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from pathlib import Path
from typing import Any, Optional, Union, Callable
import pytorch_lightning as pl
import torch
from diffusers import DDPMScheduler, DiffusionPipeline, AutoencoderKL, DDIMScheduler
from diffusers.utils.import_utils import is_xformers_available
from einops import rearrange, repeat
from transformers import CLIPTextModel, CLIPTokenizer
from videogen_hub.pipelines.streamingt2v.utils.video_utils import (
ResultProcessor,
save_videos_grid,
video_naming,
)
from . import pl_module_params_controlnet
from .diffusers_conditional.models.controlnet.controlnet import (
ControlNetModel,
)
from .diffusers_conditional.models.controlnet.unet_3d_condition import (
UNet3DConditionModel,
)
from .diffusers_conditional.models.controlnet.pipeline_text_to_video_w_controlnet_synth import (
TextToVideoSDPipeline,
)
from .diffusers_conditional.models.controlnet.processor import (
set_use_memory_efficient_attention_xformers,
)
from .diffusers_conditional.models.controlnet.mask_generator import (
MaskGenerator,
)
import warnings
# from warnings import warn
from videogen_hub.pipelines.streamingt2v.utils.iimage import IImage
from videogen_hub.pipelines.streamingt2v.utils.object_loader import instantiate_object
from videogen_hub.pipelines.streamingt2v.utils.object_loader import get_class
class VideoLDM(pl.LightningModule):
def __init__(
self,
inference_params: pl_module_params_controlnet.InferenceParams,
opt_params: pl_module_params_controlnet.OptimizerParams = None,
unet_params: pl_module_params_controlnet.UNetParams = None,
):
super().__init__()
self.inference_generator = torch.Generator(device=self.device)
self.opt_params = opt_params
self.unet_params = unet_params
print(f"Base pipeline from: {unet_params.pipeline_repo}")
print(f"Pipeline class {unet_params.pipeline_class}")
# load entire pipeline (unet, vq, text encoder,..)
state_dict_control_model = None
state_dict_fusion = None
state_dict_base_model = None
if len(opt_params.load_trained_controlnet_from_ckpt) > 0:
state_dict_ckpt = torch.load(
opt_params.load_trained_controlnet_from_ckpt,
map_location=torch.device("cpu"),
)
state_dict_ckpt = state_dict_ckpt["state_dict"]
state_dict_control_model = dict(
filter(lambda x: x[0].startswith("unet"), state_dict_ckpt.items())
)
state_dict_control_model = {
k.split("unet.")[1]: v for (k, v) in state_dict_control_model.items()
}
state_dict_fusion = dict(
filter(
lambda x: "cross_attention_merger" in x[0], state_dict_ckpt.items()
)
)
state_dict_fusion = {
k.split("base_model.")[1]: v for (k, v) in state_dict_fusion.items()
}
del state_dict_ckpt
state_dict_proj = None
state_dict_ckpt = None
if hasattr(unet_params, "use_resampler") and unet_params.use_resampler:
num_queries = unet_params.num_frames if unet_params.num_frames > 1 else None
if unet_params.use_image_tokens_ctrl:
num_queries = unet_params.num_control_input_frames
assert unet_params.frame_expansion == "none"
image_encoder = self.unet_params.image_encoder
embedding_dim = image_encoder.embedding_dim
resampler = instantiate_object(
self.unet_params.resampler_cls,
video_length=num_queries,
embedding_dim=embedding_dim,
input_tokens=image_encoder.num_tokens,
num_layers=self.unet_params.resampler_merging_layers,
aggregation=self.unet_params.aggregation,
)
state_dict_proj = None
self.resampler = resampler
self.image_encoder = image_encoder
noise_scheduler = DDPMScheduler.from_pretrained(
self.unet_params.pipeline_repo, subfolder="scheduler"
)
tokenizer = CLIPTokenizer.from_pretrained(
self.unet_params.pipeline_repo, subfolder="tokenizer"
)
text_encoder = CLIPTextModel.from_pretrained(
self.unet_params.pipeline_repo, subfolder="text_encoder"
