毕业设计基于Pyqt5+扩散模型Diffusion Models的图像恢复项目Python源码+项目说明.zip

This folder is copied from https://github.com/openai/improved-diffusion My only change is in `improved_diffusion/gaussian_diffusion.py`: added function `p_sample_image_completion`. Original readme: # improved-diffusion This is the codebase for [Improved Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2102.09672). # Usage This section of the README walks through how to train and sample from a model. ## Installation Clone this repository and navigate to it in your terminal. Then run: ``` pip install -e . ``` This should install the `improved_diffusion` python package that the scripts depend on. ## Preparing Data The training code reads images from a directory of image files. In the [datasets](datasets) folder, we have provided instructions/scripts for preparing these directories for ImageNet, LSUN bedrooms, and CIFAR-10. For creating your own dataset, simply dump all of your images into a directory with ".jpg", ".jpeg", or ".png" extensions. If you wish to train a class-conditional model, name the files like "mylabel1_XXX.jpg", "mylabel2_YYY.jpg", etc., so that the data loader knows that "mylabel1" and "mylabel2" are the labels. Subdirectories will automatically be enumerated as well, so the images can be organized into a recursive structure (although the directory names will be ignored, and the underscore prefixes are used as names). The images will automatically be scaled and center-cropped by the data-loading pipeline. Simply pass `--data_dir path/to/images` to the training script, and it will take care of the rest. ## Training To train your model, you should first decide some hyperparameters. We will split up our hyperparameters into three groups: model architecture, diffusion process, and training flags. Here are some reasonable defaults for a baseline: ``` MODEL_FLAGS="--image_size 64 --num_channels 128 --num_res_blocks 3" DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule linear" TRAIN_FLAGS="--lr 1e-4 --batch_size 128" ``` Here are some changes we experiment with, and how to set them in the flags: * **Learned sigmas:** add `--learn_sigma True` to `MODEL_FLAGS` * **Cosine schedule:** change `--noise_schedule linear` to `--noise_schedule cosine` * **Importance-sampled VLB:** add `--use_kl True` to `DIFFUSION_FLAGS` and add `--schedule_sampler loss-second-moment` to `TRAIN_FLAGS`. * **Class-conditional:** add `--class_cond True` to `MODEL_FLAGS`. Once you have setup your hyper-parameters, you can run an experiment like so: ``` python scripts/image_train.py --data_dir path/to/images $MODEL_FLAGS $DIFFUSION_FLAGS $TRAIN_FLAGS ``` You may also want to train in a distributed manner. In this case, run the same command with `mpiexec`: ``` mpiexec -n $NUM_GPUS python scripts/image_train.py --data_dir path/to/images $MODEL_FLAGS $DIFFUSION_FLAGS $TRAIN_FLAGS ``` When training in a distributed manner, you must manually divide the `--batch_size` argument by the number of ranks. In lieu of distributed training, you may use `--microbatch 16` (or `--microbatch 1` in extreme memory-limited cases) to reduce memory usage. The logs and saved models will be written to a logging directory determined by the `OPENAI_LOGDIR` environment variable. If it is not set, then a temporary directory will be created in `/tmp`. ## Sampling The above training script saves checkpoints to `.pt` files in the logging directory. These checkpoints will have names like `ema_0.9999_200000.pt` and `model200000.pt`. You will likely want to sample from the EMA models, since those produce much better samples. Once you have a path to your model, you can generate a large batch of samples like so: ``` python scripts/image_sample.py --model_path /path/to/model.pt $MODEL_FLAGS $DIFFUSION_FLAGS ``` Again, this will save results to a logging directory. Samples are saved as a large `npz` file, where `arr_0` in the file is a large batch of samples. Just like for training, you can run `image_sample.py` through MPI to use multiple GPUs and machines. You can change the number of sampling steps using the `--timestep_respacing` argument. For example, `--timestep_respacing 250` uses 250 steps to sample. Passing `--timestep_respacing ddim250` is similar, but uses the uniform stride from the [DDIM paper](https://arxiv.org/abs/2010.02502) rather than our stride. To sample using [DDIM](https://arxiv.org/abs/2010.02502), pass `--use_ddim True`. ## Models and Hyperparameters This section includes model checkpoints and run flags for the main models in the paper. Note that the batch sizes are specified for single-GPU training, even though most of these runs will not naturally fit on a single GPU. To address this, either set `--microbatch` to a small value (e.g. 4) to train on one GPU, or run with MPI and divide `--batch_size` by the number of GPUs. Unconditional ImageNet-64 with our `L_hybrid` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_uncond_100M_1500K.pt)]: ```bash MODEL_FLAGS="--image_size 64 --num_channels 128 --num_res_blocks 3 --learn_sigma True" DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine" TRAIN_FLAGS="--lr 1e-4 --batch_size 128" ``` Unconditional CIFAR-10 with our `L_hybrid` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/cifar10_uncond_50M_500K.pt)]: ```bash MODEL_FLAGS="--image_size 32 --num_channels 128 --num_res_blocks 3 --learn_sigma True --dropout 0.3" DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine" TRAIN_FLAGS="--lr 1e-4 --batch_size 128" ``` Class-conditional ImageNet-64 model (270M parameters, trained for 250K iterations) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_cond_270M_250K.pt)]: ```bash MODEL_FLAGS="--image_size 64 --num_channels 192 --num_res_blocks 3 --learn_sigma True --class_cond True" DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine --rescale_learned_sigmas False --rescale_timesteps False" TRAIN_FLAGS="--lr 3e-4 --batch_size 2048" ``` Upsampling 256x256 model (280M parameters, trained for 500K iterations) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/upsample_cond_500K.pt)]: ```bash MODEL_FLAGS="--num_channels 192 --num_res_blocks 2 --learn_sigma True --class_cond True" DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False" TRAIN_FLAGS="--lr 3e-4 --batch_size 256" ``` LSUN bedroom model (lr=1e-4) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/lsun_uncond_100M_1200K_bs128.pt)]: ```bash MODEL_FLAGS="--image_size 256 --num_channels 128 --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16" DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False" TRAIN_FLAGS="--lr 1e-4 --batch_size 128" ``` LSUN bedroom model (lr=2e-5) [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/lsun_uncond_100M_2400K_bs64.pt)]: ```bash MODEL_FLAGS="--image_size 256 --num_channels 128 --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16" DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --use_scale_shift_norm False" TRAIN_FLAGS="--lr 2e-5 --batch_size 128" ``` Unconditional ImageNet-64 with the `L_vlb` objective and cosine noise schedule [[checkpoint](https://openaipublic.blob.core.windows.net/diffusion/march-2021/imagenet64_uncond_vlb_100M_1500K.pt)]: ```bash MODEL_FLAGS="--image_size 64 --num_channels 128 --num_res_blocks 3 --learn_sigma True" DIFFUSION_FLAGS="--diffusion_steps 4000 --noise_schedule cosine --use_kl True" TRAIN_FLAGS="--lr 1e-4 --batch_size 128 --schedule_sampler loss-second-moment" ```