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from refnet.util import *
from refnet.modules.lora import LoraModules
from refnet.modules.reference_net import hack_unet_forward, hack_inference_forward
from refnet.sampling.hook import ReferenceAttentionControl
class InferenceWrapperXL(CustomizedWrapper, CustomizedColorizer):
def __init__(
self,
scalar_embedder_config,
img_embedder_config,
fg_encoder_config = None,
bg_encoder_config = None,
style_encoder_config = None,
lora_config = None,
logits_embed = False,
controller = False,
*args,
**kwargs
):
CustomizedColorizer.__init__(self, version="sdxl", *args, **kwargs)
CustomizedWrapper.__init__(self)
self.logits_embed = logits_embed
(
self.scalar_embedder,
self.img_embedder,
self.fg_encoder,
self.bg_encoder,
self.style_encoder
) = map(
lambda t: instantiate_from_config(t) if exists(t) else None,
(
scalar_embedder_config,
img_embedder_config,
fg_encoder_config,
bg_encoder_config,
style_encoder_config
)
)
self.loras = LoraModules(self, **lora_config)
if controller:
self.controller = ReferenceAttentionControl(
# time_embed_ch = self.model.diffusion_model.model_channels * 4,
reader_module = self.model.diffusion_model,
writer_module = self.bg_encoder,
# only_decoder = True
)
else:
self.controller = None
new_model_list = {
# "style_encoder": self.style_encoder,
"scalar_embedder": self.scalar_embedder,
"img_embedder": self.img_embedder,
# "controller": self.controller
}
hack_unet_forward(self.model.diffusion_model)
if exists(self.fg_encoder):
hack_inference_forward(self.fg_encoder)
new_model_list["fg_encoder"] = self.fg_encoder
if exists(self.bg_encoder):
hack_inference_forward(self.bg_encoder)
new_model_list["bg_encoder"] = self.bg_encoder
# hack_inference_forward(self.bg_encoder)
# hack_inference_forward(self.style_encoder)
self.switch_cond_modules += list(new_model_list.keys())
# self.switch_main_modules += ["controller"]
self.model_list.update(new_model_list)
def switch_to_fp16(self):
super().switch_to_fp16()
self.model.diffusion_model.map_modules.to(self.half_precision_dtype)
self.model.diffusion_model.warp_modules.to(self.half_precision_dtype)
self.model.diffusion_model.style_modules.to(self.half_precision_dtype)
self.model.diffusion_model.conv_fg.to(self.half_precision_dtype)
if exists(self.fg_encoder):
self.fg_encoder.to(self.half_precision_dtype)
self.fg_encoder.dtype = self.half_precision_dtype
self.fg_encoder.time_embed.float()
if exists(self.bg_encoder):
self.bg_encoder.to(self.half_precision_dtype)
self.bg_encoder.dtype = self.half_precision_dtype
self.bg_encoder.time_embed.float()
# self.style_encoder.to(self.half_precision_dtype)
# self.style_encoder.dtype = self.half_precision_dtype
# self.style_encoder.time_embed.float()
def switch_to_fp32(self):
super().switch_to_fp32()
self.model.diffusion_model.map_modules.float()
self.model.diffusion_model.warp_modules.float()
self.model.diffusion_model.style_modules.float()
self.fg_encoder.float()
self.bg_encoder.float()
# self.style_encoder.float()
self.fg_encoder.dtype = torch.float32
self.bg_encoder.dtype = torch.float32
# self.style_encoder.dtype = torch.float32
def rescale_size(self, x: torch.Tensor, height, width):
oh, ow = x.shape[2:]
if oh < height or ow < width:
dh, dw = height - oh, width - ow
if dh > dw:
iw = ow + int(dh * ow/oh)
ih = height
else:
ih = oh + int(dw * oh/ow)
iw = width
else:
ih, iw = oh, ow
return torch.tensor([ih]), torch.tensor([iw])
def rescale_background_size(self, x, height, width):
oh, ow = x.shape[2:]
if oh < height or ow < width:
# A simple bias to avoid deterioration caused by reference resolution
mind = max(height, width)
ih = oh + mind
iw = ow / oh * ih
else:
ih, iw = oh, ow
# rh, rw = ih / height, iw / width
return torch.tensor([ih]), torch.tensor([iw])
def get_learned_embedding(self, c, bg=False, sketch=None, mapping=False, *args, **kwargs):
clip_emb = self.cond_stage_model.encode(c, "full").detach()
wd_emb, logits = self.img_embedder.encode(c, pooled=False, return_logits=True)
cls_emb, local_emb = clip_emb[:, :1], clip_emb[:, 1:]
if self.logits_embed and exists(sketch) and mapping:
_, sketch_logits = self.img_embedder.encode(-sketch, pooled=True, return_logits=True)
logits = self.img_embedder.geometry_update(logits, sketch_logits)
if self.logits_embed:
emb = self.proj(clip_emb, logits, bg)[0]
else:
emb = self.proj(clip_emb, wd_emb, bg)
return emb.to(self.dtype), cls_emb.to(self.dtype)
def prepare_conditions(
self,
bs,
sketch,
reference,
height,
width,
control_scale = 1,
