Skip to content

GitLab

"deploy/vscode:/vscode.git/clone" did not exist on "bdcf80ce79e6e4f5027f19871b55e1c7db6125d7"
Unverified Commit 8c31925b authored by Patrick von Platen's avatar Patrick von Platen Committed by GitHub
Browse files

Get diffusers ready 🚀🚀🚀 (#101) 

* big purge

* more fixes

* finish for now
parent 33344ed9
Expand all Show whitespace changes
Inline Side-by-side
Showing with 65 additions and 4533 deletions
debug_conversion.py deleted 100755 → 0
View file @ 33344ed9
#!/usr/bin/env python3
import json
import os
from regex import P
from diffusers import UNetUnconditionalModel
from scripts.convert_ncsnpp_original_checkpoint_to_diffusers import convert_ncsnpp_checkpoint
from huggingface_hub import hf_hub_download
import torch
def convert_checkpoint(model_id, subfolder=None, checkpoint = "diffusion_model.pt", config = "config.json"):
if subfolder is not None:
checkpoint = os.path.join(subfolder, checkpoint)
config = os.path.join(subfolder, config)
original_checkpoint = torch.load(hf_hub_download(model_id, checkpoint),map_location='cpu')
config_path = hf_hub_download(model_id, config)
with open(config_path) as f:
config = json.load(f)
checkpoint = convert_ncsnpp_checkpoint(original_checkpoint, config)
def current_codebase_conversion(path):
model = UNetUnconditionalModel.from_pretrained(model_id, subfolder=subfolder, sde=True)
model.eval()
model.config.sde=False
model.save_config(path)
model.config.sde=True
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
noise = torch.randn(1, model.config.in_channels, model.config.image_size, model.config.image_size)
time_step = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
output = model(noise, time_step)
return model.state_dict()
path = f"{model_id}_converted"
currently_converted_checkpoint = current_codebase_conversion(path)
def diff_between_checkpoints(ch_0, ch_1):
all_layers_included = False
if not set(ch_0.keys()) == set(ch_1.keys()):
print(f"Contained in ch_0 and not in ch_1 (Total: {len((set(ch_0.keys()) - set(ch_1.keys())))})")
for key in sorted(list((set(ch_0.keys()) - set(ch_1.keys())))):
print(f"\t{key}")
print(f"Contained in ch_1 and not in ch_0 (Total: {len((set(ch_1.keys()) - set(ch_0.keys())))})")
for key in sorted(list((set(ch_1.keys()) - set(ch_0.keys())))):
print(f"\t{key}")
else:
print("Keys are the same between the two checkpoints")
all_layers_included = True
keys = ch_0.keys()
non_equal_keys = []
if all_layers_included:
for key in keys:
try:
if not torch.allclose(ch_0[key].cpu(), ch_1[key].cpu()):
non_equal_keys.append(f'{key}. Diff: {torch.max(torch.abs(ch_0[key].cpu() - ch_1[key].cpu()))}')
except RuntimeError as e:
print(e)
non_equal_keys.append(f'{key}. Diff in shape: {ch_0[key].size()} vs {ch_1[key].size()}')
if len(non_equal_keys):
non_equal_keys = '\n\t'.join(non_equal_keys)
print(f"These keys do not satisfy equivalence requirement:\n\t{non_equal_keys}")
else:
print("All keys are equal across checkpoints.")
diff_between_checkpoints(currently_converted_checkpoint, checkpoint)
os.makedirs( f"{model_id}_converted",exist_ok =True)
torch.save(checkpoint, f"{model_id}_converted/diffusion_model.pt")
model_ids = ["fusing/ffhq_ncsnpp","fusing/church_256-ncsnpp-ve", "fusing/celebahq_256-ncsnpp-ve",
"fusing/bedroom_256-ncsnpp-ve","fusing/ffhq_256-ncsnpp-ve","fusing/ncsnpp-ffhq-ve-dummy"
]
for model in model_ids:
print(f"converting {model}")
try:
convert_checkpoint(model)
except Exception as e:
print(e)
from tests.test_modeling_utils import PipelineTesterMixin, NCSNppModelTests
tester1 = NCSNppModelTests()
tester2 = PipelineTesterMixin()
os.environ["RUN_SLOW"] = '1'
cmd = "export RUN_SLOW=1; echo $RUN_SLOW" # or whatever command
os.system(cmd)
tester2.test_score_sde_ve_pipeline(f"{model_ids[0]}_converted")
tester1.test_output_pretrained_ve_mid(f"{model_ids[2]}_converted")
tester1.test_output_pretrained_ve_large(f"{model_ids[-1]}_converted")
run.py deleted 100755 → 0
View file @ 33344ed9
#!/usr/bin/env python3
import numpy as np
import PIL
import torch
#from configs.ve import ffhq_ncsnpp_continuous as configs
# from configs.ve import cifar10_ncsnpp_continuous as configs
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
torch.backends.cuda.matmul.allow_tf32 = False
torch.manual_seed(0)
class NewReverseDiffusionPredictor:
def __init__(self, score_fn, probability_flow=False, sigma_min=0.0, sigma_max=0.0, N=0):
super().__init__()
self.sigma_min = sigma_min
self.sigma_max = sigma_max
self.N = N
self.discrete_sigmas = torch.exp(torch.linspace(np.log(self.sigma_min), np.log(self.sigma_max), N))
self.probability_flow = probability_flow
self.score_fn = score_fn
def discretize(self, x, t):
timestep = (t * (self.N - 1)).long()
sigma = self.discrete_sigmas.to(t.device)[timestep]
adjacent_sigma = torch.where(timestep == 0, torch.zeros_like(t),
self.discrete_sigmas[timestep - 1].to(t.device))
f = torch.zeros_like(x)
G = torch.sqrt(sigma ** 2 - adjacent_sigma ** 2)
labels = self.sigma_min * (self.sigma_max / self.sigma_min) ** t
result = self.score_fn(x, labels)
rev_f = f - G[:, None, None, None] ** 2 * result * (0.5 if self.probability_flow else 1.)
