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end2end_inv.py
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end2end_inv.py
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from pipelines.SDUP_inv_pipeline import StableDiffusionUpscaleInvPipeline
from pipelines.SDUP_pipeline import StableDiffusionUpscalePipeline
from pipelines.deepfloyd_pipeline import IFPipeline
from pipelines.deepfloyd_inv_pipeline import IFInvPipeline
from pipelines.deepfloyd_SR_pipeline import IFSuperResolutionPipeline
from pipelines.deepfloyd_SR_inv_pipeline import IFSuperResolutionInvPipeline
# from pipelines.deepfloyd_SR_nti_pipeline import IFSuperResolutionInvPipeline
from pipelines.scheduler_ddim import DDIMScheduler
from pipelines.scheduler_inv import DDIMInverseScheduler
from diffusers.utils import pt_to_pil, numpy_to_pil
import torch
from IPython.display import display
import numpy as np
from PIL import Image
from diffusers.image_processor import VaeImageProcessor
import argparse
import os
### NOTE: image_processor can help to make the batch size ready
image_processor=VaeImageProcessor()
def arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--input_image', type=str, default='images/cat_hq.jpg')
parser.add_argument('--results_folder', type=str, default='output/')
# parser.add_argument('--seg_dirs', type=str, default='seg_dirs/catdog')
parser.add_argument('--prompt_str', type=str, default='a cat in an astronaut suit')
parser.add_argument('--prompt_file', type=str, default=None)
### NOTE: noise level are also hyperparameters
parser.add_argument('--noise_level_3', type=int, default=100)
parser.add_argument('--noise_level_2', type=int, default=250)
parser.add_argument('--num_inference_steps_3', type=int, default=50)
parser.add_argument('--num_inference_steps_2', type=int, default=50)
parser.add_argument('--num_inference_steps_1', type=int, default=50)
parser.add_argument('--inner_steps_3', type=int, default=21)
parser.add_argument('--inner_steps_2', type=int, default=21)
parser.add_argument('--inner_steps_1', type=int, default=51)
parser.add_argument('--scale_factor_2', type=float, default=0.3)
parser.add_argument('--scale_factor_3', type=float, default=0.3)
# parser.add_argument('--lr_3', type=float, default=51)
# parser.add_argument('--lr_2', type=float, default=1e-1)
parser.add_argument('--lr_2', type=float, default=5e-3)
### NOTE: lr_2: 0.005<= lr_2 <= 0.05 with 101 steps and 100 inference time steps ATTN: not NTI case
parser.add_argument('--lr_1', type=float, default=1e-3)
parser.add_argument('--guidance_1', type=float, default=3.0)
### NOTE: 3.5 is good >=4.0 starts getting far away
parser.add_argument('--guidance_2', type=float, default=1.0)
parser.add_argument('--guidance_3', type=float, default=1.0)
parser.add_argument('--model_path_1', type=str, default="DeepFloyd/IF-I-M-v1.0")
parser.add_argument('--model_path_2', type=str, default="DeepFloyd/IF-II-M-v1.0")
parser.add_argument('--model_path_3', type=str, default="stabilityai/stable-diffusion-x4-upscaler")
parser.add_argument('--enable_1', action='store_true')
parser.add_argument('--no_enable_1', dest='enable_1', action='store_false')
parser.set_defaults(enable_1=False)
parser.add_argument('--enable_3', action='store_true')
parser.add_argument('--no_enable_3', dest='enable_3', action='store_false')
parser.set_defaults(enable_3=False)
# parser.set_defaults(enable_3=True)
parser.add_argument('--enable_3for2', action='store_true')
parser.add_argument('--no_enable_3for2', dest='enable_3for2', action='store_false')
parser.set_defaults(enable_3for2=False)
# parser.set_defaults(enable_3for2=True)
parser.add_argument('--enable_3_float16', action='store_true')
parser.add_argument('--no_enable_3_float16', dest='enable_3_float16', action='store_false')
parser.set_defaults(enable_3_float16=False)
# parser.set_defaults(enable_3_float16=True)
parser.add_argument('--is_NPI', action='store_true')
