Merge remote-tracking branch 'origin/main'

configs/stable-diffusion/inpainting/v1-finetune-for-inpainting-laion-iaesthe.yaml

@@ -0,0 +1,144 @@
+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/checkpoints2/v1pp/v1pp-flatline-pruned.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: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
+    batch_size: 4
+    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.AddMask
+
+    # 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
+
+
+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

ldm/data/base.py

@@ -1,3 +1,5 @@
+import os
+import numpy as np
 from abc import abstractmethod
 from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
 
@@ -21,3 +23,18 @@ class Txt2ImgIterableBaseDataset(IterableDataset):
     @abstractmethod
     def __iter__(self):
         pass
+
+
+class PRNGMixin(object):
+    """
+    Adds a prng property which is a numpy RandomState which gets
+    reinitialized whenever the pid changes to avoid synchronized sampling
+    behavior when used in conjunction with multiprocessing.
+    """
+    @property
+    def prng(self):
+        currentpid = os.getpid()
+        if getattr(self, "_initpid", None) != currentpid:
+            self._initpid = currentpid
+            self._prng = np.random.RandomState()
+        return self._prng

ldm/data/inpainting/synthetic_mask.py

@@ -0,0 +1,150 @@
+from PIL import Image, ImageDraw
+import numpy as np
+
+settings = {
+    "256narrow": {
+        "p_irr": 1,
+        "min_n_irr": 4,
+        "max_n_irr": 50,
+        "max_l_irr": 40,
+        "max_w_irr": 10,
+        "min_n_box": None,
+        "max_n_box": None,
+        "min_s_box": None,
+        "max_s_box": None,
+        "marg": None,
+    },
+    "256train": {
+        "p_irr": 0.5,
+        "min_n_irr": 1,
+        "max_n_irr": 5,
+        "max_l_irr": 200,
+        "max_w_irr": 100,
+        "min_n_box": 1,
+        "max_n_box": 4,
+        "min_s_box": 30,
+        "max_s_box": 150,
+        "marg": 10,
+    },
+    "512train": {    # TODO: experimental
+            "p_irr": 0.5,
+            "min_n_irr": 1,
+            "max_n_irr": 5,
+            "max_l_irr": 450,
+            "max_w_irr": 250,
+            "min_n_box": 1,
+            "max_n_box": 4,
+            "min_s_box": 30,
+            "max_s_box": 300,
+            "marg": 10,
+        },
+}
+
+
+def gen_segment_mask(mask, start, end, brush_width):
+    mask = mask > 0
+    mask = (255 * mask).astype(np.uint8)
+    mask = Image.fromarray(mask)
+    draw = ImageDraw.Draw(mask)
+    draw.line([start, end], fill=255, width=brush_width, joint="curve")
+    mask = np.array(mask) / 255
+    return mask
+
+
+def gen_box_mask(mask, masked):
+    x_0, y_0, w, h = masked
+    mask[y_0:y_0 + h, x_0:x_0 + w] = 1
+    return mask
+
+
+def gen_round_mask(mask, masked, radius):
+    x_0, y_0, w, h = masked
+    xy = [(x_0, y_0), (x_0 + w, y_0 + w)]
+
+    mask = mask > 0
+    mask = (255 * mask).astype(np.uint8)
+    mask = Image.fromarray(mask)
+    draw = ImageDraw.Draw(mask)
+    draw.rounded_rectangle(xy, radius=radius, fill=255)
+    mask = np.array(mask) / 255
+    return mask
+
+
+def gen_large_mask(prng, img_h, img_w,
+                   marg, p_irr, min_n_irr, max_n_irr, max_l_irr, max_w_irr,
+                   min_n_box, max_n_box, min_s_box, max_s_box):
+    """
+    img_h: int, an image height
+    img_w: int, an image width
+    marg: int, a margin for a box starting coordinate
+    p_irr: float, 0 <= p_irr <= 1, a probability of a polygonal chain mask
+
+    min_n_irr: int, min number of segments
+    max_n_irr: int, max number of segments
+    max_l_irr: max length of a segment in polygonal chain
+    max_w_irr: max width of a segment in polygonal chain
+
+    min_n_box: int, min bound for the number of box primitives
+    max_n_box: int, max bound for the number of box primitives
+    min_s_box: int, min length of a box side
+    max_s_box: int, max length of a box side
+    """
+
+    mask = np.zeros((img_h, img_w))
+    uniform = prng.randint
+
+    if np.random.uniform(0, 1) < p_irr:  # generate polygonal chain
+        n = uniform(min_n_irr, max_n_irr)  # sample number of segments
+
+        for _ in range(n):
+            y = uniform(0, img_h)  # sample a starting point
+            x = uniform(0, img_w)
+
+            a = uniform(0, 360)  # sample angle
+            l = uniform(10, max_l_irr)  # sample segment length
+            w = uniform(5, max_w_irr)  # sample a segment width
+
+            # draw segment starting from (x,y) to (x_,y_) using brush of width w
+            x_ = x + l * np.sin(a)
+            y_ = y + l * np.cos(a)
+
+            mask = gen_segment_mask(mask, start=(x, y), end=(x_, y_), brush_width=w)
+            x, y = x_, y_
+    else:  # generate Box masks
+        n = uniform(min_n_box, max_n_box)  # sample number of rectangles
+
+        for _ in range(n):
+            h = uniform(min_s_box, max_s_box)  # sample box shape
+            w = uniform(min_s_box, max_s_box)
+
+            x_0 = uniform(marg, img_w - marg - w)  # sample upper-left coordinates of box
+            y_0 = uniform(marg, img_h - marg - h)
+
+            if np.random.uniform(0, 1) < 0.5:
+                mask = gen_box_mask(mask, masked=(x_0, y_0, w, h))
+            else:
+                r = uniform(0, 60)  # sample radius
+                mask = gen_round_mask(mask, masked=(x_0, y_0, w, h), radius=r)
+    return mask
+
+
+make_lama_mask = lambda prng, h, w: gen_large_mask(prng, h, w,
+                                                   **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"])
+
+if __name__ == "__main__":
+    import sys
+
+    out = sys.argv[1]
+
+    prng = np.random.RandomState(1)
+    kwargs = settings["256train"]
+    mask = gen_large_mask(prng, 256, 256, **kwargs)
+    mask = (255 * mask).astype(np.uint8)
+    mask = Image.fromarray(mask)
+    mask.save(out)

