Merge remote-tracking branch 'origin/main'
This commit is contained in:
Patrick Esser
commit
a166aa7fbf
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model:
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base_learning_rate: 7.5e-05
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target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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image_size: 64
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channels: 4
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cond_stage_trainable: false # Note: different from the one we trained before
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conditioning_key: hybrid # important
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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ckpt_path: "/fsx/stable-diffusion/stable-diffusion/checkpoints/v1pp/v1pphrflatlined2-pruned.ckpt"
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ucg_training:
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txt:
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p: 0.1
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val: ""
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 32 # unused
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in_channels: 9 # 4 data + 4 downscaled image + 1 mask
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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double_z: true
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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data:
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target: ldm.data.laion.WebDataModuleFromConfig
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params:
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tar_base: "__improvedaesthetic__"
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batch_size: 2
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num_workers: 4
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multinode: True
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min_size: 512
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max_pwatermark: 0.8
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train:
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shards: '{00000..17279}.tar -'
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shuffle: 10000
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image_key: jpg
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
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size: 512
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interpolation: 3
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- target: torchvision.transforms.RandomCrop
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params:
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size: 512
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postprocess:
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target: ldm.data.laion.AddMask
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params:
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mode: "512train-large"
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p_drop: 0.25
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# NOTE use enough shards to avoid empty validation loops in workers
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validation:
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shards: '{17280..17535}.tar -'
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shuffle: 0
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image_key: jpg
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
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size: 512
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interpolation: 3
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- target: torchvision.transforms.CenterCrop
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params:
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size: 512
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postprocess:
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target: ldm.data.laion.AddMask
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params:
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mode: "512train-large"
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p_drop: 0.25
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lightning:
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find_unused_parameters: False
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modelcheckpoint:
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params:
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every_n_train_steps: 2000
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callbacks:
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image_logger:
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target: main.ImageLogger
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params:
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disabled: False
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batch_frequency: 1000
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max_images: 4
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increase_log_steps: False
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log_first_step: False
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log_images_kwargs:
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use_ema_scope: False
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inpaint: False
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plot_progressive_rows: False
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plot_diffusion_rows: False
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N: 4
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unconditional_guidance_scale: 3.0
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unconditional_guidance_label: [""]
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ddim_steps: 100 # todo check these out for inpainting,
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ddim_eta: 1.0 # todo check these out for inpainting,
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trainer:
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benchmark: True
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val_check_interval: 5000000 # really sorry
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num_sanity_val_steps: 0
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accumulate_grad_batches: 2
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@ -242,15 +242,18 @@ class AddLR(object):
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class AddMask(PRNGMixin):
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def __init__(self, mode="512train"):
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def __init__(self, mode="512train", p_drop=0.):
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super().__init__()
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assert mode in list(MASK_MODES.keys()), f'unknown mask generation mode "{mode}"'
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self.make_mask = MASK_MODES[mode]
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self.p_drop = p_drop
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def __call__(self, sample):
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# sample['jpg'] is tensor hwc in [-1, 1] at this point
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x = sample['jpg']
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mask = self.make_mask(self.prng, x.shape[0], x.shape[1])
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if self.prng.choice(2, p=[1 - self.p_drop, self.p_drop]):
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mask = np.ones_like(mask)
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mask[mask < 0.5] = 0
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mask[mask > 0.5] = 1
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mask = torch.from_numpy(mask[..., None])
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@ -122,7 +122,6 @@ class DDPM(pl.LightningModule):
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if self.ucg_training:
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self.ucg_prng = np.random.RandomState()
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def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
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linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
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if exists(given_betas):
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@ -1603,7 +1602,9 @@ class LatentInpaintDiffusion(LatentDiffusion):
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To disable finetuning mode, set finetune_keys to None
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"""
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def __init__(self,
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finetune_keys=("model.diffusion_model.input_blocks.0.0.weight", "model_ema.diffusion_modelinput_blocks00weight"),
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finetune_keys=("model.diffusion_model.input_blocks.0.0.weight",
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"model_ema.diffusion_modelinput_blocks00weight"
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),
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concat_keys=("mask", "masked_image"),
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masked_image_key="masked_image",
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keep_finetune_dims=4, # if model was trained without concat mode before and we would like to keep these channels
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@ -1657,7 +1658,7 @@ class LatentInpaintDiffusion(LatentDiffusion):
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@torch.no_grad()
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def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
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# note: restricted to non-trainable encoders currently
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assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpaiting'
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assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting'
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z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
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force_c_encode=True, return_original_cond=True, bs=bs)
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@ -0,0 +1,30 @@
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#!/bin/bash
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# mpi version for node rank
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H=`hostname`
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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()]"`
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export NODE_RANK=${THEID}
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echo THEID=$THEID
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echo "##########################################"
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echo MASTER_ADDR=${MASTER_ADDR}
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echo MASTER_PORT=${MASTER_PORT}
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echo NODE_RANK=${NODE_RANK}
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echo WORLD_SIZE=${WORLD_SIZE}
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echo "##########################################"
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# debug environment worked great so we stick with it
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# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
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# env with pip dependencies from stable diffusion's requirements.txt
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eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
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conda activate stable
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cd /fsx/robin/stable-diffusion/stable-diffusion
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CONFIG="/fsx/robin/stable-diffusion/stable-diffusion/configs/stable-diffusion/inpainting/v1-finetune-for-inpainting-laion-aesthetic-larger-masks-and-ucfg.yaml"
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# debugging
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#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
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# detect bad gpus early on
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/bin/bash /fsx/stable-diffusion/stable-diffusion/scripts/test_gpu.sh
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python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False
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@ -0,0 +1,42 @@
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#!/bin/bash
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#SBATCH --partition=compute-od-gpu
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#SBATCH --job-name=stable-diffusion-v1-v1_inpainting_aesthetics-larger-masks
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#SBATCH --nodes 32
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#SBATCH --ntasks-per-node 1
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#SBATCH --cpus-per-gpu=4
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#SBATCH --gres=gpu:8
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#SBATCH --exclusive
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#SBATCH --output=%x_%j.out
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#SBATCH --comment "Key=Monitoring,Value=ON"
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module load intelmpi
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source /opt/intel/mpi/latest/env/vars.sh
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export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst
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all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
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export NCCL_PROTO=simple
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export PATH=/opt/amazon/efa/bin:$PATH
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export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
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export FI_EFA_FORK_SAFE=1
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export FI_LOG_LEVEL=1
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export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
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export NCCL_DEBUG=info
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export PYTHONFAULTHANDLER=1
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export CUDA_LAUNCH_BLOCKING=0
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export OMPI_MCA_mtl_base_verbose=1
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export FI_EFA_ENABLE_SHM_TRANSFER=0
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export FI_PROVIDER=efa
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export FI_EFA_TX_MIN_CREDITS=64
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export NCCL_TREE_THRESHOLD=0
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# sent to sub script
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export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
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export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
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export MASTER_PORT=12802
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export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
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export WORLD_SIZE=$COUNT_NODE
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echo go $COUNT_NODE
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echo $HOSTNAMES
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echo $WORLD_SIZE
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mpirun -n $COUNT_NODE -perhost 1 /fsx/robin/stable-diffusion/stable-diffusion/scripts/slurm/v1_inpainting_aesthetics-larger-masks-ucg/launcher.sh
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