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Commit 7353b74e authored by anton-l's avatar anton-l
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Merge remote-tracking branch 'origin/main'

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# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# 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.
import inspect
import tempfile
import unittest
import numpy as np
import torch
from diffusers import (
AutoencoderKL,
DDIMPipeline,
DDIMScheduler,
DDPMPipeline,
DDPMScheduler,
GlidePipeline,
GlideSuperResUNetModel,
GlideTextToImageUNetModel,
LatentDiffusionPipeline,
LatentDiffusionUncondPipeline,
NCSNpp,
PNDMPipeline,
PNDMScheduler,
ScoreSdeVePipeline,
ScoreSdeVeScheduler,
ScoreSdeVpPipeline,
ScoreSdeVpScheduler,
UNetLDMModel,
UNetModel,
UNetUnconditionalModel,
VQModel,
)
from diffusers.configuration_utils import ConfigMixin
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.testing_utils import floats_tensor, slow, torch_device
from diffusers.training_utils import EMAModel
# 1. LDM
def test_output_pretrained_ldm_dummy():
model = UNetUnconditionalModel.from_pretrained("fusing/unet-ldm-dummy", ldm=True)
model.eval()
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)
print(model)
import ipdb; ipdb.set_trace()
def test_output_pretrained_ldm():
model = UNetUnconditionalModel.from_pretrained("fusing/latent-diffusion-celeba-256", subfolder="unet", ldm=True)
model.eval()
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)
print(model)
import ipdb; ipdb.set_trace()
# To see the how the final model should look like
# => this is the architecture in which the model should be saved in the new format
# -> verify new repo with the following tests (in `test_modeling_utils.py`)
# - test_ldm_uncond (in PipelineTesterMixin)
# - test_output_pretrained ( in UNetLDMModelTests)
#test_output_pretrained_ldm_dummy()
#test_output_pretrained_ldm()
# 2. DDPM
def get_model(model_id):
model = UNetUnconditionalModel.from_pretrained(model_id, ldm=True)
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)
print(model)
# Repos to convert and port to google (part of https://github.com/hojonathanho/diffusion)
# - fusing/ddpm_dummy
# - fusing/ddpm-cifar10
# - https://huggingface.co/fusing/ddpm-lsun-church-ema
# - https://huggingface.co/fusing/ddpm-lsun-bedroom-ema
# - https://huggingface.co/fusing/ddpm-celeba-hq
# tests to make sure to pass
# - test_ddim_cifar10, test_ddim_lsun, test_ddpm_cifar10, test_ddim_cifar10 (in PipelineTesterMixin)
# - test_output_pretrained ( in UNetModelTests)
# e.g.
get_model("fusing/ddpm-cifar10")
# 3. NCSNpp
# Repos to convert and port to google (part of https://github.com/yang-song/score_sde)
# - https://huggingface.co/fusing/ffhq_ncsnpp
# - https://huggingface.co/fusing/church_256-ncsnpp-ve
# - https://huggingface.co/fusing/celebahq_256-ncsnpp-ve
# - https://huggingface.co/fusing/bedroom_256-ncsnpp-ve
# - https://huggingface.co/fusing/ffhq_256-ncsnpp-ve
# tests to make sure to pass
# - test_score_sde_ve_pipeline (in PipelineTesterMixin)
# - test_output_pretrained_ve_mid, test_output_pretrained_ve_large (in NCSNppModelTests)
scripts/convert_ldm_original_checkpoint_to_diffusers.py
View file @ 7353b74e
......@@ -17,6 +17,7 @@
import argparse
import json
import torch
from diffusers import VQModel, DDPMScheduler, UNetUnconditionalModel, LatentDiffusionUncondPipeline
def shave_segments(path, n_shave_prefix_segments=1):
......@@ -314,4 +315,18 @@ if __name__ == "__main__":
config = json.loads(f.read())
converted_checkpoint = convert_ldm_checkpoint(checkpoint, config)
torch.save(checkpoint, args.dump_path)
if "ldm" in config:
del config["ldm"]
model = UNetUnconditionalModel(**config)
model.load_state_dict(converted_checkpoint)
try:
scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
vqvae = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))
pipe = LatentDiffusionUncondPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except:
model.save_pretrained(args.dump_path)
scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py
View file @ 7353b74e
......@@ -20,22 +20,21 @@ import torch
from diffusers import UNetUnconditionalModel
def convert_ncsnpp_checkpoint(checkpoint, config):
"""
Takes a state dict and the path to
"""
new_model_architecture = UNetUnconditionalModel(**config)
new_model_architecture.time_steps.W.data= checkpoint['all_modules.0.W'].data
new_model_architecture.time_steps.weight.data = checkpoint['all_modules.0.W'].data
new_model_architecture.time_embedding.linear_1.weight.data = checkpoint['all_modules.1.weight'].data
new_model_architecture.time_embedding.linear_1.bias.data = checkpoint['all_modules.1.bias'].data
new_model_architecture.time_steps.W.data = checkpoint["all_modules.0.W"].data
new_model_architecture.time_steps.weight.data = checkpoint["all_modules.0.W"].data
new_model_architecture.time_embedding.linear_1.weight.data = checkpoint["all_modules.1.weight"].data
new_model_architecture.time_embedding.linear_1.bias.data = checkpoint["all_modules.1.bias"].data
new_model_architecture.time_embedding.linear_2.weight.data = checkpoint['all_modules.2.weight'].data
new_model_architecture.time_embedding.linear_2.bias.data= checkpoint['all_modules.2.bias'].data
new_model_architecture.time_embedding.linear_2.weight.data = checkpoint["all_modules.2.weight"].data
new_model_architecture.time_embedding.linear_2.bias.data = checkpoint["all_modules.2.bias"].data
new_model_architecture.conv_in.weight.data = checkpoint['all_modules.3.weight'].data
new_model_architecture.conv_in.bias.data = checkpoint['all_modules.3.bias'].data
new_model_architecture.conv_in.weight.data = checkpoint["all_modules.3.weight"].data
new_model_architecture.conv_in.bias.data = checkpoint["all_modules.3.bias"].data
new_model_architecture.conv_norm_out.weight.data = checkpoint[list(checkpoint.keys())[-4]].data
new_model_architecture.conv_norm_out.bias.data = checkpoint[list(checkpoint.keys())[-3]].data
......@@ -44,8 +43,7 @@ def convert_ncsnpp_checkpoint(checkpoint, config):
module_index = 4
def set_attention_weights(new_layer,old_checkpoint,index):
def set_attention_weights(new_layer, old_checkpoint, index):
new_layer.query.weight.data = old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
new_layer.key.weight.data = old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
new_layer.value.weight.data = old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
......@@ -60,7 +58,7 @@ def convert_ncsnpp_checkpoint(checkpoint, config):
new_layer.group_norm.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
new_layer.group_norm.bias.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
def set_resnet_weights(new_layer,old_checkpoint,index):
def set_resnet_weights(new_layer, old_checkpoint, index):
new_layer.conv1.weight.data = old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
new_layer.conv1.bias.data = old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
new_layer.norm1.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
......@@ -81,35 +79,35 @@ def convert_ncsnpp_checkpoint(checkpoint, config):
for i, block in enumerate(new_model_architecture.downsample_blocks):
has_attentions = hasattr(block, "attentions")
for j in range(len(block.resnets)):
set_resnet_weights(block.resnets[j],checkpoint, module_index)
set_resnet_weights(block.resnets[j], checkpoint, module_index)
module_index += 1
if has_attentions:
set_attention_weights(block.attentions[j],checkpoint, module_index)
set_attention_weights(block.attentions[j], checkpoint, module_index)
module_index += 1
if hasattr(block, "downsamplers") and block.downsamplers is not None:
set_resnet_weights(block.resnet_down,checkpoint, module_index)
set_resnet_weights(block.resnet_down, checkpoint, module_index)
module_index += 1
block.skip_conv.weight.data = checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
block.skip_conv.bias.data = checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
module_index += 1
set_resnet_weights(new_model_architecture.mid.resnets[0],checkpoint,module_index)
set_resnet_weights(new_model_architecture.mid.resnets[0], checkpoint, module_index)
module_index += 1
set_attention_weights(new_model_architecture.mid.attentions[0],checkpoint, module_index)
set_attention_weights(new_model_architecture.mid.attentions[0], checkpoint, module_index)
module_index += 1
set_resnet_weights(new_model_architecture.mid.resnets[1],checkpoint,module_index)
set_resnet_weights(new_model_architecture.mid.resnets[1], checkpoint, module_index)
module_index += 1
for i, block in enumerate(new_model_architecture.upsample_blocks):
has_attentions = hasattr(block, "attentions")
for j in range(len(block.resnets)):
set_resnet_weights(block.resnets[j],checkpoint, module_index)
set_resnet_weights(block.resnets[j], checkpoint, module_index)
module_index += 1
if has_attentions:
set_attention_weights(block.attentions[0],checkpoint, module_index) # why can there only be a single attention layer for up?
set_attention_weights(
block.attentions[0], checkpoint, module_index
) # why can there only be a single attention layer for up?
module_index += 1
if hasattr(block, "resnet_up") and block.resnet_up is not None:
......@@ -119,7 +117,7 @@ def convert_ncsnpp_checkpoint(checkpoint, config):
block.skip_conv.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data
block.skip_conv.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data
module_index += 1
set_resnet_weights(block.resnet_up,checkpoint, module_index)
set_resnet_weights(block.resnet_up, checkpoint, module_index)
module_index += 1
new_model_architecture.conv_norm_out.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data
......@@ -130,11 +128,16 @@ def convert_ncsnpp_checkpoint(checkpoint, config):
return new_model_architecture.state_dict()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model.pt", type=str, required=False, help="Path to the checkpoint to convert."
"--checkpoint_path",
default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model.pt",
type=str,
required=False,
help="Path to the checkpoint to convert.",
)
parser.add_argument(
......@@ -146,19 +149,35 @@ if __name__ == "__main__":
)
parser.add_argument(
"--dump_path", default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt", type=str, required=False, help="Path to the output model."
"--dump_path",
default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt",
type=str,
required=False,
help="Path to the output model.",
)
args = parser.parse_args()
checkpoint = torch.load(args.checkpoint_path, map_location="cpu")
with open(args.config_file) as f:
config = json.loads(f.read())
converted_checkpoint = convert_ncsnpp_checkpoint(
checkpoint,
config,
)
if "sde" in config:
del config["sde"]
model = UNetUnconditionalModel(**config)
model.load_state_dict(converted_checkpoint)
try:
scheduler = ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
converted_checkpoint = convert_ncsnpp_checkpoint(checkpoint, config,)
torch.save(converted_checkpoint, args.dump_path)
pipe = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
pipe.save_pretrained(args.dump_path)
except:
model.save_pretrained(args.dump_path)
tests/test_modeling_utils.py
View file @ 7353b74e
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