HeliosZhao
update mask start step
0c3bec0
from __future__ import annotations
import gc
import pathlib
import sys
import tempfile
import os
import gradio as gr
import imageio
import PIL.Image
import torch
from diffusers.utils.import_utils import is_xformers_available
from einops import rearrange
from huggingface_hub import ModelCard
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection, CLIPTextModelWithProjection
from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler, PNDMScheduler, ControlNetModel, PriorTransformer, UnCLIPScheduler
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from omegaconf import OmegaConf
from typing import Any, Callable, Dict, List, Optional, Union, Tuple
sys.path.append('Make-A-Protagonist')
from makeaprotagonist.models.unet import UNet3DConditionModel
from makeaprotagonist.pipelines.pipeline_stable_unclip_controlavideo import MakeAProtagonistStableUnCLIPPipeline, MultiControlNetModel
from makeaprotagonist.dataset.dataset import MakeAProtagonistDataset
from makeaprotagonist.util import save_videos_grid, ddim_inversion_unclip, ddim_inversion_prior
from experts.grounded_sam_mask_out import mask_out_reference_image
import ipdb
class InferencePipeline:
def __init__(self, hf_token: str | None = None):
self.hf_token = hf_token
self.pipe = None
self.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
self.model_id = None
self.conditions = None
self.masks = None
self.ddim_inv_latent = None
self.train_dataset, self.sample_indices = None, None
def clear(self) -> None:
self.model_id = None
del self.pipe
self.pipe = None
self.conditions = None
self.masks = None
self.ddim_inv_latent = None
self.train_dataset, self.sample_indices = None, None
torch.cuda.empty_cache()
gc.collect()
@staticmethod
def check_if_model_is_local(model_id: str) -> bool:
return pathlib.Path(model_id).exists()
@staticmethod
def get_model_card(model_id: str,
hf_token: str | None = None) -> ModelCard:
if InferencePipeline.check_if_model_is_local(model_id):
card_path = (pathlib.Path(model_id) / 'README.md').as_posix()
else:
card_path = model_id
return ModelCard.load(card_path, token=hf_token)
@staticmethod
def get_base_model_info(model_id: str, hf_token: str | None = None) -> str:
card = InferencePipeline.get_model_card(model_id, hf_token)
return card.data.base_model
@torch.no_grad()
def load_pipe(self, model_id: str, n_steps, seed) -> None:
if model_id == self.model_id:
return self.conditions, self.masks, self.ddim_inv_latent, self.train_dataset, self.sample_indices
base_model_id = self.get_base_model_info(model_id, self.hf_token)
pretrained_model_path = 'stabilityai/stable-diffusion-2-1-unclip-small'
# image encoding components
feature_extractor = CLIPImageProcessor.from_pretrained(pretrained_model_path, subfolder="feature_extractor")
image_encoder = CLIPVisionModelWithProjection.from_pretrained(pretrained_model_path, subfolder="image_encoder")
# image noising components
image_normalizer = StableUnCLIPImageNormalizer.from_pretrained(pretrained_model_path, subfolder="image_normalizer", torch_dtype=torch.float16,)
image_noising_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="image_noising_scheduler")
# regular denoising components
tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder", torch_dtype=torch.float16,)
vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae", torch_dtype=torch.float16,)
self.ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler')
self.ddim_inv_scheduler.set_timesteps(n_steps)
prior_model_id = "kakaobrain/karlo-v1-alpha"
data_type = torch.float16
prior = PriorTransformer.from_pretrained(prior_model_id, subfolder="prior", torch_dtype=data_type)
prior_text_model_id = "openai/clip-vit-large-patch14"
prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id)
prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type)
prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder="prior_scheduler")
prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
controlnet_model_id = ['controlnet-2-1-unclip-small-openposefull', 'controlnet-2-1-unclip-small-depth']
controlnet = MultiControlNetModel( [ControlNetModel.from_pretrained('Make-A-Protagonist/controlnet-2-1-unclip-small', subfolder=subfolder_id, torch_dtype=torch.float16) for subfolder_id in controlnet_model_id] )
unet = UNet3DConditionModel.from_pretrained(
model_id,
subfolder='unet',
torch_dtype=torch.float16,
use_auth_token=self.hf_token)
# Freeze vae and text_encoder and adapter
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
## freeze image embed
image_encoder.requires_grad_(False)
unet.requires_grad_(False)
## freeze controlnet
controlnet.requires_grad_(False)
## freeze prior
prior.requires_grad_(False)
prior_text_model.requires_grad_(False)
config_file = os.path.join('Make-A-Protagonist/configs', model_id.split('/')[-1] + '.yaml')
self.cfg = OmegaConf.load(config_file)
