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Commit d04051e3 authored by anton-l's avatar anton-l
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README.md
View file @ d04051e3
......@@ -34,6 +34,7 @@ import numpy as np
generator = torch.Generator()
generator = generator.manual_seed(6694729458485568)
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
# 1. Load models
scheduler = GaussianDDPMScheduler.from_config("fusing/ddpm-lsun-church")
......@@ -45,10 +46,10 @@ image = scheduler.sample_noise((1, model.in_channels, model.resolution, model.re
# 3. Denoise
for t in reversed(range(len(scheduler))):
# i) define coefficients for time step t
clip_image_coeff = 1 / torch.sqrt(scheduler.get_alpha_prod(t))
clip_noise_coeff = torch.sqrt(1 / scheduler.get_alpha_prod(t) - 1)
clipped_image_coeff = 1 / torch.sqrt(scheduler.get_alpha_prod(t))
clipped_noise_coeff = torch.sqrt(1 / scheduler.get_alpha_prod(t) - 1)
image_coeff = (1 - scheduler.get_alpha_prod(t - 1)) * torch.sqrt(scheduler.get_alpha(t)) / (1 - scheduler.get_alpha_prod(t))
clip_coeff = torch.sqrt(scheduler.get_alpha_prod(t - 1)) * scheduler.get_beta(t) / (1 - scheduler.get_alpha_prod(t))
clipped_coeff = torch.sqrt(scheduler.get_alpha_prod(t - 1)) * scheduler.get_beta(t) / (1 - scheduler.get_alpha_prod(t))
# ii) predict noise residual
with torch.no_grad():
......@@ -56,9 +57,9 @@ for t in reversed(range(len(scheduler))):
# iii) compute predicted image from residual
# See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
pred_mean = clip_image_coeff * image - clip_noise_coeff * noise_residual
pred_mean = clipped_image_coeff * image - clipped_noise_coeff * noise_residual
pred_mean = torch.clamp(pred_mean, -1, 1)
prev_image = clip_coeff * pred_mean + image_coeff * image
prev_image = clipped_coeff * pred_mean + image_coeff * image
# iv) sample variance
prev_variance = scheduler.sample_variance(t, prev_image.shape, device=torch_device, generator=generator)
......@@ -83,12 +84,12 @@ image_pil.save("test.png")
Example:
```python
from modeling_ddpm import DDPM
from diffusers import DiffusionPipeline
import PIL.Image
import numpy as np
# load model and scheduler
ddpm = DDPM.from_pretrained("fusing/ddpm-lsun-bedroom-pipe")
ddpm = DiffusionPipeline.from_pretrained("fusing/ddpm-lsun-bedroom")
# run pipeline in inference (sample random noise and denoise)
image = ddpm()
......
examples/sample_loop.py deleted 100755 → 0
View file @ 6292107f
#!/usr/bin/env python3
from diffusers import UNetModel, GaussianDDPMScheduler
import torch
import torch.nn.functional as F
import numpy as np
import PIL.Image
import tqdm
#torch_device = "cuda"
#
#unet = UNetModel.from_pretrained("/home/patrick/ddpm-lsun-church")
#unet.to(torch_device)
#
#TIME_STEPS = 10
#
#scheduler = GaussianDDPMScheduler.from_config("/home/patrick/ddpm-lsun-church", timesteps=TIME_STEPS)
#
#diffusion_config = {
# "beta_start": 0.0001,
# "beta_end": 0.02,
# "num_diffusion_timesteps": TIME_STEPS,
#}
#
# 2. Do one denoising step with model
#batch_size, num_channels, height, width = 1, 3, 256, 256
#
#torch.manual_seed(0)
#noise_image = torch.randn(batch_size, num_channels, height, width, device="cuda")
#
#
# Helper
#def noise_like(shape, device, repeat=False):
# def repeat_noise():
# return torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
#
# def noise():
# return torch.randn(shape, device=device)
#
# return repeat_noise() if repeat else noise()
#
#
#betas = np.linspace(diffusion_config["beta_start"], diffusion_config["beta_end"], diffusion_config["num_diffusion_timesteps"], dtype=np.float64)
#betas = torch.tensor(betas, device=torch_device)
#alphas = 1.0 - betas
#
#alphas_cumprod = torch.cumprod(alphas, axis=0)
#alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value=1.0)
#
#posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)
#posterior_mean_coef2 = (1.0 - alphas_cumprod_prev) * torch.sqrt(alphas) / (1.0 - alphas_cumprod)
#
#posterior_variance = betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
#posterior_log_variance_clipped = torch.log(posterior_variance.clamp(min=1e-20))
#
#
#sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
#sqrt_recipm1_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod - 1)
#
#
#noise_coeff = (1 - alphas) / torch.sqrt(1 - alphas_cumprod)
#coeff = 1 / torch.sqrt(alphas)
def real_fn():
# Compare the following to Algorithm 2 Sampling of paper: https://arxiv.org/pdf/2006.11239.pdf
# 1: x_t ~ N(0,1)
x_t = noise_image
# 2: for t = T, ...., 1 do
for i in reversed(range(TIME_STEPS)):
t = torch.tensor([i]).to(torch_device)
# 3: z ~ N(0, 1)
noise = noise_like(x_t.shape, torch_device)
# 4: √1αtxt − √1−αt1−α¯tθ(xt, t) + σtz
# ------------------------- MODEL ------------------------------------#
with torch.no_grad():
pred_noise = unet(x_t, t) # pred epsilon_theta
# pred_x = sqrt_recip_alphas_cumprod[t] * x_t - sqrt_recipm1_alphas_cumprod[t] * pred_noise
# pred_x.clamp_(-1.0, 1.0)
# pred mean
# posterior_mean = posterior_mean_coef1[t] * pred_x + posterior_mean_coef2[t] * x_t
# --------------------------------------------------------------------#
posterior_mean = coeff[t] * (x_t - noise_coeff[t] * pred_noise)
# ------------------------- Variance Scheduler -----------------------#
# pred variance
posterior_log_variance = posterior_log_variance_clipped[t]
b, *_, device = *x_t.shape, x_t.device
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x_t.shape) - 1)))
posterior_variance = nonzero_mask * (0.5 * posterior_log_variance).exp()
# --------------------------------------------------------------------#
x_t_1 = (posterior_mean + posterior_variance * noise).to(torch.float32)
x_t = x_t_1
print(x_t.abs().sum())
def post_process_to_image(x_t):
image = x_t.cpu().permute(0, 2, 3, 1)
image = (image + 1.0) * 127.5
image = image.numpy().astype(np.uint8)
return PIL.Image.fromarray(image[0])
from pytorch_diffusion import Diffusion
#diffusion = Diffusion.from_pretrained("lsun_church")
#samples = diffusion.denoise(1)
#
#image = post_process_to_image(samples)
#image.save("check.png")
#import ipdb; ipdb.set_trace()
device = "cuda"
scheduler = GaussianDDPMScheduler.from_config("/home/patrick/ddpm-lsun-church", timesteps=10)
import ipdb; ipdb.set_trace()
model = UNetModel.from_pretrained("/home/patrick/ddpm-lsun-church").to(device)
torch.manual_seed(0)
next_image = scheduler.sample_noise((1, model.in_channels, model.resolution, model.resolution), device=device)
for t in tqdm.tqdm(reversed(range(len(scheduler))), total=len(scheduler)):
# define coefficients for time step t
clip_image_coeff = 1 / torch.sqrt(scheduler.get_alpha_prod(t))
clip_noise_coeff = torch.sqrt(1 / scheduler.get_alpha_prod(t) - 1)
image_coeff = (1 - scheduler.get_alpha_prod(t - 1)) * torch.sqrt(scheduler.get_alpha(t)) / (1 - scheduler.get_alpha_prod(t))
clip_coeff = torch.sqrt(scheduler.get_alpha_prod(t - 1)) * scheduler.get_beta(t) / (1 - scheduler.get_alpha_prod(t))
# predict noise residual
with torch.no_grad():
noise_residual = model(next_image, t)
# compute prev image from noise
pred_mean = clip_image_coeff * next_image - clip_noise_coeff * noise_residual
pred_mean = torch.clamp(pred_mean, -1, 1)
image = clip_coeff * pred_mean + image_coeff * next_image
# sample variance
variance = scheduler.sample_variance(t, image.shape, device=device)
# sample previous image
sampled_image = image + variance
