File size: 8,252 Bytes
74da989 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | from ..tools import GlobalState, DistController
from .plugins import (
torch,
ModulePlugin,
GroupNormPlugin,
Conv3DSafeNewPligin,
Conv2DSafeNewPligin,
WanAttentionPlugin,
Conv2DSafeNewPliginStride2,
)
from diffusers.models.autoencoders.autoencoder_kl_wan import (
WanCausalConv3d,
WanAttentionBlock,
)
# from ...modules.vae import CausalConv3d, AttentionBlock
# from diffusers.models.autoencoders.autoencoder_kl_wan import WanCausalConv3d, WanAttentionBlock
class DistWrapper(object):
def __init__(
self, pipe, dist_controller: DistController, config, chunk_dim
) -> None:
super().__init__()
self.pipe = pipe
self.dist_controller = dist_controller
self.config = config
# Ensure chunk_dim is either 3 (temporal) or 4 (spatial) for proper parallel processing
assert chunk_dim == 3 or chunk_dim == 4
# Initialize global state to share distributed configuration across plugins
self.global_state = GlobalState(
{"dist_controller": dist_controller, "chunk_dim": chunk_dim}
)
self.plugin_mount()
# Configure plugin-specific hyperparameters for attention and convolution layers
plugin_configs = {
"attn": {
"padding": 24,
"top_k": 24,
"top_k_chunk_size": 24,
"attn_scale": 1.0,
"token_num_scale": True,
"dynamic_scale": True,
},
"conv_3d": {
"padding": 1,
},
"conv_layer": {},
}
self.global_state.set("plugin_configs", plugin_configs)
# torch.compile
# torch._dynamo.config.recompile_limit = 20
# Compile decoder with torch.compile for improved inference performance
with torch.no_grad():
# self.pipe.encoder = torch.compile(self.pipe.encoder)
self.pipe.decoder = torch.compile(self.pipe.decoder)
def plugin_mount(self):
self.plugins = {}
self.group_norm_plugin_mount()
self.conv_3d_plugin_mount()
self.conv_2d_plugin_stride2_mount() ##only for wan vae encoder
self.conv_2d_plugin_mount()
self.wanattention_plugin_mount()
def wanattention_plugin_mount(self):
# Mount attention plugins to WanAttentionBlock modules for distributed processing
self.plugins["wanattention"] = {}
wanattention_s = []
for module in self.pipe.encoder.named_modules():
# if self.dist_controller.is_master and module[1].__class__.__name__ == 'AttentionBlock':
# print("Encoder attn: ", module[0])
if (
"mid_block." in module[0]
and module[1].__class__.__name__ == "WanAttentionBlock"
):
wanattention_s.append(module[1])
for module in self.pipe.decoder.named_modules():
# if self.dist_controller.is_master and module[1].__class__.__name__ == 'AttentionBlock':
# print("Decoder attn: ", module[0])
if (
"mid_block." in module[0]
and module[1].__class__.__name__ == "WanAttentionBlock"
):
wanattention_s.append(module[1])
if self.dist_controller.is_master:
print(f"Found {len(wanattention_s)} wanattention_s")
for i, wanattention in enumerate(wanattention_s):
plugin_id = "wanattention", i
self.plugins["wanattention"][plugin_id] = WanAttentionPlugin(
wanattention, plugin_id, self.global_state
)
def group_norm_plugin_mount(self):
self.plugins["group_norm"] = {}
group_norms = []
for module in self.pipe.decoder.named_modules():
if ("norm_layer" in module[0]) and module[
1
].__class__.__name__ == "GroupNorm":
group_norms.append(module[1])
if self.dist_controller.is_master:
print(f"Found {len(group_norms)} group norms")
for i, group_norm in enumerate(group_norms):
plugin_id = "group_norm", i
self.plugins["group_norm"][plugin_id] = GroupNormPlugin(
group_norm, plugin_id, self.global_state
)
def conv_3d_plugin_mount(self):
self.plugins["conv_3d"] = {}
conv3d_s = []
for module in self.pipe.encoder.named_modules():
# if isinstance(module[1], CausalConv3d):
# print("Encoder conv3d: ", module[0], module[1].kernel_size[1])
if isinstance(module[1], WanCausalConv3d) and module[1].kernel_size[1] > 1:
# print(f"Found conv3d: {module[1]}")
conv3d_s.append(module[1])
for module in self.pipe.decoder.named_modules():
# if isinstance(module[1], CausalConv3d):
# print("Decoder conv3d: ", module[0], module[1].kernel_size[1])
if isinstance(module[1], WanCausalConv3d) and module[1].kernel_size[1] > 1:
# print(f"Found conv3d: {module[1]}")
conv3d_s.append(module[1])
if self.dist_controller.is_master:
print(f"Found {len(conv3d_s)} conv3d_s")
for i, conv in enumerate(conv3d_s):
plugin_id = "conv_3d", i
self.plugins["conv_3d"][plugin_id] = Conv3DSafeNewPligin(
conv, plugin_id, self.global_state
)
def conv_2d_plugin_stride2_mount(self):
self.plugins["conv_2d_stride2"] = {}
conv2d_stride2_s = []
for module in self.pipe.encoder.named_modules():
if ".resample" in module[0] and module[1].__class__.__name__ == "Conv2d":
conv2d_stride2_s.append(module[1])
if self.dist_controller.is_master:
print(f"Found {len(conv2d_stride2_s)} conv2d_stride2_s")
for i, conv in enumerate(conv2d_stride2_s):
plugin_id = "conv_2d_stride2", i
self.plugins["conv_2d_stride2"][plugin_id] = Conv2DSafeNewPliginStride2(
conv, plugin_id, self.global_state
)
def conv_2d_plugin_mount(self):
self.plugins["conv_2d"] = {}
conv2d_s = []
for module in self.pipe.decoder.named_modules():
if ".resample" in module[0] and module[1].__class__.__name__ == "Conv2d":
conv2d_s.append(module[1])
if self.dist_controller.is_master:
print(f"Found {len(conv2d_s)} conv2d_s")
for i, conv in enumerate(conv2d_s):
plugin_id = "conv_2d", i
self.plugins["conv_2d"][plugin_id] = Conv2DSafeNewPligin(
conv, plugin_id, self.global_state
)
def inference(
self,
local_pose_image,
local_latents,
config={},
pipe_configs={
"steps": 50,
"guidance_scale": 12,
"fps": 60,
"num_frames": 24 * 1,
"height": 320,
"width": 512,
"export_fps": 12,
"base_path": "./work/output",
"file_name": None,
},
plugin_configs={
"attn": {
"padding": 24,
"top_k": 24,
"top_k_chunk_size": 24,
"attn_scale": 1.0,
"token_num_scale": True,
"dynamic_scale": True,
},
"conv_3d": {
"padding": 1,
},
"conv_layer": {},
},
additional_info={},
):
# Run VAE encode-decode inference on local pose images and latents
self.plugin_mount()
# print("self.config seed: ", self.config["seed"])
self.global_state.set("plugin_configs", plugin_configs)
self.pipe = self.pipe.to(device="cuda", dtype=torch.bfloat16)
with torch.no_grad():
local_pose_image = local_pose_image.to(device="cuda", dtype=torch.bfloat16)
local_latents = local_latents.to(device="cuda", dtype=torch.bfloat16)
tmp_latents = self.pipe.encode(local_pose_image).latent_dist.mode()
latents = self.pipe.decode(local_latents, return_dict=False)[0]
return latents
|