mask_scale = 1,
merge_scale = 0.,
cond_aug = 0.,
background = None,
smask = None,
rmask = None,
mask_threshold_ref = 0.,
mask_threshold_sketch = 0.,
style_enhance = False,
fg_enhance = False,
bg_enhance = False,
latent_inpaint = False,
fg_disentangle_scale = 1.,
targets = None,
anchors = None,
controls = None,
target_scales = None,
enhances = None,
thresholds_list = None,
low_vram = False,
*args,
**kwargs
):
def prepare_style_modulations(y):
# Style enhancement part
z_ref = self.get_first_stage_encoding(warp_resize(reference, (height, width)))
if exists(background) and merge_scale > 0:
rh, rw = self.rescale_size(background, height, width)
z_bg = self.get_first_stage_encoding(warp_resize(background, (height, width)))
bg_emb, bg_cls_emb = self.get_learned_embedding(background)
scalar_embed = torch.cat(
self.scalar_embedder(torch.cat([rh, rw, ct, cl, h, w])).chunk(6), 1
).to(bg_emb.device)
bgy = torch.cat([bg_cls_emb.squeeze(1), scalar_embed], 1).to(self.dtype)
style_modulations = self.style_encoder(
torch.cat([z_ref, z_bg]),
timesteps = torch.zeros((2,), dtype=torch.long, device=z_ref.device),
context = torch.cat([emb, bg_emb]),
y = torch.cat([y, bgy])
)
for idx, m in enumerate(style_modulations):
fg, bg = m.chunk(2)
m = fg * (1-merge_scale) + merge_scale * bg
style_modulations[idx] = expand_to_batch_size(m, bs).to(self.dtype)
else:
z_bg = None
bg_emb = None
bgy = None
style_modulations = self.style_encoder(
z_ref,
timesteps = torch.zeros((1,), dtype=torch.long, device=z_ref.device),
context = emb,
y = y,
)
style_modulations = [expand_to_batch_size(m, bs).to(self.dtype) for m in style_modulations]
return style_modulations, z_bg, bg_emb, bgy
def prepare_background_latents(z_bg, bg_emb, bgy):
# Background enhancement part
bgh, bgw = background.shape[2:] if exists(background) else reference.shape[2:]
ch, cw = get_crop_scale(h, w, bgh, bgw)
if low_vram:
self.low_vram_shift(["first", "cond", "img_embedder"])
if latent_inpaint and exists(background):
hs_bg = self.get_first_stage_encoding(resize_and_crop(background, ch, cw, height, width))
bg_emb, cls_emb = self.get_learned_embedding(background)
else:
if not exists(z_bg):
bgy = torch.cat(
self.scalar_embedder(torch.tensor([ct, cl, ch, cw])).chunk(4), 1
# self.scalar_embedder(torch.tensor([bgh / bgw, h / w, ct, cl, ch, cw])).chunk(6), 1
).to(self.dtype).cuda()
if exists(background):
# bgh, bgw = self.rescale_background_size(background, height, width)
z_bg = self.get_first_stage_encoding(warp_resize(background, (height, width)))
bg_emb, cls_emb = self.get_learned_embedding(background)
# scalar_embed = torch.cat(self.scalar_embedder(torch.cat([bgh, bgw, ct, cl, h, w])).chunk(6), 1).cuda()
# bgy = torch.cat([cls_emb.squeeze(1), scalar_embed], 1).to(self.dtype)
else:
xbg = torch.where(rmask < mask_threshold_ref, reference, torch.ones_like(reference))
z_bg = self.get_first_stage_encoding(warp_resize(xbg, (height, width)))
bg_emb, cls_emb = self.get_learned_embedding(xbg)
if low_vram:
self.low_vram_shift(["bg_encoder"])
hs_bg = self.bg_encoder(
x = torch.cat([
z_bg,
# torch.where(
# smask > mask_threshold_sketch,
# torch.zeros_like(smask),
# F.interpolate(warp_resize(rmask, (height, width)), scale_factor=0.125)
# )
F.interpolate(warp_resize(smask, (height, width)), scale_factor=0.125),
F.interpolate(warp_resize(rmask, (height, width)), scale_factor=0.125)
], 1),
timesteps = torch.zeros((1,), dtype=torch.long, device=z_bg.device),
# context = bg_emb,
y = bgy.to(self.dtype),
)
return hs_bg, bg_emb
self.loras.recover_lora()
# prepare reference embedding
# manipulate = self.check_manipulate(target_scales)
c = {}
uc = [{}, {}]
self.loras.switch_lora(False)
# self.loras.recover_lora()
if exists(reference):
emb, cls_emb = self.get_learned_embedding(reference, sketch=sketch)
# rh, rw = reference.shape[2:]
# rh, rw = self.rescale_background_size(reference, height, width)
else:
emb, cls_emb = map(lambda t: torch.zeros_like(t), self.get_learned_embedding(sketch))
# rh, rw = torch.Tensor([height]), torch.Tensor([width])
ct, cl = torch.Tensor([0]), torch.Tensor([0])
# h, w = torch.Tensor([height]), torch.Tensor([width])
# scalar_embed = torch.cat(self.scalar_embedder(torch.cat([rh, rw, ct, cl, h, w])).chunk(6), 1).cuda()
# y = torch.cat([cls_emb.squeeze(1), scalar_embed], 1)
# y = self.scalar_embedder((h*w)**0.5).cuda()
# y = torch.cat(self.scalar_embedder(torch.cat([h, w])).chunk(2), 1).cuda()
h, w, score = torch.Tensor([height]), torch.Tensor([width]), torch.Tensor([7.])
