mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-10-31 03:20:40 +00:00
refactor: removes unused code and configurations (#405)
Co-authored-by: jaydrennan
This commit is contained in:
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6d39d791b1
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e1e6f8037c
@ -366,7 +366,7 @@ imagine --tile-x -w 1024 -h 512 "360 degree equirectangular panorama photograph
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Use depth maps for amazing "translations" of existing images.
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```bash
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>> imagine --model SD-2.0-depth --init-image girl_with_a_pearl_earring_large.jpg --init-image-strength 0.05 "professional headshot photo of a woman with a pearl earring" -r 4 -w 1024 -h 1024 --steps 50
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>> imagine --init-image girl_with_a_pearl_earring_large.jpg --init-image-strength 0.05 "professional headshot photo of a woman with a pearl earring" -r 4 -w 1024 -h 1024 --steps 50
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```
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<p float="left">
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<img src="tests/data/girl_with_a_pearl_earring.jpg" width="256"> ➡️
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@ -286,7 +286,7 @@ def _generate_single_image_compvis(
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if control_inputs:
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control_modes = [c.mode for c in prompt.control_inputs]
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if inpaint_method == "auto":
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if prompt.model_weights in {"SD-1.5", "SD-2.0"}:
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if prompt.model_weights in {"SD-1.5"}:
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inpaint_method = "finetune"
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else:
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inpaint_method = "controlnet"
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@ -1,68 +0,0 @@
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model:
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base_learning_rate: 1.0e-4
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target: imaginairy.modules.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: imaginairy.modules.diffusion.openaimodel.UNetModel
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params:
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use_checkpoint: True
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use_fp16: False
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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: imaginairy.modules.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: imaginairy.modules.encoders.FrozenOpenCLIPEmbedder
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params:
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freeze: True
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layer: "penultimate"
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@ -1,67 +0,0 @@
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model:
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base_learning_rate: 1.0e-4
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target: imaginairy.modules.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: imaginairy.modules.diffusion.openaimodel.UNetModel
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params:
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use_checkpoint: True
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use_fp16: False
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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: imaginairy.modules.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: imaginairy.modules.encoders.FrozenOpenCLIPEmbedder
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params:
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freeze: True
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layer: "penultimate"
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@ -1,158 +0,0 @@
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model:
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base_learning_rate: 5.0e-05
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target: imaginairy.modules.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: imaginairy.modules.diffusion.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: imaginairy.modules.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: imaginairy.modules.encoders.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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@ -1981,71 +1981,6 @@ class LatentInpaintDiffusion(LatentDiffusion):
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return z, all_conds
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class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
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"""
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condition on monocular depth estimation.
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"""
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def __init__(self, depth_stage_config, concat_keys=("midas_in",), **kwargs):
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super().__init__(concat_keys=concat_keys, **kwargs)
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self.depth_model = instantiate_from_config(depth_stage_config)
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self.depth_stage_key = concat_keys[0]
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@torch.no_grad()
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def get_input(
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self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False
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):
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# note: restricted to non-trainable encoders currently
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assert (
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not self.cond_stage_trainable
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), "trainable cond stages not yet supported for depth2img"
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z, c, x, xrec, xc = super().get_input(
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batch,
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self.first_stage_key,
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return_first_stage_outputs=True,
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force_c_encode=True,
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return_original_cond=True,
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bs=bs,
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)
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assert self.concat_keys is not None
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assert len(self.concat_keys) == 1
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c_cat = []
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for ck in self.concat_keys:
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cc = batch[ck]
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if bs is not None:
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cc = cc[:bs]
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cc = cc.to(self.device)
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cc = self.depth_model(cc)
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cc = torch.nn.functional.interpolate(
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cc,
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size=z.shape[2:],
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mode="bicubic",
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align_corners=False,
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)
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depth_min, depth_max = torch.amin(
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cc, dim=[1, 2, 3], keepdim=True
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), torch.amax(cc, dim=[1, 2, 3], keepdim=True)
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cc = 2.0 * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.0
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c_cat.append(cc)
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c_cat = torch.cat(c_cat, dim=1)
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all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
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if return_first_stage_outputs:
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return z, all_conds, x, xrec, xc
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return z, all_conds
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@torch.no_grad()
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def log_images(self, *args, **kwargs):
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log = super().log_images(*args, **kwargs)
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depth = self.depth_model(args[0][self.depth_stage_key])
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depth_min, depth_max = torch.amin(
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depth, dim=[1, 2, 3], keepdim=True
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), torch.amax(depth, dim=[1, 2, 3], keepdim=True)
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log["depth"] = 2.0 * (depth - depth_min) / (depth_max - depth_min) - 1.0
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return log
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class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
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"""
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condition on low-res image (and optionally on some spatial noise augmentation).
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@ -541,9 +541,6 @@ class ImaginePrompt(BaseModel, protected_namespaces=()):
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v = v.lower()
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if info.data.get("model") == "SD-2.0-v" and v == SolverName.PLMS:
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raise ValueError("PLMS solvers is not supported for SD-2.0-v model.")
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if info.data.get("model") == "edit" and v in (
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SolverName.PLMS,
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SolverName.DDIM,
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@ -27,12 +27,7 @@ def create_model_of_n_bytes(n):
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@pytest.mark.parametrize(
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"model_version",
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[
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# "SD-1.4",
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"SD-1.5",
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# "SD-2.0",
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# "SD-2.0-v",
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# "SD-2.1",
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# "SD-2.1-v",
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"openjourney-v1",
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"openjourney-v2",
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"openjourney-v4",
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