)
vae = AutoencoderKL.from_pretrained(
self.unet_params.pipeline_repo, subfolder="vae"
)
base_model = UNet3DConditionModel.from_pretrained(
self.unet_params.pipeline_repo,
subfolder="unet",
low_cpu_mem_usage=False,
device_map=None,
merging_mode=self.unet_params.merging_mode_base,
use_image_embedding=unet_params.use_resampler
and unet_params.use_image_tokens_main,
use_fps_conditioning=self.opt_params.use_fps_conditioning,
unet_params=unet_params,
)
if state_dict_base_model is not None:
miss, unex = base_model.load_state_dict(state_dict_base_model, strict=False)
assert len(unex) == 0
if len(miss) > 0:
warnings.warn(f"Missing keys when loading base_mode:{miss}")
del state_dict_base_model
if state_dict_fusion is not None:
miss, unex = base_model.load_state_dict(state_dict_fusion, strict=False)
assert len(unex) == 0
del state_dict_fusion
print("PIPE LOADING DONE")
self.noise_scheduler = noise_scheduler
self.tokenizer = tokenizer
self.text_encoder = text_encoder
self.vae = vae
self.unet = ControlNetModel.from_unet(
unet=base_model,
conditioning_embedding_out_channels=unet_params.conditioning_embedding_out_channels,
downsample_controlnet_cond=unet_params.downsample_controlnet_cond,
num_frames=(
unet_params.num_frames
if (
unet_params.frame_expansion != "none"
or self.unet_params.use_controlnet_mask
)
else unet_params.num_control_input_frames
),
num_frame_conditioning=unet_params.num_control_input_frames,
frame_expansion=unet_params.frame_expansion,
pre_transformer_in_cond=unet_params.pre_transformer_in_cond,
num_tranformers=unet_params.num_tranformers,
vae=AutoencoderKL.from_pretrained(
self.unet_params.pipeline_repo, subfolder="vae"
),
zero_conv_mode=unet_params.zero_conv_mode,
merging_mode=unet_params.merging_mode,
condition_encoder=unet_params.condition_encoder,
use_controlnet_mask=unet_params.use_controlnet_mask,
use_image_embedding=unet_params.use_resampler
and unet_params.use_image_tokens_ctrl,
unet_params=unet_params,
use_image_encoder_normalization=unet_params.use_image_encoder_normalization,
)
if state_dict_control_model is not None:
miss, unex = self.unet.load_state_dict(
state_dict_control_model, strict=False
)
if len(miss) > 0:
print("WARNING: Loading checkpoint for controlnet misses states")
print(miss)
if unet_params.frame_expansion == "none":
attention_params = self.unet_params.attention_mask_params
assert (
not attention_params.temporal_self_attention_only_on_conditioning
and not attention_params.spatial_attend_on_condition_frames
and not attention_params.temp_attend_on_neighborhood_of_condition_frames
)
self.mask_generator = MaskGenerator(
self.unet_params.attention_mask_params,
num_frame_conditioning=self.unet_params.num_control_input_frames,
num_frames=self.unet_params.num_frames,
)
self.mask_generator_base = MaskGenerator(
self.unet_params.attention_mask_params_base,
num_frame_conditioning=self.unet_params.num_control_input_frames,
num_frames=self.unet_params.num_frames,
)
if state_dict_proj is not None and unet_params.use_image_tokens_main:
if unet_params.use_image_tokens_main:
missing, unexpected = base_model.load_state_dict(
state_dict_proj, strict=False
)
elif unet_params.use_image_tokens_ctrl:
missing, unexpected = unet.load_state_dict(
state_dict_proj, strict=False
)
assert len(unexpected) == 0, f"Unexpected entries {unexpected}"
print(f"Missing keys state proj = {missing}")
del state_dict_proj
base_model.requires_grad_(False)
self.base_model = base_model
self.unet.requires_grad_(False)
self.text_encoder.requires_grad_(False)
self.vae.requires_grad_(False)
layers_config = opt_params.layers_config
layers_config.set_requires_grad(self)
print("CUSTOM XFORMERS ATTENTION USED.")
