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convert_consistency_decoder.py
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1131 lines (907 loc) · 43.5 KB
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import math
import os
import urllib
import warnings
from argparse import ArgumentParser
import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub.utils import insecure_hashlib
from safetensors.torch import load_file as stl
from tqdm import tqdm
from diffusers import AutoencoderKL, ConsistencyDecoderVAE, DiffusionPipeline, StableDiffusionPipeline, UNet2DModel
from diffusers.models.autoencoders.vae import Encoder
from diffusers.models.embeddings import TimestepEmbedding
from diffusers.models.unets.unet_2d_blocks import ResnetDownsampleBlock2D, ResnetUpsampleBlock2D, UNetMidBlock2D
args = ArgumentParser()
args.add_argument("--save_pretrained", required=False, default=None, type=str)
args.add_argument("--test_image", required=True, type=str)
args = args.parse_args()
def _extract_into_tensor(arr, timesteps, broadcast_shape):
# from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895
# """
res = arr[timesteps].float()
dims_to_append = len(broadcast_shape) - len(res.shape)
return res[(...,) + (None,) * dims_to_append]
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
# from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L45
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return torch.tensor(betas)
def _download(url: str, root: str):
os.makedirs(root, exist_ok=True)
filename = os.path.basename(url)
expected_sha256 = url.split("/")[-2]
download_target = os.path.join(root, filename)
if os.path.exists(download_target) and not os.path.isfile(download_target):
raise RuntimeError(f"{download_target} exists and is not a regular file")
if os.path.isfile(download_target):
if insecure_hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
return download_target
else:
warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
with tqdm(
total=int(source.info().get("Content-Length")),
ncols=80,
unit="iB",
unit_scale=True,
unit_divisor=1024,
) as loop:
while True:
buffer = source.read(8192)
if not buffer:
break
output.write(buffer)
loop.update(len(buffer))
if insecure_hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
raise RuntimeError("Model has been downloaded but the SHA256 checksum does not match")
return download_target
class ConsistencyDecoder:
def __init__(self, device="cuda:0", download_root=os.path.expanduser("~/.cache/clip")):
self.n_distilled_steps = 64
download_target = _download(
"https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt",
download_root,
)
self.ckpt = torch.jit.load(download_target).to(device)
self.device = device
sigma_data = 0.5
betas = betas_for_alpha_bar(1024, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2).to(device)
alphas = 1.0 - betas
alphas_cumprod = torch.cumprod(alphas, dim=0)
self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
sigmas = torch.sqrt(1.0 / alphas_cumprod - 1)
self.c_skip = sqrt_recip_alphas_cumprod * sigma_data**2 / (sigmas**2 + sigma_data**2)
self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5
self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5
@staticmethod
def round_timesteps(timesteps, total_timesteps, n_distilled_steps, truncate_start=True):
with torch.no_grad():
space = torch.div(total_timesteps, n_distilled_steps, rounding_mode="floor")
rounded_timesteps = (torch.div(timesteps, space, rounding_mode="floor") + 1) * space
if truncate_start:
rounded_timesteps[rounded_timesteps == total_timesteps] -= space
else:
rounded_timesteps[rounded_timesteps == total_timesteps] -= space
rounded_timesteps[rounded_timesteps == 0] += space
return rounded_timesteps
@staticmethod
def ldm_transform_latent(z, extra_scale_factor=1):
channel_means = [0.38862467, 0.02253063, 0.07381133, -0.0171294]
channel_stds = [0.9654121, 1.0440036, 0.76147926, 0.77022034]
if len(z.shape) != 4:
raise ValueError()
z = z * 0.18215
channels = [z[:, i] for i in range(z.shape[1])]
channels = [extra_scale_factor * (c - channel_means[i]) / channel_stds[i] for i, c in enumerate(channels)]
return torch.stack(channels, dim=1)
@torch.no_grad()
def __call__(
self,
features: torch.Tensor,
schedule=[1.0, 0.5],
generator=None,
):
features = self.ldm_transform_latent(features)
ts = self.round_timesteps(
torch.arange(0, 1024),
1024,
self.n_distilled_steps,
