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# Adapted from https://github.com/MichalGeyer/pnp-diffusers/blob/main/pnp.py
import spaces
import glob
import os
from pathlib import Path
import torch
import torch.nn as nn
import torchvision.transforms as T
import argparse
from PIL import Image
import yaml
from tqdm import tqdm
from transformers import logging
from diffusers import DDIMScheduler, StableDiffusionPipeline
from pnp_utils import *
from unet2d_custom import UNet2DConditionModel
from pipeline_stable_diffusion_custom import StableDiffusionPipeline
from ldm.modules.encoders.audio_projector_res import Adapter
# suppress partial model loading warning
logging.set_verbosity_error()
from diffusers import logging
logging.set_verbosity_error()
class PNP(nn.Module):
def __init__(self, sd_version="1.4", n_timesteps=50, audio_projector_ckpt_path="ckpts/audio_projector_gh.pth",
adapter_ckpt_path="ckpts/greatest_hits.pt", device="cuda",
clap_path="CLAP/msclap",
clap_weights = "ckpts/CLAP_weights_2022.pth",
):
super().__init__()
self.device = device
if sd_version == '2.1':
model_key = "stabilityai/stable-diffusion-2-1-base"
elif sd_version == '2.0':
model_key = "stabilityai/stable-diffusion-2-base"
elif sd_version == '1.5':
model_key = "runwayml/stable-diffusion-v1-5"
elif sd_version == '1.4':
model_key = "CompVis/stable-diffusion-v1-4"
print(f"model key is {model_key}")
else:
raise ValueError(f'Stable-diffusion version {sd_version} not supported.')
# Create SD models
print('Loading SD model')
pipe = StableDiffusionPipeline.from_pretrained(model_key, torch_dtype=torch.float16).to("cuda")
model_id = "CompVis/stable-diffusion-v1-4"
self.unet = UNet2DConditionModel.from_pretrained(
model_id,
subfolder="unet",
use_adapter_list=[False, True, True],
low_cpu_mem_usage=False,
device_map=None
).to("cuda")
audio_projector_path = "ckpts/audio_projector_landscape.pth"
adapter_ckpt_path = "ckpts/landscape.pt"
#self.pnp.set_audio_projector(gate_dict_path, audio_projector_path)
gate_dict = torch.load(adapter_ckpt_path)
for name, param in self.unet.named_parameters():
if "adapter" in name:
param.data = gate_dict[name]
#unet.to(self.device);
#pipe.unet = unet.to(self.device);
self.vae = pipe.vae
self.tokenizer = pipe.tokenizer
self.text_encoder = pipe.text_encoder
# self.unet = unet.to(self.device);
#pipe.unet
self.scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler")
self.scheduler.set_timesteps(n_timesteps, device=self.device)
self.latents_path = "latents_forward"
self.output_path = "PNP-results/home"
import os
os.makedirs(self.output_path, exist_ok=True)
import sys
sys.path.append(clap_path)
from CLAPWrapper import CLAPWrapper
self.audio_encoder = CLAPWrapper(clap_weights, use_cuda=True)
self.audio_projector = Adapter(audio_token_count=77, transformer_layer_count=4).cuda()
#self.audio_projector_ckpt_path = audio_projector_ckpt_path
self.sr = 44100
# self.set_audio_projector(adapter_ckpt_path, audio_projector_ckpt_path)
self.text_encoder = self.text_encoder.cuda()
self.audio_projector.load_state_dict(torch.load(audio_projector_path))
self.audio_projector_ckpt_path = audio_projector_ckpt_path
self.adapter_ckpt_path = adapter_ckpt_path
self.changed_model = False
@spaces.GPU
def set_audio_projector(self, adapter_ckpt_path, audio_projector_ckpt_path):
print(f"SETTING MODEL TO {adapter_ckpt_path}")
gate_dict = torch.load(adapter_ckpt_path)
for name, param in self.unet.named_parameters():
if "adapter" in name:
param.data = gate_dict[name]
self.unet.eval()
self.unet = self.unet.cuda()
self.audio_projector.load_state_dict(torch.load(audio_projector_ckpt_path))
self.audio_projector.eval()
self.audio_projector = self.audio_projector.cuda()
@spaces.GPU
def set_text_embeds(self, prompt, negative_prompt=""):
self.text_encoder = self.text_encoder.cuda()
self.text_embeds = self.get_text_embeds(prompt, negative_prompt)
self.pnp_guidance_embeds = self.get_text_embeds("", "").chunk(2)[0]
@spaces.GPU
def set_audio_context(self, audio_path):
self.audio_projector = self.audio_projector.cuda()
self.audio_encoder.clap.audio_encoder = self.audio_encoder.clap.audio_encoder.to("cuda")
audio_emb, _ = self.audio_encoder.get_audio_embeddings([audio_path], resample = self.sr)
dtpye_w = self.audio_projector.audio_emb_projection[0].weight.dtype
device_w = self.audio_projector.audio_emb_projection[0].weight.device
audio_emb = audio_emb.cuda()
audio_proj = self.audio_projector(audio_emb.unsqueeze(1))