rev_G = torch.zeros_like(G) if self.probability_flow else G
return rev_f, rev_G
def update_fn(self, x, t):
f, G = self.discretize(x, t)
z = torch.randn_like(x)
x_mean = x - f
x = x_mean + G[:, None, None, None] * z
return x, x_mean
class NewLangevinCorrector:
def __init__(self, score_fn, snr, n_steps, sigma_min=0.0, sigma_max=0.0):
super().__init__()
self.score_fn = score_fn
self.snr = snr
self.n_steps = n_steps
self.sigma_min = sigma_min
self.sigma_max = sigma_max
def update_fn(self, x, t):
score_fn = self.score_fn
n_steps = self.n_steps
target_snr = self.snr
# if isinstance(sde, VPSDE) or isinstance(sde, subVPSDE):
# timestep = (t * (sde.N - 1) / sde.T).long()
# alpha = sde.alphas.to(t.device)[timestep]
# else:
alpha = torch.ones_like(t)
for i in range(n_steps):
labels = self.sigma_min * (self.sigma_max / self.sigma_min) ** t
grad = score_fn(x, labels)
noise = torch.randn_like(x)
grad_norm = torch.norm(grad.reshape(grad.shape[0], -1), dim=-1).mean()
noise_norm = torch.norm(noise.reshape(noise.shape[0], -1), dim=-1).mean()
step_size = (target_snr * noise_norm / grad_norm) ** 2 * 2 * alpha
x_mean = x + step_size[:, None, None, None] * grad
x = x_mean + torch.sqrt(step_size * 2)[:, None, None, None] * noise
return x, x_mean
def save_image(x):
image_processed = np.clip(x.permute(0, 2, 3, 1).cpu().numpy() * 255, 0, 255).astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
image_pil.save("../images/hey.png")
# ckpt_filename = "exp/ve/cifar10_ncsnpp_continuous/checkpoint_24.pth"
#ckpt_filename = "exp/ve/ffhq_1024_ncsnpp_continuous/checkpoint_60.pth"
# Note usually we need to restore ema etc...
# ema restored checkpoint used from below
N = 2
sigma_min = 0.01
sigma_max = 1348
sampling_eps = 1e-5
batch_size = 1
centered = False
from diffusers import NCSNpp
model = NCSNpp.from_pretrained("/home/patrick/ffhq_ncsnpp").to(device)
model = torch.nn.DataParallel(model)
img_size = model.module.config.image_size
channels = model.module.config.num_channels
shape = (batch_size, channels, img_size, img_size)
probability_flow = False
snr = 0.15
n_steps = 1
new_corrector = NewLangevinCorrector(score_fn=model, snr=snr, n_steps=n_steps, sigma_min=sigma_min, sigma_max=sigma_max)
new_predictor = NewReverseDiffusionPredictor(score_fn=model, sigma_min=sigma_min, sigma_max=sigma_max, N=N)
with torch.no_grad():
# Initial sample
x = torch.randn(*shape) * sigma_max
x = x.to(device)
timesteps = torch.linspace(1, sampling_eps, N, device=device)
for i in range(N):
t = timesteps[i]
vec_t = torch.ones(shape[0], device=t.device) * t
x, x_mean = new_corrector.update_fn(x, vec_t)
x, x_mean = new_predictor.update_fn(x, vec_t)
x = x_mean
if centered:
x = (x + 1.) / 2.
# save_image(x)
# for 5 cifar10
x_sum = 106071.9922
x_mean = 34.52864456176758
# for 1000 cifar10
x_sum = 461.9700
x_mean = 0.1504
# for 2 for 1024
x_sum = 3382810112.0
x_mean = 1075.366455078125
def check_x_sum_x_mean(x, x_sum, x_mean):
assert (x.abs().sum() - x_sum).abs().cpu().item() < 1e-2, f"sum wrong {x.abs().sum()}"
assert (x.abs().mean() - x_mean).abs().cpu().item() < 1e-4, f"mean wrong {x.abs().mean()}"
check_x_sum_x_mean(x, x_sum, x_mean)
scripts/conversion_glide.py deleted 100644 → 0
View file @ 33344ed9
import torch
from torch import nn
from diffusers import ClassifierFreeGuidanceScheduler, DDIMScheduler, GlideSuperResUNetModel, GlideTextToImageUNetModel
from diffusers.pipelines.pipeline_glide import Glide, CLIPTextModel
from transformers import CLIPTextConfig, GPT2Tokenizer
# wget https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base.pt
state_dict = torch.load("base.pt", map_location="cpu")
state_dict = {k: nn.Parameter(v) for k, v in state_dict.items()}
### Convert the text encoder
config = CLIPTextConfig(
vocab_size=50257,
max_position_embeddings=128,
hidden_size=512,
intermediate_size=2048,
num_hidden_layers=16,
num_attention_heads=8,
use_padding_embeddings=True,
)
model = CLIPTextModel(config).eval()
tokenizer = GPT2Tokenizer(
"./glide-base/tokenizer/vocab.json", "./glide-base/tokenizer/merges.txt", pad_token="<|endoftext|>"
)
hf_encoder = model.text_model
hf_encoder.embeddings.token_embedding.weight = state_dict["token_embedding.weight"]
hf_encoder.embeddings.position_embedding.weight.data = state_dict["positional_embedding"]
hf_encoder.embeddings.padding_embedding.weight.data = state_dict["padding_embedding"]
hf_encoder.final_layer_norm.weight = state_dict["final_ln.weight"]
hf_encoder.final_layer_norm.bias = state_dict["final_ln.bias"]
for layer_idx in range(config.num_hidden_layers):
hf_layer = hf_encoder.encoder.layers[layer_idx]
hf_layer.self_attn.qkv_proj.weight = state_dict[f"transformer.resblocks.{layer_idx}.attn.c_qkv.weight"]
hf_layer.self_attn.qkv_proj.bias = state_dict[f"transformer.resblocks.{layer_idx}.attn.c_qkv.bias"]
hf_layer.self_attn.out_proj.weight = state_dict[f"transformer.resblocks.{layer_idx}.attn.c_proj.weight"]
hf_layer.self_attn.out_proj.bias = state_dict[f"transformer.resblocks.{layer_idx}.attn.c_proj.bias"]