truncate_start=False,
)
shape = (
features.size(0),
3,
8 * features.size(2),
8 * features.size(3),
)
x_start = torch.zeros(shape, device=features.device, dtype=features.dtype)
schedule_timesteps = [int((1024 - 1) * s) for s in schedule]
for i in schedule_timesteps:
t = ts[i].item()
t_ = torch.tensor([t] * features.shape[0]).to(self.device)
# noise = torch.randn_like(x_start)
noise = torch.randn(x_start.shape, dtype=x_start.dtype, generator=generator).to(device=x_start.device)
x_start = (
_extract_into_tensor(self.sqrt_alphas_cumprod, t_, x_start.shape) * x_start
+ _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t_, x_start.shape) * noise
)
c_in = _extract_into_tensor(self.c_in, t_, x_start.shape)
import torch.nn.functional as F
from diffusers import UNet2DModel
if isinstance(self.ckpt, UNet2DModel):
input = torch.concat([c_in * x_start, F.upsample_nearest(features, scale_factor=8)], dim=1)
model_output = self.ckpt(input, t_).sample
else:
model_output = self.ckpt(c_in * x_start, t_, features=features)
B, C = x_start.shape[:2]
model_output, _ = torch.split(model_output, C, dim=1)
pred_xstart = (
_extract_into_tensor(self.c_out, t_, x_start.shape) * model_output
+ _extract_into_tensor(self.c_skip, t_, x_start.shape) * x_start
).clamp(-1, 1)
x_start = pred_xstart
return x_start
def save_image(image, name):
import numpy as np
from PIL import Image
image = image[0].cpu().numpy()
image = (image + 1.0) * 127.5
image = image.clip(0, 255).astype(np.uint8)
image = Image.fromarray(image.transpose(1, 2, 0))
image.save(name)
def load_image(uri, size=None, center_crop=False):
import numpy as np
from PIL import Image
image = Image.open(uri)
if center_crop:
image = image.crop(
(
(image.width - min(image.width, image.height)) // 2,
(image.height - min(image.width, image.height)) // 2,
(image.width + min(image.width, image.height)) // 2,
(image.height + min(image.width, image.height)) // 2,
)
)
if size is not None:
image = image.resize(size)
image = torch.tensor(np.array(image).transpose(2, 0, 1)).unsqueeze(0).float()
image = image / 127.5 - 1.0
return image
class TimestepEmbedding_(nn.Module):
def __init__(self, n_time=1024, n_emb=320, n_out=1280) -> None:
super().__init__()
self.emb = nn.Embedding(n_time, n_emb)
self.f_1 = nn.Linear(n_emb, n_out)
self.f_2 = nn.Linear(n_out, n_out)
def forward(self, x) -> torch.Tensor:
x = self.emb(x)
x = self.f_1(x)
x = F.silu(x)
return self.f_2(x)
class ImageEmbedding(nn.Module):
def __init__(self, in_channels=7, out_channels=320) -> None:
super().__init__()
self.f = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
def forward(self, x) -> torch.Tensor:
return self.f(x)
class ImageUnembedding(nn.Module):
def __init__(self, in_channels=320, out_channels=6) -> None:
super().__init__()
self.gn = nn.GroupNorm(32, in_channels)
self.f = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
def forward(self, x) -> torch.Tensor:
return self.f(F.silu(self.gn(x)))
class ConvResblock(nn.Module):
def __init__(self, in_features=320, out_features=320) -> None:
super().__init__()
self.f_t = nn.Linear(1280, out_features * 2)
self.gn_1 = nn.GroupNorm(32, in_features)
self.f_1 = nn.Conv2d(in_features, out_features, kernel_size=3, padding=1)
self.gn_2 = nn.GroupNorm(32, out_features)
self.f_2 = nn.Conv2d(out_features, out_features, kernel_size=3, padding=1)
skip_conv = in_features != out_features
self.f_s = nn.Conv2d(in_features, out_features, kernel_size=1, padding=0) if skip_conv else nn.Identity()
def forward(self, x, t):
x_skip = x
t = self.f_t(F.silu(t))
t = t.chunk(2, dim=1)
t_1 = t[0].unsqueeze(dim=2).unsqueeze(dim=3) + 1
t_2 = t[1].unsqueeze(dim=2).unsqueeze(dim=3)
gn_1 = F.silu(self.gn_1(x))
f_1 = self.f_1(gn_1)
gn_2 = self.gn_2(f_1)
return self.f_s(x_skip) + self.f_2(F.silu(gn_2 * t_1 + t_2))
# Also ConvResblock
class Downsample(nn.Module):
def __init__(self, in_channels=320) -> None:
super().__init__()
self.f_t = nn.Linear(1280, in_channels * 2)
self.gn_1 = nn.GroupNorm(32, in_channels)
self.f_1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)
self.gn_2 = nn.GroupNorm(32, in_channels)
self.f_2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)
def forward(self, x, t) -> torch.Tensor:
x_skip = x
t = self.f_t(F.silu(t))
t_1, t_2 = t.chunk(2, dim=1)
t_1 = t_1.unsqueeze(2).unsqueeze(3) + 1
t_2 = t_2.unsqueeze(2).unsqueeze(3)
gn_1 = F.silu(self.gn_1(x))
avg_pool2d = F.avg_pool2d(gn_1, kernel_size=(2, 2), stride=None)
f_1 = self.f_1(avg_pool2d)
gn_2 = self.gn_2(f_1)
f_2 = self.f_2(F.silu(t_2 + (t_1 * gn_2)))
return f_2 + F.avg_pool2d(x_skip, kernel_size=(2, 2), stride=None)
# Also ConvResblock
class Upsample(nn.Module):
def __init__(self, in_channels=1024) -> None:
super().__init__()
self.f_t = nn.Linear(1280, in_channels * 2)
self.gn_1 = nn.GroupNorm(32, in_channels)
self.f_1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)
self.gn_2 = nn.GroupNorm(32, in_channels)
self.f_2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)
def forward(self, x, t) -> torch.Tensor:
x_skip = x
t = self.f_t(F.silu(t))
t_1, t_2 = t.chunk(2, dim=1)
t_1 = t_1.unsqueeze(2).unsqueeze(3) + 1
t_2 = t_2.unsqueeze(2).unsqueeze(3)
gn_1 = F.silu(self.gn_1(x))
upsample = F.upsample_nearest(gn_1, scale_factor=2)
f_1 = self.f_1(upsample)
gn_2 = self.gn_2(f_1)
f_2 = self.f_2(F.silu(t_2 + (t_1 * gn_2)))
return f_2 + F.upsample_nearest(x_skip, scale_factor=2)
class ConvUNetVAE(nn.Module):
def __init__(self) -> None:
super().__init__()
self.embed_image = ImageEmbedding()
self.embed_time = TimestepEmbedding_()
down_0 = nn.ModuleList(
[
ConvResblock(320, 320),
ConvResblock(320, 320),
ConvResblock(320, 320),
Downsample(320),
]
)
down_1 = nn.ModuleList(
[
ConvResblock(320, 640),
ConvResblock(640, 640),
ConvResblock(640, 640),
Downsample(640),
]
)
down_2 = nn.ModuleList(
[
ConvResblock(640, 1024),
ConvResblock(1024, 1024),
ConvResblock(1024, 1024),
Downsample(1024),
]
)
down_3 = nn.ModuleList(
[
ConvResblock(1024, 1024),
ConvResblock(1024, 1024),
ConvResblock(1024, 1024),
]
)
self.down = nn.ModuleList(
[
down_0,
down_1,
down_2,
down_3,
]
)
self.mid = nn.ModuleList(
[
ConvResblock(1024, 1024),
ConvResblock(1024, 1024),
]
)
up_3 = nn.ModuleList(
[
ConvResblock(1024 * 2, 1024),
ConvResblock(1024 * 2, 1024),
ConvResblock(1024 * 2, 1024),
ConvResblock(1024 * 2, 1024),
Upsample(1024),
]
)
up_2 = nn.ModuleList(
[
ConvResblock(1024 * 2, 1024),
ConvResblock(1024 * 2, 1024),
ConvResblock(1024 * 2, 1024),
ConvResblock(1024 + 640, 1024),
Upsample(1024),
]
)
up_1 = nn.ModuleList(
[
ConvResblock(1024 + 640, 640),
ConvResblock(640 * 2, 640),
ConvResblock(640 * 2, 640),
ConvResblock(320 + 640, 640),
Upsample(640),
]
)
up_0 = nn.ModuleList(
[
ConvResblock(320 + 640, 320),
ConvResblock(320 * 2, 320),
ConvResblock(320 * 2, 320),
ConvResblock(320 * 2, 320),
]
)
self.up = nn.ModuleList(
[
up_0,
up_1,
up_2,
up_3,
]
)
self.output = ImageUnembedding()
def forward(self, x, t, features) -> torch.Tensor:
converted = hasattr(self, "converted") and self.converted
x = torch.cat([x, F.upsample_nearest(features, scale_factor=8)], dim=1)
if converted:
t = self.time_embedding(self.time_proj(t))
else:
t = self.embed_time(t)
x = self.embed_image(x)
skips = [x]
for i, down in enumerate(self.down):
if converted and i in [0, 1, 2, 3]:
x, skips_ = down(x, t)
for skip in skips_:
skips.append(skip)
else:
for block in down:
x = block(x, t)
skips.append(x)
print(x.float().abs().sum())
if converted:
x = self.mid(x, t)
else:
for i in range(2):
x = self.mid[i](x, t)
print(x.float().abs().sum())
for i, up in enumerate(self.up[::-1]):
if converted and i in [0, 1, 2, 3]:
skip_4 = skips.pop()
skip_3 = skips.pop()
skip_2 = skips.pop()
skip_1 = skips.pop()
skips_ = (skip_1, skip_2, skip_3, skip_4)
x = up(x, skips_, t)
else:
for block in up:
if isinstance(block, ConvResblock):
x = torch.concat([x, skips.pop()], dim=1)
x = block(x, t)
return self.output(x)
def rename_state_dict_key(k):
k = k.replace("blocks.", "")
for i in range(5):
k = k.replace(f"down_{i}_", f"down.{i}.")
k = k.replace(f"conv_{i}.", f"{i}.")
k = k.replace(f"up_{i}_", f"up.{i}.")
k = k.replace(f"mid_{i}", f"mid.{i}")
k = k.replace("upsamp.", "4.")
k = k.replace("downsamp.", "3.")
k = k.replace("f_t.w", "f_t.weight").replace("f_t.b", "f_t.bias")
k = k.replace("f_1.w", "f_1.weight").replace("f_1.b", "f_1.bias")
k = k.replace("f_2.w", "f_2.weight").replace("f_2.b", "f_2.bias")
k = k.replace("f_s.w", "f_s.weight").replace("f_s.b", "f_s.bias")
k = k.replace("f.w", "f.weight").replace("f.b", "f.bias")
k = k.replace("gn_1.g", "gn_1.weight").replace("gn_1.b", "gn_1.bias")
k = k.replace("gn_2.g", "gn_2.weight").replace("gn_2.b", "gn_2.bias")
k = k.replace("gn.g", "gn.weight").replace("gn.b", "gn.bias")
return k
def rename_state_dict(sd, embedding):
sd = {rename_state_dict_key(k): v for k, v in sd.items()}
