, k, v):
  class SelfAttention (line 122) | class SelfAttention(nn.Module):
    method __init__ (line 123) | def __init__(self, dim: int, num_heads: int, eps: float = 1e-6):
    method forward (line 138) | def forward(self, x, freqs):
  class CrossAttention (line 148) | class CrossAttention(nn.Module):
    method __init__ (line 149) | def __init__(self, dim: int, num_heads: int, eps: float = 1e-6, has_im...
    method forward (line 169) | def forward(self, x: torch.Tensor, y: torch.Tensor):
  class DiTBlock (line 187) | class DiTBlock(nn.Module):
    method __init__ (line 188) | def __init__(self, has_image_input: bool, dim: int, num_heads: int, ff...
    method forward (line 204) | def forward(self, x, context, cam_emb, t_mod, freqs, freqs_mvs):
  class MLP (line 243) | class MLP(torch.nn.Module):
    method __init__ (line 244) | def __init__(self, in_dim, out_dim):
    method forward (line 254) | def forward(self, x):
  class Head (line 258) | class Head(nn.Module):
    method __init__ (line 259) | def __init__(self, dim: int, out_dim: int, patch_size: Tuple[int, int,...
    method forward (line 267) | def forward(self, x, t_mod):
  class WanModel (line 273) | class WanModel(torch.nn.Module):
    method __init__ (line 274) | def __init__(
    method patchify (line 319) | def patchify(self, x: torch.Tensor):
    method unpatchify (line 325) | def unpatchify(self, x: torch.Tensor, grid_size: torch.Tensor):
    method forward (line 332) | def forward(self,
    method state_dict_converter (line 396) | def state_dict_converter():
  class WanModelStateDictConverter (line 400) | class WanModelStateDictConverter:
    method __init__ (line 401) | def __init__(self):
    method from_diffusers (line 404) | def from_diffusers(self, state_dict):
    method from_civitai (line 481) | def from_civitai(self, state_dict):

FILE: diffsynth/models/wan_video_image_encoder.py
  class SelfAttention (line 14) | class SelfAttention(nn.Module):
    method __init__ (line 16) | def __init__(self, dim, num_heads, dropout=0.1, eps=1e-5):
    method forward (line 31) | def forward(self, x, mask):
    method __init__ (line 236) | def __init__(self,
    method forward (line 255) | def forward(self, x):
  class AttentionBlock (line 53) | class AttentionBlock(nn.Module):
    method __init__ (line 55) | def __init__(self, dim, num_heads, post_norm, dropout=0.1, eps=1e-5):
    method forward (line 70) | def forward(self, x, mask):
    method __init__ (line 291) | def __init__(self,
    method forward (line 323) | def forward(self, x):
  class XLMRoberta (line 80) | class XLMRoberta(nn.Module):
    method __init__ (line 85) | def __init__(self,
    method forward (line 122) | def forward(self, ids):
  function xlm_roberta_large (line 150) | def xlm_roberta_large(pretrained=False,
  function pos_interpolate (line 203) | def pos_interpolate(pos, seq_len):
  class QuickGELU (line 222) | class QuickGELU(nn.Module):
    method forward (line 224) | def forward(self, x):
  class LayerNorm (line 228) | class LayerNorm(nn.LayerNorm):
    method forward (line 230) | def forward(self, x):
  class SelfAttention (line 234) | class SelfAttention(nn.Module):
    method __init__ (line 16) | def __init__(self, dim, num_heads, dropout=0.1, eps=1e-5):
    method forward (line 31) | def forward(self, x, mask):
    method __init__ (line 236) | def __init__(self,
    method forward (line 255) | def forward(self, x):
  class SwiGLU (line 271) | class SwiGLU(nn.Module):
    method __init__ (line 273) | def __init__(self, dim, mid_dim):
    method forward (line 283) | def forward(self, x):
  class AttentionBlock (line 289) | class AttentionBlock(nn.Module):
    method __init__ (line 55) | def __init__(self, dim, num_heads, post_norm, dropout=0.1, eps=1e-5):
    method forward (line 70) | def forward(self, x, mask):
    method __init__ (line 291) | def __init__(self,
    method forward (line 323) | def forward(self, x):
  class AttentionPool (line 333) | class AttentionPool(nn.Module):
    method __init__ (line 335) | def __init__(self,
    method forward (line 363) | def forward(self, x):
  class VisionTransformer (line 386) | class VisionTransformer(nn.Module):
    method __init__ (line 388) | def __init__(self,
    method forward (line 456) | def forward(self, x, interpolation=False, use_31_block=False):
  class CLIP (line 481) | class CLIP(nn.Module):
    method __init__ (line 483) | def __init__(self,
    method forward (line 571) | def forward(self, imgs, txt_ids):
    method init_weights (line 582) | def init_weights(self):
    method param_groups (line 601) | def param_groups(self):
  class XLMRobertaWithHead (line 617) | class XLMRobertaWithHead(XLMRoberta):
    method __init__ (line 619) | def __init__(self, **kwargs):
    method forward (line 629) | def forward(self, ids):
  class XLMRobertaCLIP (line 642) | class XLMRobertaCLIP(nn.Module):
    method __init__ (line 644) | def __init__(self,
    method forward (line 710) | def forward(self, imgs, txt_ids):
    method param_groups (line 722) | def param_groups(self):
  function _clip (line 738) | def _clip(pretrained=False,
  function clip_xlm_roberta_vit_h_14 (line 822) | def clip_xlm_roberta_vit_h_14(
  class WanImageEncoder (line 852) | class WanImageEncoder(torch.nn.Module):
    method __init__ (line 854) | def __init__(self):
    method encode_image (line 864) | def encode_image(self, videos):
    method state_dict_converter (line 883) | def state_dict_converter():
  class WanImageEncoderStateDictConverter (line 887) | class WanImageEncoderStateDictConverter:
    method __init__ (line 888) | def __init__(self):
    method from_diffusers (line 891) | def from_diffusers(self, state_dict):
    method from_civitai (line 894) | def from_civitai(self, state_dict):

FILE: diffsynth/models/wan_video_text_encoder.py
  function fp16_clamp (line 8) | def fp16_clamp(x):
  class GELU (line 15) | class GELU(nn.Module):
    method forward (line 17) | def forward(self, x):
  class T5LayerNorm (line 22) | class T5LayerNorm(nn.Module):
    method __init__ (line 24) | def __init__(self, dim, eps=1e-6):
    method forward (line 30) | def forward(self, x):
  class T5Attention (line 38) | class T5Attention(nn.Module):
    method __init__ (line 40) | def __init__(self, dim, dim_attn, num_heads, dropout=0.1):
    method forward (line 55) | def forward(self, x, context=None, mask=None, pos_bias=None):
  class T5FeedForward (line 92) | class T5FeedForward(nn.Module):
    method __init__ (line 94) | def __init__(self, dim, dim_ffn, dropout=0.1):
    method forward (line 105) | def forward(self, x):
  class T5SelfAttention (line 113) | class T5SelfAttention(nn.Module):
    method __init__ (line 115) | def __init__(self,
    method forward (line 139) | def forward(self, x, mask=None, pos_bias=None):
  class T5RelativeEmbedding (line 147) | class T5RelativeEmbedding(nn.Module):
    method __init__ (line 149) | def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128):
    method forward (line 159) | def forward(self, lq, lk):
    method _relative_position_bucket (line 171) | def _relative_position_bucket(self, rel_pos):
  function init_weights (line 192) | def init_weights(m):
  class WanTextEncoder (line 209) | class WanTextEncoder(torch.nn.Module):
    method __init__ (line 211) | def __init__(self,
    method forward (line 245) | def forward(self, ids, mask=None):
    method state_dict_converter (line 257) | def state_dict_converter():
  class WanTextEncoderStateDictConverter (line 261) | class WanTextEncoderStateDictConverter:
    method __init__ (line 262) | def __init__(self):
    method from_diffusers (line 265) | def from_diffusers(self, state_dict):
    method from_civitai (line 268) | def from_civitai(self, state_dict):

FILE: diffsynth/models/wan_video_vae.py
  function check_is_instance (line 11) | def check_is_instance(model, module_class):
  function block_causal_mask (line 19) | def block_causal_mask(x, block_size):
  class CausalConv3d (line 33) | class CausalConv3d(nn.Conv3d):
    method __init__ (line 38) | def __init__(self, *args, **kwargs):
    method forward (line 44) | def forward(self, x, cache_x=None):
  class RMS_norm (line 55) | class RMS_norm(nn.Module):
    method __init__ (line 57) | def __init__(self, dim, channel_first=True, images=True, bias=False):
    method forward (line 67) | def forward(self, x):
  class Upsample (line 73) | class Upsample(nn.Upsample):
    method forward (line 75) | def forward(self, x):
  class Resample (line 82) | class Resample(nn.Module):
    method __init__ (line 84) | def __init__(self, dim, mode):
    method forward (line 120) | def forward(self, x, feat_cache=None, feat_idx=[0]):
    method init_weight (line 176) | def init_weight(self, conv):
    method init_weight2 (line 187) | def init_weight2(self, conv):
  class ResidualBlock (line 198) | class ResidualBlock(nn.Module):
    method __init__ (line 200) | def __init__(self, in_dim, out_dim, dropout=0.0):
    method forward (line 214) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  class AttentionBlock (line 235) | class AttentionBlock(nn.Module):
    method __init__ (line 240) | def __init__(self, dim):
    method forward (line 252) | def forward(self, x):
  class Encoder3d (line 276) | class Encoder3d(nn.Module):
    method __init__ (line 278) | def __init__(self,
    method forward (line 328) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  class Decoder3d (line 379) | class Decoder3d(nn.Module):
    method __init__ (line 381) | def __init__(self,
    method forward (line 432) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  function count_conv3d (line 484) | def count_conv3d(model):
  class VideoVAE_ (line 492) | class VideoVAE_(nn.Module):
    method __init__ (line 494) | def __init__(self,
    method forward (line 519) | def forward(self, x):
    method encode (line 525) | def encode(self, x, scale):
    method decode (line 552) | def decode(self, z, scale):
    method reparameterize (line 577) | def reparameterize(self, mu, log_var):
    method sample (line 582) | def sample(self, imgs, deterministic=False):
    method clear_cache (line 589) | def clear_cache(self):
  class WanVideoVAE (line 599) | class WanVideoVAE(nn.Module):
    method __init__ (line 601) | def __init__(self, z_dim=16):
    method build_1d_mask (line 621) | def build_1d_mask(self, length, left_bound, right_bound, border_width):
    method build_mask (line 630) | def build_mask(self, data, is_bound, border_width):
    method tiled_decode (line 643) | def tiled_decode(self, hidden_states, device, tile_size, tile_stride):
    method tiled_encode (line 695) | def tiled_encode(self, video, device, tile_size, tile_stride):
    method single_encode (line 746) | def single_encode(self, video, device):
    method single_decode (line 752) | def single_decode(self, hidden_state, device):
    method encode (line 758) | def encode(self, videos, device, tiled=False, tile_size=(34, 34), tile...
    method decode (line 776) | def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34...
    method state_dict_converter (line 792) | def state_dict_converter():
  class WanVideoVAEStateDictConverter (line 796) | class WanVideoVAEStateDictConverter:
    method __init__ (line 798) | def __init__(self):
    method from_civitai (line 801) | def from_civitai(self, state_dict):

FILE: diffsynth/pipelines/base.py
  class BasePipeline (line 8) | class BasePipeline(torch.nn.Module):
    method __init__ (line 10) | def __init__(self, device="cuda", torch_dtype=torch.float16, height_di...
    method check_resize_height_width (line 20) | def check_resize_height_width(self, height, width):
    method preprocess_image (line 30) | def preprocess_image(self, image):
    method preprocess_images (line 35) | def preprocess_images(self, images):
    method vae_output_to_image (line 39) | def vae_output_to_image(self, vae_output):
    method vae_output_to_video (line 45) | def vae_output_to_video(self, vae_output):
    method merge_latents (line 51) | def merge_latents(self, value, latents, masks, scales, blur_kernel_siz...
    method control_noise_via_local_prompts (line 66) | def control_noise_via_local_prompts(self, prompt_emb_global, prompt_em...
    method extend_prompt (line 79) | def extend_prompt(self, prompt, local_prompts, masks, mask_scales):
    method enable_cpu_offload (line 91) | def enable_cpu_offload(self):
    method load_models_to_device (line 95) | def load_models_to_device(self, loadmodel_names=[]):
    method generate_noise (line 124) | def generate_noise(self, shape, seed=None, device="cpu", dtype=torch.f...

