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import math
from typing import Callable
import torch
from einops import rearrange, repeat
from torch import Tensor
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]) -> dict[str, Tensor]:
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 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)
# img = rearrange(img, "b c d -> 1 (b c) d")
# img_ids = rearrange(img_ids, "b c d -> 1 (b c) d")
# txt = txt[0].unsqueeze(0)
# txt_ids = txt_ids[0].unsqueeze(0)
# vec = vec[0].unsqueeze(0)
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 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
) -> Callable[[float], float]:
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:
# estimate 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],
inverse,
info,
guidance: float = 4.0,
use_solver = True,
):
# this is ignored for schnell
inject_list = [True] * info['inject_step'] + [False] * (len(timesteps[:-1]) - info['inject_step'])
if info['partial'] is not None:
timesteps = timesteps[:info['partial']]
if inverse:
timesteps = timesteps[::-1]
inject_list = inject_list[::-1]
guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
for i, (t_curr, t_prev) in enumerate(zip(timesteps[:-1], timesteps[1:])):
t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
info['t'] = t_prev if inverse else t_curr
info['inverse'] = inverse
info['second_order'] = False
info['inject'] = inject_list[i]
# import pdb;pdb.set_trace()
if use_solver:
pred = model(
img=img,
img_ids=img_ids,
txt=txt,
txt_ids=txt_ids,
y=vec,
timesteps=t_vec,
guidance=guidance_vec,
info=info
)
img_mid = img + (t_prev - t_curr) / 2 * pred
t_vec_mid = torch.full((img.shape[0],), (t_curr + (t_prev - t_curr) / 2), dtype=img.dtype, device=img.device)
info['second_order'] = True
pred_mid = model(
img=img_mid,
img_ids=img_ids,
txt=txt,
txt_ids=txt_ids,
y=vec,
timesteps=t_vec_mid,
guidance=guidance_vec,
info=info
)
# import pdb;pdb.set_trace()
first_order = (pred_mid - pred) / ((t_prev - t_curr) / 2)
img = img + (t_prev - t_curr) * pred + 0.5 * (t_prev - t_curr) ** 2 * first_order
else:
pred = model(
img=img,
img_ids=img_ids,
txt=txt,
txt_ids=txt_ids,
y=vec,
timesteps=t_vec,
guidance=guidance_vec,
info=info
)
img = img + (t_prev - t_curr) * pred
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,
)
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