Merge branch 'main' of github.com:pesser/stable-diffusion into main

configs/stable-diffusion/inpainting/v1-edgeinpainting.yaml

@@ -0,0 +1,157 @@
+model:
+  base_learning_rate: 7.5e-05
+  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid   # important
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    ckpt_path: "/fsx/stable-diffusion/stable-diffusion/checkpoints/v1pp/v1pp-flatlined-hr.ckpt"
+
+    concat_keys:
+      - mask
+      - masked_image
+      - smoothing_strength
+
+    c_concat_log_start: 1
+    c_concat_log_end: 5
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 2500 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 10  # 4 data + 4 downscaled image + 1 mask + 1 strength
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+
+data:
+  target: ldm.data.laion.WebDataModuleFromConfig
+  params:
+    tar_base: "__improvedaesthetic__"
+    batch_size: 2
+    num_workers: 4
+    multinode: True
+    min_size: 512
+    max_pwatermark: 0.8
+    train:
+      shards: '{00000..17279}.tar -'
+      shuffle: 10000
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 512
+          interpolation: 3
+      - target: torchvision.transforms.RandomCrop
+        params:
+          size: 512
+      postprocess:
+        target: ldm.data.laion.AddEdge
+        params:
+          mode: "512train-large"
+
+    # NOTE use enough shards to avoid empty validation loops in workers
+    validation:
+      shards: '{17280..17535}.tar -'
+      shuffle: 0
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 512
+          interpolation: 3
+      - target: torchvision.transforms.CenterCrop
+        params:
+          size: 512
+      postprocess:
+        target: ldm.data.laion.AddEdge
+        params:
+          mode: "512train-large"
+
+
+lightning:
+  find_unused_parameters: False
+
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 2000
+
+  callbacks:
+    image_logger:
+      target: main.ImageLogger
+      params:
+        disabled: False
+        batch_frequency: 1000
+        max_images: 4
+        increase_log_steps: False
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          inpaint: False
+          plot_progressive_rows: False
+          plot_diffusion_rows: False
+          N: 4
+          unconditional_guidance_scale: 3.0
+          unconditional_guidance_label: [""]
+          ddim_steps: 100  # todo check these out for inpainting,
+          ddim_eta: 1.0   # todo check these out for inpainting,
+
+  trainer:
+    benchmark: True
+    val_check_interval: 5000000  # really sorry
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 2

configs/stable-diffusion/v2_pretraining.yaml

@@ -0,0 +1,131 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.001
+    linear_end: 0.015
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 16
+    channels: 16
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.22765929   # magic number
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 16    # not really needed
+        in_channels: 16
+        out_channels: 16
+        model_channels: 320    # TODO: scale model here
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 16
+        monitor: val/rec_loss
+        ckpt_path: "models/first_stage_models/kl-f16/model.ckpt"
+        ddconfig:
+          double_z: True
+          z_channels: 16
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [ 1,1,2,2,4 ]  # num_down = len(ch_mult)-1
+          num_res_blocks: 2
+          attn_resolutions: [ 16 ]
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+
+data:
+  target: ldm.data.laion.WebDataModuleFromConfig
+  params:
+    tar_base: "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/"
+    batch_size: 55
+    num_workers: 4
+    multinode: True
+    min_size: 256
+    train:
+      shards: '{000000..231317}.tar -'
+      shuffle: 10000
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 256
+          interpolation: 3
+      - target: torchvision.transforms.RandomCrop
+        params:
+          size: 256
+
+    # NOTE use enough shards to avoid empty validation loops in workers
+    validation:
+      shards: '{231318..231349}.tar -'
+      shuffle: 0
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 256
+          interpolation: 3
+      - target: torchvision.transforms.CenterCrop
+        params:
+          size: 256
+
+
+lightning:
+  find_unused_parameters: false
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 5000
+  callbacks:
+    image_logger:
+      target: main.ImageLogger
+      params:
+        batch_frequency: 5000
+        max_images: 4
+        increase_log_steps: False
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          inpaint: False
+          plot_progressive_rows: False
+          plot_diffusion_rows: False
+          N: 4
+          unconditional_guidance_scale: 3.0
+          unconditional_guidance_label: [""]
+
+  trainer:
+    benchmark: True
+    val_check_interval: 5000000 # really sorry
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 1

environment.yaml

@@ -23,6 +23,7 @@ dependencies:
     - torch-fidelity==0.3.0
     - transformers==4.3.1
     - webdataset==0.2.5
+    - kornia==0.6
     - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
     - -e git+https://github.com/openai/CLIP.git@main#egg=clip
     - -e .

ldm/data/laion.py

@@ -1,4 +1,5 @@
 import webdataset as wds
+import kornia
 from PIL import Image
 import io
 import os
@@ -185,10 +186,19 @@ class WebDataModuleFromConfig(pl.LightningDataModule):
         return loader
 
     def filter_size(self, x):
-        if self.min_size is None:
-            return True
         try:
-            return x['json']['original_width'] >= self.min_size and x['json']['original_height'] >= self.min_size and x['json']['pwatermark'] <= self.max_pwatermark
+            valid = True
+            if self.min_size is not None and self.min_size > 1:
+                try:
+                    valid = valid and x['json']['original_width'] >= self.min_size and x['json']['original_height'] >= self.min_size
+                except Exception:
+                    valid = False
+            if self.max_pwatermark is not None and self.max_pwatermark < 1.0:
+                try:
+                    valid = valid and  x['json']['pwatermark'] <= self.max_pwatermark
+                except Exception:
+                    valid = False
+            return valid
         except Exception:
             return False
 
@@ -252,6 +262,76 @@ class AddMask(PRNGMixin):
         return sample
 
 
+class AddEdge(PRNGMixin):
+    def __init__(self, mode="512train", mask_edges=True):
+        super().__init__()
+        assert mode in list(MASK_MODES.keys()), f'unknown mask generation mode "{mode}"'
+        self.make_mask = MASK_MODES[mode]
+        self.n_down_choices = [0, 1, 2]
+        self.sigma_choices = [1, 2, 3, 4, 5]
+        self.mask_edges = mask_edges
+
+    @torch.no_grad()
+    def __call__(self, sample):
+        # sample['jpg'] is tensor hwc in [-1, 1] at this point
+        x = sample['jpg']
+
+        mask = self.make_mask(self.prng, x.shape[0], x.shape[1])
+        mask[mask < 0.5] = 0
+        mask[mask > 0.5] = 1
+        mask = torch.from_numpy(mask[..., None])
+        sample['mask'] = mask
+
+        n_down_idx = self.prng.choice(len(self.n_down_choices))
+        sigma_idx = self.prng.choice(len(self.sigma_choices))
+
+        n_choices = len(self.n_down_choices)*len(self.sigma_choices)
+        raveled_idx = np.ravel_multi_index((n_down_idx, sigma_idx),
+                                           (len(self.n_down_choices), len(self.sigma_choices)))
+        normalized_idx = raveled_idx/(n_choices-1)
+
+        n_down = self.n_down_choices[n_down_idx]
+        sigma = self.sigma_choices[sigma_idx]
+
+        kernel_size = 4*sigma+1
+        kernel_size = (kernel_size, kernel_size)
+        sigma = (sigma, sigma)
+        canny = kornia.filters.Canny(
+                low_threshold=0.1,
+                high_threshold=0.2,
+                kernel_size=kernel_size,
+                sigma=sigma,
+                hysteresis=True,
+                )
+        y = (x+1.0)/2.0 # in 01
+        y = y.unsqueeze(0).permute(0, 3, 1, 2).contiguous()
+
+        # down
+        for i_down in range(n_down):
+            size = min(y.shape[-2], y.shape[-1])//2
+            y = kornia.geometry.transform.resize(y, size, antialias=True)
+
+        # edge
+        _, y = canny(y)
+
+        if n_down > 0:
+            size = x.shape[0], x.shape[1]
+            y = kornia.geometry.transform.resize(y, size, interpolation="nearest")
+
+        y = y.permute(0, 2, 3, 1)[0].expand(-1, -1, 3).contiguous()
+        y = y*2.0-1.0
+
+        if self.mask_edges:
+            sample['masked_image'] = y * (mask < 0.5)
+        else:
+            sample['masked_image'] = y
+
+        # concat normalized idx
+        sample['smoothing_strength'] = torch.ones_like(sample['mask'])*normalized_idx
+
+        return sample
+
+
 def example00():
     url = "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/000000.tar -"
     dataset = wds.WebDataset(url)

ldm/models/diffusion/ddpm.py

@@ -1608,6 +1608,8 @@ class LatentInpaintDiffusion(LatentDiffusion):
                  concat_keys=("mask", "masked_image"),
                  masked_image_key="masked_image",
                  keep_finetune_dims=4,  # if model was trained without concat mode before and we would like to keep these channels
+                 c_concat_log_start=None, # to log reconstruction of c_concat codes
+                 c_concat_log_end=None,
                  *args, **kwargs
                  ):
         ckpt_path = kwargs.pop("ckpt_path", None)
@@ -1618,6 +1620,8 @@ class LatentInpaintDiffusion(LatentDiffusion):
         self.finetune_keys = finetune_keys
         self.concat_keys = concat_keys
         self.keep_dims = keep_finetune_dims
+        self.c_concat_log_start = c_concat_log_start
+        self.c_concat_log_end = c_concat_log_end
         if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint'
         if exists(ckpt_path):
             self.init_from_ckpt(ckpt_path, ignore_keys)
@@ -1708,6 +1712,9 @@ class LatentInpaintDiffusion(LatentDiffusion):
             if ismap(xc):
                 log["original_conditioning"] = self.to_rgb(xc)
 
+        if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
+            log["c_concat_decoded"] = self.decode_first_stage(c_cat[:,self.c_concat_log_start:self.c_concat_log_end])
+
         if plot_diffusion_rows:
             # get diffusion row
             diffusion_row = list()

scripts/printckpt.py

@@ -10,6 +10,8 @@ def printit(p):
     sd = torch.load(p, map_location="cpu")
     if "global_step" in sd:
         print(f"This is global step {sd['global_step']}.")
+    if "model_ema.num_updates" in sd["state_dict"]:
+        print(f"And we got {sd['state_dict']['model_ema.num_updates']} EMA updates.")
 
 
 if __name__ == "__main__":

scripts/slurm/v1_edgeinpainting/launcher.sh

@@ -0,0 +1,41 @@
+#!/bin/bash
+
+# mpi version for node rank
+H=`hostname`
+THEID=`echo -e $HOSTNAMES  | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
+export NODE_RANK=${THEID}
+echo THEID=$THEID
+
+echo "##########################################"
+echo MASTER_ADDR=${MASTER_ADDR}
+echo MASTER_PORT=${MASTER_PORT}
+echo NODE_RANK=${NODE_RANK}
+echo WORLD_SIZE=${WORLD_SIZE}
+echo "##########################################"
+# debug environment worked great so we stick with it
+# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
+# env with pip dependencies from stable diffusion's requirements.txt
+eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
+#conda activate stable
+# torch 1.11 to avoid bug in ckpt restoring
+conda activate torch111
+cd /fsx/stable-diffusion/stable-diffusion
+
+CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/inpainting/v1-edgeinpainting.yaml"
+
+# resume and set new seed to reshuffle data
+#EXTRA="--seed 543 --resume_from_checkpoint ..."
+
+# reduce lr a bit
+#EXTRA="${EXTRA} model.params.scheduler_config.params.f_max=[0.75]"
+
+# custom logdir
+#EXTRA="${EXTRA} --logdir rlogs"
+
+# debugging
+#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
+
+# detect bad gpus early on
+/bin/bash /fsx/stable-diffusion/stable-diffusion/scripts/test_gpu.sh
+
+python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False

scripts/slurm/v1_edgeinpainting/sbatch.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+#SBATCH --partition=compute-od-gpu
+#SBATCH --job-name=stable-diffusion-v1-edgeinpainting
+#SBATCH --nodes 24
+#SBATCH --ntasks-per-node 1
+#SBATCH --cpus-per-gpu=4
+#SBATCH --gres=gpu:8
+#SBATCH --exclusive
+#SBATCH --output=%x_%j.out
+#SBATCH --comment "Key=Monitoring,Value=ON"
+#SBATCH --no-requeue
+
+module load intelmpi
+source /opt/intel/mpi/latest/env/vars.sh
+export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-install/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
+export NCCL_PROTO=simple
+export PATH=/opt/amazon/efa/bin:$PATH
+export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
+export FI_EFA_FORK_SAFE=1
+export FI_LOG_LEVEL=1
+export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
+export NCCL_DEBUG=info
+export PYTHONFAULTHANDLER=1
+export CUDA_LAUNCH_BLOCKING=0
+export OMPI_MCA_mtl_base_verbose=1
+export FI_EFA_ENABLE_SHM_TRANSFER=0
+export FI_PROVIDER=efa
+export FI_EFA_TX_MIN_CREDITS=64
+export NCCL_TREE_THRESHOLD=0
+
+# sent to sub script
+export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
+export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_PORT=12802
+export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
+export WORLD_SIZE=$COUNT_NODE
+
+echo go $COUNT_NODE
+echo $HOSTNAMES
+echo $WORLD_SIZE
+
+mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v1_edgeinpainting/launcher.sh

