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configs/alt-diffusion-inference.yaml
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model:
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base_learning_rate: 1.0e-04
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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 # Note: different from the one we trained before
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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
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 10000 ]
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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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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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_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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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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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: modules.xlmr.BertSeriesModelWithTransformation
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params:
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name: "XLMR-Large"
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configs/instruct-pix2pix.yaml
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# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
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# See more details in LICENSE.
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model:
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base_learning_rate: 1.0e-04
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target: modules.models.diffusion.ddpm_edit.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: edited
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cond_stage_key: edit
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# image_size: 64
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# image_size: 32
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image_size: 16
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channels: 4
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cond_stage_trainable: false # Note: different from the one we trained before
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conditioning_key: hybrid
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: false
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 0 ]
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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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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image_size: 32 # unused
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in_channels: 8
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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_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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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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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.FrozenCLIPEmbedder
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 128
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num_workers: 1
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wrap: false
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validation:
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target: edit_dataset.EditDataset
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params:
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path: data/clip-filtered-dataset
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cache_dir: data/
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cache_name: data_10k
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split: val
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min_text_sim: 0.2
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min_image_sim: 0.75
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min_direction_sim: 0.2
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max_samples_per_prompt: 1
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min_resize_res: 512
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max_resize_res: 512
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crop_res: 512
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output_as_edit: False
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real_input: True
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configs/v1-inference.yaml
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@@ -0,0 +1,70 @@
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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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5 |
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linear_start: 0.00085
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6 |
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linear_end: 0.0120
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7 |
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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 # Note: different from the one we trained before
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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17 |
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scale_factor: 0.18215
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use_ema: False
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 10000 ]
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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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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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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36 |
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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_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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first_stage_config:
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47 |
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target: ldm.models.autoencoder.AutoencoderKL
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48 |
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params:
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49 |
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embed_dim: 4
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50 |
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monitor: val/rec_loss
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51 |
+
ddconfig:
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double_z: true
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z_channels: 4
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54 |
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resolution: 256
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55 |
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in_channels: 3
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56 |
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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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63 |
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num_res_blocks: 2
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64 |
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attn_resolutions: []
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65 |
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dropout: 0.0
|
66 |
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lossconfig:
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67 |
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target: torch.nn.Identity
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68 |
+
|
69 |
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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configs/v1-inpainting-inference.yaml
ADDED
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model:
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base_learning_rate: 7.5e-05
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target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
|
4 |
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params:
|
5 |
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linear_start: 0.00085
|
6 |
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linear_end: 0.0120
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7 |
+
num_timesteps_cond: 1
|
8 |
+
log_every_t: 200
|
9 |
+
timesteps: 1000
|
10 |
+
first_stage_key: "jpg"
|
11 |
+
cond_stage_key: "txt"
|
12 |
+
image_size: 64
|
13 |
+
channels: 4
|
14 |
+
cond_stage_trainable: false # Note: different from the one we trained before
|
15 |
+
conditioning_key: hybrid # important
|
16 |
+
monitor: val/loss_simple_ema
|
17 |
+
scale_factor: 0.18215
|
18 |
+
finetune_keys: null
|
19 |
+
|
20 |
+
scheduler_config: # 10000 warmup steps
|
21 |
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target: ldm.lr_scheduler.LambdaLinearScheduler
|
22 |
+
params:
|
23 |
+
warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
|
24 |
+
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
|
25 |
+
f_start: [ 1.e-6 ]
|
26 |
+
f_max: [ 1. ]
|
27 |
+
f_min: [ 1. ]
|
28 |
+
|
29 |
+
unet_config:
|
30 |
+
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
31 |
+
params:
|
32 |
+
image_size: 32 # unused
|
33 |
+
in_channels: 9 # 4 data + 4 downscaled image + 1 mask
|
34 |
+
out_channels: 4
|
35 |
+
model_channels: 320
|
36 |
+
attention_resolutions: [ 4, 2, 1 ]
|
37 |
+
num_res_blocks: 2
|
38 |
+
channel_mult: [ 1, 2, 4, 4 ]
|
39 |
+
num_heads: 8
|
40 |
+
use_spatial_transformer: True
|
41 |
+
transformer_depth: 1
|
42 |
+
context_dim: 768
|
43 |
+
use_checkpoint: True
|
44 |
+
legacy: False
|
45 |
+
|
46 |
+
first_stage_config:
|
47 |
+
target: ldm.models.autoencoder.AutoencoderKL
|
48 |
+
params:
|
49 |
+
embed_dim: 4
|
50 |
+
monitor: val/rec_loss
|
51 |
+
ddconfig:
|
52 |
+
double_z: true
|
53 |
+
z_channels: 4
|
54 |
+
resolution: 256
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55 |
+
in_channels: 3
|
56 |
+
out_ch: 3
|
57 |
+
ch: 128
|
58 |
+
ch_mult:
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59 |
+
- 1
|
60 |
+
- 2
|
61 |
+
- 4
|
62 |
+
- 4
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63 |
+
num_res_blocks: 2
|
64 |
+
attn_resolutions: []
|
65 |
+
dropout: 0.0
|
66 |
+
lossconfig:
|
67 |
+
target: torch.nn.Identity
|
68 |
+
|
69 |
+
cond_stage_config:
|
70 |
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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