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mta122
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update
Browse files- __pycache__/categories.cpython-38.pyc +0 -0
- configs/finetune/finetune_bert.yaml +0 -128
- configs/finetune/finetune_clip.yaml +0 -118
- configs/finetune/finetune_generic.yaml +7 -7
- configs/finetune/finetune_multi_bert.yaml +0 -127
- configs/finetune/finetune_multi_clip.yaml +0 -118
- ldm/__pycache__/util.cpython-38.pyc +0 -0
- ldm/models/__pycache__/autoencoder.cpython-38.pyc +0 -0
- ldm/models/diffusion/__pycache__/__init__.cpython-38.pyc +0 -0
- ldm/models/diffusion/__pycache__/ddim.cpython-38.pyc +0 -0
- ldm/models/diffusion/__pycache__/ddpm.cpython-38.pyc +0 -0
- ldm/models/diffusion/__pycache__/plms.cpython-38.pyc +0 -0
- ldm/models/diffusion/ddpm.py +1 -105
- ldm/modules/__pycache__/attention.cpython-38.pyc +0 -0
- ldm/modules/__pycache__/discriminator.cpython-38.pyc +0 -0
- ldm/modules/__pycache__/ema.cpython-38.pyc +0 -0
- ldm/modules/__pycache__/x_transformer.cpython-38.pyc +0 -0
- ldm/modules/diffusionmodules/__pycache