mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-09-10 03:25:22 +00:00
fc247150fe
* Attempting a universal implementation of EasyCache, starting with flux as test; I screwed up the math a bit, but when I set it just right it works. * Fixed math to make threshold work as expected, refactored code to use EasyCacheHolder instead of a dict wrapped by object * Use sigmas from transformer_options instead of timesteps to be compatible with a greater amount of models, make end_percent work * Make log statement when not skipping useful, preparing for per-cond caching * Added DIFFUSION_MODEL wrapper around forward function for wan model * Add subsampling for heuristic inputs * Add subsampling to output_prev (output_prev_subsampled now) * Properly consider conds in EasyCache logic * Created SuperEasyCache to test what happens if caching and reuse is moved outside the scope of conds, added PREDICT_NOISE wrapper to facilitate this test * Change max reuse_threshold to 3.0 * Mark EasyCache/SuperEasyCache as experimental (beta) * Make Lumina2 compatible with EasyCache * Add EasyCache support for Qwen Image * Fix missing comma, curse you Cursor * Add EasyCache support to AceStep * Add EasyCache support to Chroma * Added EasyCache support to Cosmos Predict t2i * Make EasyCache not crash with Cosmos Predict ImagToVideo latents, but does not work well at all * Add EasyCache support to hidream * Added EasyCache support to hunyuan video * Added EasyCache support to hunyuan3d * Added EasyCache support to LTXV (not very good, but does not crash) * Implemented EasyCache for aura_flow * Renamed SuperEasyCache to LazyCache, hardcoded subsample_factor to 8 on nodes * Eatra logging when verbose is true for EasyCache
631 lines
23 KiB
Python
631 lines
23 KiB
Python
# Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py
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from __future__ import annotations
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from typing import List, Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import comfy.ldm.common_dit
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from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder
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from comfy.ldm.modules.attention import optimized_attention_masked
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from comfy.ldm.flux.layers import EmbedND
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import comfy.patcher_extension
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def modulate(x, scale):
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return x * (1 + scale.unsqueeze(1))
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#############################################################################
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# Core NextDiT Model #
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#############################################################################
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class JointAttention(nn.Module):
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"""Multi-head attention module."""
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def __init__(
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self,
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dim: int,
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n_heads: int,
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n_kv_heads: Optional[int],
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qk_norm: bool,
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operation_settings={},
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):
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"""
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Initialize the Attention module.
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Args:
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dim (int): Number of input dimensions.
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n_heads (int): Number of heads.
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n_kv_heads (Optional[int]): Number of kv heads, if using GQA.
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"""
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super().__init__()
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self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads
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self.n_local_heads = n_heads
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self.n_local_kv_heads = self.n_kv_heads
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self.n_rep = self.n_local_heads // self.n_local_kv_heads
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self.head_dim = dim // n_heads
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self.qkv = operation_settings.get("operations").Linear(
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dim,
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(n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim,
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bias=False,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.out = operation_settings.get("operations").Linear(
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n_heads * self.head_dim,
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dim,
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bias=False,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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if qk_norm:
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self.q_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.k_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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else:
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self.q_norm = self.k_norm = nn.Identity()
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@staticmethod
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def apply_rotary_emb(
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x_in: torch.Tensor,
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freqs_cis: torch.Tensor,
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) -> torch.Tensor:
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"""
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Apply rotary embeddings to input tensors using the given frequency
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tensor.
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This function applies rotary embeddings to the given query 'xq' and
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key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The
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input tensors are reshaped as complex numbers, and the frequency tensor
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is reshaped for broadcasting compatibility. The resulting tensors
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contain rotary embeddings and are returned as real tensors.
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Args:
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x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings.
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freqs_cis (torch.Tensor): Precomputed frequency tensor for complex
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exponentials.
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Returns:
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Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor
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and key tensor with rotary embeddings.
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"""
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t_ = x_in.reshape(*x_in.shape[:-1], -1, 1, 2)
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t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
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return t_out.reshape(*x_in.shape)
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def forward(
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self,
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x: torch.Tensor,
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x_mask: torch.Tensor,
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freqs_cis: torch.Tensor,
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) -> torch.Tensor:
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"""
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Args:
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x:
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x_mask:
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freqs_cis:
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Returns:
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"""
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bsz, seqlen, _ = x.shape
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xq, xk, xv = torch.split(
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self.qkv(x),
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[
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self.n_local_heads * self.head_dim,
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self.n_local_kv_heads * self.head_dim,
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self.n_local_kv_heads * self.head_dim,
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],
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dim=-1,
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)
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xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
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xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
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xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
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xq = self.q_norm(xq)
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xk = self.k_norm(xk)
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xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis)
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xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis)
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n_rep = self.n_local_heads // self.n_local_kv_heads
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if n_rep >= 1:
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xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
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xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
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output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True)
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return self.out(output)
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class FeedForward(nn.Module):
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def __init__(
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self,
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dim: int,
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hidden_dim: int,
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multiple_of: int,
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ffn_dim_multiplier: Optional[float],
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operation_settings={},
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):
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"""
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Initialize the FeedForward module.
