add powerpaint v2
This commit is contained in:
Qing
parent
ccea072dc5
commit
911f7224b6
@ -13,7 +13,7 @@ from iopaint.helper import (
|
||||
switch_mps_device,
|
||||
)
|
||||
from iopaint.schema import InpaintRequest, HDStrategy, SDSampler
|
||||
from .helper.g_diffuser_bot import expand_image, expand_image2
|
||||
from .helper.g_diffuser_bot import expand_image
|
||||
from .utils import get_scheduler
|
||||
|
||||
|
||||
@ -35,8 +35,7 @@ class InpaintModel:
|
||||
self.init_model(device, **kwargs)
|
||||
|
||||
@abc.abstractmethod
|
||||
def init_model(self, device, **kwargs):
|
||||
...
|
||||
def init_model(self, device, **kwargs): ...
|
||||
|
||||
@staticmethod
|
||||
@abc.abstractmethod
|
||||
@ -53,8 +52,7 @@ class InpaintModel:
|
||||
...
|
||||
|
||||
@staticmethod
|
||||
def download():
|
||||
...
|
||||
def download(): ...
|
||||
|
||||
def _pad_forward(self, image, mask, config: InpaintRequest):
|
||||
origin_height, origin_width = image.shape[:2]
|
||||
@ -96,7 +94,7 @@ class InpaintModel:
|
||||
# logger.info(f"hd_strategy: {config.hd_strategy}")
|
||||
if config.hd_strategy == HDStrategy.CROP:
|
||||
if max(image.shape) > config.hd_strategy_crop_trigger_size:
|
||||
logger.info(f"Run crop strategy")
|
||||
logger.info("Run crop strategy")
|
||||
boxes = boxes_from_mask(mask)
|
||||
crop_result = []
|
||||
for box in boxes:
|
||||
@ -327,14 +325,12 @@ class DiffusionInpaintModel(InpaintModel):
|
||||
padding_r = max(0, cropper_r - image_r)
|
||||
padding_b = max(0, cropper_b - image_b)
|
||||
|
||||
expanded_image, mask_image = expand_image2(
|
||||
expanded_image, mask_image = expand_image(
|
||||
cropped_image,
|
||||
left=padding_l,
|
||||
top=padding_t,
|
||||
right=padding_r,
|
||||
bottom=padding_b,
|
||||
softness=config.sd_outpainting_softness,
|
||||
space=config.sd_outpainting_space,
|
||||
)
|
||||
|
||||
# 最终扩大了的 image, BGR
|
||||
@ -381,15 +377,6 @@ class DiffusionInpaintModel(InpaintModel):
|
||||
interpolation=cv2.INTER_CUBIC,
|
||||
)
|
||||
|
||||
# blend result, copy from g_diffuser_bot
|
||||
# mask_rgb = 1.0 - np_img_grey_to_rgb(mask / 255.0)
|
||||
# inpaint_result = np.clip(
|
||||
# inpaint_result * (1.0 - mask_rgb) + image * mask_rgb, 0.0, 255.0
|
||||
# )
|
||||
# original_pixel_indices = mask < 127
|
||||
# inpaint_result[original_pixel_indices] = image[:, :, ::-1][
|
||||
# original_pixel_indices
|
||||
# ]
|
||||
return inpaint_result
|
||||
|
||||
def set_scheduler(self, config: InpaintRequest):
|
||||
@ -412,7 +399,7 @@ class DiffusionInpaintModel(InpaintModel):
|
||||
if config.sd_match_histograms:
|
||||
result = self._match_histograms(result, image[:, :, ::-1], mask)
|
||||
|
||||
# if config.sd_mask_blur != 0:
|
||||
# k = 2 * config.sd_mask_blur + 1
|
||||
# mask = cv2.GaussianBlur(mask, (k, k), 0)
|
||||
if config.use_extender and config.sd_mask_blur != 0:
|
||||
k = 2 * config.sd_mask_blur + 1
|
||||
mask = cv2.GaussianBlur(mask, (k, k), 0)
|
||||
return result, image, mask
|
||||
|
||||
@ -1,174 +1,29 @@
|
||||
# code copy from: https://github.com/parlance-zz/g-diffuser-bot
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
|
||||
def np_img_grey_to_rgb(data):
|
||||
if data.ndim == 3:
|
||||
return data
|
||||
return np.expand_dims(data, 2) * np.ones((1, 1, 3))
|
||||
|
||||
|
||||
def convolve(data1, data2): # fast convolution with fft
|
||||
if data1.ndim != data2.ndim: # promote to rgb if mismatch
|
||||
if data1.ndim < 3:
|
||||
data1 = np_img_grey_to_rgb(data1)
|
||||
if data2.ndim < 3:
|
||||
data2 = np_img_grey_to_rgb(data2)
|
||||
return ifft2(fft2(data1) * fft2(data2))
|
||||
|
||||
|
||||
def fft2(data):
|
||||
if data.ndim > 2: # multiple channels
|
||||
out_fft = np.zeros(
|
||||
(data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128
|
||||
)
|
||||
for c in range(data.shape[2]):
|
||||
c_data = data[:, :, c]
|
||||
out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
|
||||
out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
|
||||
else: # single channel
|
||||
out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
|
||||
out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
|
||||
out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
|
||||
|
||||
return out_fft
|
||||
|
||||
|
||||
def ifft2(data):
|
||||
if data.ndim > 2: # multiple channels
|
||||
out_ifft = np.zeros(
|
||||
(data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128
|
||||
)
|
||||
for c in range(data.shape[2]):
|
||||
c_data = data[:, :, c]
|
||||
out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
|
||||
out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
|
||||
else: # single channel
|
||||
out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
|
||||
out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
|
||||
out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
|
||||
|
||||
return out_ifft
|
||||
|
||||
|
||||
def get_gradient_kernel(width, height, std=3.14, mode="linear"):
|
||||
window_scale_x = float(
|
||||
width / min(width, height)
|
||||
) # for non-square aspect ratios we still want a circular kernel
|
||||
window_scale_y = float(height / min(width, height))
|
||||
if mode == "gaussian":
|
||||
x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x
|
||||
kx = np.exp(-x * x * std)
|
||||
if window_scale_x != window_scale_y:
|
||||
y = (np.arange(height) / height * 2.0 - 1.0) * window_scale_y
|
||||
ky = np.exp(-y * y * std)
|
||||
else:
|
||||
y = x
|
||||
ky = kx
|
||||
return np.outer(kx, ky)
|
||||
elif mode == "linear":
|
||||
x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x
|
||||
if window_scale_x != window_scale_y:
|
||||
y = (np.arange(height) / height * 2.0 - 1.0) * window_scale_y
|
||||
else:
|
||||
y = x
|
||||
return np.clip(1.0 - np.sqrt(np.add.outer(x * x, y * y)) * std / 3.14, 0.0, 1.0)
|
||||
else:
|
||||
raise Exception("Error: Unknown mode in get_gradient_kernel: {0}".format(mode))
|
||||
|
||||
|
||||
def image_blur(data, std=3.14, mode="linear"):
|
||||
width = data.shape[0]
|
||||
height = data.shape[1]
|
||||
kernel = get_gradient_kernel(width, height, std, mode=mode)
|
||||
return np.real(convolve(data, kernel / np.sqrt(np.sum(kernel * kernel))))
|
||||
|
||||
|
||||
def soften_mask(mask_img, softness, space):
|
||||
if softness == 0:
|
||||
return mask_img
|
||||
softness = min(softness, 1.0)
|
||||
space = np.clip(space, 0.0, 1.0)
|
||||
original_max_opacity = np.max(mask_img)
|
||||
out_mask = mask_img <= 0.0
|
||||
blurred_mask = image_blur(mask_img, 3.5 / softness, mode="linear")
|
||||
blurred_mask = np.maximum(blurred_mask - np.max(blurred_mask[out_mask]), 0.0)
|
||||
mask_img *= blurred_mask # preserve partial opacity in original input mask
|
||||
mask_img /= np.max(mask_img) # renormalize
|
||||
mask_img = np.clip(mask_img - space, 0.0, 1.0) # make space
|
||||
mask_img /= np.max(mask_img) # and renormalize again
|
||||
mask_img *= original_max_opacity # restore original max opacity
|
||||
return mask_img
|
||||
|
||||
|
||||
def expand_image(
|
||||
cv2_img, top: int, right: int, bottom: int, left: int, softness: float, space: float
|
||||
):
|
||||
def expand_image(cv2_img, top: int, right: int, bottom: int, left: int):
|
||||
assert cv2_img.shape[2] == 3
|
||||
origin_h, origin_w = cv2_img.shape[:2]
|
||||
new_width = cv2_img.shape[1] + left + right
|
||||
new_height = cv2_img.shape[0] + top + bottom
|
||||
|
||||
# TODO: which is better?
|
||||
# new_img = np.random.randint(0, 255, (new_height, new_width, 3), np.uint8)
|
||||
new_img = cv2.copyMakeBorder(
|
||||
cv2_img, top, bottom, left, right, cv2.BORDER_REPLICATE
|
||||
)
|
||||
mask_img = np.zeros((new_height, new_width), np.uint8)
|
||||
mask_img[top: top + cv2_img.shape[0], left: left + cv2_img.shape[1]] = 255
|
||||
|
||||
if softness > 0.0:
|
||||
mask_img = soften_mask(mask_img / 255.0, softness / 100.0, space / 100.0)
|
||||
mask_img = (np.clip(mask_img, 0.0, 1.0) * 255.0).astype(np.uint8)
|
||||
|
||||
mask_image = 255.0 - mask_img # extract mask from alpha channel and invert
|
||||
rgb_init_image = (
|
||||
0.0 + new_img[:, :, 0:3]
|
||||
) # strip mask from init_img leaving only rgb channels
|
||||
|
||||
hard_mask = np.zeros_like(cv2_img[:, :, 0])
|
||||
if top != 0:
|
||||
hard_mask[0: origin_h // 2, :] = 255
|
||||
if bottom != 0:
|
||||
hard_mask[origin_h // 2:, :] = 255
|
||||
if left != 0:
|
||||
hard_mask[:, 0: origin_w // 2] = 255
|
||||
if right != 0:
|
||||
hard_mask[:, origin_w // 2:] = 255
|
||||
|
||||
hard_mask = cv2.copyMakeBorder(
|
||||
hard_mask, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255
|
||||
)
|
||||
mask_image = np.where(hard_mask > 0, mask_image, 0)
|
||||
return rgb_init_image.astype(np.uint8), mask_image.astype(np.uint8)
|
||||
|
||||
|
||||
def expand_image2(
|
||||
cv2_img, top: int, right: int, bottom: int, left: int, softness: float, space: float
|
||||
):
|
||||
assert cv2_img.shape[2] == 3
|
||||
origin_h, origin_w = cv2_img.shape[:2]
|
||||
new_width = cv2_img.shape[1] + left + right
|
||||
new_height = cv2_img.shape[0] + top + bottom
|
||||
|
||||
# TODO: which is better?
|
||||
# new_img = np.random.randint(0, 255, (new_height, new_width, 3), np.uint8)
|
||||
# new_img = np.ones((new_height, new_width, 3), np.uint8) * 255
|
||||
new_img = cv2.copyMakeBorder(
|
||||
cv2_img, top, bottom, left, right, cv2.BORDER_REPLICATE
|
||||
)
|
||||
|
||||
inner_padding_left = 13 if left > 0 else 0
|
||||
inner_padding_right = 13 if right > 0 else 0
|
||||
inner_padding_top = 13 if top > 0 else 0
|
||||
inner_padding_bottom = 13 if bottom > 0 else 0
|
||||
inner_padding_left = 0 if left > 0 else 0
|
||||
inner_padding_right = 0 if right > 0 else 0
|
||||
inner_padding_top = 0 if top > 0 else 0
|
||||
inner_padding_bottom = 0 if bottom > 0 else 0
|
||||
|
||||
mask_image = np.zeros(
|
||||
(
|
||||
origin_h - inner_padding_top - inner_padding_bottom
|
||||
, origin_w - inner_padding_left - inner_padding_right
|
||||
origin_h - inner_padding_top - inner_padding_bottom,
|
||||
origin_w - inner_padding_left - inner_padding_right,
|
||||
),
|
||||
np.uint8)
|
||||
np.uint8,
|
||||
)
|
||||
mask_image = cv2.copyMakeBorder(
|
||||
mask_image,
|
||||
top + inner_padding_top,
|
||||
@ -176,11 +31,11 @@ def expand_image2(
|
||||
left + inner_padding_left,
|
||||
right + inner_padding_right,
|
||||
cv2.BORDER_CONSTANT,
|
||||
value=255
|
||||
value=255,
|
||||
)
|
||||
# k = 2*int(min(origin_h, origin_w) // 6)+1
|
||||
k = 7
|
||||
mask_image = cv2.GaussianBlur(mask_image, (k, k), 0)
|
||||
# k = 7
|
||||
# mask_image = cv2.GaussianBlur(mask_image, (k, k), 0)
|
||||
return new_img, mask_image
|
||||
|
||||
|
||||
@ -190,7 +45,7 @@ if __name__ == "__main__":
|
||||
current_dir = Path(__file__).parent.absolute().resolve()
|
||||
image_path = "/Users/cwq/code/github/IOPaint/iopaint/tests/bunny.jpeg"
|
||||
init_image = cv2.imread(str(image_path))
|
||||
init_image, mask_image = expand_image2(
|
||||
init_image, mask_image = expand_image(
|
||||
init_image,
|
||||
top=0,
|
||||
right=0,
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
141
iopaint/model/power_paint/power_paint_v2.py
Normal file
141
iopaint/model/power_paint/power_paint_v2.py
Normal file
@ -0,0 +1,141 @@
|
||||
import PIL.Image
|
||||
import cv2
|
||||
import torch
|
||||
from loguru import logger
|
||||
from transformers import CLIPTextModel, CLIPTokenizer
|
||||
import numpy as np
|
||||
|
||||
from ..base import DiffusionInpaintModel
|
||||
from ..helper.cpu_text_encoder import CPUTextEncoderWrapper
|
||||
from ..utils import (
|
||||
get_torch_dtype,
|
||||
enable_low_mem,
|
||||
is_local_files_only,
|
||||
handle_from_pretrained_exceptions,
|
||||
)
|
||||
from .powerpaint_tokenizer import task_to_prompt
|
||||
from iopaint.schema import InpaintRequest
|
||||
from .v2.BrushNet_CA import BrushNetModel
|
||||
from .v2.unet_2d_condition import UNet2DConditionModel
|
||||
|
||||
|
||||
class PowerPaintV2(DiffusionInpaintModel):
|
||||
pad_mod = 8
|
||||
min_size = 512
|
||||
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
|
||||
hf_model_id = "Sanster/PowerPaint_v2"
|
||||
|
||||
def init_model(self, device: torch.device, **kwargs):
|
||||
from .v2.pipeline_PowerPaint_Brushnet_CA import (
|
||||
StableDiffusionPowerPaintBrushNetPipeline,
|
||||
)
|
||||
from .powerpaint_tokenizer import PowerPaintTokenizer
|
||||
|
||||
use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
|
||||
model_kwargs = {"local_files_only": is_local_files_only(**kwargs)}
|
||||
if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
|
||||
logger.info("Disable Stable Diffusion Model NSFW checker")
|
||||
model_kwargs.update(
|
||||
dict(
|
||||
safety_checker=None,
|
||||
feature_extractor=None,
|
||||
requires_safety_checker=False,
|
||||
)
|
||||
)
|
||||
|
||||
text_encoder_brushnet = CLIPTextModel.from_pretrained(
|
||||
self.hf_model_id,
|
||||
subfolder="text_encoder_brushnet",
|
||||
variant="fp16",
|
||||
torch_dtype=torch_dtype,
|
||||
local_files_only=model_kwargs["local_files_only"],
|
||||
)
|
||||
unet = handle_from_pretrained_exceptions(
|
||||
UNet2DConditionModel.from_pretrained,
|
||||
pretrained_model_name_or_path=self.model_id_or_path,
|
||||
subfolder="unet",
|
||||
variant="fp16",
|
||||
torch_dtype=torch_dtype,
|
||||
local_files_only=model_kwargs["local_files_only"],
|
||||
)
|
||||
brushnet = BrushNetModel.from_pretrained(
|
||||
self.hf_model_id,
|
||||
subfolder="PowerPaint_Brushnet",
|
||||
variant="fp16",
|
||||
torch_dtype=torch_dtype,
|
||||
local_files_only=model_kwargs["local_files_only"],
|
||||
)
|
||||
pipe = handle_from_pretrained_exceptions(
|
||||
StableDiffusionPowerPaintBrushNetPipeline.from_pretrained,
|
||||
pretrained_model_name_or_path=self.model_id_or_path,
|
||||
torch_dtype=torch_dtype,
|
||||
unet=unet,
|
||||
brushnet=brushnet,
|
||||
text_encoder_brushnet=text_encoder_brushnet,
|
||||
variant="fp16",
|
||||
**model_kwargs,
|
||||
)
|
||||
pipe.tokenizer = PowerPaintTokenizer(
|
||||
CLIPTokenizer.from_pretrained(self.hf_model_id, subfolder="tokenizer")
|
||||
)
|
||||
self.model = pipe
|
||||
|
||||
enable_low_mem(self.model, kwargs.get("low_mem", False))
|
||||
|
||||
if kwargs.get("cpu_offload", False) and use_gpu:
|
||||
logger.info("Enable sequential cpu offload")
|
||||
self.model.enable_sequential_cpu_offload(gpu_id=0)
|
||||
else:
|
||||
self.model = self.model.to(device)
|
||||
if kwargs["sd_cpu_textencoder"]:
|
||||
logger.info("Run Stable Diffusion TextEncoder on CPU")
|
||||
self.model.text_encoder = CPUTextEncoderWrapper(
|
||||
self.model.text_encoder, torch_dtype
|
||||
)
|
||||
|
||||
self.callback = kwargs.pop("callback", None)
|
||||
|
||||
def forward(self, image, mask, config: InpaintRequest):
|
||||
"""Input image and output image have same size
|
||||
image: [H, W, C] RGB
|
||||
mask: [H, W, 1] 255 means area to repaint
|
||||
return: BGR IMAGE
|
||||
"""
|
||||
self.set_scheduler(config)
|
||||
|
||||
image = image * (1 - mask / 255.0)
|
||||
img_h, img_w = image.shape[:2]
|
||||
|
||||
image = PIL.Image.fromarray(image.astype(np.uint8))
|
||||
mask = PIL.Image.fromarray(mask[:, :, -1], mode="L").convert("RGB")
|
||||
|
||||
promptA, promptB, negative_promptA, negative_promptB = task_to_prompt(
|
||||
config.powerpaint_task
|
||||
)
|
||||
|
||||
output = self.model(
|
||||
image=image,
|
||||
mask=mask,
|
||||
promptA=promptA,
|
||||
promptB=promptB,
|
||||
promptU=config.prompt,
|
||||
tradoff=config.fitting_degree,
|
||||
tradoff_nag=config.fitting_degree,
|
||||
negative_promptA=negative_promptA,
|
||||
negative_promptB=negative_promptB,
|
||||
negative_promptU=config.negative_prompt,
|
||||
num_inference_steps=config.sd_steps,
|
||||
# strength=config.sd_strength,
|
||||
brushnet_conditioning_scale=1.0,
|
||||
guidance_scale=config.sd_guidance_scale,
|
||||
output_type="np",
|
||||
callback=self.callback,
|
||||
height=img_h,
|
||||
width=img_w,
|
||||
generator=torch.manual_seed(config.sd_seed),
|
||||
callback_steps=1,
|
||||
).images[0]
|
||||
|
||||
output = (output * 255).round().astype("uint8")
|
||||
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
||||
return output
|
||||
@ -1,8 +1,6 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import copy
|
||||
import random
|
||||
from typing import Any, List, Optional, Union
|
||||
from typing import Any, List, Union
|
||||
from transformers import CLIPTokenizer
|
||||
|
||||
from iopaint.schema import PowerPaintTask
|
||||
@ -14,6 +12,11 @@ def add_task_to_prompt(prompt, negative_prompt, task: PowerPaintTask):
|
||||
promptB = prompt + " P_ctxt"
|
||||
negative_promptA = negative_prompt + " P_obj"
|
||||
negative_promptB = negative_prompt + " P_obj"
|
||||
elif task == PowerPaintTask.context_aware:
|
||||
promptA = prompt + " P_ctxt"
|
||||
promptB = prompt + " P_ctxt"
|
||||
negative_promptA = negative_prompt
|
||||
negative_promptB = negative_prompt
|
||||
elif task == PowerPaintTask.shape_guided:
|
||||
promptA = prompt + " P_shape"
|
||||
promptB = prompt + " P_ctxt"
|
||||
@ -33,6 +36,18 @@ def add_task_to_prompt(prompt, negative_prompt, task: PowerPaintTask):
|
||||
return promptA, promptB, negative_promptA, negative_promptB
|
||||
|
||||
|
||||
def task_to_prompt(task: PowerPaintTask):
|
||||
promptA, promptB, negative_promptA, negative_promptB = add_task_to_prompt(
|
||||
"", "", task
|
||||
)
|
||||
return (
|
||||
promptA.strip(),
|
||||
promptB.strip(),
|
||||
negative_promptA.strip(),
|
||||
negative_promptB.strip(),
|
||||
)
|
||||
|
||||
|
||||
class PowerPaintTokenizer:
|
||||
def __init__(self, tokenizer: CLIPTokenizer):
|
||||
self.wrapped = tokenizer
|
||||
@ -237,304 +252,3 @@ class PowerPaintTokenizer:
|
||||
return text
|
||||
replaced_text = self.replace_text_with_placeholder_tokens(text)
|
||||
return replaced_text
|
||||
|
||||
|
||||
class EmbeddingLayerWithFixes(nn.Module):
|
||||
"""The revised embedding layer to support external embeddings. This design
|
||||
of this class is inspired by https://github.com/AUTOMATIC1111/stable-
|
||||
diffusion-webui/blob/22bcc7be428c94e9408f589966c2040187245d81/modules/sd_hi
|
||||
jack.py#L224 # noqa.
|
||||
|
||||
Args:
|
||||
wrapped (nn.Emebdding): The embedding layer to be wrapped.
|
||||
external_embeddings (Union[dict, List[dict]], optional): The external
|
||||
embeddings added to this layer. Defaults to None.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
wrapped: nn.Embedding,
|
||||
external_embeddings: Optional[Union[dict, List[dict]]] = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.wrapped = wrapped
|
||||
self.num_embeddings = wrapped.weight.shape[0]
|
||||
|
||||
self.external_embeddings = []
|
||||
if external_embeddings:
|
||||
self.add_embeddings(external_embeddings)
|
||||
|
||||
self.trainable_embeddings = nn.ParameterDict()
|
||||
|
||||
@property
|
||||
def weight(self):
|
||||
"""Get the weight of wrapped embedding layer."""
|
||||
return self.wrapped.weight
|
||||
|
||||
def check_duplicate_names(self, embeddings: List[dict]):
|
||||
"""Check whether duplicate names exist in list of 'external
|
||||
embeddings'.
|
||||
|
||||
Args:
|
||||
embeddings (List[dict]): A list of embedding to be check.
|
||||
"""
|
||||
names = [emb["name"] for emb in embeddings]
|
||||
assert len(names) == len(set(names)), (
|
||||
"Found duplicated names in 'external_embeddings'. Name list: " f"'{names}'"
|
||||
)
|
||||
|
||||
def check_ids_overlap(self, embeddings):
|
||||
"""Check whether overlap exist in token ids of 'external_embeddings'.
|
||||
|
||||
Args:
|
||||
embeddings (List[dict]): A list of embedding to be check.
|
||||
"""
|
||||
ids_range = [[emb["start"], emb["end"], emb["name"]] for emb in embeddings]
|
||||
ids_range.sort() # sort by 'start'
|
||||
# check if 'end' has overlapping
|
||||
for idx in range(len(ids_range) - 1):
|
||||
name1, name2 = ids_range[idx][-1], ids_range[idx + 1][-1]
|
||||
assert ids_range[idx][1] <= ids_range[idx + 1][0], (
|
||||
f"Found ids overlapping between embeddings '{name1}' " f"and '{name2}'."
|
||||
)
|
||||
|
||||
def add_embeddings(self, embeddings: Optional[Union[dict, List[dict]]]):
|
||||
"""Add external embeddings to this layer.
|
||||
|
||||
Use case:
|
||||
|
||||
>>> 1. Add token to tokenizer and get the token id.
|
||||
>>> tokenizer = TokenizerWrapper('openai/clip-vit-base-patch32')
|
||||
>>> # 'how much' in kiswahili
|
||||
>>> tokenizer.add_placeholder_tokens('ngapi', num_vec_per_token=4)
|
||||
>>>
|
||||
>>> 2. Add external embeddings to the model.
|
||||
>>> new_embedding = {
|
||||
>>> 'name': 'ngapi', # 'how much' in kiswahili
|
||||
>>> 'embedding': torch.ones(1, 15) * 4,
|
||||
>>> 'start': tokenizer.get_token_info('kwaheri')['start'],
|
||||
>>> 'end': tokenizer.get_token_info('kwaheri')['end'],
|
||||
>>> 'trainable': False # if True, will registry as a parameter
|
||||
>>> }
|
||||
>>> embedding_layer = nn.Embedding(10, 15)
|
||||
>>> embedding_layer_wrapper = EmbeddingLayerWithFixes(embedding_layer)
|
||||
>>> embedding_layer_wrapper.add_embeddings(new_embedding)
|
||||
>>>
|
||||
>>> 3. Forward tokenizer and embedding layer!
|
||||
>>> input_text = ['hello, ngapi!', 'hello my friend, ngapi?']
|
||||
>>> input_ids = tokenizer(
|
||||
>>> input_text, padding='max_length', truncation=True,
|
||||
>>> return_tensors='pt')['input_ids']
|
||||
>>> out_feat = embedding_layer_wrapper(input_ids)
|
||||
>>>
|
||||
>>> 4. Let's validate the result!
|
||||
>>> assert (out_feat[0, 3: 7] == 2.3).all()
|
||||
>>> assert (out_feat[2, 5: 9] == 2.3).all()
|
||||
|
||||
Args:
|
||||
embeddings (Union[dict, list[dict]]): The external embeddings to
|
||||
be added. Each dict must contain the following 4 fields: 'name'
|
||||
(the name of this embedding), 'embedding' (the embedding
|
||||
tensor), 'start' (the start token id of this embedding), 'end'
|
||||
(the end token id of this embedding). For example:
|
||||
`{name: NAME, start: START, end: END, embedding: torch.Tensor}`
|
||||
"""
|
||||
if isinstance(embeddings, dict):
|
||||
embeddings = [embeddings]
|
||||
|
||||
self.external_embeddings += embeddings
|
||||
self.check_duplicate_names(self.external_embeddings)
|
||||
self.check_ids_overlap(self.external_embeddings)
|
||||
|
||||
# set for trainable
|
||||
added_trainable_emb_info = []
|
||||
for embedding in embeddings:
|
||||
trainable = embedding.get("trainable", False)
|
||||
if trainable:
|
||||
name = embedding["name"]
|
||||
embedding["embedding"] = torch.nn.Parameter(embedding["embedding"])
|
||||
self.trainable_embeddings[name] = embedding["embedding"]
|
||||
added_trainable_emb_info.append(name)
|
||||
|
||||
added_emb_info = [emb["name"] for emb in embeddings]
|
||||
added_emb_info = ", ".join(added_emb_info)
|
||||
print(f"Successfully add external embeddings: {added_emb_info}.", "current")
|
||||
|
||||
if added_trainable_emb_info:
|
||||
added_trainable_emb_info = ", ".join(added_trainable_emb_info)
|
||||
print(
|
||||
"Successfully add trainable external embeddings: "
|
||||
f"{added_trainable_emb_info}",
|
||||
"current",
|
||||
)
|
||||
|
||||
def replace_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
"""Replace external input ids to 0.
|
||||
|
||||
Args:
|
||||
input_ids (torch.Tensor): The input ids to be replaced.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The replaced input ids.
|
||||
"""
|
||||
input_ids_fwd = input_ids.clone()
|
||||
input_ids_fwd[input_ids_fwd >= self.num_embeddings] = 0
|
||||
return input_ids_fwd
|
||||
|
||||
def replace_embeddings(
|
||||
self, input_ids: torch.Tensor, embedding: torch.Tensor, external_embedding: dict
|
||||
) -> torch.Tensor:
|
||||
"""Replace external embedding to the embedding layer. Noted that, in
|
||||
this function we use `torch.cat` to avoid inplace modification.
|
||||
|
||||
Args:
|
||||
input_ids (torch.Tensor): The original token ids. Shape like
|
||||
[LENGTH, ].
|
||||
embedding (torch.Tensor): The embedding of token ids after
|
||||
`replace_input_ids` function.
|
||||
external_embedding (dict): The external embedding to be replaced.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The replaced embedding.
|
||||
"""
|
||||
new_embedding = []
|
||||
|
||||
name = external_embedding["name"]
|
||||
start = external_embedding["start"]
|
||||
end = external_embedding["end"]
|
||||
target_ids_to_replace = [i for i in range(start, end)]
|
||||
ext_emb = external_embedding["embedding"]
|
||||
|
||||
# do not need to replace
|
||||
if not (input_ids == start).any():
|
||||
return embedding
|
||||
|
||||
# start replace
|
||||
s_idx, e_idx = 0, 0
|
||||
while e_idx < len(input_ids):
|
||||
if input_ids[e_idx] == start:
|
||||
if e_idx != 0:
|
||||
# add embedding do not need to replace
|
||||
new_embedding.append(embedding[s_idx:e_idx])
|
||||
|
||||
# check if the next embedding need to replace is valid
|
||||
actually_ids_to_replace = [
|
||||
int(i) for i in input_ids[e_idx : e_idx + end - start]
|
||||
]
|
||||
assert actually_ids_to_replace == target_ids_to_replace, (
|
||||
f"Invalid 'input_ids' in position: {s_idx} to {e_idx}. "
|
||||
f"Expect '{target_ids_to_replace}' for embedding "
|
||||
f"'{name}' but found '{actually_ids_to_replace}'."
|
||||
)
|
||||
|
||||
new_embedding.append(ext_emb)
|
||||
|
||||
s_idx = e_idx + end - start
|
||||
e_idx = s_idx + 1
|
||||
else:
|
||||
e_idx += 1
|
||||
|
||||
if e_idx == len(input_ids):
|
||||
new_embedding.append(embedding[s_idx:e_idx])
|
||||
|
||||
return torch.cat(new_embedding, dim=0)
|
||||
|
||||
def forward(
|
||||
self, input_ids: torch.Tensor, external_embeddings: Optional[List[dict]] = None
|
||||
):
|
||||
"""The forward function.
|
||||
|
||||
Args:
|
||||
input_ids (torch.Tensor): The token ids shape like [bz, LENGTH] or
|
||||
[LENGTH, ].
|
||||
external_embeddings (Optional[List[dict]]): The external
|
||||
embeddings. If not passed, only `self.external_embeddings`
|
||||
will be used. Defaults to None.
|
||||
|
||||
input_ids: shape like [bz, LENGTH] or [LENGTH].
|
||||
"""
|
||||
assert input_ids.ndim in [1, 2]
|
||||
if input_ids.ndim == 1:
|
||||
input_ids = input_ids.unsqueeze(0)
|
||||
|
||||
if external_embeddings is None and not self.external_embeddings:
|
||||
return self.wrapped(input_ids)
|
||||
|
||||
input_ids_fwd = self.replace_input_ids(input_ids)
|
||||
inputs_embeds = self.wrapped(input_ids_fwd)
|
||||
|
||||
vecs = []
|
||||
|
||||
if external_embeddings is None:
|
||||
external_embeddings = []
|
||||
elif isinstance(external_embeddings, dict):
|
||||
external_embeddings = [external_embeddings]
|
||||
embeddings = self.external_embeddings + external_embeddings
|
||||
|
||||
for input_id, embedding in zip(input_ids, inputs_embeds):
|
||||
new_embedding = embedding
|
||||
for external_embedding in embeddings:
|
||||
new_embedding = self.replace_embeddings(
|
||||
input_id, new_embedding, external_embedding
|
||||
)
|
||||
vecs.append(new_embedding)
|
||||
|
||||
return torch.stack(vecs)
|
||||
|
||||
|
||||
def add_tokens(
|
||||
tokenizer,
|
||||
text_encoder,
|
||||
placeholder_tokens: list,
|
||||
initialize_tokens: list = None,
|
||||
num_vectors_per_token: int = 1,
|
||||
):
|
||||
"""Add token for training.
|
||||
|
||||
# TODO: support add tokens as dict, then we can load pretrained tokens.
|
||||
"""
|
||||
if initialize_tokens is not None:
|
||||
assert len(initialize_tokens) == len(
|
||||
placeholder_tokens
|
||||
), "placeholder_token should be the same length as initialize_token"
|
||||
for ii in range(len(placeholder_tokens)):
|
||||
tokenizer.add_placeholder_token(
|
||||
placeholder_tokens[ii], num_vec_per_token=num_vectors_per_token
|
||||
)
|
||||
|
||||
# text_encoder.set_embedding_layer()
|
||||
embedding_layer = text_encoder.text_model.embeddings.token_embedding
|
||||
text_encoder.text_model.embeddings.token_embedding = EmbeddingLayerWithFixes(
|
||||
embedding_layer
|
||||
)
|
||||
embedding_layer = text_encoder.text_model.embeddings.token_embedding
|
||||
|
||||
assert embedding_layer is not None, (
|
||||
"Do not support get embedding layer for current text encoder. "
|
||||
"Please check your configuration."
|
||||
)
|
||||
initialize_embedding = []
|
||||
if initialize_tokens is not None:
|
||||
for ii in range(len(placeholder_tokens)):
|
||||
init_id = tokenizer(initialize_tokens[ii]).input_ids[1]
|
||||
temp_embedding = embedding_layer.weight[init_id]
|
||||
initialize_embedding.append(
|
||||
temp_embedding[None, ...].repeat(num_vectors_per_token, 1)
|
||||
)
|
||||
else:
|
||||
for ii in range(len(placeholder_tokens)):
|
||||
init_id = tokenizer("a").input_ids[1]
|
||||
temp_embedding = embedding_layer.weight[init_id]
|
||||
len_emb = temp_embedding.shape[0]
|
||||
init_weight = (torch.rand(num_vectors_per_token, len_emb) - 0.5) / 2.0
|
||||
initialize_embedding.append(init_weight)
|
||||
|
||||
# initialize_embedding = torch.cat(initialize_embedding,dim=0)
|
||||
|
||||
token_info_all = []
|
||||
for ii in range(len(placeholder_tokens)):
|
||||
token_info = tokenizer.get_token_info(placeholder_tokens[ii])
|
||||
token_info["embedding"] = initialize_embedding[ii]
|
||||
token_info["trainable"] = True
|
||||
token_info_all.append(token_info)
|
||||
embedding_layer.add_embeddings(token_info_all)
|
||||
|
||||
933
iopaint/model/power_paint/v2/BrushNet_CA.py
Normal file
933
iopaint/model/power_paint/v2/BrushNet_CA.py
Normal file
@ -0,0 +1,933 @@
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
||||
from diffusers.utils import BaseOutput, logging
|
||||
from diffusers.models.attention_processor import (
|
||||
ADDED_KV_ATTENTION_PROCESSORS,
|
||||
CROSS_ATTENTION_PROCESSORS,
|
||||
AttentionProcessor,
|
||||
AttnAddedKVProcessor,
|
||||
AttnProcessor,
|
||||
)
|
||||
from diffusers.models.embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, \
|
||||
TimestepEmbedding, Timesteps
|
||||
from diffusers.models.modeling_utils import ModelMixin
|
||||
from .unet_2d_blocks import (
|
||||
CrossAttnDownBlock2D,
|
||||
DownBlock2D,
|
||||
get_down_block,
|
||||
get_mid_block,
|
||||
get_up_block
|
||||
)
|
||||
|
||||
from .unet_2d_condition import UNet2DConditionModel
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
@dataclass
|
||||
class BrushNetOutput(BaseOutput):
|
||||
"""
|
||||
The output of [`BrushNetModel`].
|
||||
|
||||
Args:
|
||||
up_block_res_samples (`tuple[torch.Tensor]`):
|
||||
A tuple of upsample activations at different resolutions for each upsampling block. Each tensor should
|
||||
be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
|
||||
used to condition the original UNet's upsampling activations.
|
||||
down_block_res_samples (`tuple[torch.Tensor]`):
|
||||
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
|
||||
be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
|
||||
used to condition the original UNet's downsampling activations.
|
||||
mid_down_block_re_sample (`torch.Tensor`):
|
||||
The activation of the midde block (the lowest sample resolution). Each tensor should be of shape
|
||||
`(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
|
||||
Output can be used to condition the original UNet's middle block activation.
|
||||
"""
|
||||
|
||||
up_block_res_samples: Tuple[torch.Tensor]
|
||||
down_block_res_samples: Tuple[torch.Tensor]
|
||||
mid_block_res_sample: torch.Tensor
|
||||
|
||||
|
||||
class BrushNetModel(ModelMixin, ConfigMixin):
|
||||
"""
|
||||
A BrushNet model.
|
||||
|
||||
Args:
|
||||
in_channels (`int`, defaults to 4):
|
||||
The number of channels in the input sample.
|
||||
flip_sin_to_cos (`bool`, defaults to `True`):
|
||||
Whether to flip the sin to cos in the time embedding.
|
||||
freq_shift (`int`, defaults to 0):
|
||||
The frequency shift to apply to the time embedding.
|
||||
down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
|
||||
The tuple of downsample blocks to use.
|
||||
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
|
||||
Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
|
||||
`UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
|
||||
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
|
||||
The tuple of upsample blocks to use.
|
||||
only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
|
||||
block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
|
||||
The tuple of output channels for each block.
|
||||
layers_per_block (`int`, defaults to 2):
|
||||
The number of layers per block.
|
||||
downsample_padding (`int`, defaults to 1):
|
||||
The padding to use for the downsampling convolution.
|
||||
mid_block_scale_factor (`float`, defaults to 1):
|
||||
The scale factor to use for the mid block.
|
||||
act_fn (`str`, defaults to "silu"):
|
||||
The activation function to use.
|
||||
norm_num_groups (`int`, *optional*, defaults to 32):
|
||||
The number of groups to use for the normalization. If None, normalization and activation layers is skipped
|
||||
in post-processing.
|
||||
norm_eps (`float`, defaults to 1e-5):
|
||||
The epsilon to use for the normalization.
|
||||
cross_attention_dim (`int`, defaults to 1280):
|
||||
The dimension of the cross attention features.
|
||||
transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
|
||||
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
|
||||
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
|
||||
[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
|
||||
encoder_hid_dim (`int`, *optional*, defaults to None):
|
||||
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
|
||||
dimension to `cross_attention_dim`.
|
||||
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
|
||||
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
|
||||
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
|
||||
attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
|
||||
The dimension of the attention heads.
|
||||
use_linear_projection (`bool`, defaults to `False`):
|
||||
class_embed_type (`str`, *optional*, defaults to `None`):
|
||||
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
|
||||
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
|
||||
addition_embed_type (`str`, *optional*, defaults to `None`):
|
||||
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
|
||||
"text". "text" will use the `TextTimeEmbedding` layer.
|
||||
num_class_embeds (`int`, *optional*, defaults to 0):
|
||||
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
|
||||
class conditioning with `class_embed_type` equal to `None`.
|
||||
upcast_attention (`bool`, defaults to `False`):
|
||||
resnet_time_scale_shift (`str`, defaults to `"default"`):
|
||||
Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
|
||||
projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
|
||||
The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
|
||||
`class_embed_type="projection"`.
|
||||
brushnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
|
||||
The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
|
||||
conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
|
||||
The tuple of output channel for each block in the `conditioning_embedding` layer.
|
||||
global_pool_conditions (`bool`, defaults to `False`):
|
||||
TODO(Patrick) - unused parameter.
|
||||
addition_embed_type_num_heads (`int`, defaults to 64):
|
||||
The number of heads to use for the `TextTimeEmbedding` layer.
|
||||
"""
|
||||
|
||||
_supports_gradient_checkpointing = True
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int = 4,
|
||||
conditioning_channels: int = 5,
|
||||
flip_sin_to_cos: bool = True,
|
||||
freq_shift: int = 0,
|
||||
down_block_types: Tuple[str, ...] = (
|
||||
"CrossAttnDownBlock2D",
|
||||
"CrossAttnDownBlock2D",
|
||||
"CrossAttnDownBlock2D",
|
||||
"DownBlock2D",
|
||||
),
|
||||
mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
|
||||
up_block_types: Tuple[str, ...] = (
|
||||
"UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"
|
||||
),
|
||||
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
||||
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
|
||||
layers_per_block: int = 2,
|
||||
downsample_padding: int = 1,
|
||||
mid_block_scale_factor: float = 1,
|
||||
act_fn: str = "silu",
|
||||
norm_num_groups: Optional[int] = 32,
|
||||
norm_eps: float = 1e-5,
|
||||
cross_attention_dim: int = 1280,
|
||||
transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
|
||||
encoder_hid_dim: Optional[int] = None,
|
||||
encoder_hid_dim_type: Optional[str] = None,
|
||||
attention_head_dim: Union[int, Tuple[int, ...]] = 8,
|
||||
num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
|
||||
use_linear_projection: bool = False,
|
||||
class_embed_type: Optional[str] = None,
|
||||
addition_embed_type: Optional[str] = None,
|
||||
addition_time_embed_dim: Optional[int] = None,
|
||||
num_class_embeds: Optional[int] = None,
|
||||
upcast_attention: bool = False,
|
||||
resnet_time_scale_shift: str = "default",
|
||||
projection_class_embeddings_input_dim: Optional[int] = None,
|
||||
brushnet_conditioning_channel_order: str = "rgb",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||||
global_pool_conditions: bool = False,
|
||||
addition_embed_type_num_heads: int = 64,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
# If `num_attention_heads` is not defined (which is the case for most models)
|
||||
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
|
||||
# The reason for this behavior is to correct for incorrectly named variables that were introduced
|
||||
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
|
||||
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
|
||||
# which is why we correct for the naming here.
|
||||
num_attention_heads = num_attention_heads or attention_head_dim
|
||||
|
||||
# Check inputs
|
||||
if len(down_block_types) != len(up_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
|
||||
)
|
||||
|
||||
if len(block_out_channels) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if isinstance(transformer_layers_per_block, int):
|
||||
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
|
||||
|
||||
# input
|
||||
conv_in_kernel = 3
|
||||
conv_in_padding = (conv_in_kernel - 1) // 2
|
||||
self.conv_in_condition = nn.Conv2d(
|
||||
in_channels + conditioning_channels, block_out_channels[0], kernel_size=conv_in_kernel,
|
||||
padding=conv_in_padding
|
||||
)
|
||||
|
||||
# time
|
||||
time_embed_dim = block_out_channels[0] * 4
|
||||
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
||||
timestep_input_dim = block_out_channels[0]
|
||||
self.time_embedding = TimestepEmbedding(
|
||||
timestep_input_dim,
|
||||
time_embed_dim,
|
||||
act_fn=act_fn,
|
||||
)
|
||||
|
||||
if encoder_hid_dim_type is None and encoder_hid_dim is not None:
|
||||
encoder_hid_dim_type = "text_proj"
|
||||
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
|
||||
logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
|
||||
|
||||
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
|
||||
raise ValueError(
|
||||
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
|
||||
)
|
||||
|
||||
if encoder_hid_dim_type == "text_proj":
|
||||
self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
|
||||
elif encoder_hid_dim_type == "text_image_proj":
|
||||
# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
||||
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
||||
# case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
|
||||
self.encoder_hid_proj = TextImageProjection(
|
||||
text_embed_dim=encoder_hid_dim,
|
||||
image_embed_dim=cross_attention_dim,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
)
|
||||
|
||||
elif encoder_hid_dim_type is not None:
|
||||
raise ValueError(
|
||||
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
|
||||
)
|
||||
else:
|
||||
self.encoder_hid_proj = None
|
||||
|
||||
# class embedding
|
||||
if class_embed_type is None and num_class_embeds is not None:
|
||||
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
||||
elif class_embed_type == "timestep":
|
||||
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
||||
elif class_embed_type == "identity":
|
||||
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
||||
elif class_embed_type == "projection":
|
||||
if projection_class_embeddings_input_dim is None:
|
||||
raise ValueError(
|
||||
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
|
||||
)
|
||||
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
|
||||
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
|
||||
# 2. it projects from an arbitrary input dimension.
|
||||
#
|
||||
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
|
||||
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
|
||||
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
|
||||
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
||||
else:
|
||||
self.class_embedding = None
|
||||
|
||||
if addition_embed_type == "text":
|
||||
if encoder_hid_dim is not None:
|
||||
text_time_embedding_from_dim = encoder_hid_dim
|
||||
else:
|
||||
text_time_embedding_from_dim = cross_attention_dim
|
||||
|
||||
self.add_embedding = TextTimeEmbedding(
|
||||
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
|
||||
)
|
||||
elif addition_embed_type == "text_image":
|
||||
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
|
||||
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
|
||||
# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
|
||||
self.add_embedding = TextImageTimeEmbedding(
|
||||
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
|
||||
)
|
||||
elif addition_embed_type == "text_time":
|
||||
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
|
||||
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
||||
|
||||
elif addition_embed_type is not None:
|
||||
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
|
||||
|
||||
self.down_blocks = nn.ModuleList([])
|
||||
self.brushnet_down_blocks = nn.ModuleList([])
|
||||
|
||||
if isinstance(only_cross_attention, bool):
|
||||
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
||||
|
||||
if isinstance(attention_head_dim, int):
|
||||
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
||||
|
||||
if isinstance(num_attention_heads, int):
|
||||
num_attention_heads = (num_attention_heads,) * len(down_block_types)
|
||||
|
||||
# down
|
||||
output_channel = block_out_channels[0]
|
||||
|
||||
brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
brushnet_block = zero_module(brushnet_block)
|
||||
self.brushnet_down_blocks.append(brushnet_block)
|
||||
|
||||
for i, down_block_type in enumerate(down_block_types):
|
||||
input_channel = output_channel
|
||||
output_channel = block_out_channels[i]
|
||||
is_final_block = i == len(block_out_channels) - 1
|
||||
|
||||
down_block = get_down_block(
|
||||
down_block_type,
|
||||
num_layers=layers_per_block,
|
||||
transformer_layers_per_block=transformer_layers_per_block[i],
|
||||
in_channels=input_channel,
|
||||
out_channels=output_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
add_downsample=not is_final_block,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
resnet_groups=norm_num_groups,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
num_attention_heads=num_attention_heads[i],
|
||||
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
||||
downsample_padding=downsample_padding,
|
||||
use_linear_projection=use_linear_projection,
|
||||
only_cross_attention=only_cross_attention[i],
|
||||
upcast_attention=upcast_attention,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
)
|
||||
self.down_blocks.append(down_block)
|
||||
|
||||
for _ in range(layers_per_block):
|
||||
brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
brushnet_block = zero_module(brushnet_block)
|
||||
self.brushnet_down_blocks.append(brushnet_block)
|
||||
|
||||
if not is_final_block:
|
||||
brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
brushnet_block = zero_module(brushnet_block)
|
||||
self.brushnet_down_blocks.append(brushnet_block)
|
||||
|
||||
# mid
|
||||
mid_block_channel = block_out_channels[-1]
|
||||
|
||||
brushnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
|
||||
brushnet_block = zero_module(brushnet_block)
|
||||
self.brushnet_mid_block = brushnet_block
|
||||
|
||||
self.mid_block = get_mid_block(
|
||||
mid_block_type,
|
||||
transformer_layers_per_block=transformer_layers_per_block[-1],
|
||||
in_channels=mid_block_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
output_scale_factor=mid_block_scale_factor,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
num_attention_heads=num_attention_heads[-1],
|
||||
resnet_groups=norm_num_groups,
|
||||
use_linear_projection=use_linear_projection,
|
||||
upcast_attention=upcast_attention,
|
||||
)
|
||||
|
||||
# count how many layers upsample the images
|
||||
self.num_upsamplers = 0
|
||||
|
||||
# up
|
||||
reversed_block_out_channels = list(reversed(block_out_channels))
|
||||
reversed_num_attention_heads = list(reversed(num_attention_heads))
|
||||
reversed_transformer_layers_per_block = (list(reversed(transformer_layers_per_block)))
|
||||
only_cross_attention = list(reversed(only_cross_attention))
|
||||
|
||||
output_channel = reversed_block_out_channels[0]
|
||||
|
||||
self.up_blocks = nn.ModuleList([])
|
||||
self.brushnet_up_blocks = nn.ModuleList([])
|
||||
|
||||
for i, up_block_type in enumerate(up_block_types):
|
||||
is_final_block = i == len(block_out_channels) - 1
|
||||
|
||||
prev_output_channel = output_channel
|
||||
output_channel = reversed_block_out_channels[i]
|
||||
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
|
||||
|
||||
# add upsample block for all BUT final layer
|
||||
if not is_final_block:
|
||||
add_upsample = True
|
||||
self.num_upsamplers += 1
|
||||
else:
|
||||
add_upsample = False
|
||||
|
||||
up_block = get_up_block(
|
||||
up_block_type,
|
||||
num_layers=layers_per_block + 1,
|
||||
transformer_layers_per_block=reversed_transformer_layers_per_block[i],
|
||||
in_channels=input_channel,
|
||||
out_channels=output_channel,
|
||||
prev_output_channel=prev_output_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
add_upsample=add_upsample,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
resolution_idx=i,
|
||||
resnet_groups=norm_num_groups,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
num_attention_heads=reversed_num_attention_heads[i],
|
||||
use_linear_projection=use_linear_projection,
|
||||
only_cross_attention=only_cross_attention[i],
|
||||
upcast_attention=upcast_attention,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
||||
)
|
||||
self.up_blocks.append(up_block)
|
||||
prev_output_channel = output_channel
|
||||
|
||||
for _ in range(layers_per_block + 1):
|
||||
brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
brushnet_block = zero_module(brushnet_block)
|
||||
self.brushnet_up_blocks.append(brushnet_block)
|
||||
|
||||
if not is_final_block:
|
||||
brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
brushnet_block = zero_module(brushnet_block)
|
||||
self.brushnet_up_blocks.append(brushnet_block)
|
||||
|
||||
@classmethod
|
||||
def from_unet(
|
||||
cls,
|
||||
unet: UNet2DConditionModel,
|
||||
brushnet_conditioning_channel_order: str = "rgb",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
|
||||
load_weights_from_unet: bool = True,
|
||||
conditioning_channels: int = 5,
|
||||
):
|
||||
r"""
|
||||
Instantiate a [`BrushNetModel`] from [`UNet2DConditionModel`].
|
||||
|
||||
Parameters:
|
||||
unet (`UNet2DConditionModel`):
|
||||
The UNet model weights to copy to the [`BrushNetModel`]. All configuration options are also copied
|
||||
where applicable.
|
||||
"""
|
||||
transformer_layers_per_block = (
|
||||
unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1
|
||||
)
|
||||
encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None
|
||||
encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None
|
||||
addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None
|
||||
addition_time_embed_dim = (
|
||||
unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None
|
||||
)
|
||||
|
||||
brushnet = cls(
|
||||
in_channels=unet.config.in_channels,
|
||||
conditioning_channels=conditioning_channels,
|
||||
flip_sin_to_cos=unet.config.flip_sin_to_cos,
|
||||
freq_shift=unet.config.freq_shift,
|
||||
# down_block_types=['DownBlock2D','DownBlock2D','DownBlock2D','DownBlock2D'],
|
||||
down_block_types=["CrossAttnDownBlock2D",
|
||||
"CrossAttnDownBlock2D",
|
||||
"CrossAttnDownBlock2D",
|
||||
"DownBlock2D", ],
|
||||
# mid_block_type='MidBlock2D',
|
||||
mid_block_type="UNetMidBlock2DCrossAttn",
|
||||
# up_block_types=['UpBlock2D','UpBlock2D','UpBlock2D','UpBlock2D'],
|
||||
up_block_types=["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
|
||||
only_cross_attention=unet.config.only_cross_attention,
|
||||
block_out_channels=unet.config.block_out_channels,
|
||||
layers_per_block=unet.config.layers_per_block,
|
||||
downsample_padding=unet.config.downsample_padding,
|
||||
mid_block_scale_factor=unet.config.mid_block_scale_factor,
|
||||
act_fn=unet.config.act_fn,
|
||||
norm_num_groups=unet.config.norm_num_groups,
|
||||
norm_eps=unet.config.norm_eps,
|
||||
cross_attention_dim=unet.config.cross_attention_dim,
|
||||
transformer_layers_per_block=transformer_layers_per_block,
|
||||
encoder_hid_dim=encoder_hid_dim,
|
||||
encoder_hid_dim_type=encoder_hid_dim_type,
|
||||
attention_head_dim=unet.config.attention_head_dim,
|
||||
num_attention_heads=unet.config.num_attention_heads,
|
||||
use_linear_projection=unet.config.use_linear_projection,
|
||||
class_embed_type=unet.config.class_embed_type,
|
||||
addition_embed_type=addition_embed_type,
|
||||
addition_time_embed_dim=addition_time_embed_dim,
|
||||
num_class_embeds=unet.config.num_class_embeds,
|
||||
upcast_attention=unet.config.upcast_attention,
|
||||
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
||||
projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
|
||||
brushnet_conditioning_channel_order=brushnet_conditioning_channel_order,
|
||||
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
||||
)
|
||||
|
||||
if load_weights_from_unet:
|
||||
conv_in_condition_weight = torch.zeros_like(brushnet.conv_in_condition.weight)
|
||||
conv_in_condition_weight[:, :4, ...] = unet.conv_in.weight
|
||||
conv_in_condition_weight[:, 4:8, ...] = unet.conv_in.weight
|
||||
brushnet.conv_in_condition.weight = torch.nn.Parameter(conv_in_condition_weight)
|
||||
brushnet.conv_in_condition.bias = unet.conv_in.bias
|
||||
|
||||
brushnet.time_proj.load_state_dict(unet.time_proj.state_dict())
|
||||
brushnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
|
||||
|
||||
if brushnet.class_embedding:
|
||||
brushnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
|
||||
|
||||
brushnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(), strict=False)
|
||||
brushnet.mid_block.load_state_dict(unet.mid_block.state_dict(), strict=False)
|
||||
brushnet.up_blocks.load_state_dict(unet.up_blocks.state_dict(), strict=False)
|
||||
|
||||
return brushnet.to(unet.dtype)
|
||||
|
||||
@property
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||||
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
||||
r"""
|
||||
Returns:
|
||||
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||||
indexed by its weight name.
|
||||
"""
|
||||
# set recursively
|
||||
processors = {}
|
||||
|
||||
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||||
if hasattr(module, "get_processor"):
|
||||
processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||||
|
||||
return processors
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_add_processors(name, module, processors)
|
||||
|
||||
return processors
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||||
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
||||
r"""
|
||||
Sets the attention processor to use to compute attention.
|
||||
|
||||
Parameters:
|
||||
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
||||
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
||||
for **all** `Attention` layers.
|
||||
|
||||
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
||||
processor. This is strongly recommended when setting trainable attention processors.
|
||||
|
||||
"""
|
||||
count = len(self.attn_processors.keys())
|
||||
|
||||
if isinstance(processor, dict) and len(processor) != count:
|
||||
raise ValueError(
|
||||
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||||
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||||
)
|
||||
|
||||
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||||
if hasattr(module, "set_processor"):
|
||||
if not isinstance(processor, dict):
|
||||
module.set_processor(processor)
|
||||
else:
|
||||
module.set_processor(processor.pop(f"{name}.processor"))
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_attn_processor(name, module, processor)
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
|
||||
def set_default_attn_processor(self):
|
||||
"""
|
||||
Disables custom attention processors and sets the default attention implementation.
|
||||
"""
|
||||
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||||
processor = AttnAddedKVProcessor()
|
||||
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
|
||||
processor = AttnProcessor()
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
|
||||
)
|
||||
|
||||
self.set_attn_processor(processor)
|
||||
|
||||
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
|
||||
def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
|
||||
r"""
|
||||
Enable sliced attention computation.
|
||||
|
||||
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
|
||||
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
|
||||
|
||||
Args:
|
||||
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
||||
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
|
||||
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
|
||||
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
||||
must be a multiple of `slice_size`.
|
||||
"""
|
||||
sliceable_head_dims = []
|
||||
|
||||
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
sliceable_head_dims.append(module.sliceable_head_dim)
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_retrieve_sliceable_dims(child)
|
||||
|
||||
# retrieve number of attention layers
|
||||
for module in self.children():
|
||||
fn_recursive_retrieve_sliceable_dims(module)
|
||||
|
||||
num_sliceable_layers = len(sliceable_head_dims)
|
||||
|
||||
if slice_size == "auto":
|
||||
# half the attention head size is usually a good trade-off between
|
||||
# speed and memory
|
||||
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
||||
elif slice_size == "max":
|
||||
# make smallest slice possible
|
||||
slice_size = num_sliceable_layers * [1]
|
||||
|
||||
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
||||
|
||||
if len(slice_size) != len(sliceable_head_dims):
|
||||
raise ValueError(
|
||||
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
||||
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
||||
)
|
||||
|
||||
for i in range(len(slice_size)):
|
||||
size = slice_size[i]
|
||||
dim = sliceable_head_dims[i]
|
||||
if size is not None and size > dim:
|
||||
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
||||
|
||||
# Recursively walk through all the children.
|
||||
# Any children which exposes the set_attention_slice method
|
||||
# gets the message
|
||||
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
module.set_attention_slice(slice_size.pop())
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_set_attention_slice(child, slice_size)
|
||||
|
||||
reversed_slice_size = list(reversed(slice_size))
|
||||
for module in self.children():
|
||||
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
|
||||
if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
def forward(
|
||||
self,
|
||||
sample: torch.FloatTensor,
|
||||
timestep: Union[torch.Tensor, float, int],
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
brushnet_cond: torch.FloatTensor,
|
||||
conditioning_scale: float = 1.0,
|
||||
class_labels: Optional[torch.Tensor] = None,
|
||||
timestep_cond: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
||||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
guess_mode: bool = False,
|
||||
return_dict: bool = True,
|
||||
) -> Union[BrushNetOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]:
|
||||
"""
|
||||
The [`BrushNetModel`] forward method.
|
||||
|
||||
Args:
|
||||
sample (`torch.FloatTensor`):
|
||||
The noisy input tensor.
|
||||
timestep (`Union[torch.Tensor, float, int]`):
|
||||
The number of timesteps to denoise an input.
|
||||
encoder_hidden_states (`torch.Tensor`):
|
||||
The encoder hidden states.
|
||||
brushnet_cond (`torch.FloatTensor`):
|
||||
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
conditioning_scale (`float`, defaults to `1.0`):
|
||||
The scale factor for BrushNet outputs.
|
||||
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
|
||||
timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
|
||||
timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
|
||||
embeddings.
|
||||
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
|
||||
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
|
||||
negative values to the attention scores corresponding to "discard" tokens.
|
||||
added_cond_kwargs (`dict`):
|
||||
Additional conditions for the Stable Diffusion XL UNet.
|
||||
cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
|
||||
A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
|
||||
guess_mode (`bool`, defaults to `False`):
|
||||
In this mode, the BrushNet encoder tries its best to recognize the input content of the input even if
|
||||
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
|
||||
return_dict (`bool`, defaults to `True`):
|
||||
Whether or not to return a [`~models.brushnet.BrushNetOutput`] instead of a plain tuple.
|
||||
|
||||
Returns:
|
||||
[`~models.brushnet.BrushNetOutput`] **or** `tuple`:
|
||||
If `return_dict` is `True`, a [`~models.brushnet.BrushNetOutput`] is returned, otherwise a tuple is
|
||||
returned where the first element is the sample tensor.
|
||||
"""
|
||||
# check channel order
|
||||
channel_order = self.config.brushnet_conditioning_channel_order
|
||||
|
||||
if channel_order == "rgb":
|
||||
# in rgb order by default
|
||||
...
|
||||
elif channel_order == "bgr":
|
||||
brushnet_cond = torch.flip(brushnet_cond, dims=[1])
|
||||
else:
|
||||
raise ValueError(f"unknown `brushnet_conditioning_channel_order`: {channel_order}")
|
||||
|
||||
# prepare attention_mask
|
||||
if attention_mask is not None:
|
||||
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
||||
attention_mask = attention_mask.unsqueeze(1)
|
||||
|
||||
# 1. time
|
||||
timesteps = timestep
|
||||
if not torch.is_tensor(timesteps):
|
||||
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
||||
# This would be a good case for the `match` statement (Python 3.10+)
|
||||
is_mps = sample.device.type == "mps"
|
||||
if isinstance(timestep, float):
|
||||
dtype = torch.float32 if is_mps else torch.float64
|
||||
else:
|
||||
dtype = torch.int32 if is_mps else torch.int64
|
||||
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
||||
elif len(timesteps.shape) == 0:
|
||||
timesteps = timesteps[None].to(sample.device)
|
||||
|
||||
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
||||
timesteps = timesteps.expand(sample.shape[0])
|
||||
|
||||
t_emb = self.time_proj(timesteps)
|
||||
|
||||
# timesteps does not contain any weights and will always return f32 tensors
|
||||
# but time_embedding might actually be running in fp16. so we need to cast here.
|
||||
# there might be better ways to encapsulate this.
|
||||
t_emb = t_emb.to(dtype=sample.dtype)
|
||||
|
||||
emb = self.time_embedding(t_emb, timestep_cond)
|
||||
aug_emb = None
|
||||
|
||||
if self.class_embedding is not None:
|
||||
if class_labels is None:
|
||||
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
||||
|
||||
if self.config.class_embed_type == "timestep":
|
||||
class_labels = self.time_proj(class_labels)
|
||||
|
||||
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
||||
emb = emb + class_emb
|
||||
|
||||
if self.config.addition_embed_type is not None:
|
||||
if self.config.addition_embed_type == "text":
|
||||
aug_emb = self.add_embedding(encoder_hidden_states)
|
||||
|
||||
elif self.config.addition_embed_type == "text_time":
|
||||
if "text_embeds" not in added_cond_kwargs:
|
||||
raise ValueError(
|
||||
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
|
||||
)
|
||||
text_embeds = added_cond_kwargs.get("text_embeds")
|
||||
if "time_ids" not in added_cond_kwargs:
|
||||
raise ValueError(
|
||||
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
|
||||
)
|
||||
time_ids = added_cond_kwargs.get("time_ids")
|
||||
time_embeds = self.add_time_proj(time_ids.flatten())
|
||||
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
|
||||
|
||||
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
|
||||
add_embeds = add_embeds.to(emb.dtype)
|
||||
aug_emb = self.add_embedding(add_embeds)
|
||||
|
||||
emb = emb + aug_emb if aug_emb is not None else emb
|
||||
|
||||
# 2. pre-process
|
||||
brushnet_cond = torch.concat([sample, brushnet_cond], 1)
|
||||
sample = self.conv_in_condition(brushnet_cond)
|
||||
|
||||
# 3. down
|
||||
down_block_res_samples = (sample,)
|
||||
for downsample_block in self.down_blocks:
|
||||
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
||||
sample, res_samples = downsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
||||
|
||||
down_block_res_samples += res_samples
|
||||
|
||||
# 4. PaintingNet down blocks
|
||||
brushnet_down_block_res_samples = ()
|
||||
for down_block_res_sample, brushnet_down_block in zip(down_block_res_samples, self.brushnet_down_blocks):
|
||||
down_block_res_sample = brushnet_down_block(down_block_res_sample)
|
||||
brushnet_down_block_res_samples = brushnet_down_block_res_samples + (down_block_res_sample,)
|
||||
|
||||
# 5. mid
|
||||
if self.mid_block is not None:
|
||||
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
|
||||
sample = self.mid_block(
|
||||
sample,
|
||||
emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
)
|
||||
else:
|
||||
sample = self.mid_block(sample, emb)
|
||||
|
||||
# 6. BrushNet mid blocks
|
||||
brushnet_mid_block_res_sample = self.brushnet_mid_block(sample)
|
||||
|
||||
# 7. up
|
||||
up_block_res_samples = ()
|
||||
for i, upsample_block in enumerate(self.up_blocks):
|
||||
is_final_block = i == len(self.up_blocks) - 1
|
||||
|
||||
res_samples = down_block_res_samples[-len(upsample_block.resnets):]
|
||||
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
|
||||
|
||||
# if we have not reached the final block and need to forward the
|
||||
# upsample size, we do it here
|
||||
if not is_final_block:
|
||||
upsample_size = down_block_res_samples[-1].shape[2:]
|
||||
|
||||
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
|
||||
sample, up_res_samples = upsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
res_hidden_states_tuple=res_samples,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
upsample_size=upsample_size,
|
||||
attention_mask=attention_mask,
|
||||
return_res_samples=True
|
||||
)
|
||||
else:
|
||||
sample, up_res_samples = upsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
res_hidden_states_tuple=res_samples,
|
||||
upsample_size=upsample_size,
|
||||
return_res_samples=True
|
||||
)
|
||||
|
||||
up_block_res_samples += up_res_samples
|
||||
|
||||
# 8. BrushNet up blocks
|
||||
brushnet_up_block_res_samples = ()
|
||||
for up_block_res_sample, brushnet_up_block in zip(up_block_res_samples, self.brushnet_up_blocks):
|
||||
up_block_res_sample = brushnet_up_block(up_block_res_sample)
|
||||
brushnet_up_block_res_samples = brushnet_up_block_res_samples + (up_block_res_sample,)
|
||||
|
||||
# 6. scaling
|
||||
if guess_mode and not self.config.global_pool_conditions:
|
||||
scales = torch.logspace(-1, 0,
|
||||
len(brushnet_down_block_res_samples) + 1 + len(brushnet_up_block_res_samples),
|
||||
device=sample.device) # 0.1 to 1.0
|
||||
scales = scales * conditioning_scale
|
||||
|
||||
brushnet_down_block_res_samples = [sample * scale for sample, scale in zip(brushnet_down_block_res_samples,
|
||||
scales[:len(
|
||||
brushnet_down_block_res_samples)])]
|
||||
brushnet_mid_block_res_sample = brushnet_mid_block_res_sample * scales[len(brushnet_down_block_res_samples)]
|
||||
brushnet_up_block_res_samples = [sample * scale for sample, scale in zip(brushnet_up_block_res_samples,
|
||||
scales[
|
||||
len(brushnet_down_block_res_samples) + 1:])]
|
||||
else:
|
||||
brushnet_down_block_res_samples = [sample * conditioning_scale for sample in
|
||||
brushnet_down_block_res_samples]
|
||||
brushnet_mid_block_res_sample = brushnet_mid_block_res_sample * conditioning_scale
|
||||
brushnet_up_block_res_samples = [sample * conditioning_scale for sample in brushnet_up_block_res_samples]
|
||||
|
||||
if self.config.global_pool_conditions:
|
||||
brushnet_down_block_res_samples = [
|
||||
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_down_block_res_samples
|
||||
]
|
||||
brushnet_mid_block_res_sample = torch.mean(brushnet_mid_block_res_sample, dim=(2, 3), keepdim=True)
|
||||
brushnet_up_block_res_samples = [
|
||||
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_up_block_res_samples
|
||||
]
|
||||
|
||||
if not return_dict:
|
||||
return (brushnet_down_block_res_samples, brushnet_mid_block_res_sample, brushnet_up_block_res_samples)
|
||||
|
||||
return BrushNetOutput(
|
||||
down_block_res_samples=brushnet_down_block_res_samples,
|
||||
mid_block_res_sample=brushnet_mid_block_res_sample,
|
||||
up_block_res_samples=brushnet_up_block_res_samples
|
||||
)
|
||||
|
||||
|
||||
def zero_module(module):
|
||||
for p in module.parameters():
|
||||
nn.init.zeros_(p)
|
||||
return module
|
||||
1494
iopaint/model/power_paint/v2/pipeline_PowerPaint_Brushnet_CA.py
Normal file
1494
iopaint/model/power_paint/v2/pipeline_PowerPaint_Brushnet_CA.py
Normal file
File diff suppressed because it is too large
Load Diff
3811
iopaint/model/power_paint/v2/unet_2d_blocks.py
Normal file
3811
iopaint/model/power_paint/v2/unet_2d_blocks.py
Normal file
File diff suppressed because it is too large
Load Diff
1543
iopaint/model/power_paint/v2/unet_2d_condition.py
Normal file
1543
iopaint/model/power_paint/v2/unet_2d_condition.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -8,6 +8,7 @@ from iopaint.download import scan_models
|
||||
from iopaint.helper import switch_mps_device
|
||||
from iopaint.model import models, ControlNet, SD, SDXL
|
||||
from iopaint.model.brushnet.brushnet_wrapper import BrushNetWrapper
|
||||
from iopaint.model.power_paint.power_paint_v2 import PowerPaintV2
|
||||
from iopaint.model.utils import torch_gc, is_local_files_only
|
||||
from iopaint.schema import InpaintRequest, ModelInfo, ModelType
|
||||
|
||||
@ -33,6 +34,8 @@ class ModelManager:
|
||||
self.enable_brushnet = kwargs.get("enable_brushnet", False)
|
||||
self.brushnet_method = kwargs.get("brushnet_method", None)
|
||||
|
||||
self.enable_powerpaint_v2 = kwargs.get("enable_powerpaint_v2", False)
|
||||
|
||||
self.model = self.init_model(name, device, **kwargs)
|
||||
|
||||
@property
|
||||
@ -62,6 +65,9 @@ class ModelManager:
|
||||
if model_info.support_brushnet and self.enable_brushnet:
|
||||
return BrushNetWrapper(device, **kwargs)
|
||||
|
||||
if model_info.support_powerpaint_v2 and self.enable_powerpaint_v2:
|
||||
return PowerPaintV2(device, **kwargs)
|
||||
|
||||
if model_info.name in models:
|
||||
return models[name](device, **kwargs)
|
||||
|
||||
@ -91,10 +97,12 @@ class ModelManager:
|
||||
Returns:
|
||||
BGR image
|
||||
"""
|
||||
if not config.enable_brushnet:
|
||||
if config.enable_controlnet:
|
||||
self.switch_controlnet_method(config)
|
||||
if not config.enable_controlnet:
|
||||
if config.enable_brushnet:
|
||||
self.switch_brushnet_method(config)
|
||||
|
||||
self.enable_disable_powerpaint_v2(config)
|
||||
self.enable_disable_freeu(config)
|
||||
self.enable_disable_lcm_lora(config)
|
||||
return self.model(image, mask, config).astype(np.uint8)
|
||||
@ -213,6 +221,25 @@ class ModelManager:
|
||||
else:
|
||||
logger.info(f"Enable controlnet: {config.controlnet_method}")
|
||||
|
||||
def enable_disable_powerpaint_v2(self, config: InpaintRequest):
|
||||
if not self.available_models[self.name].support_powerpaint_v2:
|
||||
return
|
||||
|
||||
if self.enable_powerpaint_v2 != config.enable_powerpaint_v2:
|
||||
self.enable_powerpaint_v2 = config.enable_powerpaint_v2
|
||||
pipe_components = {"vae": self.model.model.vae}
|
||||
|
||||
self.model = self.init_model(
|
||||
self.name,
|
||||
switch_mps_device(self.name, self.device),
|
||||
pipe_components=pipe_components,
|
||||
**self.kwargs,
|
||||
)
|
||||
if config.enable_powerpaint_v2:
|
||||
logger.info("Enable PowerPaintV2")
|
||||
else:
|
||||
logger.info("Disable PowerPaintV2")
|
||||
|
||||
def enable_disable_freeu(self, config: InpaintRequest):
|
||||
if str(self.model.device) == "mps":
|
||||
return
|
||||
|
||||
@ -119,6 +119,13 @@ class ModelInfo(BaseModel):
|
||||
ModelType.DIFFUSERS_SD,
|
||||
]
|
||||
|
||||
@computed_field
|
||||
@property
|
||||
def support_powerpaint_v2(self) -> bool:
|
||||
return self.model_type in [
|
||||
ModelType.DIFFUSERS_SD,
|
||||
]
|
||||
|
||||
@computed_field
|
||||
@property
|
||||
def support_freeu(self) -> bool:
|
||||
@ -225,8 +232,9 @@ class FREEUConfig(BaseModel):
|
||||
b2: float = 1.4
|
||||
|
||||
|
||||
class PowerPaintTask(str, Enum):
|
||||
class PowerPaintTask(Choices):
|
||||
text_guided = "text-guided"
|
||||
context_aware = "context-aware"
|
||||
shape_guided = "shape-guided"
|
||||
object_remove = "object-remove"
|
||||
outpainting = "outpainting"
|
||||
@ -387,12 +395,13 @@ class InpaintRequest(BaseModel):
|
||||
|
||||
# BrushNet
|
||||
enable_brushnet: bool = Field(False, description="Enable brushnet")
|
||||
brushnet_method: str = Field(
|
||||
SD_BRUSHNET_CHOICES[0], description="Brushnet method"
|
||||
brushnet_method: str = Field(SD_BRUSHNET_CHOICES[0], description="Brushnet method")
|
||||
brushnet_conditioning_scale: float = Field(
|
||||
1.0, description="brushnet conditioning scale", ge=0.0, le=1.0
|
||||
)
|
||||
brushnet_conditioning_scale: float = Field(1.0, description="brushnet conditioning scale", ge=0.0, le=1.0)
|
||||
|
||||
# PowerPaint
|
||||
enable_powerpaint_v2: bool = Field(False, description="Enable PowerPaint v2")
|
||||
powerpaint_task: PowerPaintTask = Field(
|
||||
PowerPaintTask.text_guided, description="PowerPaint task"
|
||||
)
|
||||
@ -403,8 +412,8 @@ class InpaintRequest(BaseModel):
|
||||
le=1.0,
|
||||
)
|
||||
|
||||
@model_validator(mode='after')
|
||||
def validate_field(cls, values: 'InpaintRequest'):
|
||||
@model_validator(mode="after")
|
||||
def validate_field(cls, values: "InpaintRequest"):
|
||||
if values.sd_seed == -1:
|
||||
values.sd_seed = random.randint(1, 99999999)
|
||||
logger.info(f"Generate random seed: {values.sd_seed}")
|
||||
|
||||
@ -10,7 +10,7 @@ import pytest
|
||||
import torch
|
||||
|
||||
from iopaint.model_manager import ModelManager
|
||||
from iopaint.schema import HDStrategy, SDSampler, FREEUConfig
|
||||
from iopaint.schema import HDStrategy, SDSampler, FREEUConfig, PowerPaintTask
|
||||
|
||||
current_dir = Path(__file__).parent.absolute().resolve()
|
||||
save_dir = current_dir / "result"
|
||||
@ -35,7 +35,7 @@ def test_runway_brushnet(device, sampler):
|
||||
sd_freeu=True,
|
||||
sd_freeu_config=FREEUConfig(),
|
||||
enable_brushnet=True,
|
||||
brushnet_method=SD_BRUSHNET_CHOICES[0]
|
||||
brushnet_method=SD_BRUSHNET_CHOICES[0],
|
||||
)
|
||||
cfg.sd_sampler = sampler
|
||||
|
||||
@ -49,38 +49,64 @@ def test_runway_brushnet(device, sampler):
|
||||
|
||||
|
||||
@pytest.mark.parametrize("device", ["cuda", "mps"])
|
||||
@pytest.mark.parametrize("sampler", [SDSampler.dpm_plus_plus_2m_karras])
|
||||
@pytest.mark.parametrize(
|
||||
"name",
|
||||
[
|
||||
"v1-5-pruned-emaonly.safetensors",
|
||||
],
|
||||
)
|
||||
def test_brushnet_local_file_path(device, sampler, name):
|
||||
@pytest.mark.parametrize("sampler", [SDSampler.dpm_plus_plus_2m])
|
||||
def test_runway_powerpaint_v2(device, sampler):
|
||||
sd_steps = check_device(device)
|
||||
model = ModelManager(
|
||||
name=name,
|
||||
name="runwayml/stable-diffusion-v1-5",
|
||||
device=torch.device(device),
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
cpu_offload=False,
|
||||
)
|
||||
|
||||
tasks = {
|
||||
PowerPaintTask.text_guided: {
|
||||
"prompt": "face of a fox, sitting on a bench",
|
||||
"scale": 7.5,
|
||||
},
|
||||
PowerPaintTask.context_aware: {
|
||||
"prompt": "face of a fox, sitting on a bench",
|
||||
"scale": 7.5,
|
||||
},
|
||||
PowerPaintTask.shape_guided: {
|
||||
"prompt": "face of a fox, sitting on a bench",
|
||||
"scale": 7.5,
|
||||
},
|
||||
PowerPaintTask.object_remove: {
|
||||
"prompt": "",
|
||||
"scale": 12,
|
||||
},
|
||||
PowerPaintTask.outpainting: {
|
||||
"prompt": "",
|
||||
"scale": 7.5,
|
||||
},
|
||||
}
|
||||
|
||||
for task, data in tasks.items():
|
||||
cfg = get_config(
|
||||
strategy=HDStrategy.ORIGINAL,
|
||||
prompt="face of a fox, sitting on a bench",
|
||||
prompt=data["prompt"],
|
||||
negative_prompt="out of frame, lowres, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, disfigured, gross proportions, malformed limbs, watermark, signature",
|
||||
sd_steps=sd_steps,
|
||||
sd_seed=1234,
|
||||
enable_brushnet=True,
|
||||
brushnet_method=SD_BRUSHNET_CHOICES[1]
|
||||
sd_guidance_scale=data["scale"],
|
||||
enable_powerpaint_v2=True,
|
||||
powerpaint_task=task,
|
||||
sd_sampler=sampler,
|
||||
sd_mask_blur=11,
|
||||
sd_seed=42,
|
||||
# sd_keep_unmasked_area=False
|
||||
)
|
||||
cfg.sd_sampler = sampler
|
||||
if task == PowerPaintTask.outpainting:
|
||||
cfg.use_extender = True
|
||||
cfg.extender_x = -128
|
||||
cfg.extender_y = -128
|
||||
cfg.extender_width = 768
|
||||
cfg.extender_height = 768
|
||||
|
||||
assert_equal(
|
||||
model,
|
||||
cfg,
|
||||
f"brushnet_segmentation_mask_{device}.png",
|
||||
f"powerpaint_v2_{device}_{task}.png",
|
||||
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
|
||||
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
|
||||
fx=1,
|
||||
fy=1,
|
||||
)
|
||||
|
||||
@ -3,18 +3,17 @@ import os
|
||||
from iopaint.tests.utils import current_dir, check_device
|
||||
|
||||
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from iopaint.model_manager import ModelManager
|
||||
from iopaint.schema import HDStrategy, SDSampler
|
||||
from iopaint.schema import SDSampler
|
||||
from iopaint.tests.test_model import get_config, assert_equal
|
||||
|
||||
|
||||
@pytest.mark.parametrize("name", ["runwayml/stable-diffusion-inpainting"])
|
||||
@pytest.mark.parametrize("device", ["cuda", "mps", "cpu"])
|
||||
@pytest.mark.parametrize("device", ["cuda", "mps"])
|
||||
@pytest.mark.parametrize(
|
||||
"rect",
|
||||
[
|
||||
@ -23,7 +22,7 @@ from iopaint.tests.test_model import get_config, assert_equal
|
||||
[128, 0, 512 - 128 + 100, 512],
|
||||
[-100, 0, 512 - 128 + 100, 512],
|
||||
[0, 0, 512, 512 + 200],
|
||||
[0, 0, 512 + 200, 512],
|
||||
[256, 0, 512 + 200, 512],
|
||||
[-100, -100, 512 + 200, 512 + 200],
|
||||
],
|
||||
)
|
||||
@ -58,7 +57,7 @@ def test_outpainting(name, device, rect):
|
||||
|
||||
|
||||
@pytest.mark.parametrize("name", ["kandinsky-community/kandinsky-2-2-decoder-inpaint"])
|
||||
@pytest.mark.parametrize("device", ["cuda", "mps", "cpu"])
|
||||
@pytest.mark.parametrize("device", ["cuda", "mps"])
|
||||
@pytest.mark.parametrize(
|
||||
"rect",
|
||||
[
|
||||
@ -99,7 +98,7 @@ def test_kandinsky_outpainting(name, device, rect):
|
||||
|
||||
|
||||
@pytest.mark.parametrize("name", ["Sanster/PowerPaint-V1-stable-diffusion-inpainting"])
|
||||
@pytest.mark.parametrize("device", ["cuda", "mps", "cpu"])
|
||||
@pytest.mark.parametrize("device", ["cuda", "mps"])
|
||||
@pytest.mark.parametrize(
|
||||
"rect",
|
||||
[
|
||||
@ -114,7 +113,7 @@ def test_powerpaint_outpainting(name, device, rect):
|
||||
device=torch.device(device),
|
||||
disable_nsfw=True,
|
||||
sd_cpu_textencoder=False,
|
||||
low_mem=True
|
||||
low_mem=True,
|
||||
)
|
||||
cfg = get_config(
|
||||
prompt="a dog sitting on a bench in the park",
|
||||
|
||||
@ -3,9 +3,8 @@ import cv2
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from iopaint.helper import encode_pil_to_base64
|
||||
from iopaint.schema import LDMSampler, HDStrategy, InpaintRequest, SDSampler
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
current_dir = Path(__file__).parent.absolute().resolve()
|
||||
save_dir = current_dir / "result"
|
||||
@ -32,6 +31,7 @@ def assert_equal(
|
||||
):
|
||||
img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
|
||||
print(f"Input image shape: {img.shape}")
|
||||
|
||||
res = model(img, mask, config)
|
||||
ok = cv2.imwrite(
|
||||
str(save_dir / gt_name),
|
||||
|
||||
Loading…
Reference in New Issue
Block a user