hf_layer.layer_norm1.weight = state_dict[f"transformer.resblocks.{layer_idx}.ln_1.weight"]
hf_layer.layer_norm1.bias = state_dict[f"transformer.resblocks.{layer_idx}.ln_1.bias"]
hf_layer.layer_norm2.weight = state_dict[f"transformer.resblocks.{layer_idx}.ln_2.weight"]
hf_layer.layer_norm2.bias = state_dict[f"transformer.resblocks.{layer_idx}.ln_2.bias"]
hf_layer.mlp.fc1.weight = state_dict[f"transformer.resblocks.{layer_idx}.mlp.c_fc.weight"]
hf_layer.mlp.fc1.bias = state_dict[f"transformer.resblocks.{layer_idx}.mlp.c_fc.bias"]
hf_layer.mlp.fc2.weight = state_dict[f"transformer.resblocks.{layer_idx}.mlp.c_proj.weight"]
hf_layer.mlp.fc2.bias = state_dict[f"transformer.resblocks.{layer_idx}.mlp.c_proj.bias"]
### Convert the Text-to-Image UNet
text2im_model = GlideTextToImageUNetModel(
in_channels=3,
model_channels=192,
out_channels=6,
num_res_blocks=3,
attention_resolutions=(2, 4, 8),
dropout=0.1,
channel_mult=(1, 2, 3, 4),
num_heads=1,
num_head_channels=64,
num_heads_upsample=1,
use_scale_shift_norm=True,
resblock_updown=True,
transformer_dim=512,
)
text2im_model.load_state_dict(state_dict, strict=False)
text_scheduler = ClassifierFreeGuidanceScheduler(timesteps=1000, beta_schedule="squaredcos_cap_v2")
### Convert the Super-Resolution UNet
# wget https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample.pt
ups_state_dict = torch.load("upsample.pt", map_location="cpu")
superres_model = GlideSuperResUNetModel(
in_channels=6,
model_channels=192,
out_channels=6,
num_res_blocks=2,
attention_resolutions=(8, 16, 32),
dropout=0.1,
channel_mult=(1, 1, 2, 2, 4, 4),
num_heads=1,
num_head_channels=64,
num_heads_upsample=1,
use_scale_shift_norm=True,
resblock_updown=True,
)
superres_model.load_state_dict(ups_state_dict, strict=False)
upscale_scheduler = DDIMScheduler(
timesteps=1000, beta_schedule="linear", beta_start=0.0001, beta_end=0.02, tensor_format="pt"
)
glide = Glide(
text_unet=text2im_model,
text_noise_scheduler=text_scheduler,
text_encoder=model,
tokenizer=tokenizer,
upscale_unet=superres_model,
upscale_noise_scheduler=upscale_scheduler,
)
glide.save_pretrained("./glide-base")
src/diffusers/__init__.py
View file @ 8c31925b
......@@ -7,36 +7,13 @@ from .utils import is_inflect_available, is_transformers_available, is_unidecode
__version__ = "0.0.4"
from .modeling_utils import ModelMixin
from .models import (
AutoencoderKL,
NCSNpp,
UNetConditionalModel,
UNetLDMModel,
UNetModel,
UNetUnconditionalModel,
VQModel,
)
from .models import AutoencoderKL, UNetConditionalModel, UNetUnconditionalModel, VQModel
from .pipeline_utils import DiffusionPipeline
from .pipelines import (
DDIMPipeline,
DDPMPipeline,
LatentDiffusionUncondPipeline,
PNDMPipeline,
ScoreSdeVePipeline,
ScoreSdeVpPipeline,
)
from .schedulers import (
DDIMScheduler,
DDPMScheduler,
PNDMScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
ScoreSdeVpScheduler,
)
from .pipelines import DDIMPipeline, DDPMPipeline, LatentDiffusionUncondPipeline, PNDMPipeline, ScoreSdeVePipeline
from .schedulers import DDIMScheduler, DDPMScheduler, PNDMScheduler, SchedulerMixin, ScoreSdeVeScheduler
if is_transformers_available():
from .models.unet_glide import GlideSuperResUNetModel, GlideTextToImageUNetModel, GlideUNetModel
from .pipelines import GlidePipeline, LatentDiffusionPipeline
from .pipelines import LatentDiffusionPipeline
else:
from .utils.dummy_transformers_objects import *
src/diffusers/models/__init__.py
View file @ 8c31925b
......@@ -16,10 +16,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from .unet import UNetModel
from .unet_conditional import UNetConditionalModel
from .unet_glide import GlideSuperResUNetModel, GlideTextToImageUNetModel, GlideUNetModel
from .unet_ldm import UNetLDMModel
from .unet_sde_score_estimation import NCSNpp
from .unet_unconditional import UNetUnconditionalModel
from .vae import AutoencoderKL, VQModel
src/diffusers/models/embeddings.py
View file @ 8c31925b
......@@ -54,6 +54,43 @@ def get_timestep_embedding(
return emb
class TimestepEmbedding(nn.Module):
def __init__(self, channel, time_embed_dim, act_fn="silu"):
super().__init__()
self.linear_1 = nn.Linear(channel, time_embed_dim)
self.act = None
if act_fn == "silu":
self.act = nn.SiLU()
self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim)
def forward(self, sample):
sample = self.linear_1(sample)
if self.act is not None:
sample = self.act(sample)
sample = self.linear_2(sample)
return sample
class Timesteps(nn.Module):
def __init__(self, num_channels, flip_sin_to_cos, downscale_freq_shift):
super().__init__()
self.num_channels = num_channels
self.flip_sin_to_cos = flip_sin_to_cos
self.downscale_freq_shift = downscale_freq_shift
def forward(self, timesteps):
t_emb = get_timestep_embedding(
timesteps,
self.num_channels,
flip_sin_to_cos=self.flip_sin_to_cos,
downscale_freq_shift=self.downscale_freq_shift,
)
return t_emb
class GaussianFourierProjection(nn.Module):
"""Gaussian Fourier embeddings for noise levels."""
......