next_image = sampled_image
image = post_process_to_image(next_image)
image.save("example_new.png")
models/vision/ddpm/example.py
View file @ d04051e3
#!/usr/bin/env python3
import tempfile
import sys
import os
import pathlib
from modeling_ddpm import DDPM
import PIL.Image
import numpy as np
model_id = sys.argv[1]
model_ids = ["ddpm-lsun-cat", "ddpm-lsun-cat-ema", "ddpm-lsun-church-ema", "ddpm-lsun-church", "ddpm-lsun-bedroom", "ddpm-lsun-bedroom-ema", "ddpm-cifar10-ema", "ddpm-cifar10", "ddpm-celeba-hq", "ddpm-celeba-hq-ema"]
ddpm = DDPM.from_pretrained(model_id)
image = ddpm()
for model_id in model_ids:
path = os.path.join("/home/patrick/images/hf", model_id)
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
import PIL.Image
import numpy as np
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
image_pil.save("test.png")
ddpm = DDPM.from_pretrained("fusing/" + model_id)
image = ddpm(batch_size=4)
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
import ipdb; ipdb.set_trace()
for i in range(image_processed.shape[0]):
image_pil = PIL.Image.fromarray(image_processed[i])
image_pil.save(os.path.join(path, f"image_{i}.png"))
models/vision/ddpm/modeling_ddpm.py
View file @ d04051e3
......@@ -23,22 +23,23 @@ class DDPM(DiffusionPipeline):
modeling_file = "modeling_ddpm.py"
def __init__(self, unet, noise_scheduler, vqvae):
def __init__(self, unet, noise_scheduler):
super().__init__()
self.register_modules(unet=unet, noise_scheduler=noise_scheduler)
def __call__(self, generator=None, torch_device=None):
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
def __call__(self, batch_size=1, generator=None, torch_device=None):
if torch_device is None:
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
self.unet.to(torch_device)
# 1. Sample gaussian noise
image = self.noise_scheduler.sample_noise((1, self.unet.in_channels, self.unet.resolution, self.unet.resolution), device=torch_device, generator=generator)
image = self.noise_scheduler.sample_noise((batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution), device=torch_device, generator=generator)
for t in tqdm.tqdm(reversed(range(len(self.noise_scheduler))), total=len(self.noise_scheduler)):
# i) define coefficients for time step t
clip_image_coeff = 1 / torch.sqrt(self.noise_scheduler.get_alpha_prod(t))
clip_noise_coeff = torch.sqrt(1 / self.noise_scheduler.get_alpha_prod(t) - 1)
clipped_image_coeff = 1 / torch.sqrt(self.noise_scheduler.get_alpha_prod(t))
clipped_noise_coeff = torch.sqrt(1 / self.noise_scheduler.get_alpha_prod(t) - 1)
image_coeff = (1 - self.noise_scheduler.get_alpha_prod(t - 1)) * torch.sqrt(self.noise_scheduler.get_alpha(t)) / (1 - self.noise_scheduler.get_alpha_prod(t))
clip_coeff = torch.sqrt(self.noise_scheduler.get_alpha_prod(t - 1)) * self.noise_scheduler.get_beta(t) / (1 - self.noise_scheduler.get_alpha_prod(t))
clipped_coeff = torch.sqrt(self.noise_scheduler.get_alpha_prod(t - 1)) * self.noise_scheduler.get_beta(t) / (1 - self.noise_scheduler.get_alpha_prod(t))
# ii) predict noise residual
with torch.no_grad():
......@@ -46,9 +47,9 @@ class DDPM(DiffusionPipeline):
# iii) compute predicted image from residual
# See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
pred_mean = clip_image_coeff * image - clip_noise_coeff * noise_residual
pred_mean = clipped_image_coeff * image - clipped_noise_coeff * noise_residual
pred_mean = torch.clamp(pred_mean, -1, 1)
prev_image = clip_coeff * pred_mean + image_coeff * image
prev_image = clipped_coeff * pred_mean + image_coeff * image
# iv) sample variance
prev_variance = self.noise_scheduler.sample_variance(t, prev_image.shape, device=torch_device, generator=generator)
......
