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https://github.com/invoke-ai/InvokeAI
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fix(mm): add missing v2-midas-inference.yaml
This is needed for SD2 depth models. Note: These models don't seem to work.
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invokeai/configs/stable-diffusion/v2-midas-inference.yaml
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74
invokeai/configs/stable-diffusion/v2-midas-inference.yaml
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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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