parser.add_argument('--no_is_NPI', dest='is_NPI', action='store_false')
# parser.set_defaults(is_NPI=True)
parser.set_defaults(is_NPI=False)
args = parser.parse_args()
return args
if __name__=="__main__":
args = arguments()
print(args)
### NOTE: upscaler can only be float 32 to avoid overflow
if args.enable_3 or args.enable_3for2:
stage_3 = StableDiffusionUpscaleInvPipeline.from_pretrained(
args.model_path_3,
torch_dtype=torch.float32)
stage_3.scheduler = DDIMScheduler.from_config(stage_3.scheduler.config)
### NOTE: support float 16 for guidance_3>1.0 or modular them to see if help or move to 48GB GPUs
# if not args.enable_3_float16:
stage_3_rec = StableDiffusionUpscalePipeline.from_pretrained(
args.model_path_3,
torch_dtype=torch.float32)
stage_3_rec.scheduler = DDIMScheduler.from_config(stage_3_rec.scheduler.config)
stage_3.enable_model_cpu_offload()
stage_3_rec.enable_model_cpu_offload()
if args.enable_1:
stage_1 = IFInvPipeline.from_pretrained(args.model_path_1, variant="fp32", torch_dtype=torch.float32)
stage_1.enable_model_cpu_offload()
### NOTE: Get the value from the arguments
img_pth=args.input_image
noise_level_3=args.noise_level_3
noise_level_2=args.noise_level_2
bname = os.path.basename(args.input_image).split(".")[0]
### NOTE: include prompt files if provides
if args.prompt_file is None:
args.prompt_file=os.path.join(args.results_folder, f"{bname}", f"prompt.txt")
if os.path.isfile(args.prompt_file):
prompt = open(args.prompt_file).read().strip()
print(f'get prompt from file: {args.prompt_file} \n')
else:
prompt = args.prompt_str
print(f'get prompt from arguments \n')
print(prompt)
num_inference_steps_3=args.num_inference_steps_3
num_inference_steps_2=args.num_inference_steps_2
num_inference_steps_1=args.num_inference_steps_1
inner_steps_3=args.inner_steps_3
inner_steps_2=args.inner_steps_2
inner_steps_1=args.inner_steps_1
scale_factor_2=args.scale_factor_2
scale_factor_3=args.scale_factor_3
lr_2=args.lr_2
lr_1=args.lr_1
guidance_1=args.guidance_1
guidance_2=args.guidance_2
guidance_3=args.guidance_3
### NOTE: negative prompt inversion
is_NPI=args.is_NPI
### NOTE: creating saving path
saving_path=os.path.join(args.results_folder, f"{bname}",
f"CFG1_{guidance_1}_CFG3_{guidance_3}_noise3_{noise_level_3}_lr1_{lr_1}_scale3_{scale_factor_3}_NPI_{is_NPI}",
'_'.join(prompt.split(' ')))
print(f'the saving path is {saving_path}')
os.makedirs(saving_path, exist_ok=True)
### NOTE: read the images
_inv_raw_image_2 = Image.open(img_pth).convert("RGB").resize((1024,1024))
inv_raw_image_2 = image_processor.preprocess(_inv_raw_image_2)
_inv_raw_image_1 = Image.open(img_pth).convert("RGB").resize((256,256))
inv_raw_image_1 = image_processor.preprocess(_inv_raw_image_1)
_inv_raw_image_0 = Image.open(img_pth).convert("RGB").resize((64,64))
inv_raw_image_0 = image_processor.preprocess(_inv_raw_image_0)
generator = torch.manual_seed(0)
### NOTE: =================================================== 1st stage inversion start:
if args.enable_1:
prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
generator = torch.manual_seed(0)
stage_1.scheduler = DDIMInverseScheduler.from_config(stage_1.scheduler.config)
output_inv_1, inter_img_list_1_inv, _ = stage_1(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
num_inference_steps=num_inference_steps_1,
output_type="pt",
image_init=inv_raw_image_0,
guidance_scale=1.0,
)
inv_noise_1 = output_inv_1.images
torch.save(inv_noise_1.cpu(),f"{saving_path}/inv_noise_1.pt")
torch.save(inter_img_list_1_inv, f"{saving_path}/inter_img_list_1_inv.pt")
torch.cuda.empty_cache()
#################### Recon =========================
stage_1.scheduler = DDIMScheduler.from_config(stage_1.scheduler.config)
generator = torch.manual_seed(0)
output_rec_1, _, uncond_embeddings_list = stage_1(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
num_inference_steps=num_inference_steps_1,
output_type="pt",
image_init=inv_noise_1,
guidance_scale=guidance_1,
all_latents=inter_img_list_1_inv,