src/diffusers/configuration_utils.py
View file @ d04051e3
......@@ -89,6 +89,7 @@ class ConfigMixin:
self.to_json_file(output_config_file)
logger.info(f"ConfigMixinuration saved in {output_config_file}")
@classmethod
def get_config_dict(
......@@ -182,35 +183,42 @@ class ConfigMixin:
logger.info(f"loading configuration file {config_file}")
else:
logger.info(f"loading configuration file {config_file} from cache at {resolved_config_file}")
return config_dict
@classmethod
def extract_init_dict(cls, config_dict, **kwargs):
expected_keys = set(dict(inspect.signature(cls.__init__).parameters).keys())
expected_keys.remove("self")
init_dict = {}
for key in expected_keys:
if key in kwargs:
# overwrite key
config_dict[key] = kwargs.pop(key)
init_dict[key] = kwargs.pop(key)
elif key in config_dict:
# use value from config dict
init_dict[key] = config_dict.pop(key)
passed_keys = set(config_dict.keys())
unused_kwargs = kwargs
for key in passed_keys - expected_keys:
unused_kwargs[key] = config_dict.pop(key)
unused_kwargs = config_dict.update(kwargs)
passed_keys = set(init_dict.keys())
if len(expected_keys - passed_keys) > 0:
logger.warn(
f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
)
return config_dict, unused_kwargs
return init_dict, unused_kwargs
@classmethod
def from_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs):
config_dict, unused_kwargs = cls.get_config_dict(
config_dict = cls.get_config_dict(
pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs
)
model = cls(**config_dict)
init_dict, unused_kwargs = cls.extract_init_dict(config_dict, **kwargs)
model = cls(**init_dict)
if return_unused_kwargs:
return model, unused_kwargs
......
src/diffusers/dynamic_modules_utils.py 0 → 100644
View file @ d04051e3
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
"""Utilities to dynamically load objects from the Hub."""
import importlib
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from huggingface_hub import HfFolder, model_info
from transformers.utils import (
HF_MODULES_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
cached_path,
hf_bucket_url,
is_offline_mode,
logging,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def init_hf_modules():
"""
Creates the cache directory for modules with an init, and adds it to the Python path.
"""
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(HF_MODULES_CACHE)
os.makedirs(HF_MODULES_CACHE, exist_ok=True)
init_path = Path(HF_MODULES_CACHE) / "__init__.py"
if not init_path.exists():
init_path.touch()
def create_dynamic_module(name: Union[str, os.PathLike]):
"""
Creates a dynamic module in the cache directory for modules.
"""
init_hf_modules()
dynamic_module_path = Path(HF_MODULES_CACHE) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent)
os.makedirs(dynamic_module_path, exist_ok=True)
init_path = dynamic_module_path / "__init__.py"
if not init_path.exists():
init_path.touch()
def get_relative_imports(module_file):
"""
Get the list of modules that are relatively imported in a module file.
Args:
module_file (`str` or `os.PathLike`): The module file to inspect.
"""
with open(module_file, "r", encoding="utf-8") as f:
content = f.read()
# Imports of the form `import .xxx`
relative_imports = re.findall("^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE)
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE)
# Unique-ify
return list(set(relative_imports))
def get_relative_import_files(module_file):
"""
Get the list of all files that are needed for a given module. Note that this function recurses through the relative
imports (if a imports b and b imports c, it will return module files for b and c).
Args:
module_file (`str` or `os.PathLike`): The module file to inspect.
"""
no_change = False
files_to_check = [module_file]
all_relative_imports = []
# Let's recurse through all relative imports
while not no_change:
new_imports = []
for f in files_to_check:
new_imports.extend(get_relative_imports(f))
module_path = Path(module_file).parent
new_import_files = [str(module_path / m) for m in new_imports]
new_import_files = [f for f in new_import_files if f not in all_relative_imports]
files_to_check = [f"{f}.py" for f in new_import_files]
no_change = len(new_import_files) == 0
all_relative_imports.extend(files_to_check)
return all_relative_imports
def check_imports(filename):
"""
Check if the current Python environment contains all the libraries that are imported in a file.
"""
with open(filename, "r", encoding="utf-8") as f:
content = f.read()
# Imports of the form `import xxx`
imports = re.findall("^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE)
# Imports of the form `from xxx import yyy`
imports += re.findall("^\s*from\s+(\S+)\s+import", content, flags=re.MULTILINE)
# Only keep the top-level module
imports = [imp.split(".")[0] for imp in imports if not imp.startswith(".")]
# Unique-ify and test we got them all
imports = list(set(imports))
missing_packages = []
for imp in imports:
try:
importlib.import_module(imp)
except ImportError:
missing_packages.append(imp)
if len(missing_packages) > 0:
raise ImportError(
"This modeling file requires the following packages that were not found in your environment: "
f"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`"
)
return get_relative_imports(filename)
def get_class_in_module(class_name, module_path):
"""
Import a module on the cache directory for modules and extract a class from it.
"""
module_path = module_path.replace(os.path.sep, ".")
module = importlib.import_module(module_path)
return getattr(module, class_name)
def get_cached_module_file(
pretrained_model_name_or_path: Union[str, os.PathLike],
module_file: str,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
):
"""
Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
Transformers module.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
module_file (`str`):
The name of the module file containing the class to look for.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `transformers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
<Tip>
Passing `use_auth_token=True` is required when you want to use a private model.
</Tip>
Returns:
`str`: The path to the module inside the cache.
"""
# Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
submodule = "local"
try:
# Load from URL or cache if already cached
resolved_module_file = cached_path(
module_file_or_url,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
)
except EnvironmentError:
logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
raise
# Check we have all the requirements in our environment
modules_needed = check_imports(resolved_module_file)
# Now we move the module inside our cached dynamic modules.
full_submodule = TRANSFORMERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(full_submodule)
submodule_path = Path(HF_MODULES_CACHE) / full_submodule
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(resolved_module_file, submodule_path / module_file)
for module_needed in modules_needed:
module_needed = f"{module_needed}.py"
shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
return os.path.join(full_submodule, module_file)
def get_class_from_dynamic_module(
pretrained_model_name_or_path: Union[str, os.PathLike],
module_file: str,
class_name: str,
cache_dir: Optional[Union[str, os.PathLike]] = None,
force_download: bool = False,
resume_download: bool = False,
proxies: Optional[Dict[str, str]] = None,
use_auth_token: Optional[Union[bool, str]] = None,
revision: Optional[str] = None,
local_files_only: bool = False,
**kwargs,
):
"""
Extracts a class from a module file, present in the local folder or repository of a model.
<Tip warning={true}>
Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should
therefore only be called on trusted repos.
</Tip>
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
under a user or organization name, like `dbmdz/bert-base-german-cased`.
- a path to a *directory* containing a configuration file saved using the
[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
module_file (`str`):
The name of the module file containing the class to look for.
class_name (`str`):
The name of the class to import in the module.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
cache should not be used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force to (re-)download the configuration files and override the cached versions if they
exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
use_auth_token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `transformers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
local_files_only (`bool`, *optional*, defaults to `False`):
If `True`, will only try to load the tokenizer configuration from local files.
<Tip>
Passing `use_auth_token=True` is required when you want to use a private model.
</Tip>
Returns:
`type`: The class, dynamically imported from the module.
Examples:
```python
# Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
# module.
cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel")
```"""
# And lastly we get the class inside our newly created module
final_module = get_cached_module_file(
pretrained_model_name_or_path,
module_file,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
use_auth_token=use_auth_token,
revision=revision,
local_files_only=local_files_only,
)
return get_class_in_module(class_name, final_module.replace(".py", ""))
src/diffusers/models/unet_glide.py
View file @ d04051e3
......@@ -8,6 +8,7 @@ import torch.nn.functional as F
from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
def convert_module_to_f16(l):
"""
Convert primitive modules to float16.
......
