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from gaussian_diffusion import get_named_beta_schedule
from respace import SpacedDiffusion, space_timesteps
from model import (
InpaintText2ImUNet,
SuperResInpaintText2ImUnet,
SuperResText2ImUNet,
Text2ImUNet,
)
from tokenizer.bpe import get_encoder
def model_and_diffusion_defaults():
return dict(
image_size=64,
num_channels=192,
num_res_blocks=3,
channel_mult="",
num_heads=1, # Editable
num_head_channels=64, # Editable 64, 32, 18,
num_heads_upsample=-1, # Editable
attention_resolutions="32,16,8",
dropout=.1, # Editable
text_ctx=128,
xf_width=512, # Non Editable
xf_layers=16, # Non Editable
xf_heads=8, # Editable
xf_final_ln=True,
xf_padding=True,
diffusion_steps=1000, # Editable by steps of 100
noise_schedule="squaredcos_cap_v2", # Editable can be linear or squaredcos_cap_v2
timestep_respacing="",
use_scale_shift_norm=True, # Non Editable
resblock_updown=True, # Non Editable
use_fp16=True, # Editable
cache_text_emb=False,
inpaint=False,
super_res=False,
)
def model_and_diffusion_defaults_upsampler():
result = model_and_diffusion_defaults()
result.update(
dict(
image_size=256,
num_res_blocks=2,
noise_schedule="linear",
super_res=True,
)
)
return result
def create_model_and_diffusion(
image_size,
num_channels,
num_res_blocks,
channel_mult,
num_heads,
num_head_channels,
num_heads_upsample,
attention_resolutions,
dropout,
text_ctx,
xf_width,
xf_layers,
xf_heads,
xf_final_ln,
xf_padding,
diffusion_steps,
noise_schedule,
timestep_respacing,
use_scale_shift_norm,
resblock_updown,
use_fp16,
cache_text_emb,
inpaint,
super_res,
):
model = create_model(
image_size,
num_channels,
num_res_blocks,
channel_mult=channel_mult,
attention_resolutions=attention_resolutions,
num_heads=num_heads,
num_head_channels=num_head_channels,
num_heads_upsample=num_heads_upsample,
use_scale_shift_norm=use_scale_shift_norm,
dropout=dropout,
text_ctx=text_ctx,
xf_width=xf_width,
xf_layers=xf_layers,
xf_heads=xf_heads,
xf_final_ln=xf_final_ln,
xf_padding=xf_padding,
resblock_updown=resblock_updown,
use_fp16=use_fp16,
cache_text_emb=cache_text_emb,
inpaint=inpaint,
super_res=super_res,
)
diffusion = create_gaussian_diffusion(
steps=diffusion_steps,
noise_schedule=noise_schedule,
timestep_respacing=timestep_respacing,
)
return model, diffusion
def create_model(
image_size,
num_channels,
num_res_blocks,
channel_mult,
attention_resolutions,
num_heads,
num_head_channels,
num_heads_upsample,
use_scale_shift_norm,
dropout,
text_ctx,
xf_width,
xf_layers,
xf_heads,
xf_final_ln,
xf_padding,
resblock_updown,
use_fp16,
cache_text_emb,
inpaint,
super_res,
):
if channel_mult == "":
if image_size == 256:
channel_mult = (1, 1, 2, 2, 4, 4)
elif image_size == 128:
channel_mult = (1, 1, 2, 3, 4)
elif image_size == 64:
channel_mult = (1, 2, 3, 4)
else:
raise ValueError(f"unsupported image size: {image_size}")
else:
channel_mult = tuple(int(ch_mult) for ch_mult in channel_mult.split(","))
assert 2 ** (len(channel_mult) + 2) == image_size
attention_ds = []
for res in attention_resolutions.split(","):
attention_ds.append(image_size // int(res))
if inpaint and super_res:
model_cls = SuperResInpaintText2ImUnet
elif inpaint:
model_cls = InpaintText2ImUNet
elif super_res:
model_cls = SuperResText2ImUNet
else:
model_cls = Text2ImUNet
return model_cls(
text_ctx=text_ctx,
xf_width=xf_width,
xf_layers=xf_layers,
xf_heads=xf_heads,
xf_final_ln=xf_final_ln,
tokenizer=get_encoder(),
xf_padding=xf_padding,
in_channels=3,
model_channels=num_channels,
out_channels=6,
num_res_blocks=num_res_blocks,
attention_resolutions=tuple(attention_ds),
dropout=dropout,
channel_mult=channel_mult,
use_fp16=use_fp16,
num_heads=num_heads,
num_head_channels=num_head_channels,
num_heads_upsample=num_heads_upsample,
use_scale_shift_norm=use_scale_shift_norm,
resblock_updown=resblock_updown,
cache_text_emb=cache_text_emb,
)
def create_gaussian_diffusion(
steps,
noise_schedule,
timestep_respacing,
):
betas = get_named_beta_schedule(noise_schedule, steps)
if not timestep_respacing:
timestep_respacing = [steps]
return SpacedDiffusion(
use_timesteps=space_timesteps(steps, timestep_respacing),
betas=betas,
)
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