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Args:
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dim (int): Input dimension.
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hidden_dim (int): Hidden dimension of the feedforward layer.
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multiple_of (int): Value to ensure hidden dimension is a multiple
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of this value.
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ffn_dim_multiplier (float, optional): Custom multiplier for hidden
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dimension. Defaults to None.
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"""
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super().__init__()
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# custom dim factor multiplier
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if ffn_dim_multiplier is not None:
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hidden_dim = int(ffn_dim_multiplier * hidden_dim)
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hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
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self.w1 = operation_settings.get("operations").Linear(
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dim,
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hidden_dim,
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bias=False,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.w2 = operation_settings.get("operations").Linear(
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hidden_dim,
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dim,
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bias=False,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.w3 = operation_settings.get("operations").Linear(
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dim,
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hidden_dim,
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bias=False,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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# @torch.compile
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def _forward_silu_gating(self, x1, x3):
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return F.silu(x1) * x3
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def forward(self, x):
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return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
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class JointTransformerBlock(nn.Module):
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def __init__(
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self,
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layer_id: int,
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dim: int,
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n_heads: int,
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n_kv_heads: int,
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multiple_of: int,
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ffn_dim_multiplier: float,
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norm_eps: float,
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qk_norm: bool,
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modulation=True,
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operation_settings={},
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) -> None:
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"""
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Initialize a TransformerBlock.
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Args:
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layer_id (int): Identifier for the layer.
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dim (int): Embedding dimension of the input features.
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n_heads (int): Number of attention heads.
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n_kv_heads (Optional[int]): Number of attention heads in key and
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value features (if using GQA), or set to None for the same as
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query.
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multiple_of (int):
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ffn_dim_multiplier (float):
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norm_eps (float):
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"""
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super().__init__()
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self.dim = dim
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self.head_dim = dim // n_heads
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self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, operation_settings=operation_settings)
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self.feed_forward = FeedForward(
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dim=dim,
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hidden_dim=4 * dim,
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multiple_of=multiple_of,
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ffn_dim_multiplier=ffn_dim_multiplier,
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operation_settings=operation_settings,
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)
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self.layer_id = layer_id
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self.attention_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.ffn_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.attention_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.ffn_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.modulation = modulation
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if modulation:
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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operation_settings.get("operations").Linear(
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min(dim, 1024),
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4 * dim,
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bias=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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),
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)
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def forward(
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self,
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x: torch.Tensor,
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x_mask: torch.Tensor,
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freqs_cis: torch.Tensor,
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adaln_input: Optional[torch.Tensor]=None,
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):
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"""
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Perform a forward pass through the TransformerBlock.
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Args:
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x (torch.Tensor): Input tensor.
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freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
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Returns:
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torch.Tensor: Output tensor after applying attention and
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feedforward layers.
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"""
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if self.modulation:
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assert adaln_input is not None
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scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
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x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
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self.attention(
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modulate(self.attention_norm1(x), scale_msa),
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x_mask,
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freqs_cis,
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)
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)
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x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
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self.feed_forward(
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modulate(self.ffn_norm1(x), scale_mlp),
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)
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)
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else:
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assert adaln_input is None
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x = x + self.attention_norm2(
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self.attention(
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self.attention_norm1(x),
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x_mask,
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freqs_cis,
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)
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)
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x = x + self.ffn_norm2(
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self.feed_forward(
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self.ffn_norm1(x),
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)
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)
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return x
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class FinalLayer(nn.Module):
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"""
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The final layer of NextDiT.
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"""
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def __init__(self, hidden_size, patch_size, out_channels, operation_settings={}):
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super().__init__()
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self.norm_final = operation_settings.get("operations").LayerNorm(
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hidden_size,
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elementwise_affine=False,
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eps=1e-6,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.linear = operation_settings.get("operations").Linear(
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hidden_size,
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patch_size * patch_size * out_channels,
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bias=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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operation_settings.get("operations").Linear(
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min(hidden_size, 1024),
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hidden_size,
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bias=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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),
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)
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def forward(self, x, c):
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scale = self.adaLN_modulation(c)
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x = modulate(self.norm_final(x), scale)
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x = self.linear(x)
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return x
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class NextDiT(nn.Module):
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"""
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Diffusion model with a Transformer backbone.
