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Merge remote-tracking branch 'origin/main'

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anton-l 2022-07-19 17:12:48 +02:00
parent 44bb38fd8b 2ea64a08ed
commit 7353b74ec2
4 changed files with 134 additions and 523 deletions
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138
conversion.py
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@ -1,138 +0,0 @@
# 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)

17
scripts/convert_ldm_original_checkpoint_to_diffusers.py
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@ -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)

81
scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py
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@ -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 = 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_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,
)
converted_checkpoint = convert_ncsnpp_checkpoint(checkpoint, config,)
torch.save(converted_checkpoint, args.dump_path)
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]))
pipe = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
pipe.save_pretrained(args.dump_path)
except:
model.save_pretrained(args.dump_path)

407
tests/test_modeling_utils.py
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@ -22,26 +22,19 @@ import unittest
import numpy as np
import torch
from diffusers import UNetConditionalModel # TODO(Patrick) - need to write tests with it
from diffusers import UNetConditionalModel # noqa: F401 TODO(Patrick) - need to write tests with it
from diffusers import (
AutoencoderKL,
DDIMPipeline,
DDIMScheduler,
DDPMPipeline,
DDPMScheduler,
GlidePipeline,
GlideSuperResUNetModel,
GlideTextToImageUNetModel,
LatentDiffusionPipeline,
LatentDiffusionUncondPipeline,
NCSNpp,
PNDMPipeline,
PNDMScheduler,
ScoreSdeVePipeline,
ScoreSdeVeScheduler,
ScoreSdeVpPipeline,
ScoreSdeVpScheduler,
UNetLDMModel,
UNetUnconditionalModel,
VQModel,
)
@ -278,222 +271,27 @@ class UnetModelTests(ModelTesterMixin, unittest.TestCase):
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = UNetUnconditionalModel.from_pretrained(
"fusing/ddpm_dummy", output_loading_info=True, ddpm=True
)
self.assertIsNotNone(model)
# self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)["sample"]
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = UNetUnconditionalModel.from_pretrained("fusing/ddpm_dummy", ddpm=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])
with torch.no_grad():
output = model(noise, time_step)["sample"]
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase):
model_class = GlideSuperResUNetModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 6
sizes = (32, 32)
low_res_size = (4, 4)
noise = torch.randn((batch_size, num_channels // 2) + sizes).to(torch_device)
low_res = torch.randn((batch_size, 3) + low_res_size).to(torch_device)
time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
return {"sample": noise, "timestep": time_step, "low_res": low_res}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (6, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"attention_resolutions": (2,),
"channel_mult": (1, 2),
"in_channels": 6,
"out_channels": 6,
"model_channels": 32,
"num_head_channels": 8,
"num_heads_upsample": 1,
"num_res_blocks": 2,
"resblock_updown": True,
"resolution": 32,
"use_scale_shift_norm": True,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_output(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
output, _ = torch.split(output, 3, dim=1)
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
def test_from_pretrained_hub(self):
model, loading_info = GlideSuperResUNetModel.from_pretrained(
"fusing/glide-super-res-dummy", output_loading_info=True
)
self.assertIsNotNone(model)
# self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = GlideSuperResUNetModel.from_pretrained("fusing/glide-super-res-dummy")
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
noise = torch.randn(1, 3, 64, 64)
low_res = torch.randn(1, 3, 4, 4)
time_step = torch.tensor([42] * noise.shape[0])
with torch.no_grad():
output = model(noise, time_step, low_res)
output, _ = torch.split(output, 3, dim=1)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-22.8782, -23.2652, -15.3966, -22.8034, -23.3159, -15.5640, -15.3970, -15.4614, - 10.4370])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
class GlideTextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
model_class = GlideTextToImageUNetModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
transformer_dim = 32
seq_len = 16
noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device)
emb = torch.randn((batch_size, seq_len, transformer_dim)).to(torch_device)
time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
return {"sample": noise, "timestep": time_step, "transformer_out": emb}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (6, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"attention_resolutions": (2,),
"channel_mult": (1, 2),
