stable diffusion v2 configs
This commit is contained in:
Anna
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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: "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
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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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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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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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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
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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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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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model:
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base_learning_rate: 5.0e-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
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conditioning_key: hybrid
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scale_factor: 0.18215
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monitor: val/loss_simple_ema
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finetune_keys: null
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use_ema: False
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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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image_size: 32 # unused
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in_channels: 9
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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: ldm.data.laion.WebDataModuleFromConfig
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params:
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tar_base: null # for concat as in LAION-A
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p_unsafe_threshold: 0.1
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filter_word_list: "data/filters.yaml"
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max_pwatermark: 0.45
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batch_size: 8
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num_workers: 6
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multinode: True
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min_size: 512
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train:
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shards:
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- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -"
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- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -"
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- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -"
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- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -"
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- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.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:
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- "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.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: True
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modelcheckpoint:
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params:
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every_n_train_steps: 5000
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callbacks:
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metrics_over_trainsteps_checkpoint:
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params:
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every_n_train_steps: 10000
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image_logger:
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target: main.ImageLogger
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params:
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enable_autocast: False
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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: 5.0
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unconditional_guidance_label: [""]
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ddim_steps: 50 # todo check these out for depth2img,
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ddim_eta: 0.0 # todo check these out for depth2img,
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trainer:
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benchmark: True
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val_check_interval: 5000000
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num_sanity_val_steps: 0
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accumulate_grad_batches: 1
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model:
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base_learning_rate: 5.0e-07
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target: ldm.models.diffusion.ddpm.LatentDepth2ImageDiffusion
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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
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conditioning_key: hybrid
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scale_factor: 0.18215
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monitor: val/loss_simple_ema
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finetune_keys: null
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use_ema: False
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depth_stage_config:
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target: ldm.modules.midas.api.MiDaSInference
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params:
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model_type: "dpt_hybrid"
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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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image_size: 32 # unused
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in_channels: 5
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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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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
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params:
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parameterization: "v"
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low_scale_key: "lr"
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linear_start: 0.0001
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linear_end: 0.02
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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: 128
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channels: 4
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cond_stage_trainable: false
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conditioning_key: "hybrid-adm"
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monitor: val/loss_simple_ema
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scale_factor: 0.08333
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use_ema: False
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low_scale_config:
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target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation
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params:
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noise_schedule_config: # image space
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linear_start: 0.0001
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linear_end: 0.02
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max_noise_level: 350
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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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num_classes: 1000 # timesteps for noise conditioning (here constant, just need one)
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image_size: 128
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in_channels: 7
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out_channels: 4
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model_channels: 256
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attention_resolutions: [ 2,4,8]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 2, 4]
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disable_self_attentions: [True, True, True, False]
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disable_middle_self_attn: False
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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: 1024
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legacy: False
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use_linear_in_transformer: True
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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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ddconfig:
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# attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though)
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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: [ 1,2,4 ] # num_down = len(ch_mult)-1
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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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