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"""
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def __init__(
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self,
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patch_size: int = 2,
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in_channels: int = 4,
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dim: int = 4096,
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n_layers: int = 32,
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n_refiner_layers: int = 2,
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n_heads: int = 32,
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n_kv_heads: Optional[int] = None,
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multiple_of: int = 256,
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ffn_dim_multiplier: Optional[float] = None,
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norm_eps: float = 1e-5,
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qk_norm: bool = False,
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cap_feat_dim: int = 5120,
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axes_dims: List[int] = (16, 56, 56),
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axes_lens: List[int] = (1, 512, 512),
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image_model=None,
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device=None,
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dtype=None,
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operations=None,
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) -> None:
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super().__init__()
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self.dtype = dtype
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operation_settings = {"operations": operations, "device": device, "dtype": dtype}
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self.in_channels = in_channels
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self.out_channels = in_channels
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self.patch_size = patch_size
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self.x_embedder = operation_settings.get("operations").Linear(
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in_features=patch_size * patch_size * in_channels,
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out_features=dim,
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bias=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.noise_refiner = nn.ModuleList(
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[
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JointTransformerBlock(
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layer_id,
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dim,
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n_heads,
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n_kv_heads,
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multiple_of,
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ffn_dim_multiplier,
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norm_eps,
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qk_norm,
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modulation=True,
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operation_settings=operation_settings,
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)
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for layer_id in range(n_refiner_layers)
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]
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)
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self.context_refiner = nn.ModuleList(
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[
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JointTransformerBlock(
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layer_id,
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dim,
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n_heads,
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n_kv_heads,
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multiple_of,
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ffn_dim_multiplier,
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norm_eps,
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|
qk_norm,
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|
modulation=False,
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|
operation_settings=operation_settings,
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)
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for layer_id in range(n_refiner_layers)
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]
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)
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self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings)
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self.cap_embedder = nn.Sequential(
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operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
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operation_settings.get("operations").Linear(
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|
cap_feat_dim,
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|
dim,
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|
bias=True,
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|
device=operation_settings.get("device"),
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|
dtype=operation_settings.get("dtype"),
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),
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)
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|
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|
self.layers = nn.ModuleList(
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[
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JointTransformerBlock(
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layer_id,
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dim,
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n_heads,
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|
n_kv_heads,
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|
multiple_of,
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|
ffn_dim_multiplier,
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|
norm_eps,
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|
qk_norm,
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|
operation_settings=operation_settings,
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)
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|
for layer_id in range(n_layers)
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|
]