FILE: diffsynth/pipelines/cog_video.py
  class CogVideoPipeline (line 13) | class CogVideoPipeline(BasePipeline):
    method __init__ (line 15) | def __init__(self, device="cuda", torch_dtype=torch.float16):
    method fetch_models (line 26) | def fetch_models(self, model_manager: ModelManager, prompt_refiner_cla...
    method from_model_manager (line 36) | def from_model_manager(model_manager: ModelManager, prompt_refiner_cla...
    method tensor2video (line 45) | def tensor2video(self, frames):
    method encode_prompt (line 52) | def encode_prompt(self, prompt, positive=True):
    method prepare_extra_input (line 57) | def prepare_extra_input(self, latents):
    method __call__ (line 62) | def __call__(

FILE: diffsynth/pipelines/dancer.py
  function lets_dance (line 7) | def lets_dance(
  function lets_dance_xl (line 119) | def lets_dance_xl(

FILE: diffsynth/pipelines/flux_image.py
  class FluxImagePipeline (line 19) | class FluxImagePipeline(BasePipeline):
    method __init__ (line 21) | def __init__(self, device="cuda", torch_dtype=torch.float16):
    method enable_vram_management (line 37) | def enable_vram_management(self, num_persistent_param_in_dit=None):
    method denoising_model (line 136) | def denoising_model(self):
    method fetch_models (line 140) | def fetch_models(self, model_manager: ModelManager, controlnet_config_...
    method from_model_manager (line 167) | def from_model_manager(model_manager: ModelManager, controlnet_config_...
    method encode_image (line 176) | def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32):
    method decode_image (line 181) | def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=...
    method encode_prompt (line 187) | def encode_prompt(self, prompt, positive=True, t5_sequence_length=512):
    method prepare_extra_input (line 194) | def prepare_extra_input(self, latents=None, guidance=1.0):
    method apply_controlnet_mask_on_latents (line 200) | def apply_controlnet_mask_on_latents(self, latents, mask):
    method apply_controlnet_mask_on_image (line 209) | def apply_controlnet_mask_on_image(self, image, mask):
    method prepare_controlnet_input (line 218) | def prepare_controlnet_input(self, controlnet_image, controlnet_inpain...
    method prepare_ipadapter_inputs (line 242) | def prepare_ipadapter_inputs(self, images, height=384, width=384):
    method inpaint_fusion (line 248) | def inpaint_fusion(self, latents, inpaint_latents, pred_noise, fg_mask...
    method preprocess_masks (line 260) | def preprocess_masks(self, masks, height, width, dim):
    method prepare_entity_inputs (line 269) | def prepare_entity_inputs(self, entity_prompts, entity_masks, width, h...
    method prepare_latents (line 283) | def prepare_latents(self, input_image, height, width, seed, tiled, til...
    method prepare_ipadapter (line 296) | def prepare_ipadapter(self, ipadapter_images, ipadapter_scale):
    method prepare_controlnet (line 309) | def prepare_controlnet(self, controlnet_image, masks, controlnet_inpai...
    method prepare_eligen (line 324) | def prepare_eligen(self, prompt_emb_nega, eligen_entity_prompts, elige...
    method prepare_prompts (line 340) | def prepare_prompts(self, prompt, local_prompts, masks, mask_scales, t...
    method __call__ (line 353) | def __call__(
  class TeaCache (line 472) | class TeaCache:
    method __init__ (line 473) | def __init__(self, num_inference_steps, rel_l1_thresh):
    method check (line 482) | def check(self, dit: FluxDiT, hidden_states, conditioning):
    method store (line 506) | def store(self, hidden_states):
    method update (line 510) | def update(self, hidden_states):
  function lets_dance_flux (line 515) | def lets_dance_flux(

FILE: diffsynth/pipelines/hunyuan_image.py
  class ImageSizeManager (line 15) | class ImageSizeManager:
    method __init__ (line 16) | def __init__(self):
    method _to_tuple (line 20) | def _to_tuple(self, x):
    method get_fill_resize_and_crop (line 27) | def get_fill_resize_and_crop(self, src, tgt):
    method get_meshgrid (line 48) | def get_meshgrid(self, start, *args):
    method get_2d_rotary_pos_embed (line 74) | def get_2d_rotary_pos_embed(self, embed_dim, start, *args, use_real=Tr...
    method get_2d_rotary_pos_embed_from_grid (line 81) | def get_2d_rotary_pos_embed_from_grid(self, embed_dim, grid, use_real=...
    method get_1d_rotary_pos_embed (line 97) | def get_1d_rotary_pos_embed(self, dim: int, pos, theta: float = 10000....
    method calc_rope (line 112) | def calc_rope(self, height, width):
  class HunyuanDiTImagePipeline (line 125) | class HunyuanDiTImagePipeline(BasePipeline):
    method __init__ (line 127) | def __init__(self, device="cuda", torch_dtype=torch.float16):
    method denoising_model (line 141) | def denoising_model(self):
    method fetch_models (line 145) | def fetch_models(self, model_manager: ModelManager, prompt_refiner_cla...
    method from_model_manager (line 157) | def from_model_manager(model_manager: ModelManager, prompt_refiner_cla...
    method encode_image (line 166) | def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32):
    method decode_image (line 171) | def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=...
    method encode_prompt (line 177) | def encode_prompt(self, prompt, clip_skip=1, clip_skip_2=1, positive=T...
    method prepare_extra_input (line 193) | def prepare_extra_input(self, latents=None, tiled=False, tile_size=64,...
    method __call__ (line 210) | def __call__(

FILE: diffsynth/pipelines/hunyuan_video.py
  class HunyuanVideoPipeline (line 15) | class HunyuanVideoPipeline(BasePipeline):
    method __init__ (line 17) | def __init__(self, device="cuda", torch_dtype=torch.float16):
    method enable_vram_management (line 30) | def enable_vram_management(self):
    method fetch_models (line 37) | def fetch_models(self, model_manager: ModelManager):
    method from_model_manager (line 47) | def from_model_manager(model_manager: ModelManager, torch_dtype=None, ...
    method generate_crop_size_list (line 56) | def generate_crop_size_list(self, base_size=256, patch_size=32, max_ra...
    method get_closest_ratio (line 71) | def get_closest_ratio(self, height: float, width: float, ratios: list,...
    method prepare_vae_images_inputs (line 78) | def prepare_vae_images_inputs(self, semantic_images, i2v_resolution="7...
    method encode_prompt (line 105) | def encode_prompt(self, prompt, positive=True, clip_sequence_length=77...
    method prepare_extra_input (line 112) | def prepare_extra_input(self, latents=None, guidance=1.0):
    method tensor2video (line 118) | def tensor2video(self, frames):
    method encode_video (line 125) | def encode_video(self, frames, tile_size=(17, 30, 30), tile_stride=(12...
    method __call__ (line 133) | def __call__(
  class TeaCache (line 251) | class TeaCache:
    method __init__ (line 252) | def __init__(self, num_inference_steps, rel_l1_thresh):
    method check (line 261) | def check(self, dit: HunyuanVideoDiT, img, vec):
    method store (line 287) | def store(self, hidden_states):
    method update (line 291) | def update(self, hidden_states):
  function lets_dance_hunyuan_video (line 297) | def lets_dance_hunyuan_video(
  function lets_dance_hunyuan_video_i2v (line 343) | def lets_dance_hunyuan_video_i2v(

FILE: diffsynth/pipelines/omnigen_image.py
  class OmniGenCache (line 15) | class OmniGenCache(DynamicCache):
    method __init__ (line 16) | def __init__(self,
    method prefetch_layer (line 28) | def prefetch_layer(self, layer_idx: int):
    method evict_previous_layer (line 38) | def evict_previous_layer(self, layer_idx: int):
    method __getitem__ (line 50) | def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
    method update (line 74) | def update(
  class OmnigenImagePipeline (line 122) | class OmnigenImagePipeline(BasePipeline):
    method __init__ (line 124) | def __init__(self, device="cuda", torch_dtype=torch.float16):
    method denoising_model (line 135) | def denoising_model(self):
    method fetch_models (line 139) | def fetch_models(self, model_manager: ModelManager, prompt_refiner_cla...
    method from_model_manager (line 148) | def from_model_manager(model_manager: ModelManager, prompt_refiner_cla...
    method encode_image (line 157) | def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32):
    method encode_images (line 162) | def encode_images(self, images, tiled=False, tile_size=64, tile_stride...
    method decode_image (line 167) | def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=...
    method encode_prompt (line 173) | def encode_prompt(self, prompt, clip_skip=1, positive=True):
    method prepare_extra_input (line 178) | def prepare_extra_input(self, latents=None):
    method crop_position_ids_for_cache (line 182) | def crop_position_ids_for_cache(self, position_ids, num_tokens_for_img):
    method crop_attention_mask_for_cache (line 191) | def crop_attention_mask_for_cache(self, attention_mask, num_tokens_for...
    method __call__ (line 198) | def __call__(

FILE: diffsynth/pipelines/pipeline_runner.py
  class SDVideoPipelineRunner (line 8) | class SDVideoPipelineRunner:
    method __init__ (line 9) | def __init__(self, in_streamlit=False):
    method load_pipeline (line 13) | def load_pipeline(self, model_list, textual_inversion_folder, device, ...
    method load_smoother (line 35) | def load_smoother(self, model_manager, smoother_configs):
    method synthesize_video (line 40) | def synthesize_video(self, model_manager, pipe, seed, smoother, **pipe...
    method load_video (line 53) | def load_video(self, video_file, image_folder, height, width, start_fr...
    method add_data_to_pipeline_inputs (line 63) | def add_data_to_pipeline_inputs(self, data, pipeline_inputs):
    method save_output (line 72) | def save_output(self, video, output_folder, fps, config):
    method run (line 82) | def run(self, config):

FILE: diffsynth/pipelines/sd3_image.py
  class SD3ImagePipeline (line 10) | class SD3ImagePipeline(BasePipeline):
    method __init__ (line 12) | def __init__(self, device="cuda", torch_dtype=torch.float16):
    method denoising_model (line 26) | def denoising_model(self):
    method fetch_models (line 30) | def fetch_models(self, model_manager: ModelManager, prompt_refiner_cla...
    method from_model_manager (line 42) | def from_model_manager(model_manager: ModelManager, prompt_refiner_cla...
    method encode_image (line 51) | def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32):
    method decode_image (line 56) | def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=...
    method encode_prompt (line 62) | def encode_prompt(self, prompt, positive=True, t5_sequence_length=77):
    method prepare_extra_input (line 69) | def prepare_extra_input(self, latents=None):
    method __call__ (line 74) | def __call__(

FILE: diffsynth/pipelines/sd_image.py
  class SDImagePipeline (line 14) | class SDImagePipeline(BasePipeline):
    method __init__ (line 16) | def __init__(self, device="cuda", torch_dtype=torch.float16):
    method denoising_model (line 31) | def denoising_model(self):
    method fetch_models (line 35) | def fetch_models(self, model_manager: ModelManager, controlnet_config_...
    method from_model_manager (line 61) | def from_model_manager(model_manager: ModelManager, controlnet_config_...
    method encode_image (line 70) | def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32):
    method decode_image (line 75) | def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=...
    method encode_prompt (line 81) | def encode_prompt(self, prompt, clip_skip=1, positive=True):
    method prepare_extra_input (line 86) | def prepare_extra_input(self, latents=None):
    method __call__ (line 91) | def __call__(

FILE: diffsynth/pipelines/sd_video.py
  function lets_dance_with_long_video (line 14) | def lets_dance_with_long_video(
  class SDVideoPipeline (line 68) | class SDVideoPipeline(SDImagePipeline):
    method __init__ (line 70) | def __init__(self, device="cuda", torch_dtype=torch.float16, use_origi...
    method fetch_models (line 85) | def fetch_models(self, model_manager: ModelManager, controlnet_config_...
    method from_model_manager (line 116) | def from_model_manager(model_manager: ModelManager, controlnet_config_...
    method decode_video (line 125) | def decode_video(self, latents, tiled=False, tile_size=64, tile_stride...
    method encode_video (line 133) | def encode_video(self, processed_images, tiled=False, tile_size=64, ti...
    method __call__ (line 144) | def __call__(

FILE: diffsynth/pipelines/sdxl_image.py
  class SDXLImagePipeline (line 16) | class SDXLImagePipeline(BasePipeline):
    method __init__ (line 18) | def __init__(self, device="cuda", torch_dtype=torch.float16):
    method denoising_model (line 35) | def denoising_model(self):
    method fetch_models (line 39) | def fetch_models(self, model_manager: ModelManager, controlnet_config_...
    method from_model_manager (line 75) | def from_model_manager(model_manager: ModelManager, controlnet_config_...
    method encode_image (line 84) | def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32):
    method decode_image (line 89) | def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=...
    method encode_prompt (line 95) | def encode_prompt(self, prompt, clip_skip=1, clip_skip_2=2, positive=T...
    method prepare_extra_input (line 105) | def prepare_extra_input(self, latents=None):
    method __call__ (line 112) | def __call__(

FILE: diffsynth/pipelines/sdxl_video.py
  class SDXLVideoPipeline (line 15) | class SDXLVideoPipeline(SDXLImagePipeline):
    method __init__ (line 17) | def __init__(self, device="cuda", torch_dtype=torch.float16, use_origi...
    method fetch_models (line 34) | def fetch_models(self, model_manager: ModelManager, controlnet_config_...
    method from_model_manager (line 69) | def from_model_manager(model_manager: ModelManager, controlnet_config_...
    method decode_video (line 78) | def decode_video(self, latents, tiled=False, tile_size=64, tile_stride...
    method encode_video (line 86) | def encode_video(self, processed_images, tiled=False, tile_size=64, ti...
    method __call__ (line 97) | def __call__(

FILE: diffsynth/pipelines/step_video.py
  class StepVideoPipeline (line 20) | class StepVideoPipeline(BasePipeline):
    method __init__ (line 22) | def __init__(self, device="cuda", torch_dtype=torch.float16):
    method enable_vram_management (line 33) | def enable_vram_management(self, num_persistent_param_in_dit=None):
    method fetch_models (line 118) | def fetch_models(self, model_manager: ModelManager):
    method from_model_manager (line 127) | def from_model_manager(model_manager: ModelManager, torch_dtype=None, ...
    method encode_prompt (line 135) | def encode_prompt(self, prompt, positive=True):
    method tensor2video (line 143) | def tensor2video(self, frames):
    method __call__ (line 151) | def __call__(

FILE: diffsynth/pipelines/svd_video.py
  class SVDVideoPipeline (line 12) | class SVDVideoPipeline(BasePipeline):
    method __init__ (line 14) | def __init__(self, device="cuda", torch_dtype=torch.float16):
    method fetch_models (line 24) | def fetch_models(self, model_manager: ModelManager):
    method from_model_manager (line 32) | def from_model_manager(model_manager: ModelManager, **kwargs):
    method encode_image_with_clip (line 41) | def encode_image_with_clip(self, image):
    method encode_image_with_vae (line 52) | def encode_image_with_vae(self, image, noise_aug_strength, seed=None):
    method encode_video_with_vae (line 60) | def encode_video_with_vae(self, video):
    method tensor2video (line 69) | def tensor2video(self, frames):
    method calculate_noise_pred (line 76) | def calculate_noise_pred(
    method post_process_latents (line 102) | def post_process_latents(self, latents, post_normalize=True, contrast_...
    method __call__ (line 111) | def __call__(
  class SVDCLIPImageProcessor (line 192) | class SVDCLIPImageProcessor:
    method __init__ (line 193) | def __init__(self):
    method resize_with_antialiasing (line 196) | def resize_with_antialiasing(self, input, size, interpolation="bicubic...
    method _compute_padding (line 225) | def _compute_padding(self, kernel_size):
    method _filter2d (line 248) | def _filter2d(self, input, kernel):
    method _gaussian (line 271) | def _gaussian(self, window_size: int, sigma):
    method _gaussian_blur2d (line 287) | def _gaussian_blur2d(self, input, kernel_size, sigma):

FILE: diffsynth/pipelines/wan_video.py
  class WanVideoPipeline (line 23) | class WanVideoPipeline(BasePipeline):
    method __init__ (line 25) | def __init__(self, device="cuda", torch_dtype=torch.float16, tokenizer...
    method enable_vram_management (line 38) | def enable_vram_management(self, num_persistent_param_in_dit=None):
    method fetch_models (line 126) | def fetch_models(self, model_manager: ModelManager):
    method from_model_manager (line 138) | def from_model_manager(model_manager: ModelManager, torch_dtype=None, ...
    method denoising_model (line 146) | def denoising_model(self):
    method encode_prompt (line 150) | def encode_prompt(self, prompt, positive=True):
    method encode_image (line 155) | def encode_image(self, image, num_frames, height, width):
    method tensor2video (line 173) | def tensor2video(self, frames):
    method prepare_extra_input (line 180) | def prepare_extra_input(self, latents=None):
    method encode_video (line 184) | def encode_video(self, input_video, tiled=True, tile_size=(34, 34), ti...
    method decode_video (line 189) | def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_s...
    method __call__ (line 195) | def __call__(
  class TeaCache (line 288) | class TeaCache:
    method __init__ (line 289) | def __init__(self, num_inference_steps, rel_l1_thresh, model_id):
    method check (line 309) | def check(self, dit: WanModel, x, t_mod):
    method store (line 331) | def store(self, hidden_states):
    method update (line 335) | def update(self, hidden_states):
  function model_fn_wan_video (line 341) | def model_fn_wan_video(

FILE: diffsynth/pipelines/wan_video_syncammaster.py
  class WanVideoSynCamMasterPipeline (line 23) | class WanVideoSynCamMasterPipeline(BasePipeline):
    method __init__ (line 25) | def __init__(self, device="cuda", torch_dtype=torch.float16, tokenizer...
    method enable_vram_management (line 38) | def enable_vram_management(self, num_persistent_param_in_dit=None):
    method fetch_models (line 126) | def fetch_models(self, model_manager: ModelManager):
    method from_model_manager (line 138) | def from_model_manager(model_manager: ModelManager, torch_dtype=None, ...
    method denoising_model (line 146) | def denoising_model(self):
    method encode_prompt (line 150) | def encode_prompt(self, prompt, positive=True):
    method encode_image (line 155) | def encode_image(self, image, num_frames, height, width):
    method tensor2video (line 173) | def tensor2video(self, frames):
    method prepare_extra_input (line 180) | def prepare_extra_input(self, latents=None):
    method encode_video (line 184) | def encode_video(self, input_video, tiled=True, tile_size=(34, 34), ti...
    method decode_video (line 189) | def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_s...
    method __call__ (line 195) | def __call__(
  class TeaCache (line 297) | class TeaCache:
    method __init__ (line 298) | def __init__(self, num_inference_steps, rel_l1_thresh, model_id):
    method check (line 318) | def check(self, dit: WanModel, x, t_mod):
    method store (line 340) | def store(self, hidden_states):
    method update (line 344) | def update(self, hidden_states):
  function model_fn_wan_video (line 350) | def model_fn_wan_video(

FILE: diffsynth/processors/FastBlend.py
  class FastBlendSmoother (line 10) | class FastBlendSmoother(VideoProcessor):
    method __init__ (line 11) | def __init__(
    method from_model_manager (line 30) | def from_model_manager(model_manager, **kwargs):
    method inference_fast (line 34) | def inference_fast(self, frames_guide, frames_style):
    method inference_balanced (line 61) | def inference_balanced(self, frames_guide, frames_style):
    method inference_accurate (line 98) | def inference_accurate(self, frames_guide, frames_style):
    method release_vram (line 124) | def release_vram(self):
    method __call__ (line 130) | def __call__(self, rendered_frames, original_frames=None, **kwargs):

FILE: diffsynth/processors/PILEditor.py
  class ContrastEditor (line 5) | class ContrastEditor(VideoProcessor):
    method __init__ (line 6) | def __init__(self, rate=1.5):
    method from_model_manager (line 10) | def from_model_manager(model_manager, **kwargs):
    method __call__ (line 13) | def __call__(self, rendered_frames, **kwargs):
  class SharpnessEditor (line 18) | class SharpnessEditor(VideoProcessor):
    method __init__ (line 19) | def __init__(self, rate=1.5):
    method from_model_manager (line 23) | def from_model_manager(model_manager, **kwargs):
    method __call__ (line 26) | def __call__(self, rendered_frames, **kwargs):

FILE: diffsynth/processors/RIFE.py
  class RIFESmoother (line 7) | class RIFESmoother(VideoProcessor):
    method __init__ (line 8) | def __init__(self, model, device="cuda", scale=1.0, batch_size=4, inte...
    method from_model_manager (line 21) | def from_model_manager(model_manager, **kwargs):
    method process_image (line 24) | def process_image(self, image):
    method process_images (line 33) | def process_images(self, images):
    method decode_images (line 38) | def decode_images(self, images):
    method process_tensors (line 43) | def process_tensors(self, input_tensor, scale=1.0, batch_size=4):
    method __call__ (line 55) | def __call__(self, rendered_frames, **kwargs):

