sampling.py

import math
from typing import Callable

import torch
from einops import rearrange, repeat
from torch import Tensor

from modules.flux_model import Flux
from modules.conditioner import HFEmbedder


@torch.inference_mode()
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),
    )


@torch.inference_mode()
def prepare(
    t5: HFEmbedder,
    clip: HFEmbedder,
    img: Tensor,
    prompt: str | list[str],
    target_device: torch.device = torch.device("cuda:0"),
    target_dtype: torch.dtype = torch.float16,
) -> 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, device=target_device, dtype=target_dtype)
    img_ids[..., 1] = (
        img_ids[..., 1]
        + torch.arange(h // 2, device=target_device, dtype=target_dtype)[:, None]
    )
    img_ids[..., 2] = (
        img_ids[..., 2]
        + torch.arange(w // 2, device=target_device, dtype=target_dtype)[None, :]
    )
    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)

    if isinstance(prompt, str):
        prompt = [prompt]
    txt = t5(prompt).to(target_device, dtype=target_dtype)
    if txt.shape[0] == 1 and bs > 1:
        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
    txt_ids = torch.zeros(bs, txt.shape[1], 3, device=target_device, dtype=target_dtype)

    vec = clip(prompt).to(target_device, dtype=target_dtype)
    if vec.shape[0] == 1 and bs > 1:
        vec = repeat(vec, "1 ... -> bs ...", bs=bs)

    return {
        "img": img,
        "img_ids": img_ids,
        "txt": txt,
        "txt_ids": txt_ids,
        "vec": vec,
    }


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()


@torch.inference_mode()
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,
    dtype: torch.dtype = torch.bfloat16,
    device: torch.device = torch.device("cuda:0"),
):
    from tqdm import tqdm

    # this is ignored for schnell
    guidance_vec = torch.full((img.shape[0],), guidance, device=device, dtype=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=dtype, device=device)
        pred = model(
            img=img,
            img_ids=img_ids,
            txt=txt,
            txt_ids=txt_ids,
            y=vec,
            timesteps=t_vec,
            guidance=guidance_vec,
        )

        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,
    )