sd["embed_time.emb.weight"] = embedding["weight"]
return sd
# encode with stable diffusion vae
pipe = StableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
)
pipe.vae.cuda()
# construct original decoder with jitted model
decoder_consistency = ConsistencyDecoder(device="cuda:0")
# construct UNet code, overwrite the decoder with conv_unet_vae
model = ConvUNetVAE()
model.load_state_dict(
rename_state_dict(
stl("consistency_decoder.safetensors"),
stl("embedding.safetensors"),
)
)
model = model.cuda()
decoder_consistency.ckpt = model
image = load_image(args.test_image, size=(256, 256), center_crop=True)
latent = pipe.vae.encode(image.half().cuda()).latent_dist.sample()
# decode with gan
sample_gan = pipe.vae.decode(latent).sample.detach()
save_image(sample_gan, "gan.png")
# decode with conv_unet_vae
sample_consistency_orig = decoder_consistency(latent, generator=torch.Generator("cpu").manual_seed(0))
save_image(sample_consistency_orig, "con_orig.png")
########### conversion
print("CONVERSION")
print("DOWN BLOCK ONE")
block_one_sd_orig = model.down[0].state_dict()
block_one_sd_new = {}
for i in range(3):
block_one_sd_new[f"resnets.{i}.norm1.weight"] = block_one_sd_orig.pop(f"{i}.gn_1.weight")
block_one_sd_new[f"resnets.{i}.norm1.bias"] = block_one_sd_orig.pop(f"{i}.gn_1.bias")
block_one_sd_new[f"resnets.{i}.conv1.weight"] = block_one_sd_orig.pop(f"{i}.f_1.weight")
block_one_sd_new[f"resnets.{i}.conv1.bias"] = block_one_sd_orig.pop(f"{i}.f_1.bias")
block_one_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_one_sd_orig.pop(f"{i}.f_t.weight")
block_one_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_one_sd_orig.pop(f"{i}.f_t.bias")
block_one_sd_new[f"resnets.{i}.norm2.weight"] = block_one_sd_orig.pop(f"{i}.gn_2.weight")
block_one_sd_new[f"resnets.{i}.norm2.bias"] = block_one_sd_orig.pop(f"{i}.gn_2.bias")
block_one_sd_new[f"resnets.{i}.conv2.weight"] = block_one_sd_orig.pop(f"{i}.f_2.weight")
block_one_sd_new[f"resnets.{i}.conv2.bias"] = block_one_sd_orig.pop(f"{i}.f_2.bias")
block_one_sd_new["downsamplers.0.norm1.weight"] = block_one_sd_orig.pop("3.gn_1.weight")
block_one_sd_new["downsamplers.0.norm1.bias"] = block_one_sd_orig.pop("3.gn_1.bias")
block_one_sd_new["downsamplers.0.conv1.weight"] = block_one_sd_orig.pop("3.f_1.weight")
block_one_sd_new["downsamplers.0.conv1.bias"] = block_one_sd_orig.pop("3.f_1.bias")
block_one_sd_new["downsamplers.0.time_emb_proj.weight"] = block_one_sd_orig.pop("3.f_t.weight")
block_one_sd_new["downsamplers.0.time_emb_proj.bias"] = block_one_sd_orig.pop("3.f_t.bias")
block_one_sd_new["downsamplers.0.norm2.weight"] = block_one_sd_orig.pop("3.gn_2.weight")
block_one_sd_new["downsamplers.0.norm2.bias"] = block_one_sd_orig.pop("3.gn_2.bias")
block_one_sd_new["downsamplers.0.conv2.weight"] = block_one_sd_orig.pop("3.f_2.weight")
block_one_sd_new["downsamplers.0.conv2.bias"] = block_one_sd_orig.pop("3.f_2.bias")
assert len(block_one_sd_orig) == 0
block_one = ResnetDownsampleBlock2D(
in_channels=320,
out_channels=320,
temb_channels=1280,
num_layers=3,
add_downsample=True,
resnet_time_scale_shift="scale_shift",
resnet_eps=1e-5,
)
block_one.load_state_dict(block_one_sd_new)
print("DOWN BLOCK TWO")
block_two_sd_orig = model.down[1].state_dict()
block_two_sd_new = {}
for i in range(3):
block_two_sd_new[f"resnets.{i}.norm1.weight"] = block_two_sd_orig.pop(f"{i}.gn_1.weight")
block_two_sd_new[f"resnets.{i}.norm1.bias"] = block_two_sd_orig.pop(f"{i}.gn_1.bias")
block_two_sd_new[f"resnets.{i}.conv1.weight"] = block_two_sd_orig.pop(f"{i}.f_1.weight")
block_two_sd_new[f"resnets.{i}.conv1.bias"] = block_two_sd_orig.pop(f"{i}.f_1.bias")
block_two_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_two_sd_orig.pop(f"{i}.f_t.weight")
block_two_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_two_sd_orig.pop(f"{i}.f_t.bias")
block_two_sd_new[f"resnets.{i}.norm2.weight"] = block_two_sd_orig.pop(f"{i}.gn_2.weight")
block_two_sd_new[f"resnets.{i}.norm2.bias"] = block_two_sd_orig.pop(f"{i}.gn_2.bias")
block_two_sd_new[f"resnets.{i}.conv2.weight"] = block_two_sd_orig.pop(f"{i}.f_2.weight")
block_two_sd_new[f"resnets.{i}.conv2.bias"] = block_two_sd_orig.pop(f"{i}.f_2.bias")
if i == 0:
block_two_sd_new[f"resnets.{i}.conv_shortcut.weight"] = block_two_sd_orig.pop(f"{i}.f_s.weight")
block_two_sd_new[f"resnets.{i}.conv_shortcut.bias"] = block_two_sd_orig.pop(f"{i}.f_s.bias")
block_two_sd_new["downsamplers.0.norm1.weight"] = block_two_sd_orig.pop("3.gn_1.weight")
block_two_sd_new["downsamplers.0.norm1.bias"] = block_two_sd_orig.pop("3.gn_1.bias")
block_two_sd_new["downsamplers.0.conv1.weight"] = block_two_sd_orig.pop("3.f_1.weight")
block_two_sd_new["downsamplers.0.conv1.bias"] = block_two_sd_orig.pop("3.f_1.bias")
block_two_sd_new["downsamplers.0.time_emb_proj.weight"] = block_two_sd_orig.pop("3.f_t.weight")
block_two_sd_new["downsamplers.0.time_emb_proj.bias"] = block_two_sd_orig.pop("3.f_t.bias")
block_two_sd_new["downsamplers.0.norm2.weight"] = block_two_sd_orig.pop("3.gn_2.weight")
block_two_sd_new["downsamplers.0.norm2.bias"] = block_two_sd_orig.pop("3.gn_2.bias")
block_two_sd_new["downsamplers.0.conv2.weight"] = block_two_sd_orig.pop("3.f_2.weight")
block_two_sd_new["downsamplers.0.conv2.bias"] = block_two_sd_orig.pop("3.f_2.bias")
assert len(block_two_sd_orig) == 0
block_two = ResnetDownsampleBlock2D(
in_channels=320,
out_channels=640,
temb_channels=1280,
num_layers=3,
add_downsample=True,
resnet_time_scale_shift="scale_shift",
resnet_eps=1e-5,
)
block_two.load_state_dict(block_two_sd_new)
print("DOWN BLOCK THREE")
block_three_sd_orig = model.down[2].state_dict()
block_three_sd_new = {}
for i in range(3):
block_three_sd_new[f"resnets.{i}.norm1.weight"] = block_three_sd_orig.pop(f"{i}.gn_1.weight")
block_three_sd_new[f"resnets.{i}.norm1.bias"] = block_three_sd_orig.pop(f"{i}.gn_1.bias")
block_three_sd_new[f"resnets.{i}.conv1.weight"] = block_three_sd_orig.pop(f"{i}.f_1.weight")
block_three_sd_new[f"resnets.{i}.conv1.bias"] = block_three_sd_orig.pop(f"{i}.f_1.bias")
block_three_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_three_sd_orig.pop(f"{i}.f_t.weight")
block_three_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_three_sd_orig.pop(f"{i}.f_t.bias")
block_three_sd_new[f"resnets.{i}.norm2.weight"] = block_three_sd_orig.pop(f"{i}.gn_2.weight")
block_three_sd_new[f"resnets.{i}.norm2.bias"] = block_three_sd_orig.pop(f"{i}.gn_2.bias")
block_three_sd_new[f"resnets.{i}.conv2.weight"] = block_three_sd_orig.pop(f"{i}.f_2.weight")
block_three_sd_new[f"resnets.{i}.conv2.bias"] = block_three_sd_orig.pop(f"{i}.f_2.bias")
if i == 0:
block_three_sd_new[f"resnets.{i}.conv_shortcut.weight"] = block_three_sd_orig.pop(f"{i}.f_s.weight")
block_three_sd_new[f"resnets.{i}.conv_shortcut.bias"] = block_three_sd_orig.pop(f"{i}.f_s.bias")
block_three_sd_new["downsamplers.0.norm1.weight"] = block_three_sd_orig.pop("3.gn_1.weight")
block_three_sd_new["downsamplers.0.norm1.bias"] = block_three_sd_orig.pop("3.gn_1.bias")
block_three_sd_new["downsamplers.0.conv1.weight"] = block_three_sd_orig.pop("3.f_1.weight")
block_three_sd_new["downsamplers.0.conv1.bias"] = block_three_sd_orig.pop("3.f_1.bias")
block_three_sd_new["downsamplers.0.time_emb_proj.weight"] = block_three_sd_orig.pop("3.f_t.weight")
block_three_sd_new["downsamplers.0.time_emb_proj.bias"] = block_three_sd_orig.pop("3.f_t.bias")
block_three_sd_new["downsamplers.0.norm2.weight"] = block_three_sd_orig.pop("3.gn_2.weight")
block_three_sd_new["downsamplers.0.norm2.bias"] = block_three_sd_orig.pop("3.gn_2.bias")
block_three_sd_new["downsamplers.0.conv2.weight"] = block_three_sd_orig.pop("3.f_2.weight")
block_three_sd_new["downsamplers.0.conv2.bias"] = block_three_sd_orig.pop("3.f_2.bias")
assert len(block_three_sd_orig) == 0
block_three = ResnetDownsampleBlock2D(
in_channels=640,
out_channels=1024,
temb_channels=1280,
num_layers=3,
add_downsample=True,
resnet_time_scale_shift="scale_shift",
resnet_eps=1e-5,
)
block_three.load_state_dict(block_three_sd_new)
print("DOWN BLOCK FOUR")
block_four_sd_orig = model.down[3].state_dict()
block_four_sd_new = {}
for i in range(3):
block_four_sd_new[f"resnets.{i}.norm1.weight"] = block_four_sd_orig.pop(f"{i}.gn_1.weight")
block_four_sd_new[f"resnets.{i}.norm1.bias"] = block_four_sd_orig.pop(f"{i}.gn_1.bias")
block_four_sd_new[f"resnets.{i}.conv1.weight"] = block_four_sd_orig.pop(f"{i}.f_1.weight")
block_four_sd_new[f"resnets.{i}.conv1.bias"] = block_four_sd_orig.pop(f"{i}.f_1.bias")
block_four_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_four_sd_orig.pop(f"{i}.f_t.weight")
block_four_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_four_sd_orig.pop(f"{i}.f_t.bias")
block_four_sd_new[f"resnets.{i}.norm2.weight"] = block_four_sd_orig.pop(f"{i}.gn_2.weight")
block_four_sd_new[f"resnets.{i}.norm2.bias"] = block_four_sd_orig.pop(f"{i}.gn_2.bias")