FILE: diffsynth/processors/base.py
  class VideoProcessor (line 1) | class VideoProcessor:
    method __init__ (line 2) | def __init__(self):
    method __call__ (line 5) | def __call__(self):

FILE: diffsynth/processors/sequencial_processor.py
  class AutoVideoProcessor (line 4) | class AutoVideoProcessor(VideoProcessor):
    method __init__ (line 5) | def __init__(self):
    method from_model_manager (line 9) | def from_model_manager(model_manager, processor_type, **kwargs):
  class SequencialProcessor (line 26) | class SequencialProcessor(VideoProcessor):
    method __init__ (line 27) | def __init__(self, processors=[]):
    method from_model_manager (line 31) | def from_model_manager(model_manager, configs):
    method __call__ (line 38) | def __call__(self, rendered_frames, **kwargs):

FILE: diffsynth/prompters/base_prompter.py
  function tokenize_long_prompt (line 6) | def tokenize_long_prompt(tokenizer, prompt, max_length=None):
  class BasePrompter (line 39) | class BasePrompter:
    method __init__ (line 40) | def __init__(self):
    method load_prompt_refiners (line 45) | def load_prompt_refiners(self, model_manager: ModelManager, refiner_cl...
    method load_prompt_extenders (line 50) | def load_prompt_extenders(self,model_manager:ModelManager,extender_cla...
    method process_prompt (line 57) | def process_prompt(self, prompt, positive=True):
    method extend_prompt (line 66) | def extend_prompt(self, prompt:str, positive=True):

FILE: diffsynth/prompters/cog_prompter.py
  class CogPrompter (line 7) | class CogPrompter(BasePrompter):
    method __init__ (line 8) | def __init__(
    method fetch_models (line 20) | def fetch_models(self, text_encoder: FluxTextEncoder2 = None):
    method encode_prompt_using_t5 (line 24) | def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_...
    method encode_prompt (line 38) | def encode_prompt(

FILE: diffsynth/prompters/flux_prompter.py
  class FluxPrompter (line 8) | class FluxPrompter(BasePrompter):
    method __init__ (line 9) | def __init__(
    method fetch_models (line 27) | def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_en...
    method encode_prompt_using_clip (line 32) | def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, ma...
    method encode_prompt_using_t5 (line 44) | def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_...
    method encode_prompt (line 56) | def encode_prompt(

FILE: diffsynth/prompters/hunyuan_dit_prompter.py
  class HunyuanDiTPrompter (line 8) | class HunyuanDiTPrompter(BasePrompter):
    method __init__ (line 9) | def __init__(
    method fetch_models (line 29) | def fetch_models(self, text_encoder: HunyuanDiTCLIPTextEncoder = None,...
    method encode_prompt_using_signle_model (line 34) | def encode_prompt_using_signle_model(self, prompt, text_encoder, token...
    method encode_prompt (line 53) | def encode_prompt(

FILE: diffsynth/prompters/hunyuan_video_prompter.py
  class HunyuanVideoPrompter (line 70) | class HunyuanVideoPrompter(BasePrompter):
    method __init__ (line 72) | def __init__(
    method fetch_models (line 94) | def fetch_models(self,
    method apply_text_to_template (line 109) | def apply_text_to_template(self, text, template):
    method encode_prompt_using_clip (line 119) | def encode_prompt_using_clip(self, prompt, max_length, device):
    method encode_prompt_using_llm (line 132) | def encode_prompt_using_llm(self,
    method encode_prompt_using_mllm (line 156) | def encode_prompt_using_mllm(self,
    method encode_prompt (line 236) | def encode_prompt(self,

FILE: diffsynth/prompters/kolors_prompter.py
  class SPTokenizer (line 12) | class SPTokenizer:
    method __init__ (line 13) | def __init__(self, model_path: str):
    method tokenize (line 35) | def tokenize(self, s: str, encode_special_tokens=False):
    method encode (line 50) | def encode(self, s: str, bos: bool = False, eos: bool = False) -> List...
    method decode (line 59) | def decode(self, t: List[int]) -> str:
    method decode_tokens (line 73) | def decode_tokens(self, tokens: List[str]) -> str:
    method convert_token_to_id (line 77) | def convert_token_to_id(self, token):
    method convert_id_to_token (line 83) | def convert_id_to_token(self, index):
  class ChatGLMTokenizer (line 93) | class ChatGLMTokenizer(PreTrainedTokenizer):
    method __init__ (line 98) | def __init__(self, vocab_file, padding_side="left", clean_up_tokenizat...
    method get_command (line 114) | def get_command(self, token):
    method unk_token (line 121) | def unk_token(self) -> str:
    method pad_token (line 125) | def pad_token(self) -> str:
    method pad_token_id (line 129) | def pad_token_id(self):
    method eos_token (line 133) | def eos_token(self) -> str:
    method eos_token_id (line 137) | def eos_token_id(self):
    method vocab_size (line 141) | def vocab_size(self):
    method get_vocab (line 144) | def get_vocab(self):
    method _tokenize (line 150) | def _tokenize(self, text, **kwargs):
    method _convert_token_to_id (line 153) | def _convert_token_to_id(self, token):
    method _convert_id_to_token (line 157) | def _convert_id_to_token(self, index):
    method convert_tokens_to_string (line 161) | def convert_tokens_to_string(self, tokens: List[str]) -> str:
    method save_vocabulary (line 164) | def save_vocabulary(self, save_directory, filename_prefix=None):
    method get_prefix_tokens (line 192) | def get_prefix_tokens(self):
    method build_single_message (line 196) | def build_single_message(self, role, metadata, message):
    method build_chat_input (line 203) | def build_chat_input(self, query, history=None, role="user"):
    method build_inputs_with_special_tokens (line 216) | def build_inputs_with_special_tokens(
    method _pad (line 241) | def _pad(
  class KolorsPrompter (line 307) | class KolorsPrompter(BasePrompter):
    method __init__ (line 308) | def __init__(
    method fetch_models (line 320) | def fetch_models(self, text_encoder: ChatGLMModel = None):
    method encode_prompt_using_ChatGLM (line 324) | def encode_prompt_using_ChatGLM(self, prompt, text_encoder, tokenizer,...
    method encode_prompt (line 343) | def encode_prompt(

FILE: diffsynth/prompters/omnigen_prompter.py
  function crop_arr (line 14) | def crop_arr(pil_image, max_image_size):
  class OmniGenPrompter (line 44) | class OmniGenPrompter:
    method __init__ (line 45) | def __init__(self,
    method from_pretrained (line 61) | def from_pretrained(cls, model_name):
    method process_image (line 72) | def process_image(self, image):
    method process_multi_modal_prompt (line 75) | def process_multi_modal_prompt(self, text, input_images):
    method add_prefix_instruction (line 112) | def add_prefix_instruction(self, prompt):
    method __call__ (line 121) | def __call__(self,
  class OmniGenCollator (line 172) | class OmniGenCollator:
    method __init__ (line 173) | def __init__(self, pad_token_id=2, hidden_size=3072):
    method create_position (line 177) | def create_position(self, attention_mask, num_tokens_for_output_images):
    method create_mask (line 187) | def create_mask(self, attention_mask, num_tokens_for_output_images):
    method adjust_attention_for_input_images (line 226) | def adjust_attention_for_input_images(self, attention_mask, image_sizes):
    method pad_input_ids (line 233) | def pad_input_ids(self, input_ids, image_sizes):
    method process_mllm_input (line 259) | def process_mllm_input(self, mllm_inputs, target_img_size):
    method __call__ (line 287) | def __call__(self, features):
  class OmniGenSeparateCollator (line 314) | class OmniGenSeparateCollator(OmniGenCollator):
    method __call__ (line 315) | def __call__(self, features):

FILE: diffsynth/prompters/omost.py
  function safe_str (line 95) | def safe_str(x):
  function closest_name (line 98) | def closest_name(input_str, options):
  class Canvas (line 110) | class Canvas:
    method from_bot_response (line 112) | def from_bot_response(response: str):
    method __init__ (line 124) | def __init__(self):
    method set_global_description (line 132) | def set_global_description(self, description: str, detailed_descriptio...
    method add_local_description (line 153) | def add_local_description(self, location: str, offset: str, area: str,...
    method process (line 198) | def process(self):
  class OmostPromter (line 235) | class OmostPromter(torch.nn.Module):
    method __init__ (line 237) | def __init__(self,model = None,tokenizer = None, template = "",device=...
    method from_model_manager (line 259) | def from_model_manager(model_manager: ModelManager):
    method __call__ (line 270) | def __call__(self,prompt_dict:dict):

FILE: diffsynth/prompters/prompt_refiners.py
  class BeautifulPrompt (line 6) | class BeautifulPrompt(torch.nn.Module):
    method __init__ (line 7) | def __init__(self, tokenizer_path=None, model=None, template=""):
    method from_model_manager (line 15) | def from_model_manager(model_manager: ModelManager):
    method __call__ (line 32) | def __call__(self, raw_prompt, positive=True, **kwargs):
  class QwenPrompt (line 57) | class QwenPrompt(torch.nn.Module):
    method __init__ (line 60) | def __init__(self, tokenizer_path=None, model=None, system_prompt=""):
    method from_model_manager (line 68) | def from_model_manager(model_nameger: ModelManager):
    method __call__ (line 79) | def __call__(self, raw_prompt, positive=True, **kwargs):
  class Translator (line 111) | class Translator(torch.nn.Module):
    method __init__ (line 112) | def __init__(self, tokenizer_path=None, model=None):
    method from_model_manager (line 119) | def from_model_manager(model_manager: ModelManager):
    method __call__ (line 125) | def __call__(self, prompt, **kwargs):

FILE: diffsynth/prompters/sd3_prompter.py
  class SD3Prompter (line 8) | class SD3Prompter(BasePrompter):
    method __init__ (line 9) | def __init__(
    method fetch_models (line 33) | def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_en...
    method encode_prompt_using_clip (line 39) | def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, ma...
    method encode_prompt_using_t5 (line 51) | def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_...
    method encode_prompt (line 66) | def encode_prompt(

FILE: diffsynth/prompters/sd_prompter.py
  class SDPrompter (line 9) | class SDPrompter(BasePrompter):
    method __init__ (line 10) | def __init__(self, tokenizer_path=None):
    method fetch_models (line 21) | def fetch_models(self, text_encoder: SDTextEncoder = None):
    method add_textual_inversions_to_model (line 25) | def add_textual_inversions_to_model(self, textual_inversion_dict, text...
    method add_textual_inversions_to_tokenizer (line 39) | def add_textual_inversions_to_tokenizer(self, textual_inversion_dict, ...
    method load_textual_inversions (line 48) | def load_textual_inversions(self, model_paths):
    method encode_prompt (line 63) | def encode_prompt(self, prompt, clip_skip=1, device="cuda", positive=T...

