Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,529 Bytes
5004324 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import torch
from typing import Optional, Union
from huggingface_hub import hf_hub_download
from .sr_pipeline import KandiSuperResPipeline
from KandiSuperRes.model.unet import UNet
from KandiSuperRes.model.unet_sr import UNet as UNet_sr
from KandiSuperRes.movq import MoVQ
def get_sr_model(
device: Union[str, torch.device],
weights_path: Optional[str] = None,
dtype: Union[str, torch.dtype] = torch.float16
) -> (UNet_sr, Optional[dict], Optional[torch.Tensor]):
unet = UNet_sr(
init_channels=128,
model_channels=128,
num_channels=3,
time_embed_dim=512,
groups=32,
dim_mult=(1, 2, 4, 8),
num_resnet_blocks=(2,4,8,8),
add_cross_attention=(False, False, False, False),
add_self_attention=(False, False, False, False),
feature_pooling_type='attention',
lowres_cond =True
)
if weights_path:
state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
try:
unet.load_state_dict(state_dict['unet'])
except:
unet.load_state_dict(state_dict)
unet.to(device=device, dtype=dtype).eval()
return unet
def get_T2I_unet(
device: Union[str, torch.device],
weights_path: Optional[str] = None,
dtype: Union[str, torch.dtype] = torch.float32,
) -> (UNet, Optional[torch.Tensor], Optional[dict]):
unet = UNet(
model_channels=384,
num_channels=4,
init_channels=192,
time_embed_dim=1536,
context_dim=4096,
groups=32,
head_dim=64,
expansion_ratio=4,
compression_ratio=2,
dim_mult=(1, 2, 4, 8),
num_blocks=(3, 3, 3, 3),
add_cross_attention=(False, True, True, True),
add_self_attention=(False, True, True, True),
)
null_embedding = None
if weights_path:
state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
null_embedding = state_dict['null_embedding']
unet.load_state_dict(state_dict['unet'])
unet.to(device=device, dtype=dtype).eval()
return unet, null_embedding
def get_movq(
device: Union[str, torch.device],
weights_path: Optional[str] = None,
dtype: Union[str, torch.dtype] = torch.float32,
) -> MoVQ:
generator_config = {
'double_z': False,
'z_channels': 4,
'resolution': 256,
'in_channels': 3,
'out_ch': 3,
'ch': 256,
'ch_mult': [1, 2, 2, 4],
'num_res_blocks': 2,
'attn_resolutions': [32],
'dropout': 0.0,
'tile_sample_min_size': 1024,
'tile_overlap_factor_enc': 0.0,
'tile_overlap_factor_dec': 0.25,
'use_tiling': True
}
movq = MoVQ(generator_config)
if weights_path:
state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
movq.load_state_dict(state_dict)
movq.to(device=device, dtype=dtype).eval()
return movq
def get_SR_pipeline(
device: Union[str, torch.device],
fp16: bool = True,
flash: bool = True,
scale: int = 2,
cache_dir: str = '/tmp/KandiSuperRes/',
movq_path: str = None,
refiner_path: str = None,
unet_sr_path: str = None,
) -> KandiSuperResPipeline:
if flash:
if scale == 2:
device_map = {
'movq': device, 'refiner': device, 'sr_model': device
}
dtype = torch.float16 if fp16 else torch.float32
dtype_map = {
'movq': torch.float32, 'refiner': dtype, 'sr_model': dtype
}
if movq_path is None:
print('Download movq weights')
movq_path = hf_hub_download(
repo_id="ai-forever/Kandinsky3.1", filename='weights/movq.pt', cache_dir=cache_dir
)
if refiner_path is None:
print('Download refiner weights')
refiner_path = hf_hub_download(
repo_id="ai-forever/Kandinsky3.1", filename='weights/kandinsky3_flash.pt', cache_dir=cache_dir
)
if unet_sr_path is None:
print('Download KandiSuperRes Flash weights')
unet_sr_path = hf_hub_download(
repo_id="ai-forever/KandiSuperRes", filename='KandiSuperRes_flash_x2.pt', cache_dir=cache_dir
)
sr_model = get_sr_model(device_map['sr_model'], unet_sr_path, dtype=dtype_map['sr_model'])
movq = get_movq(device_map['movq'], movq_path, dtype=dtype_map['movq'])
refiner, _ = get_T2I_unet(device_map['refiner'], refiner_path, dtype=dtype_map['refiner'])
return KandiSuperResPipeline(
scale, device_map, dtype_map, flash, sr_model, movq, refiner
)
else:
print('Flash model for x4 scale is not implemented.')
else:
if unet_sr_path is None:
if scale == 4:
unet_sr_path = hf_hub_download(
repo_id="ai-forever/KandiSuperRes", filename='KandiSuperRes.ckpt', cache_dir=cache_dir
)
elif scale == 2:
unet_sr_path = hf_hub_download(
repo_id="ai-forever/KandiSuperRes", filename='KandiSuperRes_x2.ckpt', cache_dir=cache_dir
)
dtype = torch.float16 if fp16 else torch.float32
sr_model = get_sr_model(device, unet_sr_path, dtype=dtype)
return KandiSuperResPipeline(scale, device, dtype, flash, sr_model) |