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# ------------------------------------------------------------------------------------
# Karlo-v1.0.alpha
# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
# source: https://github.com/kakaobrain/karlo/blob/3c68a50a16d76b48a15c181d1c5a5e0879a90f85/karlo/sampler/t2i.py#L15
# ------------------------------------------------------------------------------------
from typing import Iterator
import torch
import torchvision.transforms.functional as TVF
from torchvision.transforms import InterpolationMode
from .template import BaseSampler, CKPT_PATH
class T2ISampler(BaseSampler):
"""
A sampler for text-to-image generation.
:param root_dir: directory for model checkpoints.
:param sampling_type: ["default", "fast"]
"""
def __init__(
self,
root_dir: str,
sampling_type: str = "default",
):
super().__init__(root_dir, sampling_type)
@classmethod
def from_pretrained(
cls,
root_dir: str,
clip_model_path: str,
clip_stat_path: str,
sampling_type: str = "default",
):
model = cls(
root_dir=root_dir,
sampling_type=sampling_type,
)
model.load_clip(clip_model_path)
model.load_prior(
f"{CKPT_PATH['prior']}",
clip_stat_path=clip_stat_path,
prior_config="configs/karlo/prior_1B_vit_l.yaml"
)
model.load_decoder(f"{CKPT_PATH['decoder']}", decoder_config="configs/karlo/decoder_900M_vit_l.yaml")
model.load_sr_64_256(CKPT_PATH["sr_256"], sr_config="configs/karlo/improved_sr_64_256_1.4B.yaml")
return model
def preprocess(
self,
prompt: str,
bsz: int,
):
"""Setup prompts & cfg scales"""
prompts_batch = [prompt for _ in range(bsz)]
prior_cf_scales_batch = [self._prior_cf_scale] * len(prompts_batch)
prior_cf_scales_batch = torch.tensor(prior_cf_scales_batch, device="cuda")
decoder_cf_scales_batch = [self._decoder_cf_scale] * len(prompts_batch)
decoder_cf_scales_batch = torch.tensor(decoder_cf_scales_batch, device="cuda")
""" Get CLIP text feature """
clip_model = self._clip
tokenizer = self._tokenizer
max_txt_length = self._prior.model.text_ctx
tok, mask = tokenizer.padded_tokens_and_mask(prompts_batch, max_txt_length)
cf_token, cf_mask = tokenizer.padded_tokens_and_mask([""], max_txt_length)
if not (cf_token.shape == tok.shape):
cf_token = cf_token.expand(tok.shape[0], -1)
cf_mask = cf_mask.expand(tok.shape[0], -1)
tok = torch.cat([tok, cf_token], dim=0)
mask = torch.cat([mask, cf_mask], dim=0)
tok, mask = tok.to(device="cuda"), mask.to(device="cuda")
txt_feat, txt_feat_seq = clip_model.encode_text(tok)
return (
prompts_batch,
prior_cf_scales_batch,
decoder_cf_scales_batch,
txt_feat,
txt_feat_seq,
tok,
mask,
)
def __call__(
self,
prompt: str,
bsz: int,
progressive_mode=None,
) -> Iterator[torch.Tensor]:
assert progressive_mode in ("loop", "stage", "final")
with torch.no_grad(), torch.cuda.amp.autocast():
(
prompts_batch,
prior_cf_scales_batch,
decoder_cf_scales_batch,
txt_feat,
txt_feat_seq,
tok,
mask,
) = self.preprocess(
prompt,
bsz,
)
""" Transform CLIP text feature into image feature """
img_feat = self._prior(
txt_feat,
txt_feat_seq,
mask,
prior_cf_scales_batch,
timestep_respacing=self._prior_sm,
)
""" Generate 64x64px images """
images_64_outputs = self._decoder(
txt_feat,
txt_feat_seq,
tok,
mask,
img_feat,
cf_guidance_scales=decoder_cf_scales_batch,
timestep_respacing=self._decoder_sm,
)
images_64 = None
for k, out in enumerate(images_64_outputs):
images_64 = out
if progressive_mode == "loop":
yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
if progressive_mode == "stage":
yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
images_64 = torch.clamp(images_64, -1, 1)
""" Upsample 64x64 to 256x256 """
images_256 = TVF.resize(
images_64,
[256, 256],
interpolation=InterpolationMode.BICUBIC,
antialias=True,
)
images_256_outputs = self._sr_64_256(
images_256, timestep_respacing=self._sr_sm
)
for k, out in enumerate(images_256_outputs):
images_256 = out
if progressive_mode == "loop":
yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
if progressive_mode == "stage":
yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
yield torch.clamp(images_256 * 0.5 + 0.5, 0.0, 1.0)
class PriorSampler(BaseSampler):
"""
A sampler for text-to-image generation, but only the prior.
:param root_dir: directory for model checkpoints.
:param sampling_type: ["default", "fast"]
"""
def __init__(
self,
root_dir: str,
sampling_type: str = "default",
):
super().__init__(root_dir, sampling_type)
@classmethod
def from_pretrained(
cls,
root_dir: str,
clip_model_path: str,
clip_stat_path: str,
sampling_type: str = "default",
):
model = cls(
root_dir=root_dir,
sampling_type=sampling_type,
)
model.load_clip(clip_model_path)
model.load_prior(
f"{CKPT_PATH['prior']}",
clip_stat_path=clip_stat_path,
prior_config="configs/karlo/prior_1B_vit_l.yaml"
)
return model
def preprocess(
self,
prompt: str,
bsz: int,
):
"""Setup prompts & cfg scales"""
prompts_batch = [prompt for _ in range(bsz)]
prior_cf_scales_batch = [self._prior_cf_scale] * len(prompts_batch)
prior_cf_scales_batch = torch.tensor(prior_cf_scales_batch, device="cuda")
decoder_cf_scales_batch = [self._decoder_cf_scale] * len(prompts_batch)
decoder_cf_scales_batch = torch.tensor(decoder_cf_scales_batch, device="cuda")
""" Get CLIP text feature """
clip_model = self._clip
tokenizer = self._tokenizer
max_txt_length = self._prior.model.text_ctx
tok, mask = tokenizer.padded_tokens_and_mask(prompts_batch, max_txt_length)
cf_token, cf_mask = tokenizer.padded_tokens_and_mask([""], max_txt_length)
if not (cf_token.shape == tok.shape):
cf_token = cf_token.expand(tok.shape[0], -1)
cf_mask = cf_mask.expand(tok.shape[0], -1)
tok = torch.cat([tok, cf_token], dim=0)
mask = torch.cat([mask, cf_mask], dim=0)
tok, mask = tok.to(device="cuda"), mask.to(device="cuda")
txt_feat, txt_feat_seq = clip_model.encode_text(tok)
return (
prompts_batch,
prior_cf_scales_batch,
decoder_cf_scales_batch,
txt_feat,
txt_feat_seq,
tok,
mask,
)
def __call__(
self,
prompt: str,
bsz: int,
progressive_mode=None,
) -> Iterator[torch.Tensor]:
assert progressive_mode in ("loop", "stage", "final")
with torch.no_grad(), torch.cuda.amp.autocast():
(
prompts_batch,
prior_cf_scales_batch,
decoder_cf_scales_batch,
txt_feat,
txt_feat_seq,
tok,
mask,
) = self.preprocess(
prompt,
bsz,
)
""" Transform CLIP text feature into image feature """
img_feat = self._prior(
txt_feat,
txt_feat_seq,
mask,
prior_cf_scales_batch,
timestep_respacing=self._prior_sm,
)
yield img_feat
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