src/diffusers/pipeline_utils.py
View file @ d04051e3
......@@ -23,6 +23,7 @@ from huggingface_hub import snapshot_download
from transformers.utils import logging
from .configuration_utils import ConfigMixin
from .dynamic_modules_utils import get_class_from_dynamic_module
INDEX_FILE = "diffusion_model.pt"
......@@ -54,19 +55,23 @@ class DiffusionPipeline(ConfigMixin):
class_name = module.__class__.__name__
register_dict = {name: (library, class_name)}
register_dict["_module"] = self.__module__
# save model index config
self.register(**register_dict)
# set models
setattr(self, name, module)
register_dict = {"_module" : self.__module__.split(".")[-1] + ".py"}
self.register(**register_dict)
def save_pretrained(self, save_directory: Union[str, os.PathLike]):
self.save_config(save_directory)
model_index_dict = self._dict_to_save
model_index_dict.pop("_class_name")
model_index_dict.pop("_module")
for name, (library_name, class_name) in self._dict_to_save.items():
importable_classes = LOADABLE_CLASSES[library_name]
......@@ -90,17 +95,27 @@ class DiffusionPipeline(ConfigMixin):
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
# use snapshot download here to get it working from from_pretrained
cached_folder = snapshot_download(pretrained_model_name_or_path)
config_dict, pipeline_kwargs = cls.get_config_dict(cached_folder)
module = pipeline_kwargs["_module"]
# TODO(Suraj) - make from hub import work
# Make `ddpm = DiffusionPipeline.from_pretrained("fusing/ddpm-lsun-bedroom-pipe")` work
# Add Sylvains code from transformers
if not os.path.isdir(pretrained_model_name_or_path):
cached_folder = snapshot_download(pretrained_model_name_or_path)
else:
cached_folder = pretrained_model_name_or_path
config_dict = cls.get_config_dict(cached_folder)
module = config_dict["_module"]
class_name_ = config_dict["_class_name"]
if class_name_ == cls.__name__:
pipeline_class = cls
else:
pipeline_class = get_class_from_dynamic_module(cached_folder, module, class_name_, cached_folder)
init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
init_kwargs = {}
for name, (library_name, class_name) in config_dict.items():
for name, (library_name, class_name) in init_dict.items():
importable_classes = LOADABLE_CLASSES[library_name]
if library_name == module:
......@@ -118,9 +133,12 @@ class DiffusionPipeline(ConfigMixin):
load_method = getattr(class_obj, load_method_name)
loaded_sub_model = load_method(os.path.join(cached_folder, name))
if os.path.isdir(os.path.join(cached_folder, name)):
loaded_sub_model = load_method(os.path.join(cached_folder, name))
else:
loaded_sub_model = load_method(cached_folder)
init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
model = cls(**init_kwargs)
model = pipeline_class(**init_kwargs)
return model
src/diffusers/schedulers/gaussian_ddpm.py
View file @ d04051e3
......@@ -108,7 +108,7 @@ class GaussianDDPMScheduler(nn.Module, ConfigMixin):
def sample_variance(self, time_step, shape, device, generator=None):
variance = self.log_variance[time_step]
nonzero_mask = torch.tensor([1 - (time_step == 0)], device=device).float()[None, :].repeat(shape[0], 1)
nonzero_mask = torch.tensor([1 - (time_step == 0)], device=device).float()[None, :]
noise = self.sample_noise(shape, device=device, generator=generator)
......
tests/test_modeling_utils.py
View file @ d04051e3
......@@ -22,6 +22,8 @@ from distutils.util import strtobool
import torch
from diffusers import GaussianDDPMScheduler, UNetModel
from diffusers.pipeline_utils import DiffusionPipeline
from models.vision.ddpm.modeling_ddpm import DDPM
global_rng = random.Random()
......@@ -76,7 +78,7 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None):
class ModelTesterMixin(unittest.TestCase):
@property
def dummy_input(self):
batch_size = 1
batch_size = 4
num_channels = 3
sizes = (32, 32)
......@@ -124,10 +126,10 @@ class SamplerTesterMixin(unittest.TestCase):
# 3. Denoise
for t in reversed(range(len(scheduler))):
# i) define coefficients for time step t
clip_image_coeff = 1 / torch.sqrt(scheduler.get_alpha_prod(t))
clip_noise_coeff = torch.sqrt(1 / scheduler.get_alpha_prod(t) - 1)
clipped_image_coeff = 1 / torch.sqrt(scheduler.get_alpha_prod(t))