scripts/slurm/v1_iahr_torch111_ucg/launcher.sh

@@ -27,7 +27,8 @@ CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_impro
 #EXTRA="--seed 718 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-22T07-45-07_v1_improvedaesthetics/checkpoints/last.ckpt"
 #EXTRA="--seed 719 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-22T12-32-32_v1_improvedaestheticsv1_iahr_torch111/checkpoints/last.ckpt"
 #EXTRA="--seed 720 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-23T07-52-21_v1_improvedaestheticsv1_iahr_torch111/checkpoints/last.ckpt"
-EXTRA="--seed 721 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-24T19-07-33_v1_improvedaestheticsv1_iahr_torch111/checkpoints/last.ckpt"
+#EXTRA="--seed 721 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-24T19-07-33_v1_improvedaestheticsv1_iahr_torch111/checkpoints/last.ckpt"
+EXTRA="--seed 722 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-29T10-26-01_v1_improvedaestheticsv1_iahr_torch111_ucg/checkpoints/last.ckpt"
 
 # only images >= 512 and pwatermark <= 0.4999
 EXTRA="${EXTRA} data.params.min_size=512 data.params.max_pwatermark=0.4999"
@@ -35,6 +36,9 @@ EXTRA="${EXTRA} data.params.min_size=512 data.params.max_pwatermark=0.4999"
 # unconditional guidance training
 EXTRA="${EXTRA} model.params.ucg_training.txt.p=0.1 model.params.ucg_training.txt.val=''"
 
+# reduce lr a bit
+EXTRA="${EXTRA} model.params.scheduler_config.params.f_max=[0.5]"
+
 # postfix
 EXTRA="${EXTRA} -f v1_iahr_torch111_ucg"
 

scripts/slurm/v2_pretraining/launcher.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+
+# mpi version for node rank
+H=`hostname`
+THEID=`echo -e $HOSTNAMES  | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
+export NODE_RANK=${THEID}
+echo THEID=$THEID
+
+echo "##########################################"
+echo MASTER_ADDR=${MASTER_ADDR}
+echo MASTER_PORT=${MASTER_PORT}
+echo NODE_RANK=${NODE_RANK}
+echo WORLD_SIZE=${WORLD_SIZE}
+echo "##########################################"
+# debug environment worked great so we stick with it
+# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
+# env with pip dependencies from stable diffusion's requirements.txt
+eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
+#conda activate stable
+# torch 1.11 to avoid bug in ckpt restoring
+conda activate torch111
+cd /fsx/stable-diffusion/stable-diffusion
+
+CONFIG=configs/stable-diffusion/v2_pretraining.yaml
+
+# resume and set new seed to reshuffle data
+#EXTRA="--seed 542 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints/v2-256/216k-256.ckpt"
+EXTRA="--seed 543 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-31T23-35-31_v2_pretraining/checkpoints/last.ckpt"
+
+# reduce lr a bit
+#EXTRA="${EXTRA} model.params.scheduler_config.params.f_max=[0.75]"
+
+# custom logdir
+#EXTRA="${EXTRA} --logdir rlogs"
+
+# debugging
+#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
+
+# detect bad gpus early on
+/bin/bash /fsx/stable-diffusion/stable-diffusion/scripts/test_gpu.sh
+
+python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA

scripts/slurm/v2_pretraining/sbatch.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+#SBATCH --partition=compute-od-gpu
+#SBATCH --job-name=stable-diffusion-v2-pretraining
+#SBATCH --nodes 32
+#SBATCH --ntasks-per-node 1
+#SBATCH --cpus-per-gpu=4
+#SBATCH --gres=gpu:8
+#SBATCH --exclusive
+#SBATCH --output=%x_%j.out
+#SBATCH --comment "Key=Monitoring,Value=ON"
+#SBATCH --no-requeue
+
+module load intelmpi
+source /opt/intel/mpi/latest/env/vars.sh
+export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-install/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
+export NCCL_PROTO=simple
+export PATH=/opt/amazon/efa/bin:$PATH
+export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
+export FI_EFA_FORK_SAFE=1
+export FI_LOG_LEVEL=1
+export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
+export NCCL_DEBUG=info
+export PYTHONFAULTHANDLER=1
+export CUDA_LAUNCH_BLOCKING=0
+export OMPI_MCA_mtl_base_verbose=1
+export FI_EFA_ENABLE_SHM_TRANSFER=0
+export FI_PROVIDER=efa
+export FI_EFA_TX_MIN_CREDITS=64
+export NCCL_TREE_THRESHOLD=0
+
+# sent to sub script
+export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
+export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_PORT=12802
+export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
+export WORLD_SIZE=$COUNT_NODE
+
+echo go $COUNT_NODE
+echo $HOSTNAMES
+echo $WORLD_SIZE
+
+mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v2_pretraining/launcher.sh