"in_channels": 3,
"out_channels": 6,
"model_channels": 32,
"num_head_channels": 8,
"num_heads_upsample": 1,
"num_res_blocks": 2,
"resblock_updown": True,
"resolution": 32,
"use_scale_shift_norm": True,
"transformer_dim": 32,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_output(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
output, _ = torch.split(output, 3, dim=1)
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
def test_from_pretrained_hub(self):
model, loading_info = GlideTextToImageUNetModel.from_pretrained(
"fusing/unet-glide-text2im-dummy", output_loading_info=True
)
self.assertIsNotNone(model)
# self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = GlideTextToImageUNetModel.from_pretrained("fusing/unet-glide-text2im-dummy")
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.resolution, model.config.resolution)).to(
torch_device
)
emb = torch.randn((1, 16, model.config.transformer_dim)).to(torch_device)
time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
model.to(torch_device)
with torch.no_grad():
output = model(noise, time_step, emb)
output, _ = torch.split(output, 3, dim=1)
output_slice = output[0, -1, -3:, -3:].cpu().flatten()
# fmt: off
expected_output_slice = torch.tensor([2.7766, -10.3558, -14.9149, -0.9376, -14.9175, -17.7679, -5.5565, -12.9521, -12.9845])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
# TODO(Patrick) - Re-add this test after having correctly added the final VE checkpoints
# def test_output_pretrained(self):
# model = UNetUnconditionalModel.from_pretrained("fusing/ddpm_dummy_update", subfolder="unet")
# 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])
#
# with torch.no_grad():
# output = model(noise, time_step)["sample"]
#
# output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
# expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
# fmt: on
# self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
@ -537,10 +335,10 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
def test_from_pretrained_hub(self):
model, loading_info = UNetUnconditionalModel.from_pretrained(
"fusing/unet-ldm-dummy", output_loading_info=True, ldm=True
"fusing/unet-ldm-dummy-update", output_loading_info=True
)
self.assertIsNotNone(model)
# self.assertEqual(len(loading_info["missing_keys"]), 0)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)["sample"]
@ -548,7 +346,7 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = UNetUnconditionalModel.from_pretrained("fusing/unet-ldm-dummy", ldm=True)
model = UNetUnconditionalModel.from_pretrained("fusing/unet-ldm-dummy-update")
model.eval()
torch.manual_seed(0)
@ -568,27 +366,30 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_output_pretrained_spatial_transformer(self):
model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial")
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)
context = torch.ones((1, 16, 64), dtype=torch.float32)
time_step = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
output = model(noise, time_step, context=context)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
# TODO(Patrick) - Re-add this test after having cleaned up LDM
# def test_output_pretrained_spatial_transformer(self):
# model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial")
# 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)
# context = torch.ones((1, 16, 64), dtype=torch.float32)
# time_step = torch.tensor([10] * noise.shape[0])
#
# with torch.no_grad():
# output = model(noise, time_step, context=context)
#
# output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
# expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890])
# fmt: on
#
# self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
#
class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
@ -641,44 +442,18 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
def test_from_pretrained_hub(self):
model, loading_info = UNetUnconditionalModel.from_pretrained(
"fusing/ncsnpp-ffhq-ve-dummy", sde=True, output_loading_info=True
"fusing/ncsnpp-ffhq-ve-dummy-update", output_loading_info=True
)
self.assertIsNotNone(model)
# self.assertEqual(len(loading_info["missing_keys"]), 0)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)
assert image is not None, "Make sure output is not None"
def test_output_pretrained_ve_small(self):
model = NCSNpp.from_pretrained("fusing/ncsnpp-cifar10-ve-dummy")
model.eval()
model.to(torch_device)
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step)
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([0.1315, 0.0741, 0.0393, 0.0455, 0.0556, 0.0180, -0.0832, -0.0644, -0.0856])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
def test_output_pretrained_ve_mid(self):
model = UNetUnconditionalModel.from_pretrained("fusing/celebahq_256-ncsnpp-ve", sde=True)
model = UNetUnconditionalModel.from_pretrained("google/ncsnpp-celebahq-256")
model.to(torch_device)
torch.manual_seed(0)
@ -703,7 +478,7 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
def test_output_pretrained_ve_large(self):
model = UNetUnconditionalModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy", sde=True)
model = UNetUnconditionalModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
model.to(torch_device)
torch.manual_seed(0)
@ -727,31 +502,6 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
def test_output_pretrained_vp(self):
model = NCSNpp.from_pretrained("fusing/cifar10-ddpmpp-vp")
model.to(torch_device)
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [9.0]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step)
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([0.3303, -0.2275, -2.8872, -0.1309, -1.2861, 3.4567, -1.0083, 2.5325, -1.3866])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class VQModelTests(ModelTesterMixin, unittest.TestCase):
model_class = VQModel
@ -802,7 +552,7 @@ class VQModelTests(ModelTesterMixin, unittest.TestCase):
def test_from_pretrained_hub(self):
model, loading_info = VQModel.from_pretrained("fusing/vqgan-dummy", output_loading_info=True)
self.assertIsNotNone(model)
# self.assertEqual(len(loading_info["missing_keys"]), 0)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)
@ -873,7 +623,7 @@ class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
def test_from_pretrained_hub(self):
model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
self.assertIsNotNone(model)
# self.assertEqual(len(loading_info["missing_keys"]), 0)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)
@ -930,7 +680,7 @@ class PipelineTesterMixin(unittest.TestCase):
@slow
def test_from_pretrained_hub(self):
model_path = "google/ddpm-cifar10"
model_path = "google/ddpm-cifar10-32"
ddpm = DDPMPipeline.from_pretrained(model_path)
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
@ -948,7 +698,7 @@ class PipelineTesterMixin(unittest.TestCase):
@slow
def test_ddpm_cifar10(self):
model_id = "google/ddpm-cifar10"
model_id = "google/ddpm-cifar10-32"
unet = UNetUnconditionalModel.from_pretrained(model_id)
scheduler = DDPMScheduler.from_config(model_id)
@ -989,7 +739,7 @@ class PipelineTesterMixin(unittest.TestCase):
@slow
def test_ddim_cifar10(self):
model_id = "google/ddpm-cifar10"
model_id = "google/ddpm-cifar10-32"
unet = UNetUnconditionalModel.from_pretrained(model_id)
scheduler = DDIMScheduler(tensor_format="pt")
@ -1009,7 +759,7 @@ class PipelineTesterMixin(unittest.TestCase):
@slow
def test_pndm_cifar10(self):
model_id = "google/ddpm-cifar10"
model_id = "google/ddpm-cifar10-32"
unet = UNetUnconditionalModel.from_pretrained(model_id)
scheduler = PNDMScheduler(tensor_format="pt")
@ -1028,7 +778,7 @@ class PipelineTesterMixin(unittest.TestCase):
@slow
def test_ldm_text2img(self):
ldm = LatentDiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large")
ldm = LatentDiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
@ -1042,7 +792,7 @@ class PipelineTesterMixin(unittest.TestCase):
@slow
def test_ldm_text2img_fast(self):
ldm = LatentDiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large")
ldm = LatentDiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
@ -1054,30 +804,15 @@ class PipelineTesterMixin(unittest.TestCase):
expected_slice = torch.tensor([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
@slow
def test_glide_text2img(self):
model_id = "fusing/glide-base"
glide = GlidePipeline.from_pretrained(model_id)
prompt = "a pencil sketch of a corgi"
generator = torch.manual_seed(0)
image = glide(prompt, generator=generator, num_inference_steps_upscale=20)
image_slice = image[0, :3, :3, -1].cpu()
assert image.shape == (1, 256, 256, 3)
expected_slice = torch.tensor([0.7119, 0.7073, 0.6460, 0.7780, 0.7423, 0.6926, 0.7378, 0.7189, 0.7784])
assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
@slow
def test_score_sde_ve_pipeline(self):
model = UNetUnconditionalModel.from_pretrained("fusing/ffhq_ncsnpp", sde=True)
model = UNetUnconditionalModel.from_pretrained("google/ncsnpp-ffhq-1024")
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
scheduler = ScoreSdeVeScheduler.from_config("fusing/ffhq_ncsnpp")
scheduler = ScoreSdeVeScheduler.from_config("google/ncsnpp-ffhq-1024")
sde_ve = ScoreSdeVePipeline(model=model, scheduler=scheduler)
@ -1099,26 +834,6 @@ class PipelineTesterMixin(unittest.TestCase):
assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2
assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4
@slow
def test_score_sde_vp_pipeline(self):
model = NCSNpp.from_pretrained("fusing/cifar10-ddpmpp-vp")
scheduler = ScoreSdeVpScheduler.from_config("fusing/cifar10-ddpmpp-vp")
sde_vp = ScoreSdeVpPipeline(model=model, scheduler=scheduler)
torch.manual_seed(0)
image = sde_vp(num_inference_steps=10)
expected_image_sum = 4183.2012
expected_image_mean = 1.3617
# on m1 mbp
# expected_image_sum = 4318.6729
# expected_image_mean = 1.4058
assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2
assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4
@slow
def test_ldm_uncond(self):
ldm = LatentDiffusionUncondPipeline.from_pretrained("CompVis/ldm-celebahq-256")