third_party/src/flux_edit/sampling.py

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,
    )