src/diffusers/models/unet.py deleted 100644 → 0
View file @ 33344ed9
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# helpers functions
import torch
from torch import nn
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import get_timestep_embedding
from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
from .unet_new import UNetMidBlock2D
def nonlinearity(x):
# swish
return x * torch.sigmoid(x)
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
class UNetModel(ModelMixin, ConfigMixin):
def __init__(
self,
ch=128,
out_ch=3,
ch_mult=(1, 1, 2, 2, 4, 4),
num_res_blocks=2,
attn_resolutions=(16,),
dropout=0.0,
resamp_with_conv=True,
in_channels=3,
resolution=256,
):
super().__init__()
self.register_to_config(
ch=ch,
out_ch=out_ch,
ch_mult=ch_mult,
num_res_blocks=num_res_blocks,
attn_resolutions=attn_resolutions,
dropout=dropout,
resamp_with_conv=resamp_with_conv,
in_channels=in_channels,
resolution=resolution,
)
ch_mult = tuple(ch_mult)
self.ch = ch
self.temb_ch = self.ch * 4
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
# timestep embedding
self.temb = nn.Module()
self.temb.dense = nn.ModuleList(
[
torch.nn.Linear(self.ch, self.temb_ch),
torch.nn.Linear(self.temb_ch, self.temb_ch),
]
)
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
curr_res = resolution
in_ch_mult = (1,) + ch_mult
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch * in_ch_mult[i_level]
block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(
ResnetBlock2D(
in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout
)
)
block_in = block_out
if curr_res in attn_resolutions:
attn.append(AttentionBlock(block_in, overwrite_qkv=True))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions - 1:
down.downsample = Downsample2D(block_in, use_conv=resamp_with_conv, padding=0)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock2D(
in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout
)
self.mid.attn_1 = AttentionBlock(block_in, overwrite_qkv=True)
self.mid.block_2 = ResnetBlock2D(
in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout
)
self.mid_new = UNetMidBlock2D(in_channels=block_in, temb_channels=self.temb_ch, dropout=dropout)
self.mid_new.resnets[0] = self.mid.block_1
self.mid_new.attentions[0] = self.mid.attn_1
self.mid_new.resnets[1] = self.mid.block_2
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch * ch_mult[i_level]
skip_in = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks + 1):
if i_block == self.num_res_blocks:
skip_in = ch * in_ch_mult[i_level]
block.append(
ResnetBlock2D(
in_channels=block_in + skip_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout,
)
)
block_in = block_out
if curr_res in attn_resolutions:
attn.append(AttentionBlock(block_in, overwrite_qkv=True))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample2D(block_in, use_conv=resamp_with_conv)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
def forward(self, sample, timesteps):
x = sample
assert x.shape[2] == x.shape[3] == self.resolution
if not torch.is_tensor(timesteps):
timesteps = torch.tensor([timesteps], dtype=torch.long, device=x.device)
# timestep embedding
temb = get_timestep_embedding(timesteps, self.ch)
temb = self.temb.dense[0](temb)
temb = nonlinearity(temb)
temb = self.temb.dense[1](temb)
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level != self.num_resolutions - 1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = self.mid_new(hs[-1], temb)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks + 1):
h = self.up[i_level].block[i_block](torch.cat([h, hs.pop()], dim=1), temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
src/diffusers/models/unet_conditional.py
View file @ 8c31925b
import functools
import math
from typing import Dict, Union
import numpy as np
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import AttentionBlock, SpatialTransformer
from .embeddings import GaussianFourierProjection, get_timestep_embedding
from .resnet import Downsample2D, FirDownsample2D, FirUpsample2D, ResnetBlock2D, Upsample2D
from .unet_new import UNetMidBlock2DCrossAttn, get_down_block, get_up_block
class Combine(nn.Module):
"""Combine information from skip connections."""
def __init__(self, dim1, dim2, method="cat"):
super().__init__()
# 1x1 convolution with DDPM initialization.
self.Conv_0 = nn.Conv2d(dim1, dim2, kernel_size=1, padding=0)
self.method = method
# def forward(self, x, y):
# h = self.Conv_0(x)
# if self.method == "cat":
# return torch.cat([h, y], dim=1)
# elif self.method == "sum":
# return h + y
# else:
# raise ValueError(f"Method {self.method} not recognized.")
class TimestepEmbedding(nn.Module):
def __init__(self, channel, time_embed_dim, act_fn="silu"):
super().__init__()
self.linear_1 = nn.Linear(channel, time_embed_dim)
self.act = None
if act_fn == "silu":
self.act = nn.SiLU()
self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim)
def forward(self, sample):
sample = self.linear_1(sample)
if self.act is not None:
sample = self.act(sample)
sample = self.linear_2(sample)
return sample
class Timesteps(nn.Module):
def __init__(self, num_channels, flip_sin_to_cos, downscale_freq_shift):
super().__init__()
self.num_channels = num_channels
self.flip_sin_to_cos = flip_sin_to_cos
self.downscale_freq_shift = downscale_freq_shift
def forward(self, timesteps):
t_emb = get_timestep_embedding(
timesteps,
self.num_channels,
flip_sin_to_cos=self.flip_sin_to_cos,
downscale_freq_shift=self.downscale_freq_shift,
)
return t_emb
from .embeddings import TimestepEmbedding, Timesteps
from .unet_blocks import UNetMidBlock2DCrossAttn, get_down_block, get_up_block
class UNetConditionalModel(ModelMixin, ConfigMixin):
......@@ -124,38 +62,7 @@ class UNetConditionalModel(ModelMixin, ConfigMixin):
downscale_freq_shift=0,
mid_block_scale_factor=1,
center_input_sample=False,
# TODO(PVP) - to delete later at release
# IMPORTANT: NOT RELEVANT WHEN REVIEWING API
# ======================================
# LDM
attention_resolutions=(4, 2, 1),
# DDPM
out_ch=None,
resolution=None,
attn_resolutions=None,
resamp_with_conv=None,
ch_mult=None,
ch=None,
ddpm=False,
# SDE
sde=False,
nf=None,
fir=None,
progressive=None,
progressive_combine=None,
scale_by_sigma=None,
skip_rescale=None,
num_channels=None,
centered=False,
conditional=True,
conv_size=3,
fir_kernel=(1, 3, 3, 1),
fourier_scale=16,
init_scale=0.0,
progressive_input="input_skip",
resnet_num_groups=32,
continuous=True,
ldm=False,
resnet_num_groups=30,