if is_xformers_available():
set_use_memory_efficient_attention_xformers(
self.unet,
num_frame_conditioning=self.unet_params.num_control_input_frames,
num_frames=self.unet_params.num_frames,
attention_mask_params=self.unet_params.attention_mask_params,
)
set_use_memory_efficient_attention_xformers(
self.base_model,
num_frame_conditioning=self.unet_params.num_control_input_frames,
num_frames=self.unet_params.num_frames,
attention_mask_params=self.unet_params.attention_mask_params_base,
)
if len(inference_params.scheduler_cls) > 0:
inf_scheduler_class = get_class(inference_params.scheduler_cls)
else:
inf_scheduler_class = DDIMScheduler
inf_scheduler = inf_scheduler_class.from_pretrained(
self.unet_params.pipeline_repo, subfolder="scheduler"
)
inference_pipeline = TextToVideoSDPipeline(
vae=self.vae,
text_encoder=self.text_encoder,
tokenizer=self.tokenizer,
unet=self.base_model,
controlnet=self.unet,
scheduler=inf_scheduler,
)
inference_pipeline.set_noise_generator(self.opt_params.noise_generator)
inference_pipeline.enable_vae_slicing()
inference_pipeline.set_progress_bar_config(disable=True)
self.inference_params = inference_params
self.inference_pipeline = inference_pipeline
self.result_processor = ResultProcessor(
fps=self.inference_params.frame_rate,
n_frames=self.inference_params.video_length,
)
def on_start(self):
datamodule = self.trainer._data_connector._datahook_selector.datamodule
pipe_id_model = self.unet_params.pipeline_repo
for dataset_key in ["video_dataset", "image_dataset", "predict_dataset"]:
dataset = getattr(datamodule, dataset_key, None)
if dataset is not None and hasattr(dataset, "model_id"):
pipe_id_data = dataset.model_id
assert (
pipe_id_model == pipe_id_data
), f"Model and Dataloader need the same pipeline path. Found '{pipe_id_model}' and '{dataset_key}.model_id={pipe_id_data}'. Consider setting '--data.{dataset_key}.model_id={pipe_id_data}'"
self.result_processor.set_logger(self.logger)
def on_predict_start(self) -> None:
self.on_start()
# pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
# pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
# pipe.set_progress_bar_config(disable=True)
# self.first_stage = pipe.to(self.device)
def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
cfg = self.trainer.predict_cfg
result_file_stem = cfg["result_file_stem"]
storage_fol = Path(cfg["predict_dir"])
prompts = [cfg["prompt"]]
inference_params: pl_module_params_controlnet.InferenceParams = (
self.inference_params
)
conditioning_type = inference_params.conditioning_type
# n_autoregressive_generations = inference_params.n_autoregressive_generations
n_autoregressive_generations = cfg["n_autoregressive_generations"]
mode = inference_params.mode
start_from_real_input = inference_params.start_from_real_input
assert isinstance(prompts, list)
prompts = n_autoregressive_generations * prompts
self.inference_generator.manual_seed(self.inference_params.seed)
assert (
self.unet_params.num_control_input_frames
== self.inference_params.video_length // 2
), f"currently we assume to have an equal size for and second half of the frame interval, e.g. 16 frames, and we condition on 8. Current setup: {self.unet_params.num_frame_conditioning} and {self.inference_params.video_length}"
chunks_conditional = []
batch_size = 1
shape = (
batch_size,
self.inference_pipeline.unet.config.in_channels,
self.inference_params.video_length,
self.inference_pipeline.unet.config.sample_size,
self.inference_pipeline.unet.config.sample_size,
)
for idx, prompt in enumerate(prompts):
if idx > 0:
content = sample * 2 - 1
content_latent = (
self.vae.encode(content).latent_dist.sample()
* self.vae.config.scaling_factor
)
content_latent = rearrange(content_latent, "F C W H -> 1 C F W H")
content_latent = (
content_latent[:, :, self.unet_params.num_control_input_frames :]
.detach()
.clone()
)
if hasattr(self.inference_pipeline, "noise_generator"):
latents = self.inference_pipeline.noise_generator.sample_noise(
shape=shape,
device=self.device,
dtype=self.dtype,
generator=self.inference_generator,
