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https://github.com/Wan-Video/Wan2.1.git
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312 lines
14 KiB
Python
312 lines
14 KiB
Python
from dataclasses import dataclass
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import torch
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from torch import Tensor, nn
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from .modules.layers import (
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DoubleStreamBlock,
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EmbedND,
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LastLayer,
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MLPEmbedder,
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SingleStreamBlock,
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timestep_embedding,
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DistilledGuidance,
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ChromaModulationOut,
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SigLIPMultiFeatProjModel,
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)
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from .modules.lora import LinearLora, replace_linear_with_lora
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@dataclass
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class FluxParams:
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in_channels: int
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out_channels: int
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vec_in_dim: int
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context_in_dim: int
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hidden_size: int
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mlp_ratio: float
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num_heads: int
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depth: int
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depth_single_blocks: int
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axes_dim: list[int]
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theta: int
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qkv_bias: bool
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guidance_embed: bool
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chroma: bool = False
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eso: bool = False
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class Flux(nn.Module):
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"""
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Transformer model for flow matching on sequences.
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"""
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def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0):
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# This function slices up the modulations tensor which has the following layout:
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# single : num_single_blocks * 3 elements
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# double_img : num_double_blocks * 6 elements
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# double_txt : num_double_blocks * 6 elements
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# final : 2 elements
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if block_type == "final":
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return (tensor[:, -2:-1, :], tensor[:, -1:, :])
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single_block_count = self.params.depth_single_blocks
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double_block_count = self.params.depth
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offset = 3 * idx
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if block_type == "single":
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return ChromaModulationOut.from_offset(tensor, offset)
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# Double block modulations are 6 elements so we double 3 * idx.
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offset *= 2
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if block_type in {"double_img", "double_txt"}:
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# Advance past the single block modulations.
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offset += 3 * single_block_count
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if block_type == "double_txt":
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# Advance past the double block img modulations.
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offset += 6 * double_block_count
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return (
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ChromaModulationOut.from_offset(tensor, offset),
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ChromaModulationOut.from_offset(tensor, offset + 3),
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)
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raise ValueError("Bad block_type")
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def __init__(self, params: FluxParams):
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super().__init__()
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self.params = params
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self.in_channels = params.in_channels
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self.out_channels = params.out_channels
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self.chroma = params.chroma
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if params.hidden_size % params.num_heads != 0:
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raise ValueError(
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f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
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)
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pe_dim = params.hidden_size // params.num_heads
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if sum(params.axes_dim) != pe_dim:
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raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
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self.hidden_size = params.hidden_size
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self.num_heads = params.num_heads
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self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
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self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
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self.guidance_in = (
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MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
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)
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self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
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if self.chroma:
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self.distilled_guidance_layer = DistilledGuidance(
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in_dim=64,
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hidden_dim=5120,
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out_dim=3072,
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n_layers=5,
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)
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else:
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self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
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self.vector_in = MLPEmbedder(params.vec_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=params.mlp_ratio,
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qkv_bias=params.qkv_bias,
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chroma_modulation = self.chroma,
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)
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for _ in range(params.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=params.mlp_ratio, chroma_modulation = self.chroma)
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for _ in range(params.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, chroma_modulation = self.chroma)
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def preprocess_loras(self, model_type, sd):
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new_sd = {}
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if len(sd) == 0: return sd
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def swap_scale_shift(weight):
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shift, scale = weight.chunk(2, dim=0)
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new_weight = torch.cat([scale, shift], dim=0)
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return new_weight
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first_key= next(iter(sd))
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if first_key.startswith("lora_unet_"):
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new_sd = {}
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print("Converting Lora Safetensors format to Lora Diffusers format")
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repl_list = ["linear1", "linear2", "modulation", "img_attn", "txt_attn", "img_mlp", "txt_mlp", "img_mod", "txt_mod"]
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src_list = ["_" + k + "." for k in repl_list]
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src_list2 = ["_" + k + "_" for k in repl_list]
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tgt_list = ["." + k + "." for k in repl_list]
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for k,v in sd.items():
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k = k.replace("lora_unet_blocks_","diffusion_model.blocks.")
