v1 edgeinpainting
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Patrick Esser
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157
configs/stable-diffusion/inpainting/v1-edgeinpainting.yaml
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157
configs/stable-diffusion/inpainting/v1-edgeinpainting.yaml
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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/v1pp-flatlined-hr.ckpt"
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concat_keys:
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- mask
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- masked_image
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- smoothing_strength
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c_concat_log_start: 1
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c_concat_log_end: 5
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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 ]
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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: 10 # 4 data + 4 downscaled image + 1 mask + 1 strength
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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.AddEdge
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params:
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mode: "512train-large"
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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.AddEdge
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params:
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mode: "512train-large"
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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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@ -23,6 +23,7 @@ dependencies:
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- torch-fidelity==0.3.0
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- transformers==4.3.1
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- webdataset==0.2.5
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- kornia==0.6
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- -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
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- -e git+https://github.com/openai/CLIP.git@main#egg=clip
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- -e .
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@ -1,4 +1,5 @@
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import webdataset as wds
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import kornia
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from PIL import Image
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import io
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import os
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@ -258,6 +259,76 @@ class AddMask(PRNGMixin):
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return sample
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class AddEdge(PRNGMixin):
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def __init__(self, mode="512train", mask_edges=True):
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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.n_down_choices = [0, 1, 2]
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self.sigma_choices = [1, 2, 3, 4, 5]
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self.mask_edges = mask_edges
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@torch.no_grad()
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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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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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sample['mask'] = mask
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n_down_idx = self.prng.choice(len(self.n_down_choices))
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sigma_idx = self.prng.choice(len(self.sigma_choices))
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n_choices = len(self.n_down_choices)*len(self.sigma_choices)
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raveled_idx = np.ravel_multi_index((n_down_idx, sigma_idx),
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(len(self.n_down_choices), len(self.sigma_choices)))
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normalized_idx = raveled_idx/(n_choices-1)
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n_down = self.n_down_choices[n_down_idx]
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sigma = self.sigma_choices[sigma_idx]
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kernel_size = 4*sigma+1
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kernel_size = (kernel_size, kernel_size)
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sigma = (sigma, sigma)
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canny = kornia.filters.Canny(
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low_threshold=0.1,
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high_threshold=0.2,
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kernel_size=kernel_size,
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sigma=sigma,
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hysteresis=True,
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)
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y = (x+1.0)/2.0 # in 01
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y = y.unsqueeze(0).permute(0, 3, 1, 2).contiguous()
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# down
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for i_down in range(n_down):
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size = min(y.shape[-2], y.shape[-1])//2
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y = kornia.geometry.transform.resize(y, size, antialias=True)
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# edge
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_, y = canny(y)
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if n_down > 0:
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size = x.shape[0], x.shape[1]
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y = kornia.geometry.transform.resize(y, size, interpolation="nearest")
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y = y.permute(0, 2, 3, 1)[0].expand(-1, -1, 3).contiguous()
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y = y*2.0-1.0
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if self.mask_edges:
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sample['masked_image'] = y * (mask < 0.5)
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else:
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sample['masked_image'] = y
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# concat normalized idx
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sample['smoothing_strength'] = torch.ones_like(sample['mask'])*normalized_idx
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return sample
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def example00():
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url = "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/000000.tar -"
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dataset = wds.WebDataset(url)
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@ -1607,6 +1607,8 @@ class LatentInpaintDiffusion(LatentDiffusion):
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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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c_concat_log_start=None, # to log reconstruction of c_concat codes
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c_concat_log_end=None,
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*args, **kwargs
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):
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ckpt_path = kwargs.pop("ckpt_path", None)
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@ -1617,6 +1619,8 @@ class LatentInpaintDiffusion(LatentDiffusion):
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self.finetune_keys = finetune_keys
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self.concat_keys = concat_keys
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self.keep_dims = keep_finetune_dims
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self.c_concat_log_start = c_concat_log_start
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self.c_concat_log_end = c_concat_log_end
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if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint'
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if exists(ckpt_path):
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self.init_from_ckpt(ckpt_path, ignore_keys)
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@ -1707,6 +1711,9 @@ class LatentInpaintDiffusion(LatentDiffusion):
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if ismap(xc):
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log["original_conditioning"] = self.to_rgb(xc)
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if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
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log["c_concat_decoded"] = self.decode_first_stage(c_cat[:,self.c_concat_log_start:self.c_concat_log_end])
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if plot_diffusion_rows:
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# get diffusion row
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diffusion_row = list()
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41
scripts/slurm/v1_edgeinpainting/launcher.sh
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scripts/slurm/v1_edgeinpainting/launcher.sh
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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 "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
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#conda activate stable
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# torch 1.11 to avoid bug in ckpt restoring
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conda activate torch111
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cd /fsx/stable-diffusion/stable-diffusion
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CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/inpainting/v1-edgeinpainting.yaml"
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# resume and set new seed to reshuffle data
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#EXTRA="--seed 543 --resume_from_checkpoint ..."
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# reduce lr a bit
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#EXTRA="${EXTRA} model.params.scheduler_config.params.f_max=[0.75]"
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# custom logdir
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#EXTRA="${EXTRA} --logdir rlogs"
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# debugging
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#EXTRA="${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 ${WORLD_SIZE} --scale_lr False
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scripts/slurm/v1_edgeinpainting/sbatch.sh
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scripts/slurm/v1_edgeinpainting/sbatch.sh
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#!/bin/bash
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#SBATCH --partition=compute-od-gpu
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#SBATCH --job-name=stable-diffusion-v1-edgeinpainting
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#SBATCH --nodes 24
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#SBATCH --ntasks-per-node 1
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#SBATCH --cpus-per-gpu=4
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#SBATCH --gres=gpu:8
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#SBATCH --exclusive
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#SBATCH --output=%x_%j.out
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#SBATCH --comment "Key=Monitoring,Value=ON"
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#SBATCH --no-requeue
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module load intelmpi
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source /opt/intel/mpi/latest/env/vars.sh
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export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-install/lib:/opt/aws-ofi-nccl/lib:$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/stable-diffusion/stable-diffusion/scripts/slurm/v1_edgeinpainting/launcher.sh
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