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from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler
from transformers import CLIPTextModel, CLIPTokenizer, logging
import torch
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from PIL import Image
# Supress some unnecessary warnings when loading the CLIPTextModel
logging.set_verbosity_error()
# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
# Loading components we'll use
tokenizer = CLIPTokenizer.from_pretrained(
"openai/clip-vit-large-patch14",
)
text_encoder = CLIPTextModel.from_pretrained(
"openai/clip-vit-large-patch14",
).to(device)
vae = AutoencoderKL.from_pretrained(
"CompVis/stable-diffusion-v1-4",
subfolder = "vae",
).to(device)
unet = UNet2DConditionModel.from_pretrained(
"CompVis/stable-diffusion-v1-4",
subfolder = "unet",
).to(device)
beta_start,beta_end = 0.00085,0.012
scheduler = DDIMScheduler(
beta_start=beta_start,
beta_end=beta_end,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
clip_sample=False,
set_alpha_to_one=False,
)
# convert PIL image to latents
def encode(img):
with torch.no_grad():
latent = vae.encode(tfms.ToTensor()(img).unsqueeze(0).to(device)*2-1)
latent = 0.18215 * latent.latent_dist.sample()
return latent
# convert latents to PIL image
def decode(latent):
latent = (1 / 0.18215) * latent
with torch.no_grad():
img = vae.decode(latent).sample
img = (img / 2 + 0.5).clamp(0, 1)
img = img.detach().cpu().permute(0, 2, 3, 1).numpy()
img = (img * 255).round().astype("uint8")
return Image.fromarray(img[0])
# convert prompt into text embeddings, also unconditional embeddings
def prep_text(prompt):
text_input = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embedding = text_encoder(
text_input.input_ids.to(device)
)[0]
uncond_input = tokenizer(
"",
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
uncond_embedding = text_encoder(
uncond_input.input_ids.to(device)
)[0]
return torch.cat([uncond_embedding, text_embedding])
def magic_mix(
img, # specifies the layout semantics
prompt, # specifies the content semantics
kmin=0.3,
kmax=0.6,
v=0.5, # interpolation constant
seed=42,
steps=50,
guidance_scale=7.5,
):
tmin = steps- int(kmin*steps)
tmax = steps- int(kmax*steps)
text_embeddings = prep_text(prompt)
scheduler.set_timesteps(steps)
width, height = img.size
encoded = encode(img)
torch.manual_seed(seed)
noise = torch.randn(
(1,unet.in_channels,height // 8,width // 8),
).to(device)
latents = scheduler.add_noise(
encoded,
noise,
timesteps=scheduler.timesteps[tmax]
)
input = torch.cat([latents]*2)
input = scheduler.scale_model_input(input, scheduler.timesteps[tmax])
with torch.no_grad():
pred = unet(
input,
scheduler.timesteps[tmax],
encoder_hidden_states=text_embeddings,
).sample
pred_uncond, pred_text = pred.chunk(2)
pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)
latents = scheduler.step(pred, scheduler.timesteps[tmax], latents).prev_sample
for i, t in enumerate(tqdm(scheduler.timesteps)):
if i > tmax:
if i < tmin: # layout generation phase
orig_latents = scheduler.add_noise(
encoded,
noise,
timesteps=t
)
input = (v*latents) + (1-v)*orig_latents # interpolating between layout noise and conditionally generated noise to preserve layout sematics
input = torch.cat([input]*2)
else: # content generation phase
input = torch.cat([latents]*2)
input = scheduler.scale_model_input(input, t)
with torch.no_grad():
pred = unet(
input,
t,
encoder_hidden_states=text_embeddings,
).sample
pred_uncond, pred_text = pred.chunk(2)
pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)
latents = scheduler.step(pred, t, latents).prev_sample
return decode(latents)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("img_file", type=str, help="image file to provide the layout semantics for the mixing process")
parser.add_argument("prompt", type=str, help="prompt to provide the content semantics for the mixing process")
parser.add_argument("out_file", type=str, help="filename to save the generation to")
parser.add_argument("--kmin", type=float, default=0.3)
parser.add_argument("--kmax", type=float, default=0.6)
parser.add_argument("--v", type=float, default=0.5)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--steps", type=int, default=50)
parser.add_argument("--guidance_scale", type=float, default=7.5)
args = parser.parse_args()
img = Image.open(args.img_file)
out_img = magic_mix(
img,
args.prompt,
args.kmin,
args.kmax,
args.v,
args.seed,
args.steps,
args.guidance_scale
)
out_img.save(args.out_file) |