):
super().__init__()
# register all __init__ params to be accessible via `self.config.<...>`
......@@ -175,21 +82,13 @@ class UNetConditionalModel(ModelMixin, ConfigMixin):
num_head_channels=num_head_channels,
flip_sin_to_cos=flip_sin_to_cos,
downscale_freq_shift=downscale_freq_shift,
attention_resolutions=attention_resolutions,
attn_resolutions=attn_resolutions,
mid_block_scale_factor=mid_block_scale_factor,
resnet_num_groups=resnet_num_groups,
center_input_sample=center_input_sample,
)
self.ldm = ldm
# TODO(PVP) - to delete later at release
# IMPORTANT: NOT RELEVANT WHEN REVIEWING API
# ======================================
self.image_size = image_size
time_embed_dim = block_channels[0] * 4
# ======================================
# input
self.conv_in = nn.Conv2d(in_channels, block_channels[0], kernel_size=3, padding=(1, 1))
......@@ -264,57 +163,18 @@ class UNetConditionalModel(ModelMixin, ConfigMixin):
prev_output_channel = output_channel
# out
num_groups_out = resnet_num_groups if resnet_num_groups is not None else min(block_channels[0] // 4, 32)
self.conv_norm_out = nn.GroupNorm(num_channels=block_channels[0], num_groups=num_groups_out, eps=resnet_eps)
self.conv_norm_out = nn.GroupNorm(num_channels=block_channels[0], num_groups=resnet_num_groups, eps=resnet_eps)
self.conv_act = nn.SiLU()
self.conv_out = nn.Conv2d(block_channels[0], out_channels, 3, padding=1)
# ======================== Out ====================
# =========== TO DELETE AFTER CONVERSION ==========
# TODO(PVP) - to delete later at release
# IMPORTANT: NOT RELEVANT WHEN REVIEWING API
# ======================================
self.is_overwritten = False
if ldm:
num_heads = 8
num_head_channels = -1
transformer_depth = 1
use_spatial_transformer = True
context_dim = 1280
legacy = False
model_channels = block_channels[0]
channel_mult = tuple([x // model_channels for x in block_channels])
self.init_for_ldm(
in_channels,
model_channels,
channel_mult,
num_res_blocks,
dropout,
time_embed_dim,
attention_resolutions,
num_head_channels,
num_heads,
legacy,
False,
transformer_depth,
context_dim,
conv_resample,
out_channels,
)
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
) -> Dict[str, torch.FloatTensor]:
# TODO(PVP) - to delete later at release
# IMPORTANT: NOT RELEVANT WHEN REVIEWING API
# ======================================
if not self.is_overwritten:
self.set_weights()
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
......@@ -329,7 +189,6 @@ class UNetConditionalModel(ModelMixin, ConfigMixin):
emb = self.time_embedding(t_emb)
# 2. pre-process
skip_sample = sample
sample = self.conv_in(sample)
# 3. down
......@@ -349,7 +208,6 @@ class UNetConditionalModel(ModelMixin, ConfigMixin):
sample = self.mid(sample, emb, encoder_hidden_states=encoder_hidden_states)
# 5. up
skip_sample = None
for upsample_block in self.upsample_blocks:
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
......@@ -374,259 +232,3 @@ class UNetConditionalModel(ModelMixin, ConfigMixin):
output = {"sample": sample}
return output
# !!!IMPORTANT - ALL OF THE FOLLOWING CODE WILL BE DELETED AT RELEASE TIME AND SHOULD NOT BE TAKEN INTO CONSIDERATION WHEN EVALUATING THE API ###
# =================================================================================================================================================
def set_weights(self):
self.is_overwritten = True
if self.ldm:
self.time_embedding.linear_1.weight.data = self.time_embed[0].weight.data
self.time_embedding.linear_1.bias.data = self.time_embed[0].bias.data
self.time_embedding.linear_2.weight.data = self.time_embed[2].weight.data
self.time_embedding.linear_2.bias.data = self.time_embed[2].bias.data
self.conv_in.weight.data = self.input_blocks[0][0].weight.data
self.conv_in.bias.data = self.input_blocks[0][0].bias.data
# ================ SET WEIGHTS OF ALL WEIGHTS ==================
for i, input_layer in enumerate(self.input_blocks[1:]):
block_id = i // (self.config.num_res_blocks + 1)
layer_in_block_id = i % (self.config.num_res_blocks + 1)
if layer_in_block_id == 2:
self.downsample_blocks[block_id].downsamplers[0].conv.weight.data = input_layer[0].op.weight.data
self.downsample_blocks[block_id].downsamplers[0].conv.bias.data = input_layer[0].op.bias.data
elif len(input_layer) > 1:
self.downsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
self.downsample_blocks[block_id].attentions[layer_in_block_id].set_weight(input_layer[1])
else:
self.downsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
self.mid.resnets[0].set_weight(self.middle_block[0])
self.mid.resnets[1].set_weight(self.middle_block[2])
self.mid.attentions[0].set_weight(self.middle_block[1])
for i, input_layer in enumerate(self.output_blocks):
block_id = i // (self.config.num_res_blocks + 1)
layer_in_block_id = i % (self.config.num_res_blocks + 1)
if len(input_layer) > 2:
self.upsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
self.upsample_blocks[block_id].attentions[layer_in_block_id].set_weight(input_layer[1])
self.upsample_blocks[block_id].upsamplers[0].conv.weight.data = input_layer[2].conv.weight.data
self.upsample_blocks[block_id].upsamplers[0].conv.bias.data = input_layer[2].conv.bias.data
elif len(input_layer) > 1 and "Upsample2D" in input_layer[1].__class__.__name__:
self.upsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
self.upsample_blocks[block_id].upsamplers[0].conv.weight.data = input_layer[1].conv.weight.data
self.upsample_blocks[block_id].upsamplers[0].conv.bias.data = input_layer[1].conv.bias.data
elif len(input_layer) > 1:
self.upsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
self.upsample_blocks[block_id].attentions[layer_in_block_id].set_weight(input_layer[1])
else:
self.upsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
self.conv_norm_out.weight.data = self.out[0].weight.data
self.conv_norm_out.bias.data = self.out[0].bias.data
self.conv_out.weight.data = self.out[2].weight.data
self.conv_out.bias.data = self.out[2].bias.data
self.remove_ldm()
def init_for_ldm(
self,
in_channels,
model_channels,
channel_mult,
num_res_blocks,
dropout,
time_embed_dim,
attention_resolutions,
num_head_channels,
num_heads,
legacy,
use_spatial_transformer,
transformer_depth,
context_dim,
conv_resample,
out_channels,
):
# TODO(PVP) - delete after weight conversion
class TimestepEmbedSequential(nn.Sequential):
"""
A sequential module that passes timestep embeddings to the children that support it as an extra input.
