813 lines
38 KiB
Python
813 lines
38 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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# This file contains code that is adapted from
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# https://github.com/black-forest-labs/flux.git
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import math
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import torch
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from torch import Tensor, nn
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from collections import OrderedDict
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from functools import partial
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from einops import rearrange, repeat
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from scepter.modules.model.base_model import BaseModel
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from scepter.modules.model.registry import BACKBONES
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from scepter.modules.utils.config import dict_to_yaml
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from scepter.modules.utils.distribute import we
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from scepter.modules.utils.file_system import FS
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from torch.utils.checkpoint import checkpoint_sequential
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from torch.nn.utils.rnn import pad_sequence
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from .layers import (DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder,
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SingleStreamBlock, timestep_embedding)
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@BACKBONES.register_class()
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class Flux(BaseModel):
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"""
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Transformer backbone Diffusion model with RoPE.
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"""
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para_dict = {
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'IN_CHANNELS': {
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'value': 64,
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'description': "model's input channels."
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},
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'OUT_CHANNELS': {
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'value': 64,
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'description': "model's output channels."
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},
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'HIDDEN_SIZE': {
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'value': 1024,
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'description': "model's hidden size."
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},
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'NUM_HEADS': {
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'value': 16,
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'description': 'number of heads in the transformer.'
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},
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'AXES_DIM': {
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'value': [16, 56, 56],
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'description': 'dimensions of the axes of the positional encoding.'
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},
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'THETA': {
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'value': 10_000,
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'description': 'theta for positional encoding.'
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},
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'VEC_IN_DIM': {
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'value': 768,
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'description': 'dimension of the vector input.'
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},
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'GUIDANCE_EMBED': {
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'value': False,
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'description': 'whether to use guidance embedding.'
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},
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'CONTEXT_IN_DIM': {
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'value': 4096,
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'description': 'dimension of the context input.'
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},
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'MLP_RATIO': {
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'value': 4.0,
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'description': 'ratio of mlp hidden size to hidden size.'
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},
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'QKV_BIAS': {
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'value': True,
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'description': 'whether to use bias in qkv projection.'
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},
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'DEPTH': {
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'value': 19,
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'description': 'number of transformer blocks.'
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},
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'DEPTH_SINGLE_BLOCKS': {
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'value':
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38,
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'description':
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'number of transformer blocks in the single stream block.'
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},
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'USE_GRAD_CHECKPOINT': {
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'value': False,
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'description': 'whether to use gradient checkpointing.'
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}
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}
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def __init__(self, cfg, logger=None):
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super().__init__(cfg, logger=logger)
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self.in_channels = cfg.IN_CHANNELS
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self.out_channels = cfg.get('OUT_CHANNELS', self.in_channels)
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hidden_size = cfg.get('HIDDEN_SIZE', 1024)
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num_heads = cfg.get('NUM_HEADS', 16)
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axes_dim = cfg.AXES_DIM
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theta = cfg.THETA
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vec_in_dim = cfg.VEC_IN_DIM
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self.guidance_embed = cfg.GUIDANCE_EMBED
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context_in_dim = cfg.CONTEXT_IN_DIM
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mlp_ratio = cfg.MLP_RATIO
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qkv_bias = cfg.QKV_BIAS
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depth = cfg.DEPTH
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depth_single_blocks = cfg.DEPTH_SINGLE_BLOCKS
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self.use_grad_checkpoint = cfg.get("USE_GRAD_CHECKPOINT", False)
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self.attn_backend = cfg.get("ATTN_BACKEND", "pytorch")
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self.cache_pretrain_model = cfg.get("CACHE_PRETRAIN_MODEL", False)
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self.lora_model = cfg.get("DIFFUSERS_LORA_MODEL", None)
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self.comfyui_lora_model = cfg.get("COMFYUI_LORA_MODEL", None)
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self.swift_lora_model = cfg.get("SWIFT_LORA_MODEL", None)
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self.blackforest_lora_model = cfg.get("BLACKFOREST_LORA_MODEL", None)
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self.pretrain_adapter = cfg.get("PRETRAIN_ADAPTER", None)
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if hidden_size % num_heads != 0:
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raise ValueError(
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f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}"
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)
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pe_dim = hidden_size // num_heads
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if sum(axes_dim) != pe_dim:
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raise ValueError(
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f"Got {axes_dim} but expected positional dim {pe_dim}")
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self.hidden_size = hidden_size