FILE: diffsynth/prompters/sdxl_prompter.py
  class SDXLPrompter (line 9) | class SDXLPrompter(BasePrompter):
    method __init__ (line 10) | def __init__(
    method fetch_models (line 28) | def fetch_models(self, text_encoder: SDXLTextEncoder = None, text_enco...
    method encode_prompt (line 33) | def encode_prompt(

FILE: diffsynth/prompters/stepvideo_prompter.py
  class StepVideoPrompter (line 8) | class StepVideoPrompter(BasePrompter):
    method __init__ (line 10) | def __init__(
    method fetch_models (line 21) | def fetch_models(self, text_encoder_1: HunyuanDiTCLIPTextEncoder = Non...
    method encode_prompt_using_clip (line 25) | def encode_prompt_using_clip(self, prompt, max_length, device):
    method encode_prompt_using_llm (line 40) | def encode_prompt_using_llm(self, prompt, max_length, device):
    method encode_prompt (line 44) | def encode_prompt(self,

FILE: diffsynth/prompters/wan_prompter.py
  function basic_clean (line 11) | def basic_clean(text):
  function whitespace_clean (line 17) | def whitespace_clean(text):
  function canonicalize (line 23) | def canonicalize(text, keep_punctuation_exact_string=None):
  class HuggingfaceTokenizer (line 36) | class HuggingfaceTokenizer:
    method __init__ (line 38) | def __init__(self, name, seq_len=None, clean=None, **kwargs):
    method __call__ (line 48) | def __call__(self, sequence, **kwargs):
    method _clean (line 74) | def _clean(self, text):
  class WanPrompter (line 84) | class WanPrompter(BasePrompter):
    method __init__ (line 86) | def __init__(self, tokenizer_path=None, text_len=512):
    method fetch_tokenizer (line 92) | def fetch_tokenizer(self, tokenizer_path=None):
    method fetch_models (line 96) | def fetch_models(self, text_encoder: WanTextEncoder = None):
    method encode_prompt (line 99) | def encode_prompt(self, prompt, positive=True, device="cuda"):

FILE: diffsynth/schedulers/continuous_ode.py
  class ContinuousODEScheduler (line 4) | class ContinuousODEScheduler():
    method __init__ (line 6) | def __init__(self, num_inference_steps=100, sigma_max=700.0, sigma_min...
    method set_timesteps (line 13) | def set_timesteps(self, num_inference_steps=100, denoising_strength=1....
    method step (line 21) | def step(self, model_output, timestep, sample, to_final=False):
    method return_to_timestep (line 36) | def return_to_timestep(self, timestep, sample, sample_stablized):
    method add_noise (line 41) | def add_noise(self, original_samples, noise, timestep):
    method training_target (line 48) | def training_target(self, sample, noise, timestep):
    method training_weight (line 55) | def training_weight(self, timestep):

FILE: diffsynth/schedulers/ddim.py
  class EnhancedDDIMScheduler (line 4) | class EnhancedDDIMScheduler():
    method __init__ (line 6) | def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_...
    method rescale_zero_terminal_snr (line 22) | def rescale_zero_terminal_snr(self, alphas_cumprod):
    method set_timesteps (line 41) | def set_timesteps(self, num_inference_steps, denoising_strength=1.0, *...
    method denoise (line 53) | def denoise(self, model_output, sample, alpha_prod_t, alpha_prod_t_prev):
    method step (line 67) | def step(self, model_output, timestep, sample, to_final=False):
    method return_to_timestep (line 81) | def return_to_timestep(self, timestep, sample, sample_stablized):
    method add_noise (line 87) | def add_noise(self, original_samples, noise, timestep):
    method training_target (line 94) | def training_target(self, sample, noise, timestep):
    method training_weight (line 104) | def training_weight(self, timestep):

FILE: diffsynth/schedulers/flow_match.py
  class FlowMatchScheduler (line 5) | class FlowMatchScheduler():
    method __init__ (line 7) | def __init__(self, num_inference_steps=100, num_train_timesteps=1000, ...
    method set_timesteps (line 18) | def set_timesteps(self, num_inference_steps=100, denoising_strength=1....
    method step (line 40) | def step(self, model_output, timestep, sample, to_final=False, **kwargs):
    method return_to_timestep (line 53) | def return_to_timestep(self, timestep, sample, sample_stablized):
    method add_noise (line 62) | def add_noise(self, original_samples, noise, timestep):
    method training_target (line 71) | def training_target(self, sample, noise, timestep):
    method training_weight (line 76) | def training_weight(self, timestep):

FILE: diffsynth/trainers/text_to_image.py
  class LightningModelForT2ILoRA (line 10) | class LightningModelForT2ILoRA(pl.LightningModule):
    method __init__ (line 11) | def __init__(
    method load_models (line 25) | def load_models(self):
    method freeze_parameters (line 30) | def freeze_parameters(self):
    method add_lora_to_model (line 37) | def add_lora_to_model(self, model, lora_rank=4, lora_alpha=4, lora_tar...
    method training_step (line 67) | def training_step(self, batch, batch_idx):
    method configure_optimizers (line 98) | def configure_optimizers(self):
    method on_save_checkpoint (line 104) | def on_save_checkpoint(self, checkpoint):
  function add_general_parsers (line 119) | def add_general_parsers(parser):
  function launch_training_task (line 267) | def launch_training_task(model, args):

FILE: diffsynth/vram_management/layers.py
  function cast_to (line 5) | def cast_to(weight, dtype, device):
  class AutoWrappedModule (line 11) | class AutoWrappedModule(torch.nn.Module):
    method __init__ (line 12) | def __init__(self, module: torch.nn.Module, offload_dtype, offload_dev...
    method offload (line 23) | def offload(self):
    method onload (line 28) | def onload(self):
    method forward (line 33) | def forward(self, *args, **kwargs):
  class AutoWrappedLinear (line 41) | class AutoWrappedLinear(torch.nn.Linear):
    method __init__ (line 42) | def __init__(self, module: torch.nn.Linear, offload_dtype, offload_dev...
    method offload (line 55) | def offload(self):
    method onload (line 60) | def onload(self):
    method forward (line 65) | def forward(self, x, *args, **kwargs):
  function enable_vram_management_recursively (line 74) | def enable_vram_management_recursively(model: torch.nn.Module, module_ma...
  function enable_vram_management (line 92) | def enable_vram_management(model: torch.nn.Module, module_map: dict, mod...

FILE: inference_syncammaster.py
  class Camera (line 16) | class Camera(object):
    method __init__ (line 17) | def __init__(self, c2w):
  class TextCameraDataset (line 22) | class TextCameraDataset(torch.utils.data.Dataset):
    method __init__ (line 23) | def __init__(self, base_path, metadata_path, args, max_num_frames=81, ...
    method crop_and_resize (line 44) | def crop_and_resize(self, image):
    method load_frames_using_imageio (line 55) | def load_frames_using_imageio(self, file_path, max_num_frames, start_f...
    method is_image (line 82) | def is_image(self, file_path):
    method load_video (line 89) | def load_video(self, file_path):
    method parse_matrix (line 95) | def parse_matrix(self, matrix_str):
    method get_relative_pose (line 104) | def get_relative_pose(self, cam_params):
    method __getitem__ (line 121) | def __getitem__(self, data_id):
    method __len__ (line 163) | def __len__(self):
  function parse_args (line 166) | def parse_args():

FILE: train_syncammaster.py
  class TextVideoDataset (line 21) | class TextVideoDataset(torch.utils.data.Dataset):
    method __init__ (line 22) | def __init__(self, base_path, metadata_path, max_num_frames=81, frame_...
    method crop_and_resize (line 42) | def crop_and_resize(self, image):
    method load_frames_using_imageio (line 53) | def load_frames_using_imageio(self, file_path, max_num_frames, start_f...
    method load_video (line 80) | def load_video(self, file_path):
    method is_image (line 86) | def is_image(self, file_path):
    method load_image (line 93) | def load_image(self, file_path):
    method __getitem__ (line 102) | def __getitem__(self, data_id):
    method __len__ (line 124) | def __len__(self):
  class LightningModelForDataProcess (line 129) | class LightningModelForDataProcess(pl.LightningModule):
    method __init__ (line 130) | def __init__(self, text_encoder_path, vae_path, image_encoder_path=Non...
    method test_step (line 141) | def test_step(self, batch, batch_idx):
  class Camera (line 165) | class Camera(object):
    method __init__ (line 166) | def __init__(self, c2w):
  class TensorDataset (line 173) | class TensorDataset(torch.utils.data.Dataset):
    method __init__ (line 174) | def __init__(self, base_path, metadata_path, steps_per_epoch):
    method parse_matrix (line 184) | def parse_matrix(self, matrix_str):
    method get_relative_pose (line 193) | def get_relative_pose(self, cam_params):
    method __getitem__ (line 208) | def __getitem__(self, index):
    method __len__ (line 266) | def __len__(self):
  class LightningModelForTrain (line 271) | class LightningModelForTrain(pl.LightningModule):
    method __init__ (line 272) | def __init__(
    method freeze_parameters (line 327) | def freeze_parameters(self):
    method training_step (line 334) | def training_step(self, batch, batch_idx):
    method configure_optimizers (line 373) | def configure_optimizers(self):
    method on_save_checkpoint (line 379) | def on_save_checkpoint(self, checkpoint):
  function parse_args (line 393) | def parse_args():
  function data_process (line 561) | def data_process(args):
  function train (line 594) | def train(args):

FILE: vis_cam.py
  class CameraPoseVisualizer (line 9) | class CameraPoseVisualizer:
    method __init__ (line 10) | def __init__(self, xlim, ylim, zlim):
    method extrinsic2pyramid (line 23) | def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=9/16,...
    method customize_legend (line 41) | def customize_legend(self, list_label):
    method colorbar (line 49) | def colorbar(self, max_frame_length):
    method show (line 54) | def show(self):
  function get_args (line 60) | def get_args():
  function get_c2w (line 74) | def get_c2w(w2cs, transform_matrix, relative_c2w=True):
  function parse_matrix (line 89) | def parse_matrix(matrix_str):