block_four_sd_new[f"resnets.{i}.conv2.weight"] = block_four_sd_orig.pop(f"{i}.f_2.weight")
block_four_sd_new[f"resnets.{i}.conv2.bias"] = block_four_sd_orig.pop(f"{i}.f_2.bias")
assert len(block_four_sd_orig) == 0
block_four = ResnetDownsampleBlock2D(
in_channels=1024,
out_channels=1024,
temb_channels=1280,
num_layers=3,
add_downsample=False,
resnet_time_scale_shift="scale_shift",
resnet_eps=1e-5,
)
block_four.load_state_dict(block_four_sd_new)
print("MID BLOCK 1")
mid_block_one_sd_orig = model.mid.state_dict()
mid_block_one_sd_new = {}
for i in range(2):
mid_block_one_sd_new[f"resnets.{i}.norm1.weight"] = mid_block_one_sd_orig.pop(f"{i}.gn_1.weight")
mid_block_one_sd_new[f"resnets.{i}.norm1.bias"] = mid_block_one_sd_orig.pop(f"{i}.gn_1.bias")
mid_block_one_sd_new[f"resnets.{i}.conv1.weight"] = mid_block_one_sd_orig.pop(f"{i}.f_1.weight")
mid_block_one_sd_new[f"resnets.{i}.conv1.bias"] = mid_block_one_sd_orig.pop(f"{i}.f_1.bias")
mid_block_one_sd_new[f"resnets.{i}.time_emb_proj.weight"] = mid_block_one_sd_orig.pop(f"{i}.f_t.weight")
mid_block_one_sd_new[f"resnets.{i}.time_emb_proj.bias"] = mid_block_one_sd_orig.pop(f"{i}.f_t.bias")
mid_block_one_sd_new[f"resnets.{i}.norm2.weight"] = mid_block_one_sd_orig.pop(f"{i}.gn_2.weight")
mid_block_one_sd_new[f"resnets.{i}.norm2.bias"] = mid_block_one_sd_orig.pop(f"{i}.gn_2.bias")
mid_block_one_sd_new[f"resnets.{i}.conv2.weight"] = mid_block_one_sd_orig.pop(f"{i}.f_2.weight")
mid_block_one_sd_new[f"resnets.{i}.conv2.bias"] = mid_block_one_sd_orig.pop(f"{i}.f_2.bias")
assert len(mid_block_one_sd_orig) == 0
mid_block_one = UNetMidBlock2D(
in_channels=1024,
temb_channels=1280,
num_layers=1,
resnet_time_scale_shift="scale_shift",
resnet_eps=1e-5,
add_attention=False,
)
mid_block_one.load_state_dict(mid_block_one_sd_new)
print("UP BLOCK ONE")
up_block_one_sd_orig = model.up[-1].state_dict()
up_block_one_sd_new = {}
for i in range(4):
up_block_one_sd_new[f"resnets.{i}.norm1.weight"] = up_block_one_sd_orig.pop(f"{i}.gn_1.weight")
up_block_one_sd_new[f"resnets.{i}.norm1.bias"] = up_block_one_sd_orig.pop(f"{i}.gn_1.bias")
up_block_one_sd_new[f"resnets.{i}.conv1.weight"] = up_block_one_sd_orig.pop(f"{i}.f_1.weight")
up_block_one_sd_new[f"resnets.{i}.conv1.bias"] = up_block_one_sd_orig.pop(f"{i}.f_1.bias")
up_block_one_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_one_sd_orig.pop(f"{i}.f_t.weight")
up_block_one_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_one_sd_orig.pop(f"{i}.f_t.bias")
up_block_one_sd_new[f"resnets.{i}.norm2.weight"] = up_block_one_sd_orig.pop(f"{i}.gn_2.weight")
up_block_one_sd_new[f"resnets.{i}.norm2.bias"] = up_block_one_sd_orig.pop(f"{i}.gn_2.bias")
up_block_one_sd_new[f"resnets.{i}.conv2.weight"] = up_block_one_sd_orig.pop(f"{i}.f_2.weight")
up_block_one_sd_new[f"resnets.{i}.conv2.bias"] = up_block_one_sd_orig.pop(f"{i}.f_2.bias")
up_block_one_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_one_sd_orig.pop(f"{i}.f_s.weight")
up_block_one_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_one_sd_orig.pop(f"{i}.f_s.bias")
up_block_one_sd_new["upsamplers.0.norm1.weight"] = up_block_one_sd_orig.pop("4.gn_1.weight")
up_block_one_sd_new["upsamplers.0.norm1.bias"] = up_block_one_sd_orig.pop("4.gn_1.bias")
up_block_one_sd_new["upsamplers.0.conv1.weight"] = up_block_one_sd_orig.pop("4.f_1.weight")
up_block_one_sd_new["upsamplers.0.conv1.bias"] = up_block_one_sd_orig.pop("4.f_1.bias")
up_block_one_sd_new["upsamplers.0.time_emb_proj.weight"] = up_block_one_sd_orig.pop("4.f_t.weight")
up_block_one_sd_new["upsamplers.0.time_emb_proj.bias"] = up_block_one_sd_orig.pop("4.f_t.bias")
up_block_one_sd_new["upsamplers.0.norm2.weight"] = up_block_one_sd_orig.pop("4.gn_2.weight")
up_block_one_sd_new["upsamplers.0.norm2.bias"] = up_block_one_sd_orig.pop("4.gn_2.bias")
up_block_one_sd_new["upsamplers.0.conv2.weight"] = up_block_one_sd_orig.pop("4.f_2.weight")
up_block_one_sd_new["upsamplers.0.conv2.bias"] = up_block_one_sd_orig.pop("4.f_2.bias")
assert len(up_block_one_sd_orig) == 0
up_block_one = ResnetUpsampleBlock2D(
in_channels=1024,
prev_output_channel=1024,
out_channels=1024,
temb_channels=1280,
num_layers=4,
add_upsample=True,
resnet_time_scale_shift="scale_shift",
resnet_eps=1e-5,
)
up_block_one.load_state_dict(up_block_one_sd_new)
print("UP BLOCK TWO")
up_block_two_sd_orig = model.up[-2].state_dict()
up_block_two_sd_new = {}
for i in range(4):
