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import os
import numpy as np
from typing import List, Union
import PIL
import torch
import torch.utils.data
import torch.utils.checkpoint
from diffusers.pipeline_utils import DiffusionPipeline
from tqdm.auto import tqdm
from video_diffusion.common.image_util import make_grid, annotate_image
from video_diffusion.common.image_util import save_gif_mp4_folder_type
import cv2
class SampleLogger:
def __init__(
self,
editing_prompts: List[str],
clip_length: int,
logdir: str,
subdir: str = "sample",
num_samples_per_prompt: int = 1,
sample_seeds: List[int] = None,
num_inference_steps: int = 20,
guidance_scale: float = 7,
strength: float = None,
annotate: bool = False,
annotate_size: int = 15,
make_grid: bool = True,
grid_column_size: int = 2,
layout_mask_dir: str = None, # New parameter for the layout mask directory
layouts_masks_orders: List[str]=None,
stride: int = 1,
n_sample_frame: int = 8,
start_sample_frame: int = None,
sampling_rate: int = 1,
**args
) -> None:
self.editing_prompts = editing_prompts
self.clip_length = clip_length
self.guidance_scale = guidance_scale
self.num_inference_steps = num_inference_steps
self.strength = strength
if sample_seeds is None:
max_num_samples_per_prompt = int(1e5)
if num_samples_per_prompt > max_num_samples_per_prompt:
raise ValueError
sample_seeds = torch.randint(0, max_num_samples_per_prompt, (num_samples_per_prompt,))
sample_seeds = sorted(sample_seeds.numpy().tolist())
self.sample_seeds = sample_seeds
self.logdir = os.path.join(logdir, subdir)
os.makedirs(self.logdir, exist_ok=True)
self.annotate = annotate
self.annotate_size = annotate_size
self.make_grid = make_grid
self.grid_column_size = grid_column_size
self.layout_mask_dir = layout_mask_dir # Initialize layout_mask_dir
self.layout_mask_orders = layouts_masks_orders
self.stride = stride
self.n_sample_frame = n_sample_frame
self.start_sample_frame = start_sample_frame
self.sampling_rate = sampling_rate
def _read_mask(self, mask_path, index: int, dest_size=(64, 64)):
mask_path = os.path.join(mask_path, f"{index:05d}.png")
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
mask = (mask > 0).astype(np.uint8)
mask = cv2.resize(mask, dest_size, interpolation=cv2.INTER_NEAREST)
mask = mask[np.newaxis, ...]
return mask
def get_frame_indices(self, index):
if self.start_sample_frame is not None:
frame_start = self.start_sample_frame + self.stride * index
else:
frame_start = self.stride * index
return (frame_start + i * self.sampling_rate for i in range(self.n_sample_frame))
def read_layout_and_merge_masks(self, index):
layouts_all, masks_all = [],[]
for idx,layout_mask_order_per in enumerate(self.layout_mask_orders):
layout_ = []
for layout_name in layout_mask_order_per: # Loop over prompts
frame_indices = self.get_frame_indices(index % self.clip_length)
layout_mask_dir = os.path.join(self.layout_mask_dir, layout_name)
mask = [self._read_mask(layout_mask_dir, i) for i in frame_indices]
masks = np.stack(mask)
layout_.append(masks)
layout_ = np.stack(layout_)
merged_masks = []
for i in range(int(self.n_sample_frame)):
merged_mask_frame = np.sum(layout_[:, i, :, :], axis=0)
merged_mask_frame = (merged_mask_frame > 0).astype(np.uint8)
merged_masks.append(merged_mask_frame)
masks = rearrange(np.stack(merged_masks), "f c h w -> c f h w")
masks = torch.from_numpy(masks).half()
layouts = rearrange(layout_, "s f c h w -> f s c h w")
layouts = torch.from_numpy(layouts).half()
layouts_all.append(layouts)
masks_all.append(mask)
return masks_all, layouts_all
def log_sample_images(
self, pipeline: DiffusionPipeline,
device: torch.device, step: int,
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
masks: Union[torch.FloatTensor, PIL.Image.Image] = None,
layouts : Union[torch.FloatTensor, PIL.Image.Image] = None,
latents: torch.FloatTensor = None,
control: torch.FloatTensor = None,
controlnet_conditioning_scale = None,
negative_prompt: Union[str, List[str]] = None,
blending_percentage = None,
trajs = None,
flatten_res = None,
source_prompt = None,
inject_step = None,
old_qk = None,
use_pnp = None,
cluster_inversion_feature = None,
vis_cross_attn = None,
attn_inversion_dict = None,
):
torch.cuda.empty_cache()
samples_all = []
attention_all = []
# handle input image
if image is not None:
input_pil_images = pipeline.numpy_to_pil(tensor_to_numpy(image))[0]
samples_all.append(input_pil_images)
# samples_all.append([
# annotate_image(image, "input sequence", font_size=self.annotate_size) for image in input_pil_images
# ])
#masks_all, layouts_all = self.read_layout_and_merge_masks()
#for idx, (prompt, masks, layouts) in enumerate(tqdm(zip(self.editing_prompts, masks_all, layouts_all), desc="Generating sample images")):
for idx, prompt in enumerate(tqdm(self.editing_prompts, desc="Generating sample images")):
for seed in self.sample_seeds:
generator = torch.Generator(device=device)
generator.manual_seed(seed)
sequence_return = pipeline(
prompt=prompt,
image=image, # torch.Size([8, 3, 512, 512])
latent_mask=masks,
layouts = layouts,
strength=self.strength,
generator=generator,
num_inference_steps=self.num_inference_steps,
clip_length=self.clip_length,
guidance_scale=self.guidance_scale,
num_images_per_prompt=1,
# used in null inversion
control = control,
controlnet_conditioning_scale = controlnet_conditioning_scale,
latents = latents,
#uncond_embeddings_list = uncond_embeddings_list,
blending_percentage = blending_percentage,
logdir = self.logdir,
trajs = trajs,
flatten_res = flatten_res,
negative_prompt=negative_prompt,
source_prompt=source_prompt,
inject_step=inject_step,
old_qk=old_qk,
use_pnp=use_pnp,
cluster_inversion_feature= cluster_inversion_feature,
vis_cross_attn = vis_cross_attn,
attn_inversion_dict=attn_inversion_dict,
)
sequence = sequence_return.images[0]
torch.cuda.empty_cache()
if self.annotate:
images = [
annotate_image(image, prompt, font_size=self.annotate_size) for image in sequence
]
else:
images = sequence
if self.make_grid:
samples_all.append(images)
save_path = os.path.join(self.logdir, f"step_{step}_{idx}_{seed}.gif")
save_gif_mp4_folder_type(images, save_path)
if self.make_grid:
samples_all = [make_grid(images, cols=int(np.ceil(np.sqrt(len(samples_all))))) for images in zip(*samples_all)]
save_path = os.path.join(self.logdir, f"step_{step}.gif")
save_gif_mp4_folder_type(samples_all, save_path)
return samples_all
from einops import rearrange
def tensor_to_numpy(image, b=1):
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
image = image.cpu().float().numpy()
image = rearrange(image, "(b f) c h w -> b f h w c", b=b)
return image |