mirror of
https://github.com/invoke-ai/InvokeAI
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110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
# Initially pulled from https://github.com/black-forest-labs/flux
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import math
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from typing import Callable
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import torch
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from einops import rearrange, repeat
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from torch import Tensor
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def get_noise(
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num_samples: int,
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height: int,
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width: int,
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device: torch.device,
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dtype: torch.dtype,
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seed: int,
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):
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# We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
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rand_device = "cpu"
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rand_dtype = torch.float16
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return torch.randn(
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num_samples,
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16,
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# allow for packing
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2 * math.ceil(height / 16),
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2 * math.ceil(width / 16),
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device=rand_device,
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dtype=rand_dtype,
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generator=torch.Generator(device=rand_device).manual_seed(seed),
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).to(device=device, dtype=dtype)
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def time_shift(mu: float, sigma: float, t: Tensor):
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return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
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def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
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m = (y2 - y1) / (x2 - x1)
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b = y1 - m * x1
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return lambda x: m * x + b
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def get_schedule(
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num_steps: int,
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image_seq_len: int,
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base_shift: float = 0.5,
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max_shift: float = 1.15,
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shift: bool = True,
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) -> list[float]:
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# extra step for zero
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timesteps = torch.linspace(1, 0, num_steps + 1)
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# shifting the schedule to favor high timesteps for higher signal images
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if shift:
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# estimate mu based on linear estimation between two points
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mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
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timesteps = time_shift(mu, 1.0, timesteps)
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return timesteps.tolist()
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def unpack(x: Tensor, height: int, width: int) -> Tensor:
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return rearrange(
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x,
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"b (h w) (c ph pw) -> b c (h ph) (w pw)",
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h=math.ceil(height / 16),
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w=math.ceil(width / 16),
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ph=2,
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pw=2,
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)
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def pack(x: Tensor) -> Tensor:
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# Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
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return rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
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def generate_img_ids(h: int, w: int, batch_size: int, device: torch.device, dtype: torch.dtype) -> Tensor:
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img_ids = torch.zeros(h // 2, w // 2, 3, device=device, dtype=dtype)
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img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=device, dtype=dtype)[:, None]
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img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=device, dtype=dtype)[None, :]
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img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size)
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return img_ids
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def prepare_latent_img_patches(img: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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"""Convert an input image in latent space to patches for diffusion.
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This implementation was extracted from:
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https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/sampling.py#L32
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Args:
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img (torch.Tensor): Input image in latent space.
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Returns:
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tuple[Tensor, Tensor]: (img, img_ids), as defined in the original flux repo.
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"""
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bs, c, h, w = img.shape
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img = pack(img)
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# Generate patch position ids.
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img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device, dtype=img.dtype)
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img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device, dtype=img.dtype)[:, None]
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img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device, dtype=img.dtype)[None, :]
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img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
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return img, img_ids
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