up_block_two_sd_new[f"resnets.{i}.norm1.weight"] = up_block_two_sd_orig.pop(f"{i}.gn_1.weight")
up_block_two_sd_new[f"resnets.{i}.norm1.bias"] = up_block_two_sd_orig.pop(f"{i}.gn_1.bias")
up_block_two_sd_new[f"resnets.{i}.conv1.weight"] = up_block_two_sd_orig.pop(f"{i}.f_1.weight")
up_block_two_sd_new[f"resnets.{i}.conv1.bias"] = up_block_two_sd_orig.pop(f"{i}.f_1.bias")
up_block_two_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_two_sd_orig.pop(f"{i}.f_t.weight")
up_block_two_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_two_sd_orig.pop(f"{i}.f_t.bias")
up_block_two_sd_new[f"resnets.{i}.norm2.weight"] = up_block_two_sd_orig.pop(f"{i}.gn_2.weight")
up_block_two_sd_new[f"resnets.{i}.norm2.bias"] = up_block_two_sd_orig.pop(f"{i}.gn_2.bias")
up_block_two_sd_new[f"resnets.{i}.conv2.weight"] = up_block_two_sd_orig.pop(f"{i}.f_2.weight")
up_block_two_sd_new[f"resnets.{i}.conv2.bias"] = up_block_two_sd_orig.pop(f"{i}.f_2.bias")
up_block_two_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_two_sd_orig.pop(f"{i}.f_s.weight")
up_block_two_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_two_sd_orig.pop(f"{i}.f_s.bias")
up_block_two_sd_new["upsamplers.0.norm1.weight"] = up_block_two_sd_orig.pop("4.gn_1.weight")
up_block_two_sd_new["upsamplers.0.norm1.bias"] = up_block_two_sd_orig.pop("4.gn_1.bias")
up_block_two_sd_new["upsamplers.0.conv1.weight"] = up_block_two_sd_orig.pop("4.f_1.weight")
up_block_two_sd_new["upsamplers.0.conv1.bias"] = up_block_two_sd_orig.pop("4.f_1.bias")
up_block_two_sd_new["upsamplers.0.time_emb_proj.weight"] = up_block_two_sd_orig.pop("4.f_t.weight")
up_block_two_sd_new["upsamplers.0.time_emb_proj.bias"] = up_block_two_sd_orig.pop("4.f_t.bias")
up_block_two_sd_new["upsamplers.0.norm2.weight"] = up_block_two_sd_orig.pop("4.gn_2.weight")
up_block_two_sd_new["upsamplers.0.norm2.bias"] = up_block_two_sd_orig.pop("4.gn_2.bias")
up_block_two_sd_new["upsamplers.0.conv2.weight"] = up_block_two_sd_orig.pop("4.f_2.weight")
up_block_two_sd_new["upsamplers.0.conv2.bias"] = up_block_two_sd_orig.pop("4.f_2.bias")
assert len(up_block_two_sd_orig) == 0
up_block_two = ResnetUpsampleBlock2D(
in_channels=640,
prev_output_channel=1024,
out_channels=1024,
temb_channels=1280,
num_layers=4,
add_upsample=True,
resnet_time_scale_shift="scale_shift",
resnet_eps=1e-5,
)
up_block_two.load_state_dict(up_block_two_sd_new)
print("UP BLOCK THREE")
up_block_three_sd_orig = model.up[-3].state_dict()
up_block_three_sd_new = {}
for i in range(4):
up_block_three_sd_new[f"resnets.{i}.norm1.weight"] = up_block_three_sd_orig.pop(f"{i}.gn_1.weight")
up_block_three_sd_new[f"resnets.{i}.norm1.bias"] = up_block_three_sd_orig.pop(f"{i}.gn_1.bias")
up_block_three_sd_new[f"resnets.{i}.conv1.weight"] = up_block_three_sd_orig.pop(f"{i}.f_1.weight")
up_block_three_sd_new[f"resnets.{i}.conv1.bias"] = up_block_three_sd_orig.pop(f"{i}.f_1.bias")
up_block_three_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_three_sd_orig.pop(f"{i}.f_t.weight")
up_block_three_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_three_sd_orig.pop(f"{i}.f_t.bias")
up_block_three_sd_new[f"resnets.{i}.norm2.weight"] = up_block_three_sd_orig.pop(f"{i}.gn_2.weight")
up_block_three_sd_new[f"resnets.{i}.norm2.bias"] = up_block_three_sd_orig.pop(f"{i}.gn_2.bias")
up_block_three_sd_new[f"resnets.{i}.conv2.weight"] = up_block_three_sd_orig.pop(f"{i}.f_2.weight")
up_block_three_sd_new[f"resnets.{i}.conv2.bias"] = up_block_three_sd_orig.pop(f"{i}.f_2.bias")
up_block_three_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_three_sd_orig.pop(f"{i}.f_s.weight")
up_block_three_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_three_sd_orig.pop(f"{i}.f_s.bias")
up_block_three_sd_new["upsamplers.0.norm1.weight"] = up_block_three_sd_orig.pop("4.gn_1.weight")
up_block_three_sd_new["upsamplers.0.norm1.bias"] = up_block_three_sd_orig.pop("4.gn_1.bias")
up_block_three_sd_new["upsamplers.0.conv1.weight"] = up_block_three_sd_orig.pop("4.f_1.weight")
up_block_three_sd_new["upsamplers.0.conv1.bias"] = up_block_three_sd_orig.pop("4.f_1.bias")
up_block_three_sd_new["upsamplers.0.time_emb_proj.weight"] = up_block_three_sd_orig.pop("4.f_t.weight")
up_block_three_sd_new["upsamplers.0.time_emb_proj.bias"] = up_block_three_sd_orig.pop("4.f_t.bias")
up_block_three_sd_new["upsamplers.0.norm2.weight"] = up_block_three_sd_orig.pop("4.gn_2.weight")