null_inner_steps = inner_steps_1,
is_NPI=is_NPI,
learning_rate=lr_1,
)
image_rec_1 = output_rec_1.images
stage_1.to('cpu')
pt_to_pil(image_rec_1)[0].save(f"{saving_path}/if_stage_I_rec.png")
torch.save(image_rec_1.detach().to('cpu'), f"{saving_path}/image_rec_1.pt")
torch.save(uncond_embeddings_list, f"{saving_path}/uncond_embeddings_list.pt")
# torch.save(inter_img_list_1_rec, f"{saving_path}/inter_img_list_1_rec.pt")
del stage_1
torch.cuda.empty_cache()
### NOTE: =================================================== 1st stage inversion END:
# ### NOTE: =================================================== 2nd stage inversion start: upscaler for stage 2
if args.enable_3for2:
with torch.no_grad():
latent = stage_3.prepare_image_latents(inv_raw_image_1.cuda(), 1, stage_3.vae.dtype, 'cuda', generator=generator)
torch.cuda.empty_cache()
stage_3.scheduler = DDIMScheduler.from_config(stage_3.scheduler.config)
image_rec_1 = torch.load(f"{saving_path}/image_rec_1.pt")
generator = torch.manual_seed(0)
latent_tuple_2_inv, _ = stage_3(
prompt=prompt,
image=image_rec_1,
noise_level=noise_level_3,
generator=generator,
output_type="latent",
guidance_scale=guidance_3,
latents=latent.float(),
scale_factor=scale_factor_3,
inner_steps=inner_steps_3,
num_inference_steps=num_inference_steps_3,
)
# ### NOTE: 0.3 <= loss scale_factor <= 0.5
stage_3.to('cpu')
torch.cuda.empty_cache()
latent_2_inv=latent_tuple_2_inv.images
torch.save(latent_2_inv.to('cpu').detach(), f'{saving_path}/latent_2_inv.pt')
# torch.save(latent_2_inv_list, f'{saving_path}/latent_2_inv_list.pt')
generator = torch.manual_seed(0)
# image_tuple_2_rec, latent_2_rec = stage_3_rec(
image_tuple_2_rec, _ = stage_3_rec(
prompt=prompt,
image=image_rec_1,
noise_level=noise_level_3,
generator=generator,
output_type="pt",
guidance_scale=guidance_3,
latents=latent_2_inv,
num_inference_steps=num_inference_steps_3,
)
stage_3_rec.to('cpu')
torch.cuda.empty_cache()
image_2_rec=image_tuple_2_rec.images
torch.save(image_2_rec.detach().to('cpu'), f"{saving_path}/image_2_rec.pt")
pil_image_2_rec = pt_to_pil(image_2_rec)
# pil_image_2_rec = image_processor.pt_to_numpy(image_2_rec)
# pil_image_2_rec = image_processor.numpy_to_pil(pil_image_2_rec)
pil_image_2_rec[0].save(f"{saving_path}/if_stage_III4II_rec.png")
### NOTE: =================================================== 2nd stage inversion END:
# ### NOTE: =================================================== 3rd stage inversion start:
if args.enable_3:
image_2_rec = torch.load(f"{saving_path}/image_2_rec.pt")
with torch.no_grad():
latent = stage_3.prepare_image_latents(inv_raw_image_2.cuda(), 1, stage_3.vae.dtype, 'cuda', generator=generator)
torch.cuda.empty_cache()
stage_3.scheduler = DDIMScheduler.from_config(stage_3.scheduler.config)
generator = torch.manual_seed(0)
latent_tuple_3_inv, _ = stage_3(
prompt=prompt,
image=image_2_rec,
noise_level=noise_level_3,
generator=generator,
output_type="latent",
guidance_scale=guidance_3,
latents=latent.float(),
scale_factor=scale_factor_3,
inner_steps=inner_steps_3,
num_inference_steps=num_inference_steps_3,
)
# ### NOTE: 0.3 <= loss scale_factor <= 0.5. >=0.7 not good
stage_3.to('cpu')
latent_3_inv=latent_tuple_3_inv.images
torch.save(latent_3_inv.to('cpu').detach(), f'{saving_path}/latent_3_inv.pt')
# torch.save(latent_list, f'{saving_path}/latent_3_inv_list.pt')
torch.cuda.empty_cache()
generator = torch.manual_seed(0)
image_tuple_3_rec, latent_3_rec = stage_3_rec(
prompt=prompt,
image=image_2_rec,
noise_level=noise_level_3,
generator=generator,
output_type="pil",
guidance_scale=guidance_3,
latents=latent_3_inv,
num_inference_steps=num_inference_steps_3,
)
stage_3_rec.to('cpu')
image_3_rec=image_tuple_3_rec.images
image_3_rec[0].save(f"{saving_path}/if_stage_III_rec.png")
### NOTE: =================================================== 3rd stage inversion END:
### TODO: modulizing them into functions and save something (latents) for comparison.