Merge remote-tracking branch 'origin/main'

configs/stable-diffusion/inpainting/v1-finetune-for-inpainting-laion-aesthetic-larger-masks.yaml

@@ -0,0 +1,149 @@
+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"
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
+        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: 9  # 4 data + 4 downscaled image + 1 mask
+        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.AddMask
+        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.AddMask
+        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/upscaling/upscale-v1-with-f16.yaml

@@ -0,0 +1,214 @@
+model:
+  base_learning_rate: 5.0e-05
+  target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
+  params:
+    low_scale_key: "lr"
+    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: 32
+    channels: 16
+    cond_stage_trainable: false
+    conditioning_key: "hybrid-adm"
+    monitor: val/loss_simple_ema
+    scale_factor: 0.22765929   # magic number
+
+    low_scale_config:
+      target: ldm.modules.encoders.modules.LowScaleEncoder
+      params:
+        scale_factor: 0.18215
+        linear_start: 0.00085
+        linear_end: 0.0120
+        timesteps: 1000
+        max_noise_level: 250
+        output_size: null
+        model_config:
+          target: ldm.models.autoencoder.AutoencoderKL
+          params:
+            embed_dim: 4
+            monitor: val/rec_loss
+            ckpt_path: "/fsx/stable-diffusion/stable-diffusion/models/first_stage_models/kl-f8/model.ckpt"
+            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
+
+    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:
+        num_classes: 251  #  timesteps for noise conditoining
+        image_size: 64    # not really needed
+        in_channels: 20
+        out_channels: 16
+        model_channels: 128
+        attention_resolutions: [ 8, 4, 2 ] # -> at 32, 16, 8
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 6, 8 ]
+        # -> res, ds: (64, 1), (32, 2), (16, 4), (6, 8), (4, 16)
+        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: "/fsx/stable-diffusion/stable-diffusion/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:   # TODO: finetune here later
+#  target: ldm.data.laion.WebDataModuleFromConfig
+#  params:
+#    tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
+#    batch_size: 10
+#    num_workers: 4
+#    train:
+#      shards: '{00000..17279}.tar -'
+#      shuffle: 10000
+#      image_key: jpg
+#      image_transforms:
+#      - target: torchvision.transforms.Resize
+#        params:
+#          size: 1024
+#          interpolation: 3
+#      - target: torchvision.transforms.RandomCrop
+#        params:
+#          size: 1024
+#      postprocess:
+#              target: ldm.data.laion.AddLR
+#              params:
+#                factor: 2
+#
+#    # 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: 1024
+#          interpolation: 3
+#      - target: torchvision.transforms.CenterCrop
+#        params:
+#          size: 1024
+#      postprocess:
+#              target: ldm.data.laion.AddLR
+#              params:
+#                factor: 2
+
+data:
+  target: ldm.data.laion.WebDataModuleFromConfig
+  params:
+    tar_base: "__improvedaesthetic__"
+    batch_size: 28
+    num_workers: 4
+    multinode: True
+    min_size: 512
+    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.AddLR
+        params:
+          factor: 2
+
+    # 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.AddLR
+        params:
+          factor: 2
+
+
+lightning:
+  find_unused_parameters: False
+
+  callbacks:
+    image_logger:
+      target: main.ImageLogger
+      params:
+        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: [""]
+
+  trainer:
+    benchmark: True
+    val_check_interval: 5000000  # really sorry
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 2

ldm/data/inpainting/synthetic_mask.py

@@ -38,6 +38,18 @@ settings = {
             "max_s_box": 300,
             "marg": 10,
         },
+    "512train-large": {    # TODO: experimental
+            "p_irr": 0.5,
+            "min_n_irr": 1,
+            "max_n_irr": 5,
+            "max_l_irr": 450,
+            "max_w_irr": 400,
+            "min_n_box": 1,
+            "max_n_box": 4,
+            "min_s_box": 75,
+            "max_s_box": 450,
+            "marg": 10,
+        },
 }
 
 
@@ -128,14 +140,18 @@ def gen_large_mask(prng, img_h, img_w,
     return mask
 
 
-make_lama_mask = lambda prng, h, w: gen_large_mask(prng, h, w,
+make_lama_mask = lambda prng, h, w: gen_large_mask(prng, h, w, **settings["256train"])
-                                                   **settings["256train"])
+make_narrow_lama_mask = lambda prng, h, w: gen_large_mask(prng, h, w, **settings["256narrow"])
+make_512_lama_mask = lambda prng, h, w: gen_large_mask(prng, h, w, **settings["512train"])
+make_512_lama_mask_large = lambda prng, h, w: gen_large_mask(prng, h, w, **settings["512train-large"])
 
-make_narrow_lama_mask = lambda prng, h, w: gen_large_mask(prng, h, w,
-                                                          **settings["256narrow"])
 
-make_512_lama_mask = lambda prng, h, w: gen_large_mask(prng, h, w,
+MASK_MODES = {
-                                                       **settings["512train"])
+    "256train": make_lama_mask,
+    "256narrow": make_narrow_lama_mask,
+    "512train": make_512_lama_mask,
+    "512train-large": make_512_lama_mask_large
+}
 
 if __name__ == "__main__":
     import sys

ldm/data/laion.py

@@ -16,7 +16,7 @@ from webdataset.handlers import warn_and_continue
 
 
 from ldm.util import instantiate_from_config
-from ldm.data.inpainting.synthetic_mask import gen_large_mask, make_lama_mask, make_narrow_lama_mask, make_512_lama_mask
+from ldm.data.inpainting.synthetic_mask import gen_large_mask, MASK_MODES
 from ldm.data.base import PRNGMixin
 
 
@@ -232,9 +232,10 @@ class AddLR(object):
 
 
 class AddMask(PRNGMixin):
-    def __init__(self, size=512):
+    def __init__(self, mode="512train"):
         super().__init__()
-        self.make_mask = make_512_lama_mask if size == 512 else make_lama_mask
+        assert mode in list(MASK_MODES.keys()), f'unknown mask generation mode "{mode}"'
+        self.make_mask = MASK_MODES[mode]
 
     def __call__(self, sample):
         # sample['jpg'] is tensor hwc in [-1, 1] at this point

ldm/models/diffusion/ddpm.py

@@ -1487,9 +1487,10 @@ class LatentUpscaleDiffusion(LatentDiffusion):
         x_low = x_low.to(memory_format=torch.contiguous_format).float()
         zx, noise_level = self.low_scale_model(x_low)
         all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
+        #import pudb; pu.db
         if log_mode:
             # TODO: maybe disable if too expensive
-            interpretability = True
+            interpretability = False
             if interpretability:
                 zx = zx[:, :, ::2, ::2]
             x_low_rec = self.low_scale_model.decode(zx)
@@ -1567,13 +1568,13 @@ class LatentUpscaleDiffusion(LatentDiffusion):
                 if k == "c_crossattn":
                     assert isinstance(c[k], list) and len(c[k]) == 1
                     uc[k] = [uc_tmp]
-                elif k == "c_adm":
+                elif k == "c_adm":  # todo: only run with text-based guidance?
                     assert isinstance(c[k], torch.Tensor)
-                    uc[k] = torch.ones_like(c[k]) * (self.low_scale_model.max_noise_level-1)
+                    uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
                 elif isinstance(c[k], list):
-                    uc[k] = [torch.zeros_like(c[k][i]) for i in range(len(c[k]))]
+                    uc[k] = [c[k][i] for i in range(len(c[k]))]
                 else:
-                    uc[k] = torch.zeros_like(c[k])
+                    uc[k] = c[k]
 
             with ema_scope("Sampling with classifier-free guidance"):
                 samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
@@ -1642,8 +1643,7 @@ class LatentInpaintDiffusion(LatentDiffusion):
                 new_entry[:, :self.keep_dims, ...] = sd[k]
                 sd[k] = new_entry
 
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False)
-            sd, strict=False)
         print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
         if len(missing) > 0:
             print(f"Missing Keys: {missing}")

ldm/modules/encoders/modules.py

@@ -255,8 +255,9 @@ class LowScaleEncoder(nn.Module):
         z = z * self.scale_factor
         noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
         z = self.q_sample(z, noise_level)
-        #z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest")  # TODO: experiment with mode
+        if self.out_size is not None:
-        z = z.repeat_interleave(2, -2).repeat_interleave(2, -1)
+            z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest")  # TODO: experiment with mode
+        # z = z.repeat_interleave(2, -2).repeat_interleave(2, -1)
         return z, noise_level
 
     def decode(self, z):

scripts/slurm/v1-upscaling-f16-pretraining-512-aesthetics/launcher.sh

@@ -0,0 +1,27 @@
+#!/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 "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
+conda activate torch111
+cd /fsx/robin/stable-diffusion/stable-diffusion
+
+CONFIG="/fsx/robin/stable-diffusion/stable-diffusion/configs/stable-diffusion/upscaling/upscale-v1-with-f16.yaml"
+
+# debugging
+#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
+
+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/v1-upscaling-f16-pretraining-512-aesthetics/sbatch.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+#SBATCH --partition=compute-od-gpu
+#SBATCH --job-name=stable-diffusion-v1-upscaling-f16-pretraining-512-aesthetics
+#SBATCH --nodes 8
+#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"
+
+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-inst
+all/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/robin/stable-diffusion/stable-diffusion/scripts/slurm/v1-upscaling-f16-pretraining-512-aesthetics/launcher.sh

scripts/slurm/v1_inpainting_aesthetics-larger-masks/launcher.sh

@@ -0,0 +1,27 @@
+#!/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 "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
+conda activate torch111
+cd /fsx/robin/stable-diffusion/stable-diffusion
+
+CONFIG="/fsx/robin/stable-diffusion/stable-diffusion/configs/stable-diffusion/inpainting/v1-finetune-for-inpainting-laion-aesthetic-larger-masks.yaml"
+
+# debugging
+#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
+
+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_inpainting_aesthetics-larger-masks/sbatch.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+#SBATCH --partition=compute-od-gpu
+#SBATCH --job-name=stable-diffusion-v1-v1_inpainting_aesthetics-larger-masks
+#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"
+
+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-inst
+all/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/robin/stable-diffusion/stable-diffusion/scripts/slurm/v1_inpainting_aesthetics-larger-masks/launcher.sh