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Commit acb948bd authored by Patrick von Platen's avatar Patrick von Platen
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models/vision/ddim/README.md deleted 100644 → 0
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<!--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.
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# Denoising Diffusion Implicit Models (DDIM)
## Overview
DDPM was proposed in [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) by *Jiaming Song, Chenlin Meng, Stefano Ermon*
The abstract from the paper is the following:
*Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.*
Tips:
- ...
- ...
This model was contributed by [???](https://huggingface.co/???). The original code can be found [here](https://github.com/hojonathanho/diffusion).
models/vision/ddim/example.py deleted 100755 → 0
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#!/usr/bin/env python3
import os
import pathlib
import numpy as np
import PIL.Image
from modeling_ddim import DDIM
model_ids = ["ddim-celeba-hq", "ddim-lsun-church", "ddim-lsun-bedroom"]
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)
ddpm = DDIM.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)
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/ddim/modeling_ddim.py deleted 100644 → 0
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# 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.
import torch
import tqdm
from diffusers import DiffusionPipeline
class DDIM(DiffusionPipeline):
def __init__(self, unet, noise_scheduler):
super().__init__()
self.register_modules(unet=unet, noise_scheduler=noise_scheduler)
def __call__(self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50):
# eta corresponds to η in paper and should be between [0, 1]
if torch_device is None:
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
self.unet.to(torch_device)
# Sample gaussian noise to begin loop
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(num_inference_steps)), total=num_inference_steps):
# 1. predict noise residual
orig_t = self.noise_scheduler.get_orig_t(t, num_inference_steps)
with torch.no_grad():
residual = self.unet(image, orig_t)
# 2. predict previous mean of image x_t-1
pred_prev_image = self.noise_scheduler.compute_prev_image_step(residual, image, t, num_inference_steps, eta)
# 3. optionally sample variance
variance = 0
if eta > 0:
noise = self.noise_scheduler.sample_noise(image.shape, device=image.device, generator=generator)
variance = self.noise_scheduler.get_variance(t).sqrt() * eta * noise
# 4. set current image to prev_image: x_t -> x_t-1
image = pred_prev_image + variance
return image
models/vision/ddim/run_ddpm.py deleted 100755 → 0
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#!/usr/bin/env python3
import torch
from diffusers import GaussianDDPMScheduler, UNetModel
model = UNetModel(dim=64, dim_mults=(1, 2, 4, 8))
diffusion = GaussianDDPMScheduler(model, image_size=128, timesteps=1000, loss_type="l1") # number of steps # L1 or L2
training_images = torch.randn(8, 3, 128, 128) # your images need to be normalized from a range of -1 to +1
loss = diffusion(training_images)
loss.backward()
# after a lot of training
sampled_images = diffusion.sample(batch_size=4)
sampled_images.shape # (4, 3, 128, 128)
models/vision/ddim/run_inference.py deleted 100755 → 0
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#!/usr/bin/env python3
# !pip install diffusers
import numpy as np
import PIL.Image
from modeling_ddim import DDIM
model_id = "fusing/ddpm-cifar10"
model_id = "fusing/ddpm-lsun-bedroom"
# load model and scheduler
ddpm = DDIM.from_pretrained(model_id)
# run pipeline in inference (sample random noise and denoise)
image = ddpm()
# process image to PIL
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])
# save image
image_pil.save("/home/patrick/images/show.png")
models/vision/ddpm/README.md deleted 100644 → 0
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<!--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.
-->
# Denoising Diffusion Probabilistic Models (DDPM)
## Overview
DDPM was proposed in [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) by *Jonathan Ho, Ajay Jain, Pieter Abbeel*.
The abstract from the paper is the following:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at this https URL*
Tips:
- ...
- ...
This model was contributed by [???](https://huggingface.co/???). The original code can be found [here](https://github.com/hojonathanho/diffusion).
![ddpm](https://user-images.githubusercontent.com/23423619/171627620-e3406711-1e20-4a99-8e30-ec5a86a465be.png)
models/vision/ddpm/example.py deleted 100755 → 0
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#!/usr/bin/env python3
import os
import pathlib
import numpy as np
import PIL.Image
from modeling_ddpm import DDPM
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",
]
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)
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)
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 deleted 100644 → 0
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# 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.
import torch
import tqdm
from diffusers import DiffusionPipeline
class DDPM(DiffusionPipeline):
def __init__(self, unet, noise_scheduler):
super().__init__()
self.register_modules(unet=unet, noise_scheduler=noise_scheduler)
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)
# Sample gaussian noise to begin loop
image = self.noise_scheduler.sample_noise(
(batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution),
device=torch_device,
generator=generator,
)
num_prediction_steps = len(self.noise_scheduler)
for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_steps):
# 1. predict noise residual
with torch.no_grad():
residual = self.unet(image, t)
# 2. predict previous mean of image x_t-1
pred_prev_image = self.noise_scheduler.compute_prev_image_step(residual, image, t)
# 3. optionally sample variance
variance = 0
if t > 0:
noise = self.noise_scheduler.sample_noise(image.shape, device=image.device, generator=generator)
variance = self.noise_scheduler.get_variance(t).sqrt() * noise
# 4. set current image to prev_image: x_t -> x_t-1
image = pred_prev_image + variance
return image
models/vision/ddpm/run_ddpm.py deleted 100755 → 0
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#!/usr/bin/env python3
import torch
from diffusers import GaussianDDPMScheduler, UNetModel
model = UNetModel(dim=64, dim_mults=(1, 2, 4, 8))
diffusion = GaussianDDPMScheduler(model, image_size=128, timesteps=1000, loss_type="l1") # number of steps # L1 or L2
training_images = torch.randn(8, 3, 128, 128) # your images need to be normalized from a range of -1 to +1
loss = diffusion(training_images)
loss.backward()
# after a lot of training
sampled_images = diffusion.sample(batch_size=4)
sampled_images.shape # (4, 3, 128, 128)
models/vision/glide/README.md deleted 100644 → 0
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# References
[GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://arxiv.org/pdf/2112.10741.pdf)
[Diffusion Models Beat GANs on Image Synthesis](https://arxiv.org/pdf/2105.05233.pdf)
\ No newline at end of file
models/vision/glide/convert_weights.py deleted 100644 → 0
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import torch
from torch import nn
from diffusers import (
ClassifierFreeGuidanceScheduler,
GlideDDIMScheduler,
GLIDESuperResUNetModel,
GLIDETextToImageUNetModel,
)
from modeling_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 = GlideDDIMScheduler(timesteps=1000, beta_schedule="linear")
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")
models/vision/glide/modeling_glide.py deleted 100644 → 0
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