2023-12-21 05:15:51 -07:00
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model:
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target: sgm.models.diffusion.DiffusionEngine
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params:
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scale_factor: 0.13025
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disable_first_stage_autocast: True
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denoiser_config:
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target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
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params:
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num_idx: 1000
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weighting_config:
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target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
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scaling_config:
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target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
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discretization_config:
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target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
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network_config:
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target: sgm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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adm_in_channels: 2816
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num_classes: sequential
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2024-05-15 13:20:40 -06:00
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use_checkpoint: False
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2023-12-21 05:15:51 -07:00
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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]
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num_res_blocks: 2
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channel_mult: [1, 2, 4]
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num_head_channels: 64
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use_spatial_transformer: True
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use_linear_in_transformer: True
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transformer_depth: [1, 2, 10] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
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context_dim: 2048
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spatial_transformer_attn_type: softmax-xformers
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legacy: False
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conditioner_config:
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target: sgm.modules.GeneralConditioner
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params:
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emb_models:
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# crossattn cond
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- is_trainable: False
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input_key: txt
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target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
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params:
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layer: hidden
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layer_idx: 11
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# crossattn and vector cond
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- is_trainable: False
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input_key: txt
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target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
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params:
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arch: ViT-bigG-14
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version: laion2b_s39b_b160k
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freeze: True
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layer: penultimate
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always_return_pooled: True
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legacy: False
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# vector cond
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- is_trainable: False
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input_key: original_size_as_tuple
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target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
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params:
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outdim: 256 # multiplied by two
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# vector cond
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- is_trainable: False
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input_key: crop_coords_top_left
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target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
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params:
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outdim: 256 # multiplied by two
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# vector cond
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- is_trainable: False
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input_key: target_size_as_tuple
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target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
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params:
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outdim: 256 # multiplied by two
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first_stage_config:
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target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
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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: [1, 2, 4, 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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