clipped_noise_coeff = torch.sqrt(1 / scheduler.get_alpha_prod(t) - 1)
image_coeff = (1 - scheduler.get_alpha_prod(t - 1)) * torch.sqrt(scheduler.get_alpha(t)) / (1 - scheduler.get_alpha_prod(t))
clip_coeff = torch.sqrt(scheduler.get_alpha_prod(t - 1)) * scheduler.get_beta(t) / (1 - scheduler.get_alpha_prod(t))
clipped_coeff = torch.sqrt(scheduler.get_alpha_prod(t - 1)) * scheduler.get_beta(t) / (1 - scheduler.get_alpha_prod(t))
# ii) predict noise residual
with torch.no_grad():
......@@ -135,9 +137,9 @@ class SamplerTesterMixin(unittest.TestCase):
# iii) compute predicted image from residual
# See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
pred_mean = clip_image_coeff * image - clip_noise_coeff * noise_residual
pred_mean = clipped_image_coeff * image - clipped_noise_coeff * noise_residual
pred_mean = torch.clamp(pred_mean, -1, 1)
prev_image = clip_coeff * pred_mean + image_coeff * image
prev_image = clipped_coeff * pred_mean + image_coeff * image
# iv) sample variance
prev_variance = scheduler.sample_variance(t, prev_image.shape, device=torch_device, generator=generator)
......@@ -174,10 +176,10 @@ class SamplerTesterMixin(unittest.TestCase):
# 3. Denoise
for t in reversed(range(len(scheduler))):
# i) define coefficients for time step t
clip_image_coeff = 1 / torch.sqrt(scheduler.get_alpha_prod(t))
clip_noise_coeff = torch.sqrt(1 / scheduler.get_alpha_prod(t) - 1)
clipped_image_coeff = 1 / torch.sqrt(scheduler.get_alpha_prod(t))
clipped_noise_coeff = torch.sqrt(1 / scheduler.get_alpha_prod(t) - 1)
image_coeff = (1 - scheduler.get_alpha_prod(t - 1)) * torch.sqrt(scheduler.get_alpha(t)) / (1 - scheduler.get_alpha_prod(t))
clip_coeff = torch.sqrt(scheduler.get_alpha_prod(t - 1)) * scheduler.get_beta(t) / (1 - scheduler.get_alpha_prod(t))
clipped_coeff = torch.sqrt(scheduler.get_alpha_prod(t - 1)) * scheduler.get_beta(t) / (1 - scheduler.get_alpha_prod(t))
# ii) predict noise residual
with torch.no_grad():
......@@ -185,9 +187,9 @@ class SamplerTesterMixin(unittest.TestCase):
# iii) compute predicted image from residual
# See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
pred_mean = clip_image_coeff * image - clip_noise_coeff * noise_residual
pred_mean = clipped_image_coeff * image - clipped_noise_coeff * noise_residual
pred_mean = torch.clamp(pred_mean, -1, 1)
prev_image = clip_coeff * pred_mean + image_coeff * image
prev_image = clipped_coeff * pred_mean + image_coeff * image
# iv) sample variance
prev_variance = scheduler.sample_variance(t, prev_image.shape, device=torch_device, generator=generator)
......@@ -199,3 +201,46 @@ class SamplerTesterMixin(unittest.TestCase):
assert image.shape == (1, 3, 256, 256)
image_slice = image[0, -1, -3:, -3:].cpu()
assert (image_slice - torch.tensor([[0.1746, 0.5125, -0.7920], [-0.5734, -0.2910, -0.1984], [0.4090, -0.7740, -0.3941]])).abs().sum() < 1e-3
class PipelineTesterMixin(unittest.TestCase):
def test_from_pretrained_save_pretrained(self):
# 1. Load models
model = UNetModel(ch=32, ch_mult=(1, 2), num_res_blocks=2, attn_resolutions=(16,), resolution=32)
schedular = GaussianDDPMScheduler(timesteps=10)
ddpm = DDPM(model, schedular)
with tempfile.TemporaryDirectory() as tmpdirname:
ddpm.save_pretrained(tmpdirname)
new_ddpm = DDPM.from_pretrained(tmpdirname)
generator = torch.Generator()
generator = generator.manual_seed(669472945848556)
image = ddpm(generator=generator)
generator = generator.manual_seed(669472945848556)
new_image = new_ddpm(generator=generator)
assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"
@slow
def test_from_pretrained_hub(self):
model_path = "fusing/ddpm-cifar10"
ddpm = DDPM.from_pretrained(model_path)
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
ddpm.noise_scheduler.num_timesteps = 10
ddpm_from_hub.noise_scheduler.num_timesteps = 10
generator = torch.Generator(device=torch_device)
generator = generator.manual_seed(669472945848556)
image = ddpm(generator=generator)
generator = generator.manual_seed(669472945848556)
new_image = ddpm_from_hub(generator=generator)
assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"
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