[
  {
    "path": ".gitignore",
    "chars": 26,
    "preview": "*__pycache__\n*.ckpt\nWan-AI"
  },
  {
    "path": "README.md",
    "chars": 12225,
    "preview": "# SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints\n\n<div align=\"center\">\n<div align=\"ce"
  },
  {
    "path": "diffsynth/__init__.py",
    "chars": 145,
    "preview": "from .data import *\nfrom .models import *\nfrom .prompters import *\nfrom .schedulers import *\nfrom .pipelines import *\nfr"
  },
  {
    "path": "diffsynth/configs/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "diffsynth/configs/model_config.py",
    "chars": 50114,
    "preview": "from typing_extensions import Literal, TypeAlias\n\nfrom ..models.sd_text_encoder import SDTextEncoder\nfrom ..models.sd_un"
  },
  {
    "path": "diffsynth/controlnets/__init__.py",
    "chars": 152,
    "preview": "from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager, FluxMultiControlNetManager\nfr"
  },
  {
    "path": "diffsynth/controlnets/controlnet_unit.py",
    "chars": 3628,
    "preview": "import torch\nimport numpy as np\nfrom .processors import Processor_id\n\n\nclass ControlNetConfigUnit:\n    def __init__(self"
  },
  {
    "path": "diffsynth/controlnets/processors.py",
    "chars": 2952,
    "preview": "from typing_extensions import Literal, TypeAlias\n\n\nProcessor_id: TypeAlias = Literal[\n    \"canny\", \"depth\", \"softedge\", "
  },
  {
    "path": "diffsynth/data/__init__.py",
    "chars": 54,
    "preview": "from .video import VideoData, save_video, save_frames\n"
  },
  {
    "path": "diffsynth/data/simple_text_image.py",
    "chars": 1775,
    "preview": "import torch, os, torchvision\nfrom torchvision import transforms\nimport pandas as pd\nfrom PIL import Image\n\n\n\nclass Text"
  },
  {
    "path": "diffsynth/data/video.py",
    "chars": 4703,
    "preview": "import imageio, os\nimport numpy as np\nfrom PIL import Image\nfrom tqdm import tqdm\n\n\nclass LowMemoryVideo:\n    def __init"
  },
  {
    "path": "diffsynth/extensions/ESRGAN/__init__.py",
    "chars": 5096,
    "preview": "import torch\nfrom einops import repeat\nfrom PIL import Image\nimport numpy as np\n\n\nclass ResidualDenseBlock(torch.nn.Modu"
  },
  {
    "path": "diffsynth/extensions/FastBlend/__init__.py",
    "chars": 2823,
    "preview": "from .runners.fast import TableManager, PyramidPatchMatcher\nfrom PIL import Image\nimport numpy as np\nimport cupy as cp\n\n"
  },
  {
    "path": "diffsynth/extensions/FastBlend/api.py",
    "chars": 20207,
    "preview": "from .runners import AccurateModeRunner, FastModeRunner, BalancedModeRunner, InterpolationModeRunner, InterpolationModeS"
  },
  {
    "path": "diffsynth/extensions/FastBlend/cupy_kernels.py",
    "chars": 4430,
    "preview": "import cupy as cp\n\nremapping_kernel = cp.RawKernel(r'''\nextern \"C\" __global__\nvoid remap(\n    const int height,\n    cons"
  },
  {
    "path": "diffsynth/extensions/FastBlend/data.py",
    "chars": 4094,
    "preview": "import imageio, os\nimport numpy as np\nfrom PIL import Image\n\n\ndef read_video(file_name):\n    reader = imageio.get_reader"
  },
  {
    "path": "diffsynth/extensions/FastBlend/patch_match.py",
    "chars": 13837,
    "preview": "from .cupy_kernels import remapping_kernel, patch_error_kernel, pairwise_patch_error_kernel\nimport numpy as np\nimport cu"
  },
  {
    "path": "diffsynth/extensions/FastBlend/runners/__init__.py",
    "chars": 202,
    "preview": "from .accurate import AccurateModeRunner\nfrom .fast import FastModeRunner\nfrom .balanced import BalancedModeRunner\nfrom "
  },
  {
    "path": "diffsynth/extensions/FastBlend/runners/accurate.py",
    "chars": 1531,
    "preview": "from ..patch_match import PyramidPatchMatcher\nimport os\nimport numpy as np\nfrom PIL import Image\nfrom tqdm import tqdm\n\n"
  },
  {
    "path": "diffsynth/extensions/FastBlend/runners/balanced.py",
    "chars": 2142,
    "preview": "from ..patch_match import PyramidPatchMatcher\nimport os\nimport numpy as np\nfrom PIL import Image\nfrom tqdm import tqdm\n\n"
  },
  {
    "path": "diffsynth/extensions/FastBlend/runners/fast.py",
    "chars": 6648,
    "preview": "from ..patch_match import PyramidPatchMatcher\nimport functools, os\nimport numpy as np\nfrom PIL import Image\nfrom tqdm im"
  },
  {
    "path": "diffsynth/extensions/FastBlend/runners/interpolation.py",
    "chars": 5286,
    "preview": "from ..patch_match import PyramidPatchMatcher\nimport os\nimport numpy as np\nfrom PIL import Image\nfrom tqdm import tqdm\n\n"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py",
    "chars": 29,
    "preview": "from .blip_pretrain import *\n"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/BLIP/blip.py",
    "chars": 3425,
    "preview": "'''\n * Adapted from BLIP (https://github.com/salesforce/BLIP)\n'''\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nim"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py",
    "chars": 1555,
    "preview": "'''\n * Adapted from BLIP (https://github.com/salesforce/BLIP)\n'''\n\nimport transformers\ntransformers.logging.set_verbosit"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/BLIP/med.py",
    "chars": 41498,
    "preview": "'''\n * Adapted from BLIP (https://github.com/salesforce/BLIP)\n * Based on huggingface code base\n * https://github.com/hu"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/BLIP/vit.py",
    "chars": 14069,
    "preview": "'''\n * Adapted from BLIP (https://github.com/salesforce/BLIP)\n * Based on timm code base\n * https://github.com/rwightman"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/__init__.py",
    "chars": 6126,
    "preview": "from modelscope import snapshot_download\nfrom typing_extensions import Literal, TypeAlias\nimport os\nfrom diffsynth.exten"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/aesthetic.py",
    "chars": 5452,
    "preview": "from typing import List, Optional\nfrom PIL import Image\nimport torch\nfrom transformers import AutoProcessor, AutoModel\nf"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/clip.py",
    "chars": 3849,
    "preview": "from typing import List, Union\nfrom PIL import Image\nimport torch\nfrom .open_clip import create_model_and_transforms, ge"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/config.py",
    "chars": 1060,
    "preview": "import os\n\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\nproject_root = os.path.abspath(os.path.join(current_"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/hps.py",
    "chars": 4777,
    "preview": "from typing import List, Union\nfrom PIL import Image\nimport torch\nfrom .open_clip import create_model_and_transforms, ge"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/imagereward.py",
    "chars": 8729,
    "preview": "import os\nimport torch\nfrom PIL import Image\nfrom typing import List, Union\nfrom torchvision.transforms import Compose, "
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/mps.py",
    "chars": 5952,
    "preview": "import numpy as np\nimport torch\nfrom PIL import Image\nfrom io import BytesIO\nfrom tqdm.auto import tqdm\nfrom transformer"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py",
    "chars": 985,
    "preview": "from .coca_model import CoCa\nfrom .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD\nfrom .factory import create_"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py",
    "chars": 17439,
    "preview": "from typing import Optional\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nimport numpy as np\nf"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/constants.py",
    "chars": 116,
    "preview": "OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)\nOPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)\n"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/factory.py",
    "chars": 15868,
    "preview": "import json\nimport logging\nimport os\nimport pathlib\nimport re\nfrom copy import deepcopy\nfrom pathlib import Path\n# from "
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/generation_utils.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py",
    "chars": 1675,
    "preview": "# HF architecture dict:\narch_dict = {\n    # https://huggingface.co/docs/transformers/model_doc/roberta#roberta\n    \"robe"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py",
    "chars": 6298,
    "preview": "\"\"\" huggingface model adapter\n\nWraps HuggingFace transformers (https://github.com/huggingface/transformers) models for u"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/loss.py",
    "chars": 10711,
    "preview": "import torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\nfrom torch.nn.utils.rnn import pad_sequence\n\ntry"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/model.py",
    "chars": 18298,
    "preview": "\"\"\" CLIP Model\n\nAdapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.\n\"\"\"\nfrom"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json",
    "chars": 324,
    "preview": "{\n    \"embed_dim\": 1024,\n    \"vision_cfg\": {\n        \"image_size\": 224,\n        \"layers\": 32,\n        \"width\": 1280,\n   "
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py",
    "chars": 7026,
    "preview": "from collections import OrderedDict\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom .utils"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/openai.py",
    "chars": 5446,
    "preview": "\"\"\" OpenAI pretrained model functions\n\nAdapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py",
    "chars": 14144,
    "preview": "import hashlib\nimport os\nimport urllib\nimport warnings\nfrom functools import partial\nfrom typing import Dict, Union\n\nfro"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py",
    "chars": 7660,
    "preview": "import argparse\nimport json\nfrom pathlib import Path\nfrom tempfile import TemporaryDirectory\nfrom typing import Optional"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py",
    "chars": 5077,
    "preview": "\"\"\" timm model adapter\n\nWraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower "
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py",
    "chars": 7541,
    "preview": "\"\"\" CLIP tokenizer\n\nCopied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.\n\"\"\"\ni"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/transform.py",
    "chars": 7874,
    "preview": "import warnings\nfrom dataclasses import dataclass, asdict\nfrom typing import Any, Dict, Optional, Sequence, Tuple, Union"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py",
    "chars": 27164,
    "preview": "from collections import OrderedDict\nimport math\nfrom typing import Callable, Optional, Sequence, Tuple\n\nimport torch\nfro"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/utils.py",
    "chars": 2223,
    "preview": "from itertools import repeat\nimport collections.abc\n\nfrom torch import nn as nn\nfrom torchvision.ops.misc import FrozenB"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/open_clip/version.py",