"""
pass
# TODO(PVP) - delete after weight conversion
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
self.time_embed = nn.Sequential(
nn.Linear(model_channels, time_embed_dim),
nn.SiLU(),
nn.Linear(time_embed_dim, time_embed_dim),
)
dims = 2
self.input_blocks = nn.ModuleList(
[TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResnetBlock2D(
in_channels=ch,
out_channels=mult * model_channels,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
overwrite_for_ldm=True,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
# num_heads = 1
dim_head = num_head_channels
layers.append(
SpatialTransformer(
ch,
num_heads,
dim_head,
depth=transformer_depth,
context_dim=context_dim,
),
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
Downsample2D(ch, use_conv=conv_resample, out_channels=out_ch, padding=1, name="op")
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
# num_heads = 1
dim_head = num_head_channels
if dim_head < 0:
dim_head = None
# TODO(Patrick) - delete after weight conversion
# init to be able to overwrite `self.mid`
self.middle_block = TimestepEmbedSequential(
ResnetBlock2D(
in_channels=ch,
out_channels=None,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
overwrite_for_ldm=True,
),
SpatialTransformer(
ch,
num_heads,
dim_head,
depth=transformer_depth,
context_dim=context_dim,
),
ResnetBlock2D(
in_channels=ch,
out_channels=None,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
overwrite_for_ldm=True,
),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, mult in list(enumerate(channel_mult))[::-1]:
for i in range(num_res_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResnetBlock2D(
in_channels=ch + ich,
out_channels=model_channels * mult,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
overwrite_for_ldm=True,
),
]
ch = model_channels * mult
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
# num_heads = 1
dim_head = num_head_channels
layers.append(
SpatialTransformer(
ch,
num_heads,
dim_head,
depth=transformer_depth,
context_dim=context_dim,
)
)
if level and i == num_res_blocks:
out_ch = ch
layers.append(Upsample2D(ch, use_conv=conv_resample, out_channels=out_ch))
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
nn.GroupNorm(num_channels=model_channels, num_groups=32, eps=1e-5),
nn.SiLU(),
nn.Conv2d(model_channels, out_channels, 3, padding=1),
)
def remove_ldm(self):
del self.time_embed
del self.input_blocks
del self.middle_block
del self.output_blocks
del self.out
src/diffusers/models/unet_glide.py deleted 100644 → 0
View file @ 33344ed9
This diff is collapsed.
src/diffusers/models/unet_ldm.py deleted 100644 → 0
View file @ 33344ed9
This diff is collapsed.
src/diffusers/models/unet_sde_score_estimation.py deleted 100644 → 0
View file @ 33344ed9
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# helpers functions
import functools
import math
import numpy as np
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import GaussianFourierProjection, get_timestep_embedding
from .resnet import Downsample2D, FirDownsample2D, FirUpsample2D, ResnetBlock2D, Upsample2D
from .unet_new import UNetMidBlock2D
class Combine(nn.Module):
"""Combine information from skip connections."""
def __init__(self, dim1, dim2, method="cat"):
super().__init__()
# 1x1 convolution with DDPM initialization.
self.Conv_0 = nn.Conv2d(dim1, dim2, kernel_size=1, padding=0)
self.method = method
def forward(self, x, y):
h = self.Conv_0(x)
if self.method == "cat":
return torch.cat([h, y], dim=1)
elif self.method == "sum":
return h + y
else:
raise ValueError(f"Method {self.method} not recognized.")
class NCSNpp(ModelMixin, ConfigMixin):
"""NCSN++ model"""
def __init__(
self,
image_size=1024,
num_channels=3,
centered=False,
attn_resolutions=(16,),
ch_mult=(1, 2, 4, 8, 16, 32, 32, 32),
conditional=True,
conv_size=3,
dropout=0.0,
embedding_type="fourier",
fir=True,
fir_kernel=(1, 3, 3, 1),
fourier_scale=16,
init_scale=0.0,
nf=16,
num_res_blocks=1,
progressive="output_skip",
progressive_combine="sum",
progressive_input="input_skip",
resamp_with_conv=True,
scale_by_sigma=True,
skip_rescale=True,
continuous=True,
):
super().__init__()
self.register_to_config(
image_size=image_size,
num_channels=num_channels,
centered=centered,
attn_resolutions=attn_resolutions,
ch_mult=ch_mult,
conditional=conditional,
conv_size=conv_size,
dropout=dropout,
embedding_type=embedding_type,
fir=fir,
fir_kernel=fir_kernel,
fourier_scale=fourier_scale,
init_scale=init_scale,
nf=nf,
num_res_blocks=num_res_blocks,
progressive=progressive,
progressive_combine=progressive_combine,
progressive_input=progressive_input,
resamp_with_conv=resamp_with_conv,
scale_by_sigma=scale_by_sigma,
skip_rescale=skip_rescale,
continuous=continuous,
)
self.act = nn.SiLU()
self.nf = nf
self.num_res_blocks = num_res_blocks
self.attn_resolutions = attn_resolutions
self.num_resolutions = len(ch_mult)
self.all_resolutions = all_resolutions = [image_size // (2**i) for i in range(self.num_resolutions)]
self.conditional = conditional
self.skip_rescale = skip_rescale
self.progressive = progressive
self.progressive_input = progressive_input
self.embedding_type = embedding_type
assert progressive in ["none", "output_skip", "residual"]
assert progressive_input in ["none", "input_skip", "residual"]
assert embedding_type in ["fourier", "positional"]
combine_method = progressive_combine.lower()
combiner = functools.partial(Combine, method=combine_method)
modules = []
# timestep/noise_level embedding; only for continuous training
if embedding_type == "fourier":
# Gaussian Fourier features embeddings.
modules.append(GaussianFourierProjection(embedding_size=nf, scale=fourier_scale))
embed_dim = 2 * nf
elif embedding_type == "positional":
embed_dim = nf
else:
raise ValueError(f"embedding type {embedding_type} unknown.")