content=content_latent if idx > 0 else None,
)
else:
latents = None
if idx == 0:
sample = cfg["video"].to(self.device)
else:
if inference_params.conditioning_type == "fixed":
context = chunks_conditional[0][
: self.unet_params.num_frame_conditioning
]
context = [context]
context = [2 * sample - 1 for sample in context]
input_frames_conditioning = torch.cat(context).detach().clone()
input_frames_conditioning = rearrange(
input_frames_conditioning, "F C W H -> 1 F C W H"
)
elif inference_params.conditioning_type == "last_chunk":
input_frames_conditioning = (
condition_input[:, -self.unet_params.num_frame_conditioning :]
.detach()
.clone()
)
elif inference_params.conditioning_type == "past":
context = [
sample[: self.unet_params.num_control_input_frames]
for sample in chunks_conditional
]
context = [2 * sample - 1 for sample in context]
input_frames_conditioning = torch.cat(context).detach().clone()
input_frames_conditioning = rearrange(
input_frames_conditioning, "F C W H -> 1 F C W H"
)
else:
raise NotImplementedError()
input_frames = (
condition_input[:, self.unet_params.num_control_input_frames :]
.detach()
.clone()
)
sample = self(
prompt,
input_frames=input_frames,
input_frames_conditioning=input_frames_conditioning,
latents=latents,
)
if hasattr(self.inference_pipeline, "reset_noise_generator_state"):
self.inference_pipeline.reset_noise_generator_state()
condition_input = rearrange(sample, "F C W H -> 1 F C W H")
condition_input = (2 * condition_input) - 1 # range: [-1,1]
# store first 16 frames, then always last 8 of a chunk
chunks_conditional.append(sample)
result_formats = self.inference_params.result_formats
# result_formats = [gif", "mp4"]
concat_video = self.inference_params.concat_video
def IImage_normalized(x):
return IImage(x, vmin=0, vmax=1)
for result_format in result_formats:
save_format = result_format.replace("eval_", "")
merged_video = None
for chunk_idx, (prompt, video) in enumerate(
zip(prompts, chunks_conditional)
):
if chunk_idx == 0:
current_video = IImage_normalized(video)
else:
current_video = IImage_normalized(
video[self.unet_params.num_control_input_frames :]
)
if merged_video is None:
merged_video = current_video
else:
merged_video &= current_video
if concat_video:
filename = video_naming(prompts[0], save_format, batch_idx, 0)
result_file_video = (storage_fol / filename).absolute().as_posix()
result_file_video = (
Path(result_file_video).parent
/ (result_file_stem + Path(result_file_video).suffix)
).as_posix()
self.result_processor.save_to_file(
video=merged_video.torch(vmin=0, vmax=1),
prompt=prompts[0],
video_filename=result_file_video,
prompt_on_vid=False,
)
def forward(
self, prompt, input_frames=None, input_frames_conditioning=None, latents=None
):
call_params = self.inference_params.to_dict()
print(f"INFERENCE PARAMS = {call_params}")
call_params["prompt"] = prompt
call_params["image"] = input_frames
call_params["num_frames"] = self.inference_params.video_length
call_params["return_dict"] = False
call_params["output_type"] = "pt_t2v"
call_params["mask_generator"] = self.mask_generator
call_params["precision"] = (
"16" if self.trainer.precision.startswith("16") else "32"
)
call_params["no_text_condition_control"] = (
self.opt_params.no_text_condition_control
)
call_params["weight_control_sample"] = self.unet_params.weight_control_sample
call_params["use_controlnet_mask"] = self.unet_params.use_controlnet_mask
call_params["skip_controlnet_branch"] = self.opt_params.skip_controlnet_branch
call_params["img_cond_resampler"] = (
self.resampler if self.unet_params.use_resampler else None
)
call_params["img_cond_encoder"] = (
self.image_encoder if self.unet_params.use_resampler else None
)
call_params["input_frames_conditioning"] = input_frames_conditioning
call_params["cfg_text_image"] = self.unet_params.cfg_text_image
call_params["use_of"] = self.unet_params.use_of
if latents is not None:
call_params["latents"] = latents
sample = self.inference_pipeline(
generator=self.inference_generator, **call_params
)
return sample
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