ldm/data/laion.py

@@ -16,6 +16,8 @@ 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.base import PRNGMixin
 
 
 class DataWithWings(torch.utils.data.IterableDataset):
@@ -229,6 +231,23 @@ class AddLR(object):
         return sample
 
 
+class AddMask(PRNGMixin):
+    def __init__(self, size=512):
+        super().__init__()
+        self.make_mask = make_512_lama_mask if size == 512 else make_lama_mask
+
+    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
+        sample['masked_image'] = x * (mask < 0.5)
+        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

@@ -1260,7 +1260,6 @@ class LatentDiffusion(DDPM):
                    use_ema_scope=True,
                    **kwargs):
         ema_scope = self.ema_scope if use_ema_scope else nullcontext
-
         use_ddim = ddim_steps is not None
 
         log = dict()
@@ -1582,6 +1581,168 @@ class LatentUpscaleDiffusion(LatentDiffusion):
         return log
 
 
+class LatentInpaintDiffusion(LatentDiffusion):
+    """
+    can either run as pure inpainting model (only concat mode) or with mixed conditionings,
+    e.g. mask as concat and text via cross-attn.
+    To disable finetuning mode, set finetune_keys to None
+     """
+    def __init__(self,
+                 finetune_keys=("model.diffusion_model.input_blocks.0.0.weight", "model_ema.diffusion_modelinput_blocks00weight"),
+                 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
+                 *args, **kwargs
+                 ):
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ignore_keys = kwargs.pop("ignore_keys", list())
+        super().__init__(*args, **kwargs)
+        self.masked_image_key = masked_image_key
+        assert self.masked_image_key in concat_keys
+        self.finetune_keys = finetune_keys
+        self.concat_keys = concat_keys
+        self.keep_dims = keep_finetune_dims
+        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)
+
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+
+            # make it explicit, finetune by including extra input channels
+            if exists(self.finetune_keys) and k in self.finetune_keys:
+                new_entry = None
+                for name, param in self.named_parameters():
+                    if name in self.finetune_keys:
+                        print(f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only")
+                        new_entry = torch.zeros_like(param)  # zero init
+                assert exists(new_entry), 'did not find matching parameter to modify'
+                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(
+            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}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    @torch.no_grad()
+    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+        # note: restricted to non-trainable encoders currently
+        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpaiting'
+        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+                                              force_c_encode=True, return_original_cond=True, bs=bs)
+
+        assert exists(self.concat_keys)
+        c_cat = list()
+        for ck in self.concat_keys:
+            cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
+            if bs is not None:
+                cc = cc[:bs]
+                cc = cc.to(self.device)
+            bchw = z.shape
+            if ck != self.masked_image_key:
+                cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
+            else:
+                cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
+            c_cat.append(cc)
+        c_cat = torch.cat(c_cat, dim=1)
+        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+        if return_first_stage_outputs:
+            return z, all_conds, x, xrec, xc
+        return z, all_conds
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+                   use_ema_scope=True,
+                   **kwargs):
+        ema_scope = self.ema_scope if use_ema_scope else nullcontext
+        use_ddim = ddim_steps is not None
+
+        log = dict()
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
+        c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption", "txt"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+                log["conditioning"] = xc
+            elif self.cond_stage_key == 'class_label':
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+                log['conditioning'] = xc
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with ema_scope("Sampling"):
+                samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+                                                         batch_size=N, ddim=use_ddim,
+                                                         ddim_steps=ddim_steps, eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+        if unconditional_guidance_scale > 1.0:
+            uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+            uc_cat = c_cat
+            uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
+            with ema_scope("Sampling with classifier-free guidance"):
+                samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+                                                 batch_size=N, ddim=use_ddim,
+                                                 ddim_steps=ddim_steps, eta=ddim_eta,
+                                                 unconditional_guidance_scale=unconditional_guidance_scale,
+                                                 unconditional_conditioning=uc_full,
+                                                 )
+                x_samples_cfg = self.decode_first_stage(samples_cfg)
+                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+        log["masked_image"] = rearrange(batch["masked_image"],
+                                        'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
+        return log
+
+
 class Layout2ImgDiffusion(LatentDiffusion):
     # TODO: move all layout-specific hacks to this class
     def __init__(self, cond_stage_key, *args, **kwargs):

scripts/slurm/v1_inpainting_improvedaesthetics_torch111/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 "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
+conda activate torch111
+cd /fsx/robin/stable-diffusion/stable-diffusion
+
+CONFIG="/fsx/robin/stable-diffusion/stable-diffusion/configs/stable-diffusion/inpainting/v1-finetune-for-inpainting-laion-iaesthe.yaml"
+
+# debugging
+#EXTRA="${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_inpainting_improvedaesthetics_torch111/sbatch.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+#SBATCH --partition=compute-od-gpu
+#SBATCH --job-name=stable-diffusion-v1-inpainting-improvedaesthetics-torch111
+#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_inpainting_improvedaesthetics_torch111/launcher.sh