mirror of https://github.com/hpcaitech/ColossalAI
128 lines
3.2 KiB
YAML
128 lines
3.2 KiB
YAML
model:
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base_learning_rate: 1.0e-4
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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parameterization: "v"
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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: image
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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
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False # we set this to false because this is an inference only config
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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: [ 1 ] # 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.e-4 ]
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f_min: [ 1.e-10 ]
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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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use_checkpoint: True
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use_fp16: True
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image_size: 32 # unused
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in_channels: 4
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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_head_channels: 64 # need to fix for flash-attn
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use_spatial_transformer: True
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use_linear_in_transformer: True
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transformer_depth: 1
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context_dim: 1024
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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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#attn_type: "vanilla-xformers"
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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.FrozenOpenCLIPEmbedder
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params:
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freeze: True
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layer: "penultimate"
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 4
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num_workers: 4
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train:
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target: ldm.data.cifar10.hf_dataset
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params:
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name: cifar10
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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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- target: torchvision.transforms.RandomHorizontalFlip
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lightning:
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trainer:
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accelerator: 'gpu'
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devices: 1
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log_gpu_memory: all
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max_epochs: 2
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precision: 16
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auto_select_gpus: False
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strategy:
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target: strategies.ColossalAIStrategy
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params:
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use_chunk: True
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enable_distributed_storage: True
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placement_policy: cuda
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force_outputs_fp32: true
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log_every_n_steps: 2
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logger: True
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default_root_dir: "/tmp/diff_log/"
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# profiler: pytorch
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logger_config:
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wandb:
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target: loggers.WandbLogger
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params:
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name: nowname
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save_dir: "/tmp/diff_log/"
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offline: opt.debug
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id: nowname
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