Wan2.1/models/flux/sampling.py

import math
from typing import Callable

import numpy as np
import torch
from einops import rearrange, repeat
from PIL import Image
from torch import Tensor

from .model import Flux
from .modules.autoencoder import AutoEncoder
from .modules.conditioner import HFEmbedder
from .modules.image_embedders import CannyImageEncoder, DepthImageEncoder, ReduxImageEncoder
from .util import PREFERED_KONTEXT_RESOLUTIONS
from einops import rearrange, repeat
from typing import Literal
import torchvision.transforms.functional as TVF


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),
        dtype=dtype,
        device=device,
        generator=torch.Generator(device=device).manual_seed(seed),
    )


def prepare_prompt(t5: HFEmbedder, clip: HFEmbedder, bs: int, prompt: str | list[str], neg: bool = False, device: str = "cuda") -> dict[str, Tensor]:
    if bs == 1 and not isinstance(prompt, str):
        bs = len(prompt)

    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 {
        "neg_txt" if neg else "txt": txt.to(device),
        "neg_txt_ids" if neg else "txt_ids": txt_ids.to(device),
        "neg_vec" if neg else "vec": vec.to(device),
    }


def prepare_img( img: Tensor) -> dict[str, Tensor]:
    bs, c, h, w = img.shape

    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)

    return {
        "img": img,
        "img_ids": img_ids.to(img.device),
    }





def prepare_redux(
    t5: HFEmbedder,
    clip: HFEmbedder,
    img: Tensor,
    prompt: str | list[str],
    encoder: ReduxImageEncoder,
    img_cond_path: str,
) -> dict[str, Tensor]:
    bs, _, h, w = img.shape
    if bs == 1 and not isinstance(prompt, str):
        bs = len(prompt)

    img_cond = Image.open(img_cond_path).convert("RGB")
    with torch.no_grad():
        img_cond = encoder(img_cond)

    img_cond = img_cond.to(torch.bfloat16)
    if img_cond.shape[0] == 1 and bs > 1:
        img_cond = repeat(img_cond, "1 ... -> bs ...", bs=bs)

    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)
    txt = torch.cat((txt, img_cond.to(txt)), dim=-2)
    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 prepare_kontext(
    ae: AutoEncoder,
    img_cond_list: list,
    seed: int,
    device: torch.device,
    target_width: int | None = None,
    target_height: int | None = None,
    bs: int = 1,

) -> tuple[dict[str, Tensor], int, int]:
    # load and encode the conditioning image

    img_cond_seq = None
    img_cond_seq_ids = None
    if img_cond_list == None: img_cond_list = []
    height_offset = 0
    width_offset = 0
    for cond_no, img_cond in enumerate(img_cond_list): 
        width, height = img_cond.size
        aspect_ratio = width / height

        # Kontext is trained on specific resolutions, using one of them is recommended
        _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS)
        width = 2 * int(width / 16)
        height = 2 * int(height / 16)

        img_cond = img_cond.resize((8 * width, 8 * height), Image.Resampling.LANCZOS)
        img_cond = np.array(img_cond)
        img_cond = torch.from_numpy(img_cond).float() / 127.5 - 1.0
        img_cond = rearrange(img_cond, "h w c -> 1 c h w")
        with torch.no_grad():
            img_cond_latents = ae.encode(img_cond.to(device))

        img_cond_latents = img_cond_latents.to(torch.bfloat16)
        img_cond_latents = rearrange(img_cond_latents, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
        if img_cond.shape[0] == 1 and bs > 1:
            img_cond_latents = repeat(img_cond_latents, "1 ... -> bs ...", bs=bs)
        img_cond = None

        # image ids are the same as base image with the first dimension set to 1
        # instead of 0
        img_cond_ids = torch.zeros(height // 2, width // 2, 3)
        img_cond_ids[..., 0] = 1
        img_cond_ids[..., 1] = img_cond_ids[..., 1] + torch.arange(height // 2)[:, None] + height_offset
        img_cond_ids[..., 2] = img_cond_ids[..., 2] + torch.arange(width // 2)[None, :] + width_offset
        img_cond_ids = repeat(img_cond_ids, "h w c -> b (h w) c", b=bs)
        height_offset +=  height // 2 
        width_offset +=  width // 2

        if target_width is None:
            target_width = 8 * width
        if target_height is None:
            target_height = 8 * height
        img_cond_ids = img_cond_ids.to(device)
        if cond_no == 0:
            img_cond_seq, img_cond_seq_ids  = img_cond_latents, img_cond_ids
        else:
            img_cond_seq, img_cond_seq_ids  =  torch.cat([img_cond_seq, img_cond_latents], dim=1), torch.cat([img_cond_seq_ids, img_cond_ids], dim=1)
        
    return_dict = {
        "img_cond_seq": img_cond_seq,
        "img_cond_seq_ids": img_cond_seq_ids,
    }
    img = get_noise(
        bs,
        target_height,
        target_width,
        device=device,
        dtype=torch.bfloat16,
        seed=seed,
    )
    return_dict.update(prepare_img(img))