    "chars": 23,
    "preview": "__version__ = '2.16.0'\n"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/pickscore.py",
    "chars": 4513,
    "preview": "import torch\nfrom PIL import Image\nfrom transformers import AutoProcessor, AutoModel\nfrom typing import List, Union\nimpo"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/trainer/__init__.py",
    "chars": 21,
    "preview": "from .models import *"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/trainer/models/__init__.py",
    "chars": 81,
    "preview": "from .base_model import *\nfrom .clip_model import *\nfrom .cross_modeling import *"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py",
    "chars": 80,
    "preview": "from dataclasses import dataclass\n\n\n\n@dataclass\nclass BaseModelConfig:\n    pass\n"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/trainer/models/clip_model.py",
    "chars": 5453,
    "preview": "from dataclasses import dataclass\nfrom transformers import CLIPModel as HFCLIPModel\nfrom transformers import AutoTokeniz"
  },
  {
    "path": "diffsynth/extensions/ImageQualityMetric/trainer/models/cross_modeling.py",
    "chars": 7956,
    "preview": "import torch\nfrom torch import einsum, nn\nimport torch.nn.functional as F\nfrom einops import rearrange, repeat\n\n# helper"
  },
  {
    "path": "diffsynth/extensions/RIFE/__init__.py",
    "chars": 10736,
    "preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom PIL import Image\n\n\ndef warp(t"
  },
  {
    "path": "diffsynth/extensions/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "diffsynth/models/__init__.py",
    "chars": 29,
    "preview": "from .model_manager import *\n"
  },
  {
    "path": "diffsynth/models/attention.py",
    "chars": 3957,
    "preview": "import torch\nfrom einops import rearrange\n\n\ndef low_version_attention(query, key, value, attn_bias=None):\n    scale = 1 "
  },
  {
    "path": "diffsynth/models/cog_dit.py",
    "chars": 17475,
    "preview": "import torch\nfrom einops import rearrange, repeat\nfrom .sd3_dit import TimestepEmbeddings\nfrom .attention import Attenti"
  },
  {
    "path": "diffsynth/models/cog_vae.py",
    "chars": 23126,
    "preview": "import torch\nfrom einops import rearrange, repeat\nfrom .tiler import TileWorker2Dto3D\n\n\n\nclass Downsample3D(torch.nn.Mod"
  },
  {
    "path": "diffsynth/models/downloader.py",
    "chars": 4763,
    "preview": "from huggingface_hub import hf_hub_download\nfrom modelscope import snapshot_download\nimport os, shutil\nfrom typing_exten"
  },
  {
    "path": "diffsynth/models/flux_controlnet.py",
    "chars": 16429,
    "preview": "import torch\nfrom einops import rearrange, repeat\nfrom .flux_dit import RoPEEmbedding, TimestepEmbeddings, FluxJointTran"
  },
  {
    "path": "diffsynth/models/flux_dit.py",
    "chars": 33956,
    "preview": "import torch\nfrom .sd3_dit import TimestepEmbeddings, AdaLayerNorm, RMSNorm\nfrom einops import rearrange\nfrom .tiler imp"
  },
  {
    "path": "diffsynth/models/flux_ipadapter.py",
    "chars": 3697,
    "preview": "from .svd_image_encoder import SVDImageEncoder\nfrom .sd3_dit import RMSNorm\nfrom transformers import CLIPImageProcessor\n"
  },
  {
    "path": "diffsynth/models/flux_text_encoder.py",
    "chars": 775,
    "preview": "import torch\nfrom transformers import T5EncoderModel, T5Config\nfrom .sd_text_encoder import SDTextEncoder\n\n\n\nclass FluxT"
  },
  {
    "path": "diffsynth/models/flux_vae.py",
    "chars": 19658,
    "preview": "from .sd3_vae_encoder import SD3VAEEncoder, SDVAEEncoderStateDictConverter\nfrom .sd3_vae_decoder import SD3VAEDecoder, S"
  },
  {
    "path": "diffsynth/models/hunyuan_dit.py",
    "chars": 20154,
    "preview": "from .attention import Attention\nfrom einops import repeat, rearrange\nimport math\nimport torch\n\n\nclass HunyuanDiTRotaryE"
  },
  {
    "path": "diffsynth/models/hunyuan_dit_text_encoder.py",
    "chars": 5565,
    "preview": "from transformers import BertModel, BertConfig, T5EncoderModel, T5Config\nimport torch\n\n\n\nclass HunyuanDiTCLIPTextEncoder"
  },
  {
    "path": "diffsynth/models/hunyuan_video_dit.py",
    "chars": 42030,
    "preview": "import torch\nfrom .sd3_dit import TimestepEmbeddings, RMSNorm\nfrom .utils import init_weights_on_device\nfrom einops impo"
  },
  {
    "path": "diffsynth/models/hunyuan_video_text_encoder.py",
    "chars": 2715,
    "preview": "from transformers import LlamaModel, LlamaConfig, DynamicCache, LlavaForConditionalGeneration\nfrom copy import deepcopy\n"
  },
  {
    "path": "diffsynth/models/hunyuan_video_vae_decoder.py",
    "chars": 19320,
    "preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\nimport numpy as np\nfrom "
  },
  {
    "path": "diffsynth/models/hunyuan_video_vae_encoder.py",
    "chars": 11066,
    "preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange, repeat\nimport numpy as "
  },
  {
    "path": "diffsynth/models/kolors_text_encoder.py",
    "chars": 72734,
    "preview": "\"\"\"\nThis model is copied from https://github.com/Kwai-Kolors/Kolors/tree/master/kolors/models.\nWe didn't modify this mod"
  },
  {
    "path": "diffsynth/models/lora.py",
    "chars": 18187,
    "preview": "import torch\nfrom .sd_unet import SDUNet\nfrom .sdxl_unet import SDXLUNet\nfrom .sd_text_encoder import SDTextEncoder\nfrom"
  },
  {
    "path": "diffsynth/models/model_manager.py",
    "chars": 20790,
    "preview": "import os, torch, json, importlib\nfrom typing import List\n\nfrom .downloader import download_models, download_customized_"
  },
  {
    "path": "diffsynth/models/omnigen.py",
    "chars": 33069,
    "preview": "# The code is revised from DiT\nimport os\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport math\nfrom safetens"
  },
  {
    "path": "diffsynth/models/sd3_dit.py",
    "chars": 27845,
    "preview": "import torch\nfrom einops import rearrange\nfrom .svd_unet import TemporalTimesteps\nfrom .tiler import TileWorker\n\n\n\nclass"
  },
  {
    "path": "diffsynth/models/sd3_text_encoder.py",
    "chars": 110544,
    "preview": "import torch\nfrom transformers import T5EncoderModel, T5Config\nfrom .sd_text_encoder import SDTextEncoder\nfrom .sdxl_tex"
  },
  {
    "path": "diffsynth/models/sd3_vae_decoder.py",
    "chars": 3006,
    "preview": "import torch\nfrom .sd_vae_decoder import VAEAttentionBlock, SDVAEDecoderStateDictConverter\nfrom .sd_unet import ResnetBl"
  },
  {
    "path": "diffsynth/models/sd3_vae_encoder.py",
    "chars": 3599,
    "preview": "import torch\nfrom .sd_unet import ResnetBlock, DownSampler\nfrom .sd_vae_encoder import VAEAttentionBlock, SDVAEEncoderSt"
  },
  {
    "path": "diffsynth/models/sd_controlnet.py",
    "chars": 48266,
    "preview": "import torch\nfrom .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler\nfrom .tiler import Tile"
  },
  {
    "path": "diffsynth/models/sd_ipadapter.py",
    "chars": 2362,
    "preview": "from .svd_image_encoder import SVDImageEncoder\nfrom .sdxl_ipadapter import IpAdapterImageProjModel, IpAdapterModule, SDX"
  },
  {
    "path": "diffsynth/models/sd_motion.py",
    "chars": 8323,
    "preview": "from .sd_unet import SDUNet, Attention, GEGLU\nimport torch\nfrom einops import rearrange, repeat\n\n\nclass TemporalTransfor"
  },
  {
    "path": "diffsynth/models/sd_text_encoder.py",
    "chars": 28750,
    "preview": "import torch\nfrom .attention import Attention\n\n\nclass CLIPEncoderLayer(torch.nn.Module):\n    def __init__(self, embed_di"
  },
  {
    "path": "diffsynth/models/sd_unet.py",
    "chars": 99140,
    "preview": "import torch, math\nfrom .attention import Attention\nfrom .tiler import TileWorker\n\n\nclass Timesteps(torch.nn.Module):\n  "
  },
  {
    "path": "diffsynth/models/sd_vae_decoder.py",
    "chars": 20845,
    "preview": "import torch\nfrom .attention import Attention\nfrom .sd_unet import ResnetBlock, UpSampler\nfrom .tiler import TileWorker\n"
  },
  {
    "path": "diffsynth/models/sd_vae_encoder.py",
    "chars": 17272,
    "preview": "import torch\nfrom .sd_unet import ResnetBlock, DownSampler\nfrom .sd_vae_decoder import VAEAttentionBlock\nfrom .tiler imp"
  },
  {
    "path": "diffsynth/models/sdxl_controlnet.py",
    "chars": 13942,
    "preview": "import torch\nfrom .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler\nfrom .sdxl_unet import "
  },
  {
    "path": "diffsynth/models/sdxl_ipadapter.py",
    "chars": 4918,
    "preview": "from .svd_image_encoder import SVDImageEncoder\nfrom transformers import CLIPImageProcessor\nimport torch\n\n\nclass IpAdapte"
  },
  {
    "path": "diffsynth/models/sdxl_motion.py",
    "chars": 4209,
    "preview": "from .sd_motion import TemporalBlock\nimport torch\n\n\n\nclass SDXLMotionModel(torch.nn.Module):\n    def __init__(self):\n   "
  },
  {
    "path": "diffsynth/models/sdxl_text_encoder.py",
    "chars": 79460,
    "preview": "import torch\nfrom .sd_text_encoder import CLIPEncoderLayer\n    \n\nclass SDXLTextEncoder(torch.nn.Module):\n    def __init_"
  },
  {
    "path": "diffsynth/models/sdxl_unet.py",
    "chars": 234786,
    "preview": "import torch\nfrom .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler\n\n\n"
  },
  {
    "path": "diffsynth/models/sdxl_vae_decoder.py",
    "chars": 762,
    "preview": "from .sd_vae_decoder import SDVAEDecoder, SDVAEDecoderStateDictConverter\n\n\nclass SDXLVAEDecoder(SDVAEDecoder):\n    def _"
  },
  {
    "path": "diffsynth/models/sdxl_vae_encoder.py",
    "chars": 762,
    "preview": "from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder\n\n\nclass SDXLVAEEncoder(SDVAEEncoder):\n    def _"
  },
  {
    "path": "diffsynth/models/stepvideo_dit.py",
    "chars": 33689,
    "preview": "# Copyright 2025 StepFun Inc. All Rights Reserved.\n# \n# Permission is hereby granted, free of charge, to any person obta"
  },
  {
    "path": "diffsynth/models/stepvideo_text_encoder.py",
    "chars": 19326,
    "preview": "# Copyright 2025 StepFun Inc. All Rights Reserved.\n# \n# Permission is hereby granted, free of charge, to any person obta"
  },
  {
    "path": "diffsynth/models/stepvideo_vae.py",
    "chars": 42397,
    "preview": "# Copyright 2025 StepFun Inc. All Rights Reserved.\n# \n# Permission is hereby granted, free of charge, to any person obta"
  },
  {
    "path": "diffsynth/models/svd_image_encoder.py",
    "chars": 60869,
    "preview": "import torch\nfrom .sd_text_encoder import CLIPEncoderLayer\n\n\nclass CLIPVisionEmbeddings(torch.nn.Module):\n    def __init"
  },
  {
    "path": "diffsynth/models/svd_unet.py",
    "chars": 200698,
    "preview": "import torch, math\nfrom einops import rearrange, repeat\nfrom .sd_unet import Timesteps, PushBlock, PopBlock, Attention, "
  },
  {
    "path": "diffsynth/models/svd_vae_decoder.py",
    "chars": 40309,
    "preview": "import torch\nfrom .attention import Attention\nfrom .sd_unet import ResnetBlock, UpSampler\nfrom .tiler import TileWorker\n"
  },
  {
    "path": "diffsynth/models/svd_vae_encoder.py",
    "chars": 12377,