modules.append(nn.Linear(embed_dim, nf * 4))
modules.append(nn.Linear(nf * 4, nf * 4))
AttnBlock = functools.partial(AttentionBlock, overwrite_linear=True, rescale_output_factor=math.sqrt(2.0))
if self.fir:
Up_sample = functools.partial(FirUpsample2D, fir_kernel=fir_kernel, use_conv=resamp_with_conv)
else:
Up_sample = functools.partial(Upsample2D, name="Conv2d_0")
if progressive == "output_skip":
self.pyramid_upsample = Up_sample(channels=None, use_conv=False)
elif progressive == "residual":
pyramid_upsample = functools.partial(Up_sample, use_conv=True)
if self.fir:
Down_sample = functools.partial(FirDownsample2D, fir_kernel=fir_kernel, use_conv=resamp_with_conv)
else:
Down_sample = functools.partial(Downsample2D, padding=0, name="Conv2d_0")
if progressive_input == "input_skip":
self.pyramid_downsample = Down_sample(channels=None, use_conv=False)
elif progressive_input == "residual":
pyramid_downsample = functools.partial(Down_sample, use_conv=True)
channels = num_channels
if progressive_input != "none":
input_pyramid_ch = channels
modules.append(nn.Conv2d(channels, nf, kernel_size=3, padding=1))
hs_c = [nf]
in_ch = nf
for i_level in range(self.num_resolutions):
# Residual blocks for this resolution
for i_block in range(num_res_blocks):
out_ch = nf * ch_mult[i_level]
modules.append(
ResnetBlock2D(
in_channels=in_ch,
out_channels=out_ch,
temb_channels=4 * nf,
output_scale_factor=np.sqrt(2.0),
non_linearity="silu",
groups=min(in_ch // 4, 32),
groups_out=min(out_ch // 4, 32),
overwrite_for_score_vde=True,
)
)
in_ch = out_ch
if all_resolutions[i_level] in attn_resolutions:
modules.append(AttnBlock(channels=in_ch))
hs_c.append(in_ch)
if i_level != self.num_resolutions - 1:
modules.append(
ResnetBlock2D(
in_channels=in_ch,
temb_channels=4 * nf,
output_scale_factor=np.sqrt(2.0),
non_linearity="silu",
groups=min(in_ch // 4, 32),
groups_out=min(out_ch // 4, 32),
overwrite_for_score_vde=True,
down=True,
kernel="fir" if self.fir else "sde_vp",
use_nin_shortcut=True,
)
)
if progressive_input == "input_skip":
modules.append(combiner(dim1=input_pyramid_ch, dim2=in_ch))
if combine_method == "cat":
in_ch *= 2
elif progressive_input == "residual":
modules.append(pyramid_downsample(channels=input_pyramid_ch, out_channels=in_ch))
input_pyramid_ch = in_ch
hs_c.append(in_ch)
# mid
self.mid = UNetMidBlock2D(
in_channels=in_ch,
temb_channels=4 * nf,
output_scale_factor=math.sqrt(2.0),
resnet_act_fn="silu",
resnet_groups=min(in_ch // 4, 32),
dropout=dropout,
)
in_ch = hs_c[-1]
modules.append(
ResnetBlock2D(
in_channels=in_ch,
temb_channels=4 * nf,
output_scale_factor=np.sqrt(2.0),
non_linearity="silu",
groups=min(in_ch // 4, 32),
groups_out=min(out_ch // 4, 32),
overwrite_for_score_vde=True,
)
)
modules.append(AttnBlock(channels=in_ch))
modules.append(
ResnetBlock2D(
in_channels=in_ch,
temb_channels=4 * nf,
output_scale_factor=np.sqrt(2.0),
non_linearity="silu",
groups=min(in_ch // 4, 32),
groups_out=min(out_ch // 4, 32),
overwrite_for_score_vde=True,
)
)
# self.mid.resnets[0] = modules[len(modules) - 3]
# self.mid.attentions[0] = modules[len(modules) - 2]
# self.mid.resnets[1] = modules[len(modules) - 1]
pyramid_ch = 0
# Upsampling block
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(num_res_blocks + 1):
out_ch = nf * ch_mult[i_level]
in_ch = in_ch + hs_c.pop()
modules.append(
ResnetBlock2D(
in_channels=in_ch,
out_channels=out_ch,
temb_channels=4 * nf,
output_scale_factor=np.sqrt(2.0),
non_linearity="silu",
groups=min(in_ch // 4, 32),
groups_out=min(out_ch // 4, 32),
overwrite_for_score_vde=True,
)
)
in_ch = out_ch
if all_resolutions[i_level] in attn_resolutions:
modules.append(AttnBlock(channels=in_ch))
if progressive != "none":
if i_level == self.num_resolutions - 1:
if progressive == "output_skip":
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6))
modules.append(nn.Conv2d(in_ch, channels, kernel_size=3, padding=1))
pyramid_ch = channels
# elif progressive == "residual":
# modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6))
# modules.append(nn.Conv2d(in_ch, in_ch, bias=True, kernel_size=3, padding=1))
# pyramid_ch = in_ch
# else:
# raise ValueError(f"{progressive} is not a valid name.")
else:
if progressive == "output_skip":
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6))
modules.append(nn.Conv2d(in_ch, channels, bias=True, kernel_size=3, padding=1))
pyramid_ch = channels
# elif progressive == "residual":
# modules.append(pyramid_upsample(channels=pyramid_ch, out_channels=in_ch))
# pyramid_ch = in_ch
# else:
# raise ValueError(f"{progressive} is not a valid name")
if i_level != 0:
modules.append(
ResnetBlock2D(
in_channels=in_ch,
temb_channels=4 * nf,
output_scale_factor=np.sqrt(2.0),
non_linearity="silu",
groups=min(in_ch // 4, 32),
groups_out=min(out_ch // 4, 32),
overwrite_for_score_vde=True,
up=True,
kernel="fir" if self.fir else "sde_vp",
use_nin_shortcut=True,
)
)
assert not hs_c
if progressive != "output_skip":
modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6))
modules.append(nn.Conv2d(in_ch, channels, kernel_size=3, padding=1))
self.all_modules = nn.ModuleList(modules)
def forward(self, sample, timestep, sigmas=None):
timesteps = timestep
x = sample
# timestep/noise_level embedding; only for continuous training
modules = self.all_modules
m_idx = 0
if self.embedding_type == "fourier":
# Gaussian Fourier features embeddings.
used_sigmas = timesteps
temb = modules[m_idx](used_sigmas)
m_idx += 1
elif self.embedding_type == "positional":
# Sinusoidal positional embeddings.
timesteps = timesteps
used_sigmas = sigmas
temb = get_timestep_embedding(timesteps, self.nf)
else:
raise ValueError(f"embedding type {self.embedding_type} unknown.")
if self.conditional:
temb = modules[m_idx](temb)
m_idx += 1
temb = modules[m_idx](self.act(temb))
m_idx += 1
else:
temb = None
# If input data is in [0, 1]
if not self.config.centered:
x = 2 * x - 1.0
# Downsampling block
input_pyramid = None
if self.progressive_input != "none":
input_pyramid = x
hs = [modules[m_idx](x)]
m_idx += 1
for i_level in range(self.num_resolutions):
# Residual blocks for this resolution
for i_block in range(self.num_res_blocks):
h = modules[m_idx](hs[-1], temb)
m_idx += 1
if h.shape[-1] in self.attn_resolutions:
h = modules[m_idx](h)
m_idx += 1
hs.append(h)
if i_level != self.num_resolutions - 1:
h = modules[m_idx](hs[-1], temb)
m_idx += 1
if self.progressive_input == "input_skip":
input_pyramid = self.pyramid_downsample(input_pyramid)
h = modules[m_idx](input_pyramid, h)
m_idx += 1
elif self.progressive_input == "residual":
input_pyramid = modules[m_idx](input_pyramid)
m_idx += 1
if self.skip_rescale:
input_pyramid = (input_pyramid + h) / np.sqrt(2.0)
else:
input_pyramid = input_pyramid + h
h = input_pyramid
hs.append(h)
h = hs[-1]
h = modules[m_idx](h, temb)
m_idx += 1
h = modules[m_idx](h)
m_idx += 1
h = modules[m_idx](h, temb)
m_idx += 1
pyramid = None
# Upsampling block
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks + 1):
h = modules[m_idx](torch.cat([h, hs.pop()], dim=1), temb)
m_idx += 1
if h.shape[-1] in self.attn_resolutions:
h = modules[m_idx](h)
m_idx += 1
if self.progressive != "none":
if i_level == self.num_resolutions - 1:
if self.progressive == "output_skip":
pyramid = self.act(modules[m_idx](h))
m_idx += 1
pyramid = modules[m_idx](pyramid)