    return return_dict, target_height, target_width


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],
    guidance: float = 4.0,
    real_guidance_scale = None,
    # extra img tokens (channel-wise)
    neg_txt: Tensor = None,
    neg_txt_ids: Tensor= None,
    neg_vec: Tensor = None,
    img_cond: Tensor | None = None,
    # extra img tokens (sequence-wise)
    img_cond_seq: Tensor | None = None,
    img_cond_seq_ids: Tensor | None = None,
    siglip_embedding = None,
    siglip_embedding_ids = None,
    callback=None,
    pipeline=None,
    loras_slists=None,
    unpack_latent = None,
    joint_pass= False,
):

    kwargs = {'pipeline': pipeline, 'callback': callback, "img_len" : img.shape[1], "siglip_embedding": siglip_embedding, "siglip_embedding_ids": siglip_embedding_ids}

    if callback != None:
        callback(-1, None, True)

    updated_num_steps= len(timesteps) -1
    if callback != None:
        from shared.utils.loras_mutipliers import update_loras_slists
        update_loras_slists(model, loras_slists, updated_num_steps)
        callback(-1, None, True, override_num_inference_steps = updated_num_steps)
    from mmgp import offload
    # this is ignored for schnell
    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:])):
        offload.set_step_no_for_lora(model, i)
        if pipeline._interrupt:
            return None

        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
        img_input = img
        img_input_ids = img_ids
        if img_cond is not None:
            img_input = torch.cat((img, img_cond), dim=-1)
        if img_cond_seq is not None:
            img_input = torch.cat((img_input, img_cond_seq), dim=1)
            img_input_ids = torch.cat((img_input_ids, img_cond_seq_ids), dim=1)
        if not joint_pass or real_guidance_scale == 1:
            pred = model(
                img=img_input,
                img_ids=img_input_ids,
                txt_list=[txt],
                txt_ids_list=[txt_ids],
                y_list=[vec],
                timesteps=t_vec,
                guidance=guidance_vec,
                **kwargs
            )[0]
            if pred == None: return None
            if real_guidance_scale> 1:
                neg_pred = model(
                    img=img_input,
                    img_ids=img_input_ids,
                    txt_list=[neg_txt],
                    txt_ids_list=[neg_txt_ids],
                    y_list=[neg_vec],
                    timesteps=t_vec,
                    guidance=guidance_vec,
                    **kwargs
                )[0]
                if neg_pred == None: return None
        else:
            pred, neg_pred = model(
                img=img_input,
                img_ids=img_input_ids,
                txt_list=[txt, neg_txt],
                txt_ids_list=[txt_ids, neg_txt_ids],
                y_list=[vec, neg_vec],
                timesteps=t_vec,
                guidance=guidance_vec,
                **kwargs
            )
            if pred == None: return None

        if real_guidance_scale > 1:
            pred = neg_pred + real_guidance_scale * (pred - neg_pred)

        img += (t_prev - t_curr) * pred
        if callback is not None:
            preview = unpack_latent(img).transpose(0,1)
            callback(i, preview, False)         


    return img

def prepare_multi_ip(
    ae: AutoEncoder,
    img_cond_list: list,
    seed: int,
    device: torch.device,
    target_width: int | None = None,
    target_height: int | None = None,
    bs: int = 1,
    pe: Literal["d", "h", "w", "o"] = "d",
) -> dict[str, Tensor]:
    ref_imgs = img_cond_list
    assert pe in ["d", "h", "w", "o"]

    ref_imgs = [
        ae.encode(
            (TVF.to_tensor(ref_img) * 2.0 - 1.0)
            .unsqueeze(0)
            .to(device, torch.float32)
        ).to(torch.bfloat16)
        for ref_img in img_cond_list
    ]

    img = get_noise( bs, target_height, target_width, device=device, dtype=torch.bfloat16, seed=seed)
    bs, c, h, w = img.shape
    # tgt img
    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)
    img_cond_seq = img_cond_seq_ids = None
    pe_shift_w, pe_shift_h = w // 2, h // 2
    for cond_no, ref_img in enumerate(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)

        if target_width is None:
            target_width = 8 * ref_w1
        if target_height is None:
            target_height = 8 * ref_h1
        ref_img_ids1 = ref_img_ids1.to(device)
        if cond_no == 0:
            img_cond_seq, img_cond_seq_ids  = ref_img, ref_img_ids1
        else:
            img_cond_seq, img_cond_seq_ids  =  torch.cat([img_cond_seq, ref_img], dim=1), torch.cat([img_cond_seq_ids, ref_img_ids1], dim=1)


        # 更新pe shift
        pe_shift_h += ref_h1 // 2
        pe_shift_w += ref_w1 // 2

    return {
        "img": img,
        "img_ids": img_ids.to(img.device),
        "img_cond_seq": img_cond_seq,
        "img_cond_seq_ids": img_cond_seq_ids,
    }, target_height, target_width


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,
    )