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self.num_heads = num_heads
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self.pe_embedder = EmbedND(dim=pe_dim, theta=theta, axes_dim=axes_dim)
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self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
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self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
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self.vector_in = MLPEmbedder(vec_in_dim, self.hidden_size)
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self.guidance_in = (MLPEmbedder(in_dim=256,
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hidden_dim=self.hidden_size)
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if self.guidance_embed else nn.Identity())
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self.txt_in = nn.Linear(context_in_dim, self.hidden_size)
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self.double_blocks = nn.ModuleList(
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[
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DoubleStreamBlock(
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self.hidden_size,
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self.num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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backend=self.attn_backend
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)
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for _ in range(depth)
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]
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)
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self.single_blocks = nn.ModuleList(
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[
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SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio, backend=self.attn_backend)
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for _ in range(depth_single_blocks)
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]
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)
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self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
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def prepare_input(self, x, context, y, x_shape=None):
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# x.shape [6, 16, 16, 16] target is [6, 16, 768, 1360]
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bs, c, h, w = x.shape
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x = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
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x_id = torch.zeros(h // 2, w // 2, 3)
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x_id[..., 1] = x_id[..., 1] + torch.arange(h // 2)[:, None]
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x_id[..., 2] = x_id[..., 2] + torch.arange(w // 2)[None, :]
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x_ids = repeat(x_id, "h w c -> b (h w) c", b=bs)
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txt_ids = torch.zeros(bs, context.shape[1], 3)
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return x, x_ids.to(x), context.to(x), txt_ids.to(x), y.to(x), h, w
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def unpack(self, x: Tensor, height: int, width: int) -> Tensor:
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return rearrange(
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x,
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"b (h w) (c ph pw) -> b c (h ph) (w pw)",
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h=math.ceil(height/2),
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w=math.ceil(width/2),
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ph=2,
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pw=2,
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)
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def merge_diffuser_lora(self, ori_sd, lora_sd, scale=1.0):
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key_map = {
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"single_blocks.{}.linear1.weight": {"key_list": [
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["transformer.single_transformer_blocks.{}.attn.to_q.lora_A.weight",
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"transformer.single_transformer_blocks.{}.attn.to_q.lora_B.weight", [0, 3072]],
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["transformer.single_transformer_blocks.{}.attn.to_k.lora_A.weight",
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"transformer.single_transformer_blocks.{}.attn.to_k.lora_B.weight", [3072, 6144]],
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["transformer.single_transformer_blocks.{}.attn.to_v.lora_A.weight",
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"transformer.single_transformer_blocks.{}.attn.to_v.lora_B.weight", [6144, 9216]],
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["transformer.single_transformer_blocks.{}.proj_mlp.lora_A.weight",
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"transformer.single_transformer_blocks.{}.proj_mlp.lora_B.weight", [9216, 21504]]
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], "num": 38},
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"single_blocks.{}.modulation.lin.weight": {"key_list": [
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["transformer.single_transformer_blocks.{}.norm.linear.lora_A.weight",
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"transformer.single_transformer_blocks.{}.norm.linear.lora_B.weight", [0, 9216]],
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], "num": 38},
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"single_blocks.{}.linear2.weight": {"key_list": [
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["transformer.single_transformer_blocks.{}.proj_out.lora_A.weight",
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"transformer.single_transformer_blocks.{}.proj_out.lora_B.weight", [0, 3072]],
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], "num": 38},
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"double_blocks.{}.txt_attn.qkv.weight": {"key_list": [
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["transformer.transformer_blocks.{}.attn.add_q_proj.lora_A.weight",
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"transformer.transformer_blocks.{}.attn.add_q_proj.lora_B.weight", [0, 3072]],
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["transformer.transformer_blocks.{}.attn.add_k_proj.lora_A.weight",
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"transformer.transformer_blocks.{}.attn.add_k_proj.lora_B.weight", [3072, 6144]],
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["transformer.transformer_blocks.{}.attn.add_v_proj.lora_A.weight",
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"transformer.transformer_blocks.{}.attn.add_v_proj.lora_B.weight", [6144, 9216]],
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], "num": 19},
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"double_blocks.{}.img_attn.qkv.weight": {"key_list": [
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["transformer.transformer_blocks.{}.attn.to_q.lora_A.weight",
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"transformer.transformer_blocks.{}.attn.to_q.lora_B.weight", [0, 3072]],
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["transformer.transformer_blocks.{}.attn.to_k.lora_A.weight",
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"transformer.transformer_blocks.{}.attn.to_k.lora_B.weight", [3072, 6144]],
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["transformer.transformer_blocks.{}.attn.to_v.lora_A.weight",
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"transformer.transformer_blocks.{}.attn.to_v.lora_B.weight", [6144, 9216]],
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], "num": 19},
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"double_blocks.{}.img_attn.proj.weight": {"key_list": [
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["transformer.transformer_blocks.{}.attn.to_out.0.lora_A.weight",
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"transformer.transformer_blocks.{}.attn.to_out.0.lora_B.weight", [0, 3072]]
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], "num": 19},
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"double_blocks.{}.txt_attn.proj.weight": {"key_list": [
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["transformer.transformer_blocks.{}.attn.to_add_out.lora_A.weight",
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"transformer.transformer_blocks.{}.attn.to_add_out.lora_B.weight", [0, 3072]]
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], "num": 19},
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"double_blocks.{}.img_mlp.0.weight": {"key_list": [
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["transformer.transformer_blocks.{}.ff.net.0.proj.lora_A.weight",
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"transformer.transformer_blocks.{}.ff.net.0.proj.lora_B.weight", [0, 12288]]
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], "num": 19},
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"double_blocks.{}.img_mlp.2.weight": {"key_list": [
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["transformer.transformer_blocks.{}.ff.net.2.lora_A.weight",
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"transformer.transformer_blocks.{}.ff.net.2.lora_B.weight", [0, 3072]]
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], "num": 19},
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"double_blocks.{}.txt_mlp.0.weight": {"key_list": [
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["transformer.transformer_blocks.{}.ff_context.net.0.proj.lora_A.weight",
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"transformer.transformer_blocks.{}.ff_context.net.0.proj.lora_B.weight", [0, 12288]]
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], "num": 19},
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"double_blocks.{}.txt_mlp.2.weight": {"key_list": [
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["transformer.transformer_blocks.{}.ff_context.net.2.lora_A.weight",
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"transformer.transformer_blocks.{}.ff_context.net.2.lora_B.weight", [0, 3072]]
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], "num": 19},
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"double_blocks.{}.img_mod.lin.weight": {"key_list": [
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["transformer.transformer_blocks.{}.norm1.linear.lora_A.weight",
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"transformer.transformer_blocks.{}.norm1.linear.lora_B.weight", [0, 18432]]
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], "num": 19},
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"double_blocks.{}.txt_mod.lin.weight": {"key_list": [
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["transformer.transformer_blocks.{}.norm1_context.linear.lora_A.weight",
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"transformer.transformer_blocks.{}.norm1_context.linear.lora_B.weight", [0, 18432]]
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], "num": 19}
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}
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cover_lora_keys = set()
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cover_ori_keys = set()
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for k, v in key_map.items():
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key_list = v["key_list"]
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block_num = v["num"]
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for block_id in range(block_num):
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for k_list in key_list:
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if k_list[0].format(block_id) in lora_sd and k_list[1].format(block_id) in lora_sd:
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cover_lora_keys.add(k_list[0].format(block_id))
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cover_lora_keys.add(k_list[1].format(block_id))
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current_weight = torch.matmul(lora_sd[k_list[0].format(block_id)].permute(1, 0),
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lora_sd[k_list[1].format(block_id)].permute(1, 0)).permute(1, 0)
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ori_sd[k.format(block_id)][k_list[2][0]:k_list[2][1], ...] += scale * current_weight
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cover_ori_keys.add(k.format(block_id))
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# lora_sd.pop(k_list[0].format(block_id))
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# lora_sd.pop(k_list[1].format(block_id))
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self.logger.info(f"merge_blackforest_lora loads lora'parameters lora-paras: \n"
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f"cover-{len(cover_lora_keys)} vs total {len(lora_sd)} \n"
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f"cover ori-{len(cover_ori_keys)} vs total {len(ori_sd)}")
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return ori_sd
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def merge_swift_lora(self, ori_sd, lora_sd, scale = 1.0):
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have_lora_keys = {}
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for k, v in lora_sd.items():
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k = k[len("model."):] if k.startswith("model.") else k
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ori_key = k.split("lora")[0] + "weight"
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if ori_key not in ori_sd:
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raise f"{ori_key} should in the original statedict"
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if ori_key not in have_lora_keys:
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have_lora_keys[ori_key] = {}
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if "lora_A" in k:
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have_lora_keys[ori_key]["lora_A"] = v
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elif "lora_B" in k:
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have_lora_keys[ori_key]["lora_B"] = v
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else:
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raise NotImplementedError
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self.logger.info(f"merge_swift_lora loads lora'parameters {len(have_lora_keys)}")
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for key, v in have_lora_keys.items():
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current_weight = torch.matmul(v["lora_A"].permute(1, 0), v["lora_B"].permute(1, 0)).permute(1, 0)
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ori_sd[key] += scale * current_weight
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return ori_sd
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def merge_blackforest_lora(self, ori_sd, lora_sd, scale = 1.0):
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have_lora_keys = {}
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cover_lora_keys = set()
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cover_ori_keys = set()
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for k, v in lora_sd.items():
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if "lora" in k:
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ori_key = k.split("lora")[0] + "weight"
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if ori_key not in ori_sd:
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raise f"{ori_key} should in the original statedict"
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if ori_key not in have_lora_keys:
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have_lora_keys[ori_key] = {}
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if "lora_A" in k:
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have_lora_keys[ori_key]["lora_A"] = v
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cover_lora_keys.add(k)
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cover_ori_keys.add(ori_key)
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elif "lora_B" in k:
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have_lora_keys[ori_key]["lora_B"] = v
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cover_lora_keys.add(k)
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cover_ori_keys.add(ori_key)
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else:
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if k in ori_sd:
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ori_sd[k] = v
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cover_lora_keys.add(k)
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cover_ori_keys.add(k)
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else:
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print("unsurpport keys: ", k)
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self.logger.info(f"merge_blackforest_lora loads lora'parameters lora-paras: \n"
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f"cover-{len(cover_lora_keys)} vs total {len(lora_sd)} \n"
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f"cover ori-{len(cover_ori_keys)} vs total {len(ori_sd)}")
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for key, v in have_lora_keys.items():
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current_weight = torch.matmul(v["lora_A"].permute(1, 0), v["lora_B"].permute(1, 0)).permute(1, 0)
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# print(key, ori_sd[key].shape, current_weight.shape)
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ori_sd[key] += scale * current_weight
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return ori_sd
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def merge_comfyui_lora(self, ori_sd, lora_sd, scale = 1.0):
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ori_key_map = {key.replace("_", ".") : key for key in ori_sd.keys()}
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parse_ckpt = OrderedDict()
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for k, v in lora_sd.items():
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if "alpha" in k:
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continue
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k = k.replace("lora_unet_", "").replace("_", ".")
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map_k = ori_key_map[k.split(".lora")[0] + ".weight"]
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if map_k not in parse_ckpt:
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parse_ckpt[map_k] = {}
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if "lora.up" in k:
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parse_ckpt[map_k]["lora_up"] = v
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elif "lora.down" in k:
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parse_ckpt[map_k]["lora_down"] = v
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if self.cache_pretrain_model:
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self.lora_dict[self.comfyui_lora_model] = {}
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for key, v in parse_ckpt.items():
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current_weight = torch.matmul(v["lora_down"].permute(1, 0), v["lora_up"].permute(1, 0)).permute(1, 0)
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self.lora_dict[self.comfyui_lora_model] = current_weight
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ori_sd[key] += scale * current_weight
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return ori_sd
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def easy_lora_merge(self, ori_sd, lora_sd, scale = 1.0):
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for key, v in lora_sd.items():
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ori_sd[key] += scale * v
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return ori_sd
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def load_pretrained_model(self, pretrained_model, lora_scale = 1.0):
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if next(self.parameters()).device.type == 'meta':
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map_location = torch.device(we.device_id)
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safe_device = we.device_id
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else:
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map_location = "cpu"
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safe_device = "cpu"
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if pretrained_model is not None:
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if not hasattr(self, "ckpt"):
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with FS.get_from(pretrained_model, wait_finish=True) as local_model:
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if local_model.endswith('safetensors'):
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from safetensors.torch import load_file as load_safetensors
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ckpt = load_safetensors(local_model, device=safe_device)
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else:
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ckpt = torch.load(local_model, map_location=map_location, weights_only=True)
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if "state_dict" in ckpt:
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ckpt = ckpt["state_dict"]
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if "model" in ckpt:
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ckpt = ckpt["model"]["model"]
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if self.cache_pretrain_model:
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self.ckpt = ckpt
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self.lora_dict = {}
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else:
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ckpt = self.ckpt
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new_ckpt = OrderedDict()
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for k, v in ckpt.items():
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if k in ("img_in.weight"):
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model_p = self.state_dict()[k]
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if v.shape != model_p.shape:
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expanded_state_dict_weight = torch.zeros_like(model_p, device=v.device)
|
|
slices = tuple(slice(0, dim) for dim in v.shape)
|
|
expanded_state_dict_weight[slices] = v
|
|
new_ckpt[k] = expanded_state_dict_weight
|
|
else:
|
|
new_ckpt[k] = v
|
|
else:
|
|
new_ckpt[k] = v
|
|
|
|
|
|
if self.lora_model is not None:
|
|
with FS.get_from(self.lora_model, wait_finish=True) as local_model:
|
|
if local_model.endswith('safetensors'):
|
|
from safetensors.torch import load_file as load_safetensors
|
|
lora_sd = load_safetensors(local_model, device=safe_device)
|
|
else:
|
|
lora_sd = torch.load(local_model, map_location=map_location, weights_only=True)
|
|
new_ckpt = self.merge_diffuser_lora(new_ckpt, lora_sd, scale=lora_scale)
|
|
if self.swift_lora_model is not None:
|
|
if not isinstance(self.swift_lora_model, list):
|
|
self.swift_lora_model = [(self.swift_lora_model, 1.0)]
|
|
for lora_model in self.swift_lora_model:
|
|
if isinstance(lora_model, str):
|
|
lora_model = (lora_model, 1.0/len(self.swift_lora_model))
|
|
print(lora_model)
|
|
self.logger.info(f"load swift lora model: {lora_model}")
|
|
with FS.get_from(lora_model[0], wait_finish=True) as local_model:
|
|
if local_model.endswith('safetensors'):
|
|
from safetensors.torch import load_file as load_safetensors
|
|
lora_sd = load_safetensors(local_model, device=safe_device)
|
|
else:
|
|
lora_sd = torch.load(local_model, map_location=map_location, weights_only=True)
|
|
new_ckpt = self.merge_swift_lora(new_ckpt, lora_sd, scale=lora_model[1])
|
|
|
|
if self.blackforest_lora_model is not None:
|
|
with FS.get_from(self.blackforest_lora_model, wait_finish=True) as local_model:
|
|
if local_model.endswith('safetensors'):
|
|
from safetensors.torch import load_file as load_safetensors
|
|
lora_sd = load_safetensors(local_model, device=safe_device)
|
|
else:
|
|
lora_sd = torch.load(local_model, map_location=map_location, weights_only=True)
|
|
new_ckpt = self.merge_blackforest_lora(new_ckpt, lora_sd, scale=lora_scale)
|
|
|
|
if self.comfyui_lora_model is not None:
|
|
if hasattr(self, "current_lora") and self.current_lora == self.comfyui_lora_model:
|
|
return
|
|
if hasattr(self, "lora_dict") and self.comfyui_lora_model in self.lora_dict:
|
|
new_ckpt = self.easy_lora_merge(new_ckpt, self.lora_dict[self.comfyui_lora_model], scale=lora_scale)
|
|
else:
|
|
with FS.get_from(self.comfyui_lora_model, wait_finish=True) as local_model:
|
|
if local_model.endswith('safetensors'):
|
|
from safetensors.torch import load_file as load_safetensors
|
|
lora_sd = load_safetensors(local_model, device=safe_device)
|
|
else:
|
|
lora_sd = torch.load(local_model, map_location=map_location, weights_only=True)
|
|
new_ckpt = self.merge_comfyui_lora(new_ckpt, lora_sd, scale=lora_scale)
|
|
if self.comfyui_lora_model:
|
|
self.current_lora = self.comfyui_lora_model
|
|
|
|
|
|
adapter_ckpt = {}
|
|
if self.pretrain_adapter is not None:
|
|
with FS.get_from(self.pretrain_adapter, wait_finish=True) as local_adapter:
|
|
if local_adapter.endswith('safetensors'):
|
|
from safetensors.torch import load_file as load_safetensors
|
|
adapter_ckpt = load_safetensors(local_adapter, device=safe_device)
|
|
else:
|
|
adapter_ckpt = torch.load(local_adapter, map_location=map_location, weights_only=True)
|
|
new_ckpt.update(adapter_ckpt)
|
|
|
|
missing, unexpected = self.load_state_dict(new_ckpt, strict=False, assign=True)
|
|
self.logger.info(
|
|
f'Restored from {pretrained_model} with {len(missing)} missing and {len(unexpected)} unexpected keys'
|
|
)
|
|
if len(missing) > 0:
|
|
self.logger.info(f'Missing Keys:\n {missing}')
|
|
if len(unexpected) > 0:
|
|
self.logger.info(f'\nUnexpected Keys:\n {unexpected}')
|
|
|
|
def forward(
|
|
self,
|
|
x: Tensor,
|
|
t: Tensor,
|
|
cond: dict = {},
|
|
guidance: Tensor | None = None,
|
|
gc_seg: int = 0
|
|
) -> Tensor:
|
|
x, x_ids, txt, txt_ids, y, h, w = self.prepare_input(x, cond["context"], cond["y"])
|
|
# running on sequences img
|
|
x = self.img_in(x)
|
|
vec = self.time_in(timestep_embedding(t, 256))
|
|
if self.guidance_embed:
|
|
if guidance is None:
|
|
raise ValueError("Didn't get guidance strength for guidance distilled model.")
|
|
vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
|
|
vec = vec + self.vector_in(y)
|
|
txt = self.txt_in(txt)
|
|
ids = torch.cat((txt_ids, x_ids), dim=1)
|
|
pe = self.pe_embedder(ids)
|
|
kwargs = dict(
|
|
vec=vec,
|
|
pe=pe,
|
|
txt_length=txt.shape[1],
|
|
)
|
|
x = torch.cat((txt, x), 1)
|
|
if self.use_grad_checkpoint and gc_seg >= 0:
|
|
x = checkpoint_sequential(
|
|
functions=[partial(block, **kwargs) for block in self.double_blocks],
|
|
segments=gc_seg if gc_seg > 0 else len(self.double_blocks),
|
|
input=x,
|
|
use_reentrant=False
|
|
)
|
|
else:
|
|
for block in self.double_blocks:
|
|
x = block(x, **kwargs)
|
|
|
|
kwargs = dict(
|
|
vec=vec,
|
|
pe=pe,
|
|
)
|
|
|
|
if self.use_grad_checkpoint and gc_seg >= 0:
|
|
x = checkpoint_sequential(
|
|
functions=[partial(block, **kwargs) for block in self.single_blocks],
|
|
segments=gc_seg if gc_seg > 0 else len(self.single_blocks),
|
|
input=x,
|
|
use_reentrant=False
|
|
)
|
|
else:
|
|
for block in self.single_blocks:
|
|
x = block(x, **kwargs)
|
|
x = x[:, txt.shape[1] :, ...]
|
|
x = self.final_layer(x, vec) # (N, T, patch_size ** 2 * out_channels) 6 64 64
|
|
x = self.unpack(x, h, w)
|
|
return x
|
|
|
|
@staticmethod
|
|
def get_config_template():
|
|
return dict_to_yaml('BACKBONE',
|
|
__class__.__name__,
|
|
Flux.para_dict,
|
|
set_name=True)
|
|
@BACKBONES.register_class()
|
|
class FluxMR(Flux):
|
|
def prepare_input(self, x, cond):
|
|
if isinstance(cond['context'], list):
|
|
context, y = torch.cat(cond["context"], dim=0).to(x), torch.cat(cond["y"], dim=0).to(x)
|
|
else:
|
|
context, y = cond['context'].to(x), cond['y'].to(x)
|
|
batch_frames, batch_frames_ids = [], []
|
|
for ix, shape in zip(x, cond["x_shapes"]):
|
|
# unpack image from sequence
|
|
ix = ix[:, :shape[0] * shape[1]].view(-1, shape[0], shape[1])
|
|
c, h, w = ix.shape
|
|
ix = rearrange(ix, "c (h ph) (w pw) -> (h w) (c ph pw)", ph=2, pw=2)
|
|
ix_id = torch.zeros(h // 2, w // 2, 3)
|
|
ix_id[..., 1] = ix_id[..., 1] + torch.arange(h // 2)[:, None]
|
|
ix_id[..., 2] = ix_id[..., 2] + torch.arange(w // 2)[None, :]
|
|
ix_id = rearrange(ix_id, "h w c -> (h w) c")
|
|
batch_frames.append([ix])
|
|
batch_frames_ids.append([ix_id])
|
|
|
|
x_list, x_id_list, mask_x_list, x_seq_length = [], [], [], []
|
|
for frames, frame_ids in zip(batch_frames, batch_frames_ids):
|
|
proj_frames = []
|
|
for idx, one_frame in enumerate(frames):
|
|
one_frame = self.img_in(one_frame)
|
|
proj_frames.append(one_frame)
|
|
ix = torch.cat(proj_frames, dim=0)
|
|
if_id = torch.cat(frame_ids, dim=0)
|
|
x_list.append(ix)
|
|
x_id_list.append(if_id)
|
|
mask_x_list.append(torch.ones(ix.shape[0]).to(ix.device, non_blocking=True).bool())
|
|
x_seq_length.append(ix.shape[0])
|
|
x = pad_sequence(tuple(x_list), batch_first=True)
|
|
x_ids = pad_sequence(tuple(x_id_list), batch_first=True).to(x) # [b,pad_seq,2] pad (0.,0.) at dim2
|
|
mask_x = pad_sequence(tuple(mask_x_list), batch_first=True)
|
|
|
|
txt = self.txt_in(context)
|
|
txt_ids = torch.zeros(context.shape[0], context.shape[1], 3).to(x)
|
|
mask_txt = torch.ones(context.shape[0], context.shape[1]).to(x.device, non_blocking=True).bool()
|
|
|
|
return x, x_ids, txt, txt_ids, y, mask_x, mask_txt, x_seq_length
|
|
|
|
def unpack(self, x: Tensor, cond: dict = None, x_seq_length: list = None) -> Tensor:
|
|
x_list = []
|
|
image_shapes = cond["x_shapes"]
|
|
for u, shape, seq_length in zip(x, image_shapes, x_seq_length):
|
|
height, width = shape
|
|
h, w = math.ceil(height / 2), math.ceil(width / 2)
|
|
u = rearrange(
|
|
u[seq_length-h*w:seq_length, ...],
|
|
"(h w) (c ph pw) -> (h ph w pw) c",
|
|
h=h,
|
|
w=w,
|
|
ph=2,
|
|
pw=2,
|
|
)
|
|
x_list.append(u)
|
|
x = pad_sequence(tuple(x_list), batch_first=True).permute(0, 2, 1)
|
|
return x
|
|
|
|
def forward(
|
|
self,
|
|
x: Tensor,
|
|
t: Tensor,
|
|
cond: dict = {},
|
|
guidance: Tensor | None = None,
|
|
gc_seg: int = 0,
|
|
**kwargs
|
|
) -> Tensor:
|
|
x, x_ids, txt, txt_ids, y, mask_x, mask_txt, seq_length_list = self.prepare_input(x, cond)
|
|
# running on sequences img
|
|
vec = self.time_in(timestep_embedding(t, 256))
|
|
if self.guidance_embed and guidance[-1] >= 0:
|
|
if guidance is None:
|
|
raise ValueError("Didn't get guidance strength for guidance distilled model.")
|
|
vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
|
|
vec = vec + self.vector_in(y)
|
|
ids = torch.cat((txt_ids, x_ids), dim=1)
|
|
pe = self.pe_embedder(ids)
|
|
|
|
mask_aside = torch.cat((mask_txt, mask_x), dim=1)
|
|
mask = mask_aside[:, None, :] * mask_aside[:, :, None]
|
|
|
|
kwargs = dict(
|
|
vec=vec,
|
|
pe=pe,
|
|
mask=mask,
|
|
txt_length = txt.shape[1],
|
|
)
|
|
x = torch.cat((txt, x), 1)
|
|
if self.use_grad_checkpoint and gc_seg >= 0:
|
|
x = checkpoint_sequential(
|
|
functions=[partial(block, **kwargs) for block in self.double_blocks],
|
|
segments=gc_seg if gc_seg > 0 else len(self.double_blocks),
|
|
input=x,
|
|
use_reentrant=False
|
|
)
|
|
else:
|
|
for block in self.double_blocks:
|
|
x = block(x, **kwargs)
|
|
|
|
kwargs = dict(
|
|
vec=vec,
|
|
pe=pe,
|
|
mask=mask,
|
|
)
|
|
|
|
if self.use_grad_checkpoint and gc_seg >= 0:
|
|
x = checkpoint_sequential(
|
|
functions=[partial(block, **kwargs) for block in self.single_blocks],
|
|
segments=gc_seg if gc_seg > 0 else len(self.single_blocks),
|
|
input=x,
|
|
use_reentrant=False
|
|
)
|
|
else:
|
|
for block in self.single_blocks:
|
|
x = block(x, **kwargs)
|
|
x = x[:, txt.shape[1]:, ...]
