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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) -> 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)
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:
# 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,
timesteps: list[float],
guidance: float = 4.0,
id_weight=1.0,
id=None,
start_step=0,
uncond_id=None,
true_cfg=1.0,
timestep_to_start_cfg=1,
neg_txt=None,
neg_txt_ids=None,
neg_vec=None,
):
# this is ignored for schnell
guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
use_true_cfg = abs(true_cfg - 1.0) > 1e-2
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)
pred = model(
img=img,
img_ids=img_ids,
txt=txt,
txt_ids=txt_ids,
y=vec,
timesteps=t_vec,
guidance=guidance_vec,
id=id if i >= start_step else None,
id_weight=id_weight,
)
if use_true_cfg and i >= timestep_to_start_cfg:
neg_pred = model(
img=img,
img_ids=img_ids,
txt=neg_txt,
txt_ids=neg_txt_ids,
y=neg_vec,
timesteps=t_vec,
guidance=guidance_vec,
id=uncond_id if i >= start_step else None,
id_weight=id_weight,
)
pred = neg_pred + true_cfg * (pred - neg_pred)
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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