y = torch.cat(self.scalar_embedder(torch.cat([(h * w) ** 0.5, score])).cuda().chunk(2), 1)
z_bg, bg_emb, bgy = None, None, None
# Style enhance part
if style_enhance:
style_modulations, z_bg, bg_emb, bgy = prepare_style_modulations(y)
for d in [c] + uc:
d.update({"style_modulations": style_modulations})
# Foreground enhance part
if fg_enhance:
assert exists(smask) and exists(rmask)
self.loras.switch_lora(True, "foreground")
if low_vram:
self.low_vram_shift(["first"])
z_fg = self.get_first_stage_encoding(warp_resize(
torch.where(rmask >= mask_threshold_ref, reference, torch.ones_like(reference)),
(height, width)
)) * fg_disentangle_scale
# z_ref = default(z_ref, self.get_first_stage_encoding(warp_resize(reference, (height, width))))
# self.loras.switch_lora(True, False)
self.loras.adjust_lora_scales(fg_disentangle_scale, "foreground")
if low_vram:
self.low_vram_shift(["fg_encoder"])
hs_fg = self.fg_encoder(
z_fg,
timesteps = torch.zeros((1,), dtype=torch.long, device=z_fg.device),
)
# hs_fg = [hs * fg_disentangle_scale for hs in hs_fg]
hs_fg = expand_to_batch_size(hs_fg, bs)
for d in [c] + uc:
d.update({
"hs_fg": hs_fg,
"inject_mask": expand_to_batch_size(smask, bs),
})
# for d in [c] + uc:
# d.update({"z_fg": expand_to_batch_size(z_fg, bs)})
# Background enhance part
if bg_enhance:
assert exists(rmask) and exists(smask)
# if not self.controller.hooked:
# self.controller.register("read", self.model.diffusion_model)
# self.loras.switch_lora(False, True)
hs_bg, bg_emb = prepare_background_latents(z_bg, bg_emb, default(bgy, y))
self.loras.switch_lora(True, "background")
if latent_inpaint and exists(background):
hs_bg = expand_to_batch_size(hs_bg, bs)
c.update({"inpaint_bg": hs_bg})
elif exists(self.controller):
# self.loras.merge_lora()
self.controller.update()
else:
hs_bg = expand_to_batch_size(hs_bg, bs)
for d in [c] + uc:
d.update({"hs_bg": hs_bg})
elif exists(self.controller):
# self.controller.reader_restore()
self.controller.clean()
if fg_enhance or bg_enhance:
# need to activate mask-guided split cross-attetnion
emb = torch.cat([emb, default(bg_emb, emb)], 1)
smask = expand_to_batch_size(smask.to(self.dtype), bs)
for d in [c] + uc:
d.update({"mask": F.interpolate(smask, scale_factor=0.125), "threshold": mask_threshold_sketch})
# if fg_enhance and bg_enhance:
# self.loras.switch_lora(True, True)
sketch = sketch.to(self.dtype)
context = expand_to_batch_size(emb, bs).to(self.dtype)
y = expand_to_batch_size(y, bs).float()
uc_context = torch.zeros_like(context)
control = []
uc_control = []
if low_vram:
self.low_vram_shift(["control_encoder"])
encoded_sketch = self.control_encoder(
torch.cat([sketch, -torch.ones_like(sketch)], 0)
)
for idx, es in enumerate(encoded_sketch):
es = es * control_scale[idx]
ec, uec = es.chunk(2)
control.append(expand_to_batch_size(ec, bs))
uc_control.append(expand_to_batch_size(uec, bs))
self.loras.merge_lora()
c.update({"control": control, "context": [context], "y": [y]})
uc[0].update({"control": control, "context": [uc_context], "y": [y]})
uc[1].update({"control": uc_control, "context": [context], "y": [y]})
return c, uc |