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Upload Visionix-alpha-inpainting.yaml

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  1. Visionix-alpha-inpainting.yaml +98 -0
Visionix-alpha-inpainting.yaml ADDED
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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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+
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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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+
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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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+
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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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+ use_checkpoint: True
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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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+
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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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+
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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