# def source_parsing(self, n_steps):
# ipdb.set_trace()
train_dataset = MakeAProtagonistDataset(**self.cfg)
train_dataset.preprocess_img_embedding(feature_extractor, image_encoder)
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=1, num_workers=0,
)
image_encoder.to(dtype=data_type)
pipe = MakeAProtagonistStableUnCLIPPipeline(
prior_tokenizer=prior_tokenizer,
prior_text_encoder=prior_text_model,
prior=prior,
prior_scheduler=prior_scheduler,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
image_normalizer=image_normalizer,
image_noising_scheduler=image_noising_scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
controlnet=controlnet,
scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
)
pipe = pipe.to(self.device)
if is_xformers_available():
pipe.unet.enable_xformers_memory_efficient_attention()
pipe.controlnet.enable_xformers_memory_efficient_attention()
self.pipe = pipe
self.model_id = model_id # type: ignore
self.vae = vae
# self.feature_extractor = feature_extractor
# self.image_encoder = image_encoder
## ddim inverse for source video
batch = next(iter(train_dataloader))
weight_dtype = torch.float16
pixel_values = batch["pixel_values"].to(weight_dtype).to(self.device)
video_length = pixel_values.shape[1]
pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
latents = self.vae.encode(pixel_values).latent_dist.sample()
latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
latents = latents * self.vae.config.scaling_factor
# ControlNet
# ipdb.set_trace()
conditions = [_condition.to(weight_dtype).to(self.device) for _, _condition in batch["conditions"].items()] # b f c h w
masks = batch["masks"].to(weight_dtype).to(self.device) # b,f,1,h,w
emb_dim = train_dataset.img_embeddings[0].size(0)
key_frame_embed = torch.zeros((1, emb_dim)).to(device=latents.device, dtype=latents.dtype) ## this is dim 0
# ipdb.set_trace()
ddim_inv_latent = ddim_inversion_unclip(
self.pipe, self.ddim_inv_scheduler, video_latent=latents,
num_inv_steps=n_steps, prompt="", image_embed=key_frame_embed, noise_level=0, seed=seed)[-1].to(weight_dtype)
self.conditions = conditions
self.masks = masks
self.ddim_inv_latent = ddim_inv_latent
self.train_dataset = train_dataset
self.sample_indices = batch["sample_indices"][0]
return conditions, masks, ddim_inv_latent, train_dataset, batch["sample_indices"][0]
def run(
self,
model_id: str,
prompt: str,
video_length: int,
fps: int,
seed: int,
n_steps: int,
guidance_scale: float,
ref_image: PIL.Image.Image,
ref_pro_prompt: str,
noise_level: int,
start_step: int,
control_pose: float,
control_depth: float,
source_pro: int = 0, # 0 or 1
source_bg: int = 0,
) -> PIL.Image.Image:
if not torch.cuda.is_available():
raise gr.Error('CUDA is not available.')
torch.cuda.empty_cache()
conditions, masks, ddim_inv_latent, _, _ = self.load_pipe(model_id, n_steps, seed)
## conditions [1,F,3,H,W]
## masks [1,F,1,H,W]
## ddim_inv_latent [1,4,F,H,W]
## NOTE this is to deal with video length
conditions = [_condition[:,:video_length] for _condition in conditions]
masks = masks[:, :video_length]
ddim_inv_latent = ddim_inv_latent[:,:,:video_length]
generator = torch.Generator(device=self.device).manual_seed(seed)
## TODO mask out reference image
# ipdb.set_trace()
ref_image = mask_out_reference_image(ref_image, ref_pro_prompt)
controlnet_conditioning_scale = [control_pose, control_depth]
prior_denoised_embeds = None
image_embed = None
if source_bg:
## using source background and changing the protagonist
prior_denoised_embeds = self.train_dataset.img_embeddings[0][None].to(device=ddim_inv_latent.device, dtype=ddim_inv_latent.dtype) # 1, 768 for UnCLIP-small
if source_pro:
# using source protagonist and changing the background
sample_indices = self.sample_indices
image_embed = [self.train_dataset.img_embeddings[idx] for idx in sample_indices]
image_embed = torch.stack(image_embed, dim=0).to(device=ddim_inv_latent.device, dtype=ddim_inv_latent.dtype) # F, 768 for UnCLIP-small # F,C
image_embed = image_embed[:video_length]
ref_image = None
# ipdb.set_trace()
out = self.pipe(
image=ref_image,
prompt=prompt,
control_image=conditions,
video_length=video_length,
width=768,
height=768,
num_inference_steps=n_steps,
guidance_scale=guidance_scale,
generator=generator,
## ddim inversion
latents=ddim_inv_latent,
## ref image embeds
noise_level=noise_level,
## controlnet
controlnet_conditioning_scale=controlnet_conditioning_scale,
## mask
masks=masks,
mask_mode='all',
mask_latent_fuse_mode = 'all',
start_step=start_step,
## edit bg and pro
prior_latents=None,
image_embeds=image_embed, # keep pro
prior_denoised_embeds=prior_denoised_embeds # keep bg
)
frames = rearrange(out.videos[0], 'c t h w -> t h w c')
frames = (frames * 255).to(torch.uint8).numpy()
out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
writer = imageio.get_writer(out_file.name, fps=fps)
for frame in frames:
writer.append_data(frame)
writer.close()
return out_file.name