Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,220 Bytes
def2fd8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 |
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates. All rights reserved.
# Copyright (c) 2024 Black Forest Labs and The XLabs-AI Team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Literal
import torch
from einops import rearrange, repeat
from torch import Tensor
from tqdm import tqdm
from .model import Flux
from .modules.conditioner import HFEmbedder
def get_noise(
num_samples: int,
height: int,
width: int,
device: torch.device,
dtype: torch.dtype,
seed: int,
):
return torch.randn(
num_samples,
16,
# allow for packing
2 * math.ceil(height / 16),
2 * math.ceil(width / 16),
device=device,
dtype=dtype,
generator=torch.Generator(device=device).manual_seed(seed),
)
def prepare(
t5: HFEmbedder,
clip: HFEmbedder,
img: Tensor,
prompt: str | list[str],
ref_img: None | Tensor=None,
pe: Literal['d', 'h', 'w', 'o'] ='d'
) -> dict[str, Tensor]:
assert pe in ['d', 'h', 'w', 'o']
bs, c, h, w = img.shape
if bs == 1 and not isinstance(prompt, str):
bs = len(prompt)
img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
if img.shape[0] == 1 and bs > 1:
img = repeat(img, "1 ... -> bs ...", bs=bs)
img_ids = torch.zeros(h // 2, w // 2, 3)
img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
if ref_img is not None:
_, _, ref_h, ref_w = ref_img.shape
ref_img = rearrange(ref_img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
if ref_img.shape[0] == 1 and bs > 1:
ref_img = repeat(ref_img, "1 ... -> bs ...", bs=bs)
ref_img_ids = torch.zeros(ref_h // 2, ref_w // 2, 3)
# img id分别在宽高偏移各自最大值
h_offset = h // 2 if pe in {'d', 'h'} else 0
w_offset = w // 2 if pe in {'d', 'w'} else 0
ref_img_ids[..., 1] = ref_img_ids[..., 1] + torch.arange(ref_h // 2)[:, None] + h_offset
ref_img_ids[..., 2] = ref_img_ids[..., 2] + torch.arange(ref_w // 2)[None, :] + w_offset
ref_img_ids = repeat(ref_img_ids, "h w c -> b (h w) c", b=bs)
if isinstance(prompt, str):
prompt = [prompt]
txt = t5(prompt)
if txt.shape[0] == 1 and bs > 1:
txt = repeat(txt, "1 ... -> bs ...", bs=bs)
txt_ids = torch.zeros(bs, txt.shape[1], 3)
vec = clip(prompt)
if vec.shape[0] == 1 and bs > 1:
vec = repeat(vec, "1 ... -> bs ...", bs=bs)
if ref_img is not None:
return {
"img": img,
"img_ids": img_ids.to(img.device),
"ref_img": ref_img,
"ref_img_ids": ref_img_ids.to(img.device),
"txt": txt.to(img.device),
"txt_ids": txt_ids.to(img.device),
"vec": vec.to(img.device),
}
else:
return {
"img": img,
"img_ids": img_ids.to(img.device),
"txt": txt.to(img.device),
"txt_ids": txt_ids.to(img.device),
"vec": vec.to(img.device),
}
def prepare_multi_ip(
t5: HFEmbedder,
clip: HFEmbedder,
img: Tensor,
prompt: str | list[str],
ref_imgs: list[Tensor] | None = None,
pe: Literal['d', 'h', 'w', 'o'] = 'd'
) -> dict[str, Tensor]:
assert pe in ['d', 'h', 'w', 'o']
bs, c, h, w = img.shape
if bs == 1 and not isinstance(prompt, str):
bs = len(prompt)
img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
if img.shape[0] == 1 and bs > 1:
img = repeat(img, "1 ... -> bs ...", bs=bs)
img_ids = torch.zeros(h // 2, w // 2, 3)
img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
ref_img_ids = []
ref_imgs_list = []
pe_shift_w, pe_shift_h = w // 2, h // 2
for ref_img in ref_imgs:
_, _, ref_h1, ref_w1 = ref_img.shape
ref_img = rearrange(ref_img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
if ref_img.shape[0] == 1 and bs > 1:
ref_img = repeat(ref_img, "1 ... -> bs ...", bs=bs)
ref_img_ids1 = torch.zeros(ref_h1 // 2, ref_w1 // 2, 3)
# img id分别在宽高偏移各自最大值
h_offset = pe_shift_h if pe in {'d', 'h'} else 0
w_offset = pe_shift_w if pe in {'d', 'w'} else 0
ref_img_ids1[..., 1] = ref_img_ids1[..., 1] + torch.arange(ref_h1 // 2)[:, None] + h_offset
ref_img_ids1[..., 2] = ref_img_ids1[..., 2] + torch.arange(ref_w1 // 2)[None, :] + w_offset
ref_img_ids1 = repeat(ref_img_ids1, "h w c -> b (h w) c", b=bs)
ref_img_ids.append(ref_img_ids1)
ref_imgs_list.append(ref_img)
# 更新pe shift
pe_shift_h += ref_h1 // 2
pe_shift_w += ref_w1 // 2
if isinstance(prompt, str):
prompt = [prompt]
txt = t5(prompt)
if txt.shape[0] == 1 and bs > 1:
txt = repeat(txt, "1 ... -> bs ...", bs=bs)
txt_ids = torch.zeros(bs, txt.shape[1], 3)
vec = clip(prompt)
if vec.shape[0] == 1 and bs > 1:
vec = repeat(vec, "1 ... -> bs ...", bs=bs)
return {
"img": img,
"img_ids": img_ids.to(img.device),
"ref_img": tuple(ref_imgs_list),
"ref_img_ids": [ref_img_id.to(img.device) for ref_img_id in ref_img_ids],
"txt": txt.to(img.device),
"txt_ids": txt_ids.to(img.device),
"vec": vec.to(img.device),
}
def time_shift(mu: float, sigma: float, t: Tensor):
return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
def get_lin_function(
x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15
):
m = (y2 - y1) / (x2 - x1)
b = y1 - m * x1
return lambda x: m * x + b
def get_schedule(
num_steps: int,
image_seq_len: int,
base_shift: float = 0.5,
max_shift: float = 1.15,
shift: bool = True,
) -> list[float]:
# extra step for zero
timesteps = torch.linspace(1, 0, num_steps + 1)
# shifting the schedule to favor high timesteps for higher signal images
if shift:
# eastimate mu based on linear estimation between two points
mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
timesteps = time_shift(mu, 1.0, timesteps)
return timesteps.tolist()
def denoise(
model: Flux,
# model input
img: Tensor,
img_ids: Tensor,
txt: Tensor,
txt_ids: Tensor,
vec: Tensor,
# sampling parameters
timesteps: list[float],
guidance: float = 4.0,
ref_img: Tensor=None,
ref_img_ids: Tensor=None,
):
i = 0
guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
for t_curr, t_prev in tqdm(zip(timesteps[:-1], timesteps[1:]), total=len(timesteps) - 1):
t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
pred = model(
img=img,
img_ids=img_ids,
ref_img=ref_img,
ref_img_ids=ref_img_ids,
txt=txt,
txt_ids=txt_ids,
y=vec,
timesteps=t_vec,
guidance=guidance_vec
)
img = img + (t_prev - t_curr) * pred
i += 1
return img
def unpack(x: Tensor, height: int, width: int) -> Tensor:
return rearrange(
x,
"b (h w) (c ph pw) -> b c (h ph) (w pw)",
h=math.ceil(height / 16),
w=math.ceil(width / 16),
ph=2,
pw=2,
)
|