up_block_three_sd_new["upsamplers.0.norm2.bias"] = up_block_three_sd_orig.pop("4.gn_2.bias")
up_block_three_sd_new["upsamplers.0.conv2.weight"] = up_block_three_sd_orig.pop("4.f_2.weight")
up_block_three_sd_new["upsamplers.0.conv2.bias"] = up_block_three_sd_orig.pop("4.f_2.bias")
assert len(up_block_three_sd_orig) == 0
up_block_three = ResnetUpsampleBlock2D(
in_channels=320,
prev_output_channel=1024,
out_channels=640,
temb_channels=1280,
num_layers=4,
add_upsample=True,
resnet_time_scale_shift="scale_shift",
resnet_eps=1e-5,
)
up_block_three.load_state_dict(up_block_three_sd_new)
print("UP BLOCK FOUR")
up_block_four_sd_orig = model.up[-4].state_dict()
up_block_four_sd_new = {}
for i in range(4):
up_block_four_sd_new[f"resnets.{i}.norm1.weight"] = up_block_four_sd_orig.pop(f"{i}.gn_1.weight")
up_block_four_sd_new[f"resnets.{i}.norm1.bias"] = up_block_four_sd_orig.pop(f"{i}.gn_1.bias")
up_block_four_sd_new[f"resnets.{i}.conv1.weight"] = up_block_four_sd_orig.pop(f"{i}.f_1.weight")
up_block_four_sd_new[f"resnets.{i}.conv1.bias"] = up_block_four_sd_orig.pop(f"{i}.f_1.bias")
up_block_four_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_four_sd_orig.pop(f"{i}.f_t.weight")
up_block_four_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_four_sd_orig.pop(f"{i}.f_t.bias")
up_block_four_sd_new[f"resnets.{i}.norm2.weight"] = up_block_four_sd_orig.pop(f"{i}.gn_2.weight")
up_block_four_sd_new[f"resnets.{i}.norm2.bias"] = up_block_four_sd_orig.pop(f"{i}.gn_2.bias")
up_block_four_sd_new[f"resnets.{i}.conv2.weight"] = up_block_four_sd_orig.pop(f"{i}.f_2.weight")
up_block_four_sd_new[f"resnets.{i}.conv2.bias"] = up_block_four_sd_orig.pop(f"{i}.f_2.bias")
up_block_four_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_four_sd_orig.pop(f"{i}.f_s.weight")
up_block_four_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_four_sd_orig.pop(f"{i}.f_s.bias")
assert len(up_block_four_sd_orig) == 0
up_block_four = ResnetUpsampleBlock2D(
in_channels=320,
prev_output_channel=640,
out_channels=320,
temb_channels=1280,
num_layers=4,
add_upsample=False,
resnet_time_scale_shift="scale_shift",
resnet_eps=1e-5,
)
up_block_four.load_state_dict(up_block_four_sd_new)
print("initial projection (conv_in)")
conv_in_sd_orig = model.embed_image.state_dict()
conv_in_sd_new = {}
conv_in_sd_new["weight"] = conv_in_sd_orig.pop("f.weight")
conv_in_sd_new["bias"] = conv_in_sd_orig.pop("f.bias")
assert len(conv_in_sd_orig) == 0
block_out_channels = [320, 640, 1024, 1024]
in_channels = 7
conv_in_kernel = 3
conv_in_padding = (conv_in_kernel - 1) // 2
conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding)
conv_in.load_state_dict(conv_in_sd_new)
print("out projection (conv_out) (conv_norm_out)")
out_channels = 6
norm_num_groups = 32
norm_eps = 1e-5
act_fn = "silu"
conv_out_kernel = 3
conv_out_padding = (conv_out_kernel - 1) // 2
conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
# uses torch.functional in orig
# conv_act = get_activation(act_fn)
conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding)
conv_norm_out.load_state_dict(model.output.gn.state_dict())
conv_out.load_state_dict(model.output.f.state_dict())
print("timestep projection (time_proj) (time_embedding)")
f1_sd = model.embed_time.f_1.state_dict()
f2_sd = model.embed_time.f_2.state_dict()
time_embedding_sd = {
"linear_1.weight": f1_sd.pop("weight"),
"linear_1.bias": f1_sd.pop("bias"),
"linear_2.weight": f2_sd.pop("weight"),
"linear_2.bias": f2_sd.pop("bias"),
}
assert len(f1_sd) == 0
assert len(f2_sd) == 0
time_embedding_type = "learned"
num_train_timesteps = 1024
time_embedding_dim = 1280
time_proj = nn.Embedding(num_train_timesteps, block_out_channels[0])
timestep_input_dim = block_out_channels[0]
time_embedding = TimestepEmbedding(timestep_input_dim, time_embedding_dim)
time_proj.load_state_dict(model.embed_time.emb.state_dict())
time_embedding.load_state_dict(time_embedding_sd)
print("CONVERT")
time_embedding.to("cuda")
time_proj.to("cuda")
conv_in.to("cuda")
block_one.to("cuda")
block_two.to("cuda")
block_three.to("cuda")
block_four.to("cuda")
mid_block_one.to("cuda")
up_block_one.to("cuda")
up_block_two.to("cuda")
up_block_three.to("cuda")
up_block_four.to("cuda")
conv_norm_out.to("cuda")
conv_out.to("cuda")
model.time_proj = time_proj
model.time_embedding = time_embedding
model.embed_image = conv_in
model.down[0] = block_one
model.down[1] = block_two
model.down[2] = block_three