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|
)
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|
self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings)
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|
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|
assert (dim // n_heads) == sum(axes_dims)
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self.axes_dims = axes_dims
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self.axes_lens = axes_lens
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self.rope_embedder = EmbedND(dim=dim // n_heads, theta=10000.0, axes_dim=axes_dims)
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|
self.dim = dim
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|
self.n_heads = n_heads
|
|
|
|
def unpatchify(
|
|
self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False
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) -> List[torch.Tensor]:
|
|
"""
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|
x: (N, T, patch_size**2 * C)
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imgs: (N, H, W, C)
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"""
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pH = pW = self.patch_size
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imgs = []
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for i in range(x.size(0)):
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H, W = img_size[i]
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begin = cap_size[i]
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|
end = begin + (H // pH) * (W // pW)
|
|
imgs.append(
|
|
x[i][begin:end]
|
|
.view(H // pH, W // pW, pH, pW, self.out_channels)
|
|
.permute(4, 0, 2, 1, 3)
|
|
.flatten(3, 4)
|
|
.flatten(1, 2)
|
|
)
|
|
|
|
if return_tensor:
|
|
imgs = torch.stack(imgs, dim=0)
|
|
return imgs
|
|
|
|
def patchify_and_embed(
|
|
self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens
|
|
) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
|
|
bsz = len(x)
|
|
pH = pW = self.patch_size
|
|
device = x[0].device
|
|
dtype = x[0].dtype
|
|
|
|
if cap_mask is not None:
|
|
l_effective_cap_len = cap_mask.sum(dim=1).tolist()
|
|
else:
|
|
l_effective_cap_len = [num_tokens] * bsz
|
|
|
|
if cap_mask is not None and not torch.is_floating_point(cap_mask):
|
|
cap_mask = (cap_mask - 1).to(dtype) * torch.finfo(dtype).max
|
|
|
|
img_sizes = [(img.size(1), img.size(2)) for img in x]
|
|
l_effective_img_len = [(H // pH) * (W // pW) for (H, W) in img_sizes]
|
|
|
|
max_seq_len = max(
|
|
(cap_len+img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len))
|
|
)
|
|
max_cap_len = max(l_effective_cap_len)
|
|
max_img_len = max(l_effective_img_len)
|
|
|
|
position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device)
|
|
|
|
for i in range(bsz):
|
|
cap_len = l_effective_cap_len[i]
|
|
img_len = l_effective_img_len[i]
|
|
H, W = img_sizes[i]
|
|
H_tokens, W_tokens = H // pH, W // pW
|
|
assert H_tokens * W_tokens == img_len
|
|
|
|
position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device)
|
|
position_ids[i, cap_len:cap_len+img_len, 0] = cap_len
|
|
row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
|
|
col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
|
|
position_ids[i, cap_len:cap_len+img_len, 1] = row_ids
|
|
position_ids[i, cap_len:cap_len+img_len, 2] = col_ids
|
|
|
|
freqs_cis = self.rope_embedder(position_ids).movedim(1, 2).to(dtype)
|
|
|
|
# build freqs_cis for cap and image individually
|
|
cap_freqs_cis_shape = list(freqs_cis.shape)
|
|
# cap_freqs_cis_shape[1] = max_cap_len
|
|
cap_freqs_cis_shape[1] = cap_feats.shape[1]
|
|
cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
|
|
|
|
img_freqs_cis_shape = list(freqs_cis.shape)
|
|
img_freqs_cis_shape[1] = max_img_len
|
|
img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
|
|
|
|
for i in range(bsz):
|
|
cap_len = l_effective_cap_len[i]
|
|
img_len = l_effective_img_len[i]
|
|
cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len]
|
|
img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len:cap_len+img_len]
|
|
|
|
# refine context
|
|
for layer in self.context_refiner:
|
|
cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis)
|
|
|
|
# refine image
|
|
flat_x = []
|
|
for i in range(bsz):
|
|
img = x[i]
|
|
C, H, W = img.size()
|
|
img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1)
|
|
flat_x.append(img)
|
|
x = flat_x
|
|
padded_img_embed = torch.zeros(bsz, max_img_len, x[0].shape[-1], device=device, dtype=x[0].dtype)
|
|
padded_img_mask = torch.zeros(bsz, max_img_len, dtype=dtype, device=device)
|
|
for i in range(bsz):
|
|
padded_img_embed[i, :l_effective_img_len[i]] = x[i]
|
|
padded_img_mask[i, l_effective_img_len[i]:] = -torch.finfo(dtype).max
|
|
|
|
padded_img_embed = self.x_embedder(padded_img_embed)
|
|
padded_img_mask = padded_img_mask.unsqueeze(1)
|
|
for layer in self.noise_refiner:
|
|
padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t)
|
|
|
|
if cap_mask is not None:
|
|
mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device)
|
|
mask[:, :max_cap_len] = cap_mask[:, :max_cap_len]
|
|
else:
|
|
mask = None
|
|
|
|
padded_full_embed = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x[0].dtype)
|
|
for i in range(bsz):
|
|
cap_len = l_effective_cap_len[i]
|
|
img_len = l_effective_img_len[i]
|
|
|
|
padded_full_embed[i, :cap_len] = cap_feats[i, :cap_len]
|
|
padded_full_embed[i, cap_len:cap_len+img_len] = padded_img_embed[i, :img_len]
|
|
|
|
return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis
|
|
|
|
def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
|
|
return comfy.patcher_extension.WrapperExecutor.new_class_executor(
|
|
self._forward,
|
|
self,
|
|
comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
|
|
).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
|
|
|
|
# def forward(self, x, t, cap_feats, cap_mask):
|
|
def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
|
|
t = 1.0 - timesteps
|
|
cap_feats = context
|
|
cap_mask = attention_mask
|
|
bs, c, h, w = x.shape
|
|
x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
|
|
"""
|
|
Forward pass of NextDiT.
|
|
t: (N,) tensor of diffusion timesteps
|
|
y: (N,) tensor of text tokens/features
|
|
"""
|
|
|
|
t = self.t_embedder(t, dtype=x.dtype) # (N, D)
|
|
adaln_input = t
|
|
|
|
cap_feats = self.cap_embedder(cap_feats) # (N, L, D) # todo check if able to batchify w.o. redundant compute
|
|
|
|
x_is_tensor = isinstance(x, torch.Tensor)
|
|
x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens)
|
|
freqs_cis = freqs_cis.to(x.device)
|
|
|
|
for layer in self.layers:
|
|
x = layer(x, mask, freqs_cis, adaln_input)
|
|
|
|
x = self.final_layer(x, adaln_input)
|
|
x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]
|
|
|
|
return -x
|
|
|