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import torch
import os
import numpy as np
import torchvision
from PIL import Image
from transformers import CLIPModel, CLIPTextModel, CLIPTokenizer, CLIPProcessor
from diffusers import AutoencoderKL, UNet2DConditionModel
def get_embeds(prompt, clip, clip_tokenizer, device="cuda"):
tokens = clip_tokenizer(
prompt,
padding="max_length",
max_length=clip_tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
return_overflowing_tokens=True,
)
embeds = clip(tokens.input_ids.to(device)).last_hidden_state
return embeds
@torch.no_grad()
def get_image_from_latent(vae, latent):
latent = latent / 0.18215
image = vae.decode(latent.to(vae.dtype)).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).detach().numpy()
image = (image[0] * 255).round().astype("uint8")
return Image.fromarray(image)
@torch.no_grad()
def get_latent_from_image(vae, image, device="cuda"):
generator = torch.cuda.manual_seed(798122)
# Resize and transpose for numpy b h w c -> torch b c h w
# image = image.resize((width, height), resample=Image.Resampling.LANCZOS)
image = np.array(image).astype(np.float16) / 255.0 * 2.0 - 1.0
image = torch.from_numpy(image[np.newaxis, ...].transpose(0, 3, 1, 2))
# If there is alpha channel, composite alpha for white, as the diffusion model does not support alpha channel
if image.shape[1] > 3:
image = image[:, :3] * image[:, 3:] + (1 - image[:, 3:])
# Move image to GPU
image = image.to(device)
# Encode image
init_latent = vae.encode(image).latent_dist.sample(generator=generator) * 0.18215
return init_latent
def load_all_models(model_path_diffusion):
clip_tokenizer = CLIPTokenizer.from_pretrained(
model_path_diffusion, subfolder="tokenizer"
)
clip_text_model = CLIPTextModel.from_pretrained(
model_path_diffusion, subfolder="text_encoder", torch_dtype=torch.float16
)
# Init diffusion model
auth_token = True # Replace this with huggingface auth token as a string if model is not already downloaded
# model_path_diffusion = "assets/models/stable-diffusion-v1-4"
unet = UNet2DConditionModel.from_pretrained(
model_path_diffusion,
subfolder="unet",
revision="fp16",
torch_dtype=torch.float16,
)
vae = AutoencoderKL.from_pretrained(
model_path_diffusion,
subfolder="vae",
revision="fp16",
torch_dtype=torch.float16,
)
# Move to GPU
device = "cuda"
unet.to(device)
vae.to(device)
clip_text_model.to(device)
model_bundle = {}
model_bundle["unet"] = unet
model_bundle["vae"] = vae
model_bundle["clip_tokenizer"] = clip_tokenizer
model_bundle["clip_text_model"] = clip_text_model
return model_bundle
@torch.no_grad()
def check_clip_score(clip_model, clip_processor, prompts=[], images=[]):
if len(prompts) == 1:
dim = 0
if len(images) == 1:
dim = 1
inputs = clip_processor(
text=prompts, images=images, return_tensors="pt", padding=True
)
inputs["pixel_values"] = torch.tensor(
inputs["pixel_values"], dtype=clip_model.dtype, device=clip_model.device
)
inputs["input_ids"] = torch.tensor(inputs["input_ids"], device=clip_model.device)
inputs["attention_mask"] = torch.tensor(
inputs["attention_mask"], device=clip_model.device
)
outputs = clip_model(**inputs)
a = clip_model.get_image_features(inputs["pixel_values"])
b = clip_model.get_text_features(inputs["input_ids"])
prob = torch.matmul(a, b.t()).softmax(dim=dim)
return prob
def get_attn(unet, use=True):
attn = []
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and "attn2" in name:
if module.attn.size() == torch.Size([8, 1024, 77]):
attn.append(module.attn)
attn = torch.cat(attn, dim=0)
attn = torch.sum(attn, dim=0)
resized = torch.zeros([64, 64, 77])
f = torchvision.transforms.Resize(size=(64, 64))
for i in range(77):
dim = int(np.sqrt(attn.shape[0]))
attn_slice = attn[..., i].view(1, dim, dim)
resized[..., i] = f(attn_slice)[0]
return resized.cpu().numpy()
def save_attn(unet):
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention" and "attn2" in name:
folder = "/tmp"
filepath = os.path.join(folder, name + ".pt")
torch.save(module.attn, filepath)
print(filepath)
def use_add_noise(unet, level, use=True):
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention":
module.use_add_noise = use
module.noise_level = level
def use_edited_attention(unet, use=True):
for name, module in unet.named_modules():
module_name = type(module).__name__
if module_name == "CrossAttention":
module.use_edited_attn = use
def prompt_token(prompt, index):
tokens = clip_tokenizer(
prompt,
padding="max_length",
max_length=clip_tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
return_overflowing_tokens=True,
).input_ids[0]
return clip_tokenizer.decode(tokens[index : index + 1])
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