|
|
x = self.final_layer(x, vec) # (N, T, patch_size ** 2 * out_channels) 6 64 64
|
|
x = self.unpack(x, cond, seq_length_list)
|
|
return x
|
|
|
|
@staticmethod
|
|
def get_config_template():
|
|
return dict_to_yaml('MODEL',
|
|
__class__.__name__,
|
|
FluxMR.para_dict,
|
|
set_name=True)
|
|
@BACKBONES.register_class()
|
|
class FluxMRACEPlus(FluxMR):
|
|
def __init__(self, cfg, logger = None):
|
|
super().__init__(cfg, logger)
|
|
def prepare_input(self, x, cond):
|
|
context, y = cond["context"], cond["y"]
|
|
batch_frames, batch_frames_ids = [], []
|
|
for ix, shape, imask, ie, ie_mask in zip(x,
|
|
cond['x_shapes'],
|
|
cond['x_mask'],
|
|
cond['edit'],
|
|
cond['edit_mask']):
|
|
# unpack image from sequence
|
|
ix = ix[:, :shape[0] * shape[1]].view(-1, shape[0], shape[1])
|
|
imask = torch.ones_like(
|
|
ix[[0], :, :]) if imask is None else imask.squeeze(0)
|
|
if len(ie) > 0:
|
|
ie = [iie.squeeze(0) for iie in ie]
|
|
ie_mask = [
|
|
torch.ones(
|
|
(ix.shape[0] * 4, ix.shape[1],
|
|
ix.shape[2])) if iime is None else iime.squeeze(0)
|
|
for iime in ie_mask
|
|
]
|
|
ie = torch.cat(ie, dim=-1)
|
|
ie_mask = torch.cat(ie_mask, dim=-1)
|
|
else:
|
|
ie, ie_mask = torch.zeros_like(ix).to(x), torch.ones_like(
|
|
imask).to(x),
|
|
ix = torch.cat([ix, ie, ie_mask], dim=0)
|
|
c, h, w = ix.shape
|
|
ix = rearrange(ix,
|
|
'c (h ph) (w pw) -> (h w) (c ph pw)',
|
|
ph=2,
|
|
pw=2)
|
|
ix_id = torch.zeros(h // 2, w // 2, 3)
|
|
ix_id[..., 1] = ix_id[..., 1] + torch.arange(h // 2)[:, None]
|
|
ix_id[..., 2] = ix_id[..., 2] + torch.arange(w // 2)[None, :]
|
|
ix_id = rearrange(ix_id, 'h w c -> (h w) c')
|
|
batch_frames.append([ix])
|
|
batch_frames_ids.append([ix_id])
|
|
x_list, x_id_list, mask_x_list, x_seq_length = [], [], [], []
|
|
for frames, frame_ids in zip(batch_frames, batch_frames_ids):
|
|
proj_frames = []
|
|
for idx, one_frame in enumerate(frames):
|
|
one_frame = self.img_in(one_frame)
|
|
proj_frames.append(one_frame)
|
|
ix = torch.cat(proj_frames, dim=0)
|
|
if_id = torch.cat(frame_ids, dim=0)
|
|
x_list.append(ix)
|
|
x_id_list.append(if_id)
|
|
mask_x_list.append(torch.ones(ix.shape[0]).to(ix.device, non_blocking=True).bool())
|
|
x_seq_length.append(ix.shape[0])
|
|
# if len(x_list) < 1: import pdb;pdb.set_trace()
|
|
x = pad_sequence(tuple(x_list), batch_first=True)
|
|
x_ids = pad_sequence(tuple(x_id_list), batch_first=True).to(x) # [b,pad_seq,2] pad (0.,0.) at dim2
|
|
mask_x = pad_sequence(tuple(mask_x_list), batch_first=True)
|
|
if isinstance(context, list):
|
|
txt_list, mask_txt_list, y_list = [], [], []
|
|
for sample_id, (ctx, yy) in enumerate(zip(context, y)):
|
|
txt_list.append(self.txt_in(ctx.to(x)))
|
|
mask_txt_list.append(torch.ones(txt_list[-1].shape[0]).to(ctx.device, non_blocking=True).bool())
|
|
y_list.append(yy.to(x))
|
|
txt = pad_sequence(tuple(txt_list), batch_first=True)
|
|
txt_ids = torch.zeros(txt.shape[0], txt.shape[1], 3).to(x)
|
|
mask_txt = pad_sequence(tuple(mask_txt_list), batch_first=True)
|
|
y = torch.cat(y_list, dim=0)
|
|
assert y.ndim == 2 and txt.ndim == 3
|
|
else:
|
|
txt = self.txt_in(context)
|
|
txt_ids = torch.zeros(context.shape[0], context.shape[1], 3).to(x)
|
|
mask_txt = torch.ones(context.shape[0], context.shape[1]).to(x.device, non_blocking=True).bool()
|
|
return x, x_ids, txt, txt_ids, y, mask_x, mask_txt, x_seq_length
|
|
|
|
@staticmethod
|
|
def get_config_template():
|
|
return dict_to_yaml('MODEL',
|
|
__class__.__name__,
|
|
FluxMRACEPlus.para_dict,
|
|
set_name=True)
|
|
|
|
@BACKBONES.register_class()
|
|
class FluxMRModiACEPlus(FluxMR):
|
|
def __init__(self, cfg, logger = None):
|
|
super().__init__(cfg, logger)
|