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k = k.replace("lora_unet__blocks_","diffusion_model.blocks.")
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k = k.replace("lora_unet_single_blocks_","diffusion_model.single_blocks.")
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k = k.replace("lora_unet_double_blocks_","diffusion_model.double_blocks.")
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for s,s2, t in zip(src_list, src_list2, tgt_list):
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k = k.replace(s,t)
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k = k.replace(s2,t)
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k = k.replace("lora_up","lora_B")
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k = k.replace("lora_down","lora_A")
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new_sd[k] = v
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elif first_key.startswith("transformer."):
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root_src = ["time_text_embed.timestep_embedder.linear_1", "time_text_embed.timestep_embedder.linear_2", "time_text_embed.text_embedder.linear_1", "time_text_embed.text_embedder.linear_2",
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"time_text_embed.guidance_embedder.linear_1", "time_text_embed.guidance_embedder.linear_2",
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"x_embedder", "context_embedder", "proj_out" ]
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root_tgt = ["time_in.in_layer", "time_in.out_layer", "vector_in.in_layer", "vector_in.out_layer",
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"guidance_in.in_layer", "guidance_in.out_layer",
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"img_in", "txt_in", "final_layer.linear" ]
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double_src = ["norm1.linear", "norm1_context.linear", "attn.norm_q", "attn.norm_k", "ff.net.0.proj", "ff.net.2", "ff_context.net.0.proj", "ff_context.net.2", "attn.to_out.0" ,"attn.to_add_out", "attn.to_out", ".attn.to_", ".attn.add_q_proj.", ".attn.add_k_proj.", ".attn.add_v_proj.", ]
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double_tgt = ["img_mod.lin", "txt_mod.lin", "img_attn.norm.query_norm", "img_attn.norm.key_norm", "img_mlp.0", "img_mlp.2", "txt_mlp.0", "txt_mlp.2", "img_attn.proj", "txt_attn.proj", "img_attn.proj", ".img_attn.", ".txt_attn.q.", ".txt_attn.k.", ".txt_attn.v."]
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single_src = ["norm.linear", "attn.norm_q", "attn.norm_k", "proj_out",".attn.to_q.", ".attn.to_k.", ".attn.to_v.", ".proj_mlp."]
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single_tgt = ["modulation.lin","norm.query_norm", "norm.key_norm", "linear2", ".linear1_attn_q.", ".linear1_attn_k.", ".linear1_attn_v.", ".linear1_mlp."]
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for k,v in sd.items():
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if k.startswith("transformer.single_transformer_blocks"):
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k = k.replace("transformer.single_transformer_blocks", "diffusion_model.single_blocks")
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for src, tgt in zip(single_src, single_tgt):
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k = k.replace(src, tgt)
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elif k.startswith("transformer.transformer_blocks"):
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k = k.replace("transformer.transformer_blocks", "diffusion_model.double_blocks")
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for src, tgt in zip(double_src, double_tgt):
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k = k.replace(src, tgt)
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else:
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k = k.replace("transformer.", "diffusion_model.")
