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Running
on
Zero
File size: 6,894 Bytes
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from typing import Union, List
import PIL
import numpy as np
import torch
import torchvision.transforms as T
from einops import repeat
from kandinsky3.model.unet import UNet
from kandinsky3.movq import MoVQ
from kandinsky3.condition_encoders import T5TextConditionEncoder
from kandinsky3.condition_processors import T5TextConditionProcessor
from kandinsky3.model.diffusion import BaseDiffusion, get_named_beta_schedule
from kandinsky3.utils import resize_image_for_diffusion, resize_mask_for_diffusion
class Kandinsky3InpaintingPipeline:
def __init__(
self,
device_map: Union[str, torch.device, dict],
dtype_map: Union[str, torch.dtype, dict],
unet: UNet,
null_embedding: torch.Tensor,
t5_processor: T5TextConditionProcessor,
t5_encoder: T5TextConditionEncoder,
movq: MoVQ,
):
self.device_map = device_map
self.dtype_map = dtype_map
self.to_pil = T.ToPILImage()
self.to_tensor = T.ToTensor()
self.unet = unet
self.null_embedding = null_embedding
self.t5_processor = t5_processor
self.t5_encoder = t5_encoder
self.movq = movq
def shared_step(self, batch: dict) -> dict:
image = batch['image']
condition_model_input = batch['text']
negative_condition_model_input = batch['negative_text']
bs = image.shape[0]
masked_latent = None
mask = batch['mask']
if 'masked_image' in batch:
masked_latent = batch['masked_image']
elif self.unet.in_layer.in_channels == 9:
masked_latent = image.masked_fill((1 - mask).bool(), 0)
else:
raise ValueError()
with torch.cuda.amp.autocast(dtype=self.dtype_map['movq']):
masked_latent = self.movq.encode(masked_latent)
mask = torch.nn.functional.interpolate(mask, size=(masked_latent.shape[2], masked_latent.shape[3]))
with torch.cuda.amp.autocast(dtype=self.dtype_map['text_encoder']):
context, context_mask = self.t5_encoder(condition_model_input)
if negative_condition_model_input is not None:
negative_context, negative_context_mask = self.t5_encoder(negative_condition_model_input)
else:
negative_context, negative_context_mask = None, None
return {
'context': context,
'context_mask': context_mask,
'negative_context': negative_context,
'negative_context_mask': negative_context_mask,
'image': image,
'masked_latent': masked_latent,
'mask': mask
}
def prepare_batch(
self,
text: str,
negative_text: str,
image: PIL.Image.Image,
mask: np.ndarray,
) -> dict:
condition_model_input, negative_condition_model_input = self.t5_processor.encode(
text=text, negative_text=negative_text
)
batch = {
'image': self.to_tensor(resize_image_for_diffusion(image.convert("RGB"))) * 2 - 1,
'mask': 1 - self.to_tensor(resize_mask_for_diffusion(mask)),
'text': condition_model_input,
'negative_text': negative_condition_model_input
}
batch['mask'] = batch['mask'].type(self.dtype_map['movq'])
batch['image'] = batch['image'].unsqueeze(0).to(self.device_map['movq'])
batch['text']['input_ids'] = batch['text']['input_ids'].unsqueeze(0).to(self.device_map['text_encoder'])
batch['text']['attention_mask'] = batch['text']['attention_mask'].unsqueeze(0).to(
self.device_map['text_encoder'])
batch['mask'] = batch['mask'].unsqueeze(0).to(self.device_map['movq'])
if negative_condition_model_input is not None:
batch['negative_text']['input_ids'] = batch['negative_text']['input_ids'].to(
self.device_map['text_encoder'])
batch['negative_text']['attention_mask'] = batch['negative_text']['attention_mask'].to(
self.device_map['text_encoder'])
return batch
def __call__(
self,
text: str,
image: PIL.Image.Image,
mask: np.ndarray,
negative_text: str = None,
images_num: int = 1,
bs: int = 1,
steps: int = 50,
guidance_weight_text: float = 4,
eta=1.0
) -> List[PIL.Image.Image]:
with torch.no_grad():
batch = self.prepare_batch(text, negative_text, image, mask)
processed = self.shared_step(batch)
betas = get_named_beta_schedule('cosine', 1000)
base_diffusion = BaseDiffusion(betas, percentile=0.95)
times = list(range(999, 0, -1000 // steps))
pil_images = []
k, m = images_num // bs, images_num % bs
for minibatch in [bs] * k + [m]:
if minibatch == 0:
continue
bs_context = repeat(processed['context'], '1 n d -> b n d', b=minibatch)
bs_context_mask = repeat(processed['context_mask'], '1 n -> b n', b=minibatch)
if processed['negative_context'] is not None:
bs_negative_context = repeat(processed['negative_context'], '1 n d -> b n d', b=minibatch)
bs_negative_context_mask = repeat(processed['negative_context_mask'], '1 n -> b n', b=minibatch)
else:
bs_negative_context, bs_negative_context_mask = None, None
mask = processed['mask'].repeat_interleave(minibatch, dim=0)
masked_latent = processed['masked_latent'].repeat_interleave(minibatch, dim=0)
minibatch = masked_latent.shape[0]
with torch.cuda.amp.autocast(dtype=self.dtype_map['unet']):
with torch.no_grad():
images = base_diffusion.p_sample_loop(
self.unet, (minibatch, 4, masked_latent.shape[2], masked_latent.shape[3]), times,
self.device_map['unet'],
bs_context, bs_context_mask, self.null_embedding, guidance_weight_text, eta,
negative_context=bs_negative_context, negative_context_mask=bs_negative_context_mask,
mask=mask, masked_latent=masked_latent, gan=False
)
with torch.cuda.amp.autocast(dtype=self.dtype_map['movq']):
images = torch.cat([self.movq.decode(image) for image in images.chunk(2)])
images = torch.clip((images + 1.) / 2., 0., 1.).cpu()
for images_chunk in images.chunk(1):
pil_images += [self.to_pil(image) for image in images_chunk]
return pil_images
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