audio_emb = torch.zeros(1, 1024).cuda()
audio_uc = self.audio_projector(audio_emb.unsqueeze(1))
self.audio_context = torch.cat([audio_uc, audio_uc, audio_proj]).cuda()
@torch.no_grad()
@spaces.GPU
def get_text_embeds(self, prompt, negative_prompt, batch_size=1):
# Tokenize text and get embeddings
text_input = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length,
truncation=True, return_tensors='pt')
input_ids = text_input.input_ids.to("cuda")
text_embeddings = self.text_encoder(input_ids)[0]
# Do the same for unconditional embeddings
uncond_input = self.tokenizer(negative_prompt, padding='max_length', max_length=self.tokenizer.model_max_length,
return_tensors='pt')
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# Cat for final embeddings
text_embeddings = torch.cat([uncond_embeddings] * batch_size + [text_embeddings] * batch_size)
return text_embeddings
@torch.no_grad()
@spaces.GPU
def decode_latent(self, latent):
self.vae = self.vae.cuda()
with torch.autocast(device_type='cuda', dtype=torch.float32):
latent = 1 / 0.18215 * latent
img = self.vae.decode(latent).sample
img = (img / 2 + 0.5).clamp(0, 1)
return img
#@torch.autocast(device_type='cuda', dtype=torch.float32)
@spaces.GPU
def get_data(self, image_path):
self.image_path = image_path
# load image
image = Image.open(image_path).convert('RGB')
image = image.resize((512, 512), resample=Image.Resampling.LANCZOS)
image = T.ToTensor()(image).to(self.device)
# get noise
latents_path = os.path.join(self.latents_path, f'noisy_latents_{self.scheduler.timesteps[0]}.pt')
noisy_latent = torch.load(latents_path).to(self.device)
return image, noisy_latent
@torch.no_grad()
@spaces.GPU
def denoise_step(self, x, t, guidance_scale):
# register the time step and features in pnp injection modules
source_latents = load_source_latents_t(t, os.path.join(self.latents_path))
latent_model_input = torch.cat([source_latents] + ([x] * 2))
register_time(self, t.item())
# compute text embeddings
text_embed_input = torch.cat([self.pnp_guidance_embeds, self.text_embeds], dim=0)
# apply the denoising network
noise_pred = self.unet(latent_model_input, t,
encoder_hidden_states=text_embed_input,
audio_context=self.audio_context)['sample']
# perform guidance
_, noise_pred_uncond, noise_pred_cond = noise_pred.chunk(3)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
# compute the denoising step with the reference model
denoised_latent = self.scheduler.step(noise_pred, t, x)['prev_sample']
return denoised_latent
@spaces.GPU
def init_pnp(self, conv_injection_t, qk_injection_t):
self.qk_injection_timesteps = self.scheduler.timesteps[:qk_injection_t] if qk_injection_t >= 0 else []
self.conv_injection_timesteps = self.scheduler.timesteps[:conv_injection_t] if conv_injection_t >= 0 else []
register_attention_control_efficient(self, self.qk_injection_timesteps)
register_conv_control_efficient(self, self.conv_injection_timesteps)
@spaces.GPU
def run_pnp(self, n_timesteps=50, pnp_f_t=0.5, pnp_attn_t=0.5,
prompt="", negative_prompt="",
audio_path="", image_path="",
cfg_scale=5):
# if not self.changed_model:
# self.set_audio_projector(self.adapter_ckpt_path, self.audio_projector_ckpt_path)
self.audio_projector = self.audio_projector.cuda()
self.set_text_embeds(prompt)
self.set_audio_context(audio_path=audio_path)
self.image, self.eps = self.get_data(image_path=image_path)
self.unet = self.unet.cuda()
pnp_f_t = int(n_timesteps * pnp_f_t)
pnp_attn_t = int(n_timesteps * pnp_attn_t)
self.init_pnp(conv_injection_t=pnp_f_t, qk_injection_t=pnp_attn_t)
edited_img = self.sample_loop(self.eps, cfg_scale=cfg_scale)
return T.ToPILImage()(edited_img[0])
@spaces.GPU
def sample_loop(self, x, cfg_scale):
with torch.autocast(device_type='cuda', dtype=torch.float32):
for i, t in enumerate(tqdm(self.scheduler.timesteps, desc="Sampling")):
x = self.denoise_step(x, t, cfg_scale)
decoded_latent = self.decode_latent(x)
T.ToPILImage()(decoded_latent[0]).save(f'{self.output_path}/output.png')
return decoded_latent
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config_path', type=str, default='config_pnp.yaml')
opt = parser.parse_args()
with open(opt.config_path, "r") as f:
config = yaml.safe_load(f)
os.makedirs(config["output_path"], exist_ok=True)
with open(os.path.join(config["output_path"], "config.yaml"), "w") as f:
yaml.dump(config, f)
seed_everything(config["seed"])
print(config)
pnp = PNP(config)
temp = pnp.run_pnp() |