    "preview": "from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder\n\n\nclass SVDVAEEncoder(SDVAEEncoder):\n    def __"
  },
  {
    "path": "diffsynth/models/tiler.py",
    "chars": 9732,
    "preview": "import torch\nfrom einops import rearrange, repeat\n\n\nclass TileWorker:\n    def __init__(self):\n        pass\n\n\n    def mas"
  },
  {
    "path": "diffsynth/models/utils.py",
    "chars": 7093,
    "preview": "import torch, os\nfrom safetensors import safe_open\nfrom contextlib import contextmanager\nimport hashlib\n\n@contextmanager"
  },
  {
    "path": "diffsynth/models/wan_video_dit.py",
    "chars": 21173,
    "preview": "import re\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nfrom typing import Tuple, Optio"
  },
  {
    "path": "diffsynth/models/wan_video_image_encoder.py",
    "chars": 28139,
    "preview": "\"\"\"\nConcise re-implementation of\n``https://github.com/openai/CLIP'' and\n``https://github.com/mlfoundations/open_clip''.\n"
  },
  {
    "path": "diffsynth/models/wan_video_text_encoder.py",
    "chars": 9131,
    "preview": "import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef fp16_clamp(x):\n    if x.dtype == t"
  },
  {
    "path": "diffsynth/models/wan_video_vae.py",
    "chars": 29483,
    "preview": "from einops import rearrange, repeat\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom tqdm impor"
  },
  {
    "path": "diffsynth/pipelines/__init__.py",
    "chars": 707,
    "preview": "from .sd_image import SDImagePipeline\nfrom .sd_video import SDVideoPipeline\nfrom .sdxl_image import SDXLImagePipeline\nfr"
  },
  {
    "path": "diffsynth/pipelines/base.py",
    "chars": 5770,
    "preview": "import torch\nimport numpy as np\nfrom PIL import Image\nfrom torchvision.transforms import GaussianBlur\n\n\n\nclass BasePipel"
  },
  {
    "path": "diffsynth/pipelines/cog_video.py",
    "chars": 5313,
    "preview": "from ..models import ModelManager, FluxTextEncoder2, CogDiT, CogVAEEncoder, CogVAEDecoder\nfrom ..prompters import CogPro"
  },
  {
    "path": "diffsynth/pipelines/dancer.py",
    "chars": 10179,
    "preview": "import torch\nfrom ..models import SDUNet, SDMotionModel, SDXLUNet, SDXLMotionModel\nfrom ..models.sd_unet import PushBloc"
  },
  {
    "path": "diffsynth/pipelines/flux_image.py",
    "chars": 29090,
    "preview": "from ..models import ModelManager, FluxDiT, SD3TextEncoder1, FluxTextEncoder2, FluxVAEDecoder, FluxVAEEncoder, FluxIpAda"
  },
  {
    "path": "diffsynth/pipelines/hunyuan_image.py",
    "chars": 11919,
    "preview": "from ..models.hunyuan_dit import HunyuanDiT\nfrom ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, Hun"
  },
  {
    "path": "diffsynth/pipelines/hunyuan_video.py",
    "chars": 17733,
    "preview": "from ..models import ModelManager, SD3TextEncoder1, HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder\nfrom ..models.hunyuan"
  },
  {
    "path": "diffsynth/pipelines/omnigen_image.py",
    "chars": 13049,
    "preview": "from ..models.omnigen import OmniGenTransformer\nfrom ..models.sdxl_vae_encoder import SDXLVAEEncoder\nfrom ..models.sdxl_"
  },
  {
    "path": "diffsynth/pipelines/pipeline_runner.py",
    "chars": 5185,
    "preview": "import os, torch, json\nfrom .sd_video import ModelManager, SDVideoPipeline, ControlNetConfigUnit\nfrom ..processors.seque"
  },
  {
    "path": "diffsynth/pipelines/sd3_image.py",
    "chars": 6291,
    "preview": "from ..models import ModelManager, SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEDecoder, SD3VAEEncod"
  },
  {
    "path": "diffsynth/pipelines/sd_image.py",
    "chars": 8501,
    "preview": "from ..models import SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder\nfrom ..m"
  },
  {
    "path": "diffsynth/pipelines/sd_video.py",
    "chars": 12000,
    "preview": "from ..models import SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder, SDMotio"
  },
  {
    "path": "diffsynth/pipelines/sdxl_image.py",
    "chars": 10335,
    "preview": "from ..models import SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapt"
  },
  {
    "path": "diffsynth/pipelines/sdxl_video.py",
    "chars": 10534,
    "preview": "from ..models import SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapt"
  },
  {
    "path": "diffsynth/pipelines/step_video.py",
    "chars": 8494,
    "preview": "from ..models import ModelManager\nfrom ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder\nfrom ..models."
  },
  {
    "path": "diffsynth/pipelines/svd_video.py",
    "chars": 11418,
    "preview": "from ..models import ModelManager, SVDImageEncoder, SVDUNet, SVDVAEEncoder, SVDVAEDecoder\nfrom ..schedulers import Conti"
  },
  {
    "path": "diffsynth/pipelines/wan_video.py",
    "chars": 16001,
    "preview": "from ..models import ModelManager\nfrom ..models.wan_video_dit import WanModel\nfrom ..models.wan_video_text_encoder impor"
  },
  {
    "path": "diffsynth/pipelines/wan_video_syncammaster.py",
    "chars": 16924,
    "preview": "from ..models import ModelManager\nfrom ..models.wan_video_dit import WanModel\nfrom ..models.wan_video_text_encoder impor"
  },
  {
    "path": "diffsynth/processors/FastBlend.py",
    "chars": 7027,
    "preview": "from PIL import Image\nimport cupy as cp\nimport numpy as np\nfrom tqdm import tqdm\nfrom ..extensions.FastBlend.patch_match"
  },
  {
    "path": "diffsynth/processors/PILEditor.py",
    "chars": 855,
    "preview": "from PIL import ImageEnhance\nfrom .base import VideoProcessor\n\n\nclass ContrastEditor(VideoProcessor):\n    def __init__(s"
  },
  {
    "path": "diffsynth/processors/RIFE.py",
    "chars": 3118,
    "preview": "import torch\nimport numpy as np\nfrom PIL import Image\nfrom .base import VideoProcessor\n\n\nclass RIFESmoother(VideoProcess"
  },
  {
    "path": "diffsynth/processors/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "diffsynth/processors/base.py",
    "chars": 118,
    "preview": "class VideoProcessor:\n    def __init__(self):\n        pass\n\n    def __call__(self):\n        raise NotImplementedError\n"
  },
  {
    "path": "diffsynth/processors/sequencial_processor.py",
    "chars": 1582,
    "preview": "from .base import VideoProcessor\n\n\nclass AutoVideoProcessor(VideoProcessor):\n    def __init__(self):\n        pass\n\n    @"
  },
  {
    "path": "diffsynth/prompters/__init__.py",
    "chars": 535,
    "preview": "from .prompt_refiners import Translator, BeautifulPrompt, QwenPrompt\nfrom .sd_prompter import SDPrompter\nfrom .sdxl_prom"
  },
  {
    "path": "diffsynth/prompters/base_prompter.py",
    "chars": 2234,
    "preview": "from ..models.model_manager import ModelManager\nimport torch\n\n\n\ndef tokenize_long_prompt(tokenizer, prompt, max_length=N"
  },
  {
    "path": "diffsynth/prompters/cog_prompter.py",
    "chars": 1474,
    "preview": "from .base_prompter import BasePrompter\nfrom ..models.flux_text_encoder import FluxTextEncoder2\nfrom transformers import"
  },
  {
    "path": "diffsynth/prompters/flux_prompter.py",
    "chars": 2703,
    "preview": "from .base_prompter import BasePrompter\nfrom ..models.flux_text_encoder import FluxTextEncoder2\nfrom ..models.sd3_text_e"
  },
  {
    "path": "diffsynth/prompters/hunyuan_dit_prompter.py",
    "chars": 2743,
    "preview": "from .base_prompter import BasePrompter\nfrom ..models.model_manager import ModelManager\nfrom ..models import HunyuanDiTC"
  },
  {
    "path": "diffsynth/prompters/hunyuan_video_prompter.py",
    "chars": 13574,
    "preview": "from .base_prompter import BasePrompter\nfrom ..models.sd3_text_encoder import SD3TextEncoder1\nfrom ..models.hunyuan_vide"
  },
  {
    "path": "diffsynth/prompters/kolors_prompter.py",
    "chars": 14110,
    "preview": "from .base_prompter import BasePrompter\nfrom ..models.model_manager import ModelManager\nimport json, os, re\nfrom typing "
  },
  {
    "path": "diffsynth/prompters/omnigen_prompter.py",
    "chars": 15398,
    "preview": "import os\nimport re\nfrom typing import Dict, List\n\nimport torch\nfrom PIL import Image\nfrom torchvision import transforms"
  },
  {
    "path": "diffsynth/prompters/omost.py",
    "chars": 15938,
    "preview": "from transformers import AutoTokenizer, TextIteratorStreamer\nimport difflib\nimport torch\nimport numpy as np\nimport re\nfr"
  },
  {
    "path": "diffsynth/prompters/prompt_refiners.py",
    "chars": 6568,
    "preview": "from transformers import AutoTokenizer\nfrom ..models.model_manager import ModelManager\nimport torch\nfrom .omost import O"
  },
  {
    "path": "diffsynth/prompters/sd3_prompter.py",
    "chars": 3901,
    "preview": "from .base_prompter import BasePrompter\nfrom ..models.model_manager import ModelManager\nfrom ..models import SD3TextEnco"
  },
  {
    "path": "diffsynth/prompters/sd_prompter.py",
    "chars": 3461,
    "preview": "from .base_prompter import BasePrompter, tokenize_long_prompt\nfrom ..models.utils import load_state_dict, search_for_emb"
  },
  {
    "path": "diffsynth/prompters/sdxl_prompter.py",
    "chars": 2451,
    "preview": "from .base_prompter import BasePrompter, tokenize_long_prompt\nfrom ..models.model_manager import ModelManager\nfrom ..mod"
  },
  {
    "path": "diffsynth/prompters/stepvideo_prompter.py",
    "chars": 2086,
    "preview": "from .base_prompter import BasePrompter\nfrom ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder\nfrom ..m"
  },
  {
    "path": "diffsynth/prompters/wan_prompter.py",
    "chars": 3477,
    "preview": "from .base_prompter import BasePrompter\nfrom ..models.wan_video_text_encoder import WanTextEncoder\nfrom transformers imp"
  },
  {
    "path": "diffsynth/schedulers/__init__.py",
    "chars": 134,
    "preview": "from .ddim import EnhancedDDIMScheduler\nfrom .continuous_ode import ContinuousODEScheduler\nfrom .flow_match import FlowM"
  },
  {
    "path": "diffsynth/schedulers/continuous_ode.py",
    "chars": 2462,
    "preview": "import torch\n\n\nclass ContinuousODEScheduler():\n\n    def __init__(self, num_inference_steps=100, sigma_max=700.0, sigma_m"
  },
  {
    "path": "diffsynth/schedulers/ddim.py",
    "chars": 5087,
    "preview": "import torch, math\n\n\nclass EnhancedDDIMScheduler():\n\n    def __init__(self, num_train_timesteps=1000, beta_start=0.00085"
  },
  {
    "path": "diffsynth/schedulers/flow_match.py",
    "chars": 3347,
    "preview": "import torch\n\n\n\nclass FlowMatchScheduler():\n\n    def __init__(self, num_inference_steps=100, num_train_timesteps=1000, s"
  },
  {
    "path": "diffsynth/tokenizer_configs/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "diffsynth/tokenizer_configs/cog/tokenizer/added_tokens.json",
    "chars": 2593,
    "preview": "{\n  \"<extra_id_0>\": 32099,\n  \"<extra_id_10>\": 32089,\n  \"<extra_id_11>\": 32088,\n  \"<extra_id_12>\": 32087,\n  \"<extra_id_13"
  },
  {
    "path": "diffsynth/tokenizer_configs/cog/tokenizer/special_tokens_map.json",
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]

// ... and 22 more files (download for full content)