cleanup

.gitignore

@@ -0,0 +1,2 @@
+*.pyc
+__pycache__

mochi_preview/t2v_synth_mochi.py

@@ -1,78 +1,22 @@
 import json
-import os
 import random
-from functools import partial
 from typing import Dict, List
 
 from safetensors.torch import load_file
 import numpy as np
 import torch
-import torch.distributed as dist
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.utils.data
-import yaml
-from einops import rearrange, repeat
-from omegaconf import OmegaConf
 from torch import nn
-from torch.distributed.fsdp import (
-    BackwardPrefetch,
-    MixedPrecision,
-    ShardingStrategy,
-)
-from torch.distributed.fsdp import (
-    FullyShardedDataParallel as FSDP,
-)
-from torch.distributed.fsdp.wrap import (
-    lambda_auto_wrap_policy,
-    transformer_auto_wrap_policy,
-)
-from transformers import T5EncoderModel, T5Tokenizer
-from transformers.models.t5.modeling_t5 import T5Block
 
 from .dit.joint_model.context_parallel import get_cp_rank_size
 from .utils import Timer
 from tqdm import tqdm
 from comfy.utils import ProgressBar
 
-T5_MODEL = "weights/T5"
 MAX_T5_TOKEN_LENGTH = 256
 
-class T5_Tokenizer:
-    """Wrapper around Hugging Face tokenizer for T5
-
-    Args:
-        model_name(str): Name of tokenizer to load.
-    """
-
-    def __init__(self):
-        self.tokenizer = T5Tokenizer.from_pretrained(T5_MODEL, legacy=False)
-
-    def __call__(self, prompt, padding, truncation, return_tensors, max_length=None):
-        """
-        Args:
-            prompt (str): The input text to tokenize.
-            padding (str): The padding strategy.
-            truncation (bool): Flag indicating whether to truncate the tokens.
-            return_tensors (str): Flag indicating whether to return tensors.
-            max_length (int): The max length of the tokens.
-        """
-        assert (
-            not max_length or max_length == MAX_T5_TOKEN_LENGTH
-        ), f"Max length must be {MAX_T5_TOKEN_LENGTH} for T5."
-
-        tokenized_output = self.tokenizer(
-            prompt,
-            padding=padding,
-            max_length=MAX_T5_TOKEN_LENGTH,  # Max token length for T5 is set here.
-            truncation=truncation,
-            return_tensors=return_tensors,
-            return_attention_mask=True,
-        )
-
-        return tokenized_output
-
-
 def unnormalize_latents(
     z: torch.Tensor,
     mean: torch.Tensor,
@@ -93,27 +37,6 @@ def unnormalize_latents(
     assert z.size(1) == mean.size(0) == std.size(0)
     return z * std.to(z) + mean.to(z)
 
-
-def setup_fsdp_sync(model, device_id, *, param_dtype, auto_wrap_policy) -> FSDP:
-    model = FSDP(
-        model,
-        sharding_strategy=ShardingStrategy.FULL_SHARD,
-        mixed_precision=MixedPrecision(
-            param_dtype=param_dtype,
-            reduce_dtype=torch.float32,
-            buffer_dtype=torch.float32,
-        ),
-        auto_wrap_policy=auto_wrap_policy,
-        backward_prefetch=BackwardPrefetch.BACKWARD_PRE,
-        limit_all_gathers=True,
-        device_id=device_id,
-        sync_module_states=True,
-        use_orig_params=True,
-    )
-    torch.cuda.synchronize()
-    return model
-
-
 def compute_packed_indices(
     N: int,
     text_mask: List[torch.Tensor],
@@ -189,12 +112,6 @@ class T2VSynthMochiModel:
         t = Timer()
         self.device = torch.device(device_id)
 
-        #self.t5_tokenizer = T5_Tokenizer()
-
-        # with t("load_text_encs"):
-        #     t5_enc = T5EncoderModel.from_pretrained(T5_MODEL)
-        #     self.t5_enc = t5_enc.eval().to(torch.bfloat16).to("cpu")
-
         with t("construct_dit"):
                 from .dit.joint_model.asymm_models_joint import (
                     AsymmDiTJoint,
@@ -223,15 +140,15 @@ class T2VSynthMochiModel:
             
             model.load_state_dict(load_file(dit_checkpoint_path))
 
-        with t("fsdp_dit"):
-            self.dit = model
-            self.dit.eval()
-            for name, param in self.dit.named_parameters():
-                params_to_keep = {"t_embedder", "x_embedder", "pos_frequencies", "t5", "norm"}
-                if not any(keyword in name for keyword in params_to_keep):
-                    param.data = param.data.to(torch.float8_e4m3fn)
-                else:
-                    param.data = param.data.to(torch.bfloat16)
+        #with t("fsdp_dit"):
+        self.dit = model
+        self.dit.eval()
+        for name, param in self.dit.named_parameters():
+            params_to_keep = {"t_embedder", "x_embedder", "pos_frequencies", "t5", "norm"}
+            if not any(keyword in name for keyword in params_to_keep):
+                param.data = param.data.to(torch.float8_e4m3fn)
+            else:
+                param.data = param.data.to(torch.bfloat16)
         
 
         vae_stats = json.load(open(vae_stats_path))

nodes.py

@@ -1,17 +1,10 @@
 import os
 import torch
-import torch.nn as nn
 import folder_paths
 import comfy.model_management as mm
 from comfy.utils import ProgressBar, load_torch_file
 from einops import rearrange
-
-from contextlib import nullcontext
-
-from PIL import Image
-import numpy as np
-import json
-
+from tqdm import tqdm
 import logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 log = logging.getLogger(__name__)
@@ -295,11 +288,11 @@ class MochiDecode:
                 # Split z into overlapping tiles and decode them separately.
                 # The tiles have an overlap to avoid seams between tiles.
                 rows = []
-                for i in range(0, height, overlap_height):
+                for i in tqdm(range(0, height, overlap_height), desc="Processing rows"):
                     row = []
-                    for j in range(0, width, overlap_width):
+                    for j in tqdm(range(0, width, overlap_width), desc="Processing columns", leave=False):
                         time = []
-                        for k in range(num_frames // frame_batch_size):
+                        for k in tqdm(range(num_frames // frame_batch_size), desc="Processing frames", leave=False):
                             remaining_frames = num_frames % frame_batch_size
                             start_frame = frame_batch_size * k + (0 if k == 0 else remaining_frames)
                             end_frame = frame_batch_size * (k + 1) + remaining_frames
@@ -316,9 +309,9 @@ class MochiDecode:
                     rows.append(row)
 
                 result_rows = []
-                for i, row in enumerate(rows):
+                for i, row in enumerate(tqdm(rows, desc="Blending rows")):
                     result_row = []
-                    for j, tile in enumerate(row):
+                    for j, tile in enumerate(tqdm(row, desc="Blending tiles", leave=False)):
                         # blend the above tile and the left tile
                         # to the current tile and add the current tile to the result row
                         if i > 0: