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import gc
import numpy as np
import numpy
import torch
from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel
from matplotlib import pyplot as plt
from pathlib import Path
from PIL import Image
from torch import autocast
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer, logging
import os
from diffusers import StableDiffusionPipeline, DiffusionPipeline
# large or small model
# configurations
height, width = 128, 128
guidance_scale = 8
custom_loss_scale = 200
batch_size = 1
torch_device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
pretrained_model_name_or_path = "CompVis/stable-diffusion-v1-4"
pipe = DiffusionPipeline.from_pretrained(
pretrained_model_name_or_path,
torch_dtype=torch.float32
).to(torch_device)
# Load SD concepts
sdconcepts = ['<morino-hon>', '<space-style>', '<tesla-bot>', '<midjourney-style>', ' <hanfu-anime-style>']
pipe.load_textual_inversion("sd-concepts-library/morino-hon-style")
pipe.load_textual_inversion("sd-concepts-library/space-style")
pipe.load_textual_inversion("sd-concepts-library/tesla-bot")
pipe.load_textual_inversion("sd-concepts-library/midjourney-style")
pipe.load_textual_inversion("sd-concepts-library/hanfu-anime-style")
# define seeds
seed_list = [1, 2, 3, 4, 5]
def custom_loss(images):
# Gradient loss
gradient_x = torch.abs(images[:, :, :, :-1] - images[:, :, :, 1:]).mean()
gradient_y = torch.abs(images[:, :, :-1, :] - images[:, :, 1:, :]).mean()
error = gradient_x + gradient_y
#Variational loss
# diff_x = torch.abs(images[:, :, :, :-1] - images[:, :, :, 1:])
# diff_y = torch.abs(images[:, :, :-1, :] - images[:, :, 1:, :])
# error = diff_x.mean() + diff_y.mean()
return error
def latents_to_pil(latents):
# bath of latents -> list of images
latents = (1 / 0.18215) * latents
with torch.no_grad():
image = pipe.vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1) # 0 to 1
image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
images = (image * 255).round().astype("uint8")
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def generate_latents(prompts, num_inference_steps, seed_nums, loss_apply=False):
generator = torch.manual_seed(seed_nums)
# scheduler
scheduler = LMSDiscreteScheduler(beta_start = 0.00085, beta_end = 0.012, beta_schedule = "scaled_linear", num_train_timesteps = 1000)
scheduler.set_timesteps(num_inference_steps)
scheduler.timesteps = scheduler.timesteps.to(torch.float32)
# text embeddings of the prompt
text_input = pipe.tokenizer(prompts, padding='max_length', max_length = pipe.tokenizer.model_max_length, truncation= True, return_tensors="pt")
input_ids = text_input.input_ids.to(torch_device)
with torch.no_grad():
text_embeddings = pipe.text_encoder(text_input.input_ids.to(torch_device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = pipe.tokenizer(
[""] * batch_size, padding="max_length", max_length= max_length, return_tensors="pt"
)
with torch.no_grad():
uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(torch_device))[0]
text_embeddings = torch.cat([uncond_embeddings,text_embeddings]) # 2,77,768
# random latent
latents = torch.randn(
(batch_size, pipe.unet.config.in_channels, height// 8, width //8),
generator = generator,
) .to(torch.float16)
latents = latents.to(torch_device)
latents = latents * scheduler.init_noise_sigma
for i, t in tqdm(enumerate(scheduler.timesteps), total = len(scheduler.timesteps)):
latent_model_input = torch.cat([latents] * 2)
sigma = scheduler.sigmas[i]
latent_model_input = scheduler.scale_model_input(latent_model_input, t)
with torch.no_grad():
noise_pred = pipe.unet(latent_model_input.to(torch.float32), t, encoder_hidden_states=text_embeddings)["sample"]
#noise_pred = pipe.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
if (loss_apply and i%5 == 0):
latents = latents.detach().requires_grad_()
#latents_x0 = scheduler.step(noise_pred,t, latents).pred_original_sample # this line does not work
latents_x0 = latents - sigma * noise_pred
# use vae to decode the image
denoised_images = pipe.vae.decode((1/ 0.18215) * latents_x0).sample / 2 + 0.5 # range(0,1)
loss = custom_loss(denoised_images) * custom_loss_scale
print(f"Custom gradient loss {loss}")
cond_grad = torch.autograd.grad(loss, latents)[0]
latents = latents.detach() - cond_grad * sigma**2
latents = scheduler.step(noise_pred,t, latents).prev_sample
return latents
# Function to convert PIL images to NumPy arrays
def pil_to_np(image):
return np.array(image)
def generate_gradio_images(prompt, num_inference_steps, loss_flag = False):
# after loss is applied
latents_list = []
for seed_no, sd in zip(seed_list, sdconcepts):
prompts = [f'{prompt} {sd}']
latents = generate_latents(prompts,num_inference_steps, seed_no, loss_apply=loss_flag)
latents_list.append(latents)
# show all
latents_list = torch.vstack(latents_list)
images = latents_to_pil(latents_list)
return images |