File size: 5,784 Bytes
0285d87 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | 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
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 = True,
annotate_size: int = 15,
make_grid: bool = True,
grid_column_size: int = 2,
prompt2prompt_edit: bool=False,
**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)
self.annotate = annotate
self.annotate_size = annotate_size
self.make_grid = make_grid
self.grid_column_size = grid_column_size
self.prompt2prompt_edit = prompt2prompt_edit
def log_sample_images(
self, pipeline: DiffusionPipeline,
device: torch.device, step: int,
image: Union[torch.FloatTensor, PIL.Image.Image] = None,
latents: torch.FloatTensor = None,
uncond_embeddings_list: List[torch.FloatTensor] = 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([
annotate_image(image, "input sequence", font_size=self.annotate_size) for image in input_pil_images
])
for idx, prompt in enumerate(tqdm(self.editing_prompts, desc="Generating sample images")):
if self.prompt2prompt_edit:
if idx == 0:
edit_type = 'save'
else:
edit_type = 'swap'
else:
edit_type = None
for seed in self.sample_seeds:
generator = torch.Generator(device=device)
generator.manual_seed(seed)
sequence_return = pipeline(
prompt=prompt,
edit_type = edit_type,
image=image, # torch.Size([8, 3, 512, 512])
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
latents = latents,
uncond_embeddings_list = uncond_embeddings_list,
# Put the source prompt at the first one, when using p2p
)
if self.prompt2prompt_edit:
sequence = sequence_return['sdimage_output'].images[0]
attention_output = sequence_return['attention_output']
if ddim_latents_all_step in sequence_return:
ddim_latents_all_step = sequence_return['ddim_latents_all_step']
else:
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
]
if self.make_grid:
samples_all.append(images)
if self.prompt2prompt_edit:
attention_all.append(attention_output)
save_path = os.path.join(self.logdir, f"step_{step}_{idx}_{seed}.gif")
save_gif_mp4_folder_type(images, save_path)
if self.prompt2prompt_edit:
save_gif_mp4_folder_type(attention_output, save_path.replace('.gif', 'atten.gif'))
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)
if self.prompt2prompt_edit:
attention_all = [make_grid(images, cols=1) for images in zip(*attention_all)]
save_gif_mp4_folder_type(attention_all, save_path.replace('.gif', 'atten.gif'))
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 |