d-edit / utils.py
niulx's picture
Update utils.py
a126919 verified
raw
history blame
12 kB
from transformers import PretrainedConfig
from PIL import Image
import torch
import numpy as np
import PIL
import os
from tqdm.auto import tqdm
from diffusers.models.attention_processor import (
AttnProcessor2_0,
LoRAAttnProcessor2_0,
LoRAXFormersAttnProcessor,
XFormersAttnProcessor,
)
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
def myroll2d(a, delta_x, delta_y):
h, w = a.shape[0], a.shape[1]
delta_x = -delta_x
delta_y = -delta_y
if isinstance(a, np.ndarray):
b = np.zeros ([h,w]).astype(np.uint8)
elif isinstance(a, torch.Tensor):
b = torch.zeros([h,w]).to(torch.uint8)
if delta_x > 0:
left_a = delta_x
right_a = w
left_b = 0
right_b = w - delta_x
else:
left_a = 0
right_a = w + delta_x
left_b = -delta_x
right_b = w
if delta_y > 0:
top_a = delta_y
bot_a = h
top_b = 0
bot_b = h-delta_y
else:
top_a = 0
bot_a = h + delta_y
top_b = -delta_y
bot_b = h
b[left_b: right_b, top_b: bot_b] = a[left_a: right_a, top_a: bot_a]
return b
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision = None, subfolder: str = "text_encoder"
):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path, subfolder=subfolder, revision=revision
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "CLIPTextModelWithProjection":
from transformers import CLIPTextModelWithProjection
return CLIPTextModelWithProjection
else:
raise ValueError(f"{model_class} is not supported.")
@torch.no_grad()
def image2latent(image, vae = None, dtype=None):
with torch.no_grad():
if type(image) is Image or type(image) is PIL.PngImagePlugin.PngImageFile or type(image) is PIL.JpegImagePlugin.JpegImageFile:
image = np.array(image)
if type(image) is torch.Tensor and image.dim() == 4:
latents = image
else:
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(device, dtype= dtype)
latents = vae.encode(image).latent_dist.sample()
latents = latents * vae.config.scaling_factor
return latents
@torch.no_grad()
def latent2image(latents, return_type = 'np', vae = None):
# needs_upcasting = vae.dtype == torch.float16 and vae.config.force_upcast
needs_upcasting = True
if needs_upcasting:
upcast_vae(vae)
latents = latents.to(next(iter(vae.post_quant_conv.parameters())).dtype)
image = vae.decode(latents /vae.config.scaling_factor, return_dict=False)[0]
if return_type == 'np':
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()#[0]
image = (image * 255).astype(np.uint8)
if needs_upcasting:
vae.to(dtype=torch.float16)
return image
def upcast_vae(vae):
dtype = vae.dtype
vae.to(dtype=torch.float32)
use_torch_2_0_or_xformers = isinstance(
vae.decoder.mid_block.attentions[0].processor,
(
AttnProcessor2_0,
XFormersAttnProcessor,
LoRAXFormersAttnProcessor,
LoRAAttnProcessor2_0,
),
)
# if xformers or torch_2_0 is used attention block does not need
# to be in float32 which can save lots of memory
if use_torch_2_0_or_xformers:
vae.post_quant_conv.to(dtype)
vae.decoder.conv_in.to(dtype)
vae.decoder.mid_block.to(dtype)
def prompt_to_emb_length_sdxl(prompt, tokenizer, text_encoder, length = None):
text_input = tokenizer(
[prompt],
padding="max_length",
max_length=length,
truncation=True,
return_tensors="pt",
)
prompt_embeds = text_encoder(text_input.input_ids.to(device),output_hidden_states=True)
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.hidden_states[-2]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
return {"prompt_embeds": prompt_embeds, "pooled_prompt_embeds": pooled_prompt_embeds}
def prompt_to_emb_length_sd(prompt, tokenizer, text_encoder, length = None):
text_input = tokenizer(
[prompt],
padding="max_length",
max_length=length,
truncation=True,
return_tensors="pt",
)
emb = text_encoder(text_input.input_ids.to(device))[0]
return emb
def sdxl_prepare_input_decom(
set_string_list,
tokenizer,
tokenizer_2,
text_encoder_1,
text_encoder_2,
length = 20,
bsz = 1,
weight_dtype = torch.float32,
resolution = 1024,
normal_token_id_list = []
):
encoder_hidden_states_list = []
pooled_prompt_embeds = 0
for m_idx in range(len(set_string_list)):
prompt_embeds_list = []
if ("#" in set_string_list[m_idx] or "$" in set_string_list[m_idx]) and m_idx not in normal_token_id_list : ###
out = prompt_to_emb_length_sdxl(
set_string_list[m_idx], tokenizer, text_encoder_1, length = length
)
else:
out = prompt_to_emb_length_sdxl(
set_string_list[m_idx], tokenizer, text_encoder_1, length = 77
)
print(m_idx, set_string_list[m_idx])
prompt_embeds, _ = out["prompt_embeds"].to(dtype=weight_dtype), out["pooled_prompt_embeds"].to(dtype=weight_dtype)
prompt_embeds = prompt_embeds.repeat(bsz, 1, 1)
prompt_embeds_list.append(prompt_embeds)
if ("#" in set_string_list[m_idx] or "$" in set_string_list[m_idx]) and m_idx not in normal_token_id_list:
out = prompt_to_emb_length_sdxl(
set_string_list[m_idx], tokenizer_2, text_encoder_2, length = length
)
else:
out = prompt_to_emb_length_sdxl(
set_string_list[m_idx], tokenizer_2, text_encoder_2, length = 77
)
print(m_idx, set_string_list[m_idx])
prompt_embeds = out["prompt_embeds"].to(dtype=weight_dtype)
pooled_prompt_embeds += out["pooled_prompt_embeds"].to(dtype=weight_dtype)
prompt_embeds = prompt_embeds.repeat(bsz, 1, 1)
prompt_embeds_list.append(prompt_embeds)
encoder_hidden_states_list.append(torch.concat(prompt_embeds_list, dim=-1))
add_text_embeds = pooled_prompt_embeds /len(set_string_list)
target_size, original_size,crops_coords_top_left = (resolution,resolution),(resolution,resolution),(0,0)
add_time_ids = list(original_size + crops_coords_top_left + target_size)
add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype,device = pooled_prompt_embeds.device) #[B,6]
return encoder_hidden_states_list, add_text_embeds, add_time_ids
def sd_prepare_input_decom(
set_string_list,
tokenizer,
text_encoder_1,
length = 20,
bsz = 1,
weight_dtype = torch.float32,
normal_token_id_list = []
):
encoder_hidden_states_list = []
for m_idx in range(len(set_string_list)):
if ("#" in set_string_list[m_idx] or "$" in set_string_list[m_idx]) and m_idx not in normal_token_id_list : ###
encoder_hidden_states = prompt_to_emb_length_sd(
set_string_list[m_idx], tokenizer, text_encoder_1, length = length
)
else:
encoder_hidden_states = prompt_to_emb_length_sd(
set_string_list[m_idx], tokenizer, text_encoder_1, length = 77
)
print(m_idx, set_string_list[m_idx])
encoder_hidden_states = encoder_hidden_states.repeat(bsz, 1, 1)
encoder_hidden_states_list.append(encoder_hidden_states.to(dtype=weight_dtype))
return encoder_hidden_states_list
def load_mask (input_folder):
np_mask_dtype = 'uint8'
mask_np_list = []
mask_label_list = []
files = [
file_name for file_name in os.listdir(input_folder) \
if "mask" in file_name and ".npy" in file_name \
and "_" in file_name and "Edited" not in file_name
]
files = sorted(files, key = lambda x: int(x.split("_")[0][4:]))
for idx, file_name in enumerate(files):
if "mask" in file_name and ".npy" in file_name and "_" in file_name \
and "Edited" not in file_name:
mask_np = np.load(os.path.join(input_folder, file_name)).astype(np_mask_dtype)
mask_np_list.append(mask_np)
mask_label = file_name.split("_")[1][:-4]
mask_label_list.append(mask_label)
mask_list = []
for mask_np in mask_np_list:
mask = torch.from_numpy(mask_np)
mask_list.append(mask)
try:
assert torch.all(sum(mask_list)==1)
except:
print("please check mask")
# plt.imsave( "out_mask.png", mask_list_edit[0])
return mask_list, mask_label_list
def load_image(image_path, left=0, right=0, top=0, bottom=0, size = 512):
if type(image_path) is str:
image = np.array(Image.open(image_path))[:, :, :3]
else:
image = image_path
h, w, c = image.shape
left = min(left, w-1)
right = min(right, w - left - 1)
top = min(top, h - left - 1)
bottom = min(bottom, h - top - 1)
image = image[top:h-bottom, left:w-right]
h, w, c = image.shape
if h < w:
offset = (w - h) // 2
image = image[:, offset:offset + h]
elif w < h:
offset = (h - w) // 2
image = image[offset:offset + w]
image = np.array(Image.fromarray(image).resize((size, size)))
return image
def mask_union_torch(*masks):
masks = [m.to(torch.float) for m in masks]
res = sum(masks)>0
return res
def load_mask_edit(input_folder):
np_mask_dtype = 'uint8'
mask_np_list = []
mask_label_list = []
files = [file_name for file_name in os.listdir(input_folder) if "mask" in file_name and ".npy" in file_name and "_" in file_name and "Edited" in file_name and "-1" not in file_name]
files = sorted(files, key = lambda x: int(x.split("_")[0][10:]))
for idx, file_name in enumerate(files):
if "mask" in file_name and ".npy" in file_name and "_" in file_name and "Edited" in file_name and "-1" not in file_name:
mask_np = np.load(os.path.join(input_folder, file_name)).astype(np_mask_dtype)
mask_np_list.append(mask_np)
mask_label = file_name.split("_")[1][:-4]
# mask_label = mask_label.split("-")[0]
mask_label_list.append(mask_label)
mask_list = []
for mask_np in mask_np_list:
mask = torch.from_numpy(mask_np)
mask_list.append(mask)
try:
assert torch.all(sum(mask_list)==1)
except:
print("Make sure maskEdited is in the folder, if not, generate using the UI")
import pdb; pdb.set_trace()
return mask_list, mask_label_list
def save_images(images,filename, num_rows=1, offset_ratio=0.02):
if type(images) is list:
num_empty = len(images) % num_rows
elif images.ndim == 4:
num_empty = images.shape[0] % num_rows
else:
images = [images]
num_empty = 0
empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
num_items = len(images)
folder = os.path.dirname(filename)
for i, image in enumerate(images):
pil_img = Image.fromarray(image)
name = filename.split("/")[-1]
name = name.split(".")[-2]+"_{}".format(i) +"."+filename.split(".")[-1]
pil_img.save(os.path.join(folder, name))
print("saved to ", os.path.join(folder, name))