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for src, tgt in zip(root_src, root_tgt):
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k = k.replace(src, tgt)
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if "norm_out.linear" in k:
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if "lora_B" in k:
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v = swap_scale_shift(v)
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k = k.replace("norm_out.linear", "final_layer.adaLN_modulation.1")
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new_sd[k] = v
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# elif not first_key.startswith("diffusion_model.") and not first_key.startswith("transformer."):
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# for k,v in sd.items():
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# if "double" in k:
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# k = k.replace(".processor.proj_lora1.", ".img_attn.proj.lora_")
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# k = k.replace(".processor.proj_lora2.", ".txt_attn.proj.lora_")
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# k = k.replace(".processor.qkv_lora1.", ".img_attn.qkv.lora_")
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# k = k.replace(".processor.qkv_lora2.", ".txt_attn.qkv.lora_")
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# else:
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# k = k.replace(".processor.qkv_lora.", ".linear1_qkv.lora_")
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# k = k.replace(".processor.proj_lora.", ".linear2.lora_")
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# k = "diffusion_model." + k
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# new_sd[k] = v
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# from mmgp import safetensors2
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# safetensors2.torch_write_file(new_sd, "fff.safetensors")
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else:
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new_sd = sd
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return new_sd
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def forward(
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self,
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img: Tensor,
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img_ids: Tensor,
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txt_list,
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txt_ids_list,
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timesteps: Tensor,
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y_list,
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img_len = 0,
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guidance: Tensor | None = None,
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callback= None,
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pipeline =None,
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siglip_embedding = None,
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siglip_embedding_ids = None,
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) -> Tensor:
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sz = len(txt_list)
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# running on sequences img
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img = self.img_in(img)
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img_list = [img] if sz==1 else [img, img.clone()]
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if self.chroma:
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mod_index_length = 344
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distill_timestep = timestep_embedding(timesteps, 16).to(img.device, img.dtype)
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guidance = torch.tensor([0.]* distill_timestep.shape[0])
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distil_guidance = timestep_embedding(guidance, 16).to(img.device, img.dtype)
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modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype)
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modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype)
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timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype)
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input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype)
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mod_vectors = self.distilled_guidance_layer(input_vec)
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else:
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vec = self.time_in(timestep_embedding(timesteps, 256))
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if self.params.guidance_embed:
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if guidance is None:
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raise ValueError("Didn't get guidance strength for guidance distilled model.")
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vec += self.guidance_in(timestep_embedding(guidance, 256))
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vec_list = [ vec + self.vector_in(y) for y in y_list]
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img = None
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txt_list = [self.txt_in(txt) for txt in txt_list ]
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if siglip_embedding is not None:
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txt_list = [torch.cat((siglip_embedding, txt) , dim=1) for txt in txt_list]
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txt_ids_list = [torch.cat((siglip_embedding_ids, txt_id) , dim=1) for txt_id in txt_ids_list]
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pe_list = [self.pe_embedder(torch.cat((txt_ids, img_ids), dim=1)) for txt_ids in txt_ids_list]
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for i, block in enumerate(self.double_blocks):
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if self.chroma: vec_list = [( self.get_modulations(mod_vectors, "double_img", idx=i), self.get_modulations(mod_vectors, "double_txt", idx=i))] * sz
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if callback != None:
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callback(-1, None, False, True)
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if pipeline._interrupt:
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return [None] * sz
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for img, txt, pe, vec in zip(img_list, txt_list, pe_list, vec_list):
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img[...], txt[...] = block(img=img, txt=txt, vec=vec, pe=pe)
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img = txt = pe = vec= None
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img_list = [torch.cat((txt, img), 1) for txt, img in zip(txt_list, img_list)]
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for i, block in enumerate(self.single_blocks):
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if self.chroma: vec_list= [self.get_modulations(mod_vectors, "single", idx=i)] * sz
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if callback != None:
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callback(-1, None, False, True)
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if pipeline._interrupt:
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return [None] * sz
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for img, pe, vec in zip(img_list, pe_list, vec_list):
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img[...]= block(x=img, vec=vec, pe=pe)
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img = pe = vec = None
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img_list = [ img[:, txt.shape[1] : txt.shape[1] + img_len, ...] for img, txt in zip(img_list, txt_list)]
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if self.chroma: vec_list = [self.get_modulations(mod_vectors, "final")] * sz
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out_list = []
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for i, (img, vec) in enumerate(zip(img_list, vec_list)):
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out_list.append( self.final_layer(img, vec)) # (N, T, patch_size ** 2 * out_channels)
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img_list[i] = img = vec = None
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return out_list
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class FluxLoraWrapper(Flux):
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def __init__(
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self,
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lora_rank: int = 128,
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lora_scale: float = 1.0,
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*args,
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**kwargs,
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) -> None:
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super().__init__(*args, **kwargs)
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self.lora_rank = lora_rank
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replace_linear_with_lora(
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self,
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max_rank=lora_rank,
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scale=lora_scale,
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)
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def set_lora_scale(self, scale: float) -> None:
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for module in self.modules():
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if isinstance(module, LinearLora):
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module.set_scale(scale=scale)
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