|
def prepare_input(self, x, cond):
|
|
context, y = cond["context"], cond["y"]
|
|
batch_frames, batch_frames_ids = [], []
|
|
for ix, shape, imask, ie, im, ie_mask in zip(x,
|
|
cond['x_shapes'],
|
|
cond['x_mask'],
|
|
cond['edit'],
|
|
cond['modify'],
|
|
cond['edit_mask']):
|
|
# unpack image from sequence
|
|
ix = ix[:, :shape[0] * shape[1]].view(-1, shape[0], shape[1])
|
|
imask = torch.ones_like(
|
|
ix[[0], :, :]) if imask is None else imask.squeeze(0)
|
|
if len(ie) > 0:
|
|
ie = [iie.squeeze(0) for iie in ie]
|
|
im = [iim.squeeze(0) for iim in im]
|
|
ie_mask = [
|
|
torch.ones(
|
|
(ix.shape[0] * 4, ix.shape[1],
|
|
ix.shape[2])) if iime is None else iime.squeeze(0)
|
|
for iime in ie_mask
|
|
]
|
|
im = torch.cat(im, dim=-1)
|
|
ie = torch.cat(ie, dim=-1)
|
|
ie_mask = torch.cat(ie_mask, dim=-1)
|
|
else:
|
|
ie, im, ie_mask = torch.zeros_like(ix).to(x), torch.zeros_like(ix).to(x), torch.ones_like(
|
|
imask).to(x),
|
|
ix = torch.cat([ix, ie, im, ie_mask], dim=0)
|
|
c, h, w = ix.shape
|
|
ix = rearrange(ix,
|
|
'c (h ph) (w pw) -> (h w) (c ph pw)',
|
|
ph=2,
|
|
pw=2)
|
|
ix_id = torch.zeros(h // 2, w // 2, 3)
|
|
ix_id[..., 1] = ix_id[..., 1] + torch.arange(h // 2)[:, None]
|
|
ix_id[..., 2] = ix_id[..., 2] + torch.arange(w // 2)[None, :]
|
|
ix_id = rearrange(ix_id, 'h w c -> (h w) c')
|
|
batch_frames.append([ix])
|
|
batch_frames_ids.append([ix_id])
|
|
x_list, x_id_list, mask_x_list, x_seq_length = [], [], [], []
|
|
for frames, frame_ids in zip(batch_frames, batch_frames_ids):
|
|
proj_frames = []
|
|
for idx, one_frame in enumerate(frames):
|
|
one_frame = self.img_in(one_frame)
|
|
proj_frames.append(one_frame)
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ix = torch.cat(proj_frames, dim=0)
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|
if_id = torch.cat(frame_ids, dim=0)
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|
x_list.append(ix)
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|
x_id_list.append(if_id)
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|
mask_x_list.append(torch.ones(ix.shape[0]).to(ix.device, non_blocking=True).bool())
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|
x_seq_length.append(ix.shape[0])
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|
# if len(x_list) < 1: import pdb;pdb.set_trace()
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|
x = pad_sequence(tuple(x_list), batch_first=True)
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|
x_ids = pad_sequence(tuple(x_id_list), batch_first=True).to(x) # [b,pad_seq,2] pad (0.,0.) at dim2
|
|
mask_x = pad_sequence(tuple(mask_x_list), batch_first=True)
|
|
if isinstance(context, list):
|
|
txt_list, mask_txt_list, y_list = [], [], []
|
|
for sample_id, (ctx, yy) in enumerate(zip(context, y)):
|
|
txt_list.append(self.txt_in(ctx.to(x)))
|
|
mask_txt_list.append(torch.ones(txt_list[-1].shape[0]).to(ctx.device, non_blocking=True).bool())
|
|
y_list.append(yy.to(x))
|
|
txt = pad_sequence(tuple(txt_list), batch_first=True)
|
|
txt_ids = torch.zeros(txt.shape[0], txt.shape[1], 3).to(x)
|
|
mask_txt = pad_sequence(tuple(mask_txt_list), batch_first=True)
|
|
y = torch.cat(y_list, dim=0)
|
|
assert y.ndim == 2 and txt.ndim == 3
|
|
else:
|
|
txt = self.txt_in(context)
|
|
txt_ids = torch.zeros(context.shape[0], context.shape[1], 3).to(x)
|
|
mask_txt = torch.ones(context.shape[0], context.shape[1]).to(x.device, non_blocking=True).bool()
|
|
return x, x_ids, txt, txt_ids, y, mask_x, mask_txt, x_seq_length
|
|
|
|
@staticmethod
|
|
def get_config_template():
|
|
return dict_to_yaml('MODEL',
|
|
__class__.__name__,
|
|
FluxMRACEPlus.para_dict,
|
|
set_name=True)
|