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Zero
Running
on
Zero
File size: 10,528 Bytes
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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 | list[str], use_spatial_condition=False, use_share_weight_referencenet=False, share_position_embedding=False) -> 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)
if use_spatial_condition:
if share_position_embedding:
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_ids = torch.cat([img_ids, img_ids], dim=1)
else:
img_ids = torch.zeros(h // 2, w, 3)
img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
img_ids[..., 2] = img_ids[..., 2] + torch.arange(w)[None, :]
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
elif use_share_weight_referencenet:
if share_position_embedding:
single_img_ids = torch.zeros(h // 2, w // 2, 3)
single_img_ids[..., 1] = single_img_ids[..., 1] + torch.arange(h // 2)[:, None]
single_img_ids[..., 2] = single_img_ids[..., 2] + torch.arange(w // 2)[None, :]
single_img_ids = repeat(single_img_ids, "h w c -> b (h w) c", b=bs)
img_ids = torch.cat([single_img_ids, single_img_ids], dim=1)
else:
# single_img_position_embedding
single_img_ids = torch.zeros(h // 2, w // 2, 3)
single_img_ids[..., 1] = single_img_ids[..., 1] + torch.arange(h // 2)[:, None]
single_img_ids[..., 2] = single_img_ids[..., 2] + torch.arange(w // 2)[None, :]
single_img_ids = repeat(single_img_ids, "h w c -> b (h w) c", b=bs)
# ref_and_noise_img_position_embedding
img_ids = torch.zeros(h // 2, w, 3)
img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
img_ids[..., 2] = img_ids[..., 2] + torch.arange(w)[None, :]
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
else:
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)
if not use_share_weight_referencenet:
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),
}
else:
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),
"single_img_ids": single_img_ids.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,
neg_txt: Tensor,
neg_txt_ids: Tensor,
neg_vec: Tensor,
# sampling parameters
timesteps: list[float],
guidance: float = 4.0,
true_gs = 1,
timestep_to_start_cfg=0,
# ip-adapter parameters
image_proj: Tensor=None,
neg_image_proj: Tensor=None,
ip_scale: Tensor | float = 1.0,
neg_ip_scale: Tensor | float = 1.0,
source_image: Tensor=None,
use_share_weight_referencenet=False,
single_img_ids=None,
neg_single_img_ids=None,
single_block_refnet=False,
double_block_refnet=False,
):
i = 0
# this is ignored for schnell
guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
for t_curr, t_prev in zip(timesteps[:-1], timesteps[1:]):
t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
if source_image is not None: # spatial condition or refnet
img = torch.cat([source_image, img],dim=-2)
pred = model(
img=img,
img_ids=img_ids,
txt=txt,
txt_ids=txt_ids,
y=vec,
timesteps=t_vec,
guidance=guidance_vec,
image_proj=image_proj,
ip_scale=ip_scale,
use_share_weight_referencenet=use_share_weight_referencenet,
single_img_ids=single_img_ids,
single_block_refnet=single_block_refnet,
double_block_refnet=double_block_refnet,
)
if 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,
image_proj=neg_image_proj,
ip_scale=neg_ip_scale,
use_share_weight_referencenet=use_share_weight_referencenet,
single_img_ids=neg_single_img_ids,
single_block_refnet=single_block_refnet,
double_block_refnet=double_block_refnet,
)
pred = neg_pred + true_gs * (pred - neg_pred)
if use_share_weight_referencenet:
zero_buffer = torch.zeros_like(pred)
pred = torch.cat([zero_buffer, pred], dim=1)
img = img + (t_prev - t_curr) * pred
if (source_image is not None): # spatial condition or refnet
latent_length = img.shape[-2] // 2
img = img[:,latent_length:,:]
i += 1
return img
def denoise_controlnet(
model: Flux,
controlnet:None,
# model input
img: Tensor,
img_ids: Tensor,
txt: Tensor,
txt_ids: Tensor,
vec: Tensor,
neg_txt: Tensor,
neg_txt_ids: Tensor,
neg_vec: Tensor,
controlnet_cond,
# sampling parameters
timesteps: list[float],
guidance: float = 4.0,
true_gs = 1,
controlnet_gs=0.7,
timestep_to_start_cfg=0,
# ip-adapter parameters
image_proj: Tensor=None,
neg_image_proj: Tensor=None,
ip_scale: Tensor | float = 1,
neg_ip_scale: Tensor | float = 1,
):
# this is ignored for schnell
i = 0
guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
for t_curr, t_prev in zip(timesteps[:-1], timesteps[1:]):
t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
block_res_samples = controlnet(
img=img,
img_ids=img_ids,
controlnet_cond=controlnet_cond,
txt=txt,
txt_ids=txt_ids,
y=vec,
timesteps=t_vec,
guidance=guidance_vec,
)
pred = model(
img=img,
img_ids=img_ids,
txt=txt,
txt_ids=txt_ids,
y=vec,
timesteps=t_vec,
guidance=guidance_vec,
block_controlnet_hidden_states=[i * controlnet_gs for i in block_res_samples],
image_proj=image_proj,
ip_scale=ip_scale,
)
if i >= timestep_to_start_cfg:
neg_block_res_samples = controlnet(
img=img,
img_ids=img_ids,
controlnet_cond=controlnet_cond,
txt=neg_txt,
txt_ids=neg_txt_ids,
y=neg_vec,
timesteps=t_vec,
guidance=guidance_vec,
)
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,
block_controlnet_hidden_states=[i * controlnet_gs for i in neg_block_res_samples],
image_proj=neg_image_proj,
ip_scale=neg_ip_scale,
)
pred = neg_pred + true_gs * (pred - neg_pred)
img = img + (t_prev - t_curr) * pred
i += 1
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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