m_idx += 1
# elif self.progressive == "residual":
# pyramid = self.act(modules[m_idx](h))
# m_idx += 1
# pyramid = modules[m_idx](pyramid)
# m_idx += 1
# else:
# raise ValueError(f"{self.progressive} is not a valid name.")
else:
if self.progressive == "output_skip":
pyramid_h = self.act(modules[m_idx](h))
m_idx += 1
pyramid_h = modules[m_idx](pyramid_h)
m_idx += 1
skip_sample = self.pyramid_upsample(pyramid)
pyramid = skip_sample + pyramid_h
# elif self.progressive == "residual":
# pyramid = modules[m_idx](pyramid)
# m_idx += 1
# if self.skip_rescale:
# pyramid = (pyramid + h) / np.sqrt(2.0)
# else:
# pyramid = pyramid + h
# h = pyramid
# else:
# raise ValueError(f"{self.progressive} is not a valid name")
if i_level != 0:
h = modules[m_idx](h, temb)
m_idx += 1
assert not hs
if self.progressive == "output_skip":
h = pyramid
else:
h = self.act(modules[m_idx](h))
m_idx += 1
h = modules[m_idx](h)
m_idx += 1
assert m_idx == len(modules)
if self.config.scale_by_sigma:
used_sigmas = used_sigmas.reshape((x.shape[0], *([1] * len(x.shape[1:]))))
h = h / used_sigmas
return h
src/diffusers/models/unet_unconditional.py
View file @ 8c31925b
This diff is collapsed.
src/diffusers/pipelines/__init__.py
View file @ 8c31925b
......@@ -4,9 +4,7 @@ from .ddpm import DDPMPipeline
from .latent_diffusion_uncond import LatentDiffusionUncondPipeline
from .pndm import PNDMPipeline
from .score_sde_ve import ScoreSdeVePipeline
from .score_sde_vp import ScoreSdeVpPipeline
if is_transformers_available():
from .glide import GlidePipeline
from .latent_diffusion import LatentDiffusionPipeline
src/diffusers/pipelines/glide/__init__.py deleted 100644 → 0
View file @ 33344ed9
from ...utils import is_transformers_available
if is_transformers_available():
from .pipeline_glide import CLIPTextModel, GlidePipeline
src/diffusers/pipelines/glide/pipeline_glide.py deleted 100644 → 0
View file @ 33344ed9
This diff is collapsed.
src/diffusers/pipelines/score_sde_vp/__init__.py deleted 100644 → 0
View file @ 33344ed9
from .pipeline_score_sde_vp import ScoreSdeVpPipeline
src/diffusers/pipelines/score_sde_vp/pipeline_score_sde_vp.py deleted 100644 → 0
View file @ 33344ed9
#!/usr/bin/env python3
import torch
from diffusers import DiffusionPipeline
# TODO(Patrick, Anton, Suraj) - rename `x` to better variable names
class ScoreSdeVpPipeline(DiffusionPipeline):
def __init__(self, model, scheduler):
super().__init__()
self.register_modules(model=model, scheduler=scheduler)
def __call__(self, num_inference_steps=1000, generator=None):
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
img_size = self.model.config.image_size
channels = self.model.config.num_channels
shape = (1, channels, img_size, img_size)
model = self.model.to(device)
x = torch.randn(*shape).to(device)
self.scheduler.set_timesteps(num_inference_steps)
for t in self.scheduler.timesteps:
t = t * torch.ones(shape[0], device=device)
scaled_t = t * (num_inference_steps - 1)
# TODO add corrector
with torch.no_grad():
result = model(x, scaled_t)
x, x_mean = self.scheduler.step_pred(result, x, t)
x_mean = (x_mean + 1.0) / 2.0
return x_mean
tests/test_modeling_utils.py
View file @ 8c31925b
......@@ -255,8 +255,6 @@ class UnetModelTests(ModelTesterMixin, unittest.TestCase):
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"ch": 32,
"ch_mult": (1, 2),
"block_channels": (32, 64),
"down_blocks": ("UNetResDownBlock2D", "UNetResAttnDownBlock2D"),
"up_blocks": ("UNetResAttnUpBlock2D", "UNetResUpBlock2D"),
......@@ -264,8 +262,6 @@ class UnetModelTests(ModelTesterMixin, unittest.TestCase):
"out_channels": 3,
"in_channels": 3,
"num_res_blocks": 2,
"attn_resolutions": (16,),
"resolution": 32,
"image_size": 32,
}
inputs_dict = self.dummy_input
......@@ -322,13 +318,11 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
"in_channels": 4,
"out_channels": 4,
"num_res_blocks": 2,
"attention_resolutions": (16,),
"block_channels": (32, 64),
"num_head_channels": 32,
"conv_resample": True,
"down_blocks": ("UNetResDownBlock2D", "UNetResDownBlock2D"),
"up_blocks": ("UNetResUpBlock2D", "UNetResUpBlock2D"),
"ldm": True,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
......@@ -529,8 +523,8 @@ class VQModelTests(ModelTesterMixin, unittest.TestCase):
"ch": 64,
"out_ch": 3,
"num_res_blocks": 1,
"attn_resolutions": [],
"in_channels": 3,
"attn_resolutions": [],
"resolution": 32,
"z_channels": 3,
"n_embed": 256,
......@@ -605,11 +599,11 @@ class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
"ch_mult": (1,),
"embed_dim": 4,
"in_channels": 3,
"attn_resolutions": [],
"num_res_blocks": 1,
"out_ch": 3,
"resolution": 32,
"z_channels": 4,
"attn_resolutions": [],
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
......@@ -655,7 +649,6 @@ class PipelineTesterMixin(unittest.TestCase):
model = UNetUnconditionalModel(
block_channels=(32, 64),
num_res_blocks=2,
attn_resolutions=(16,),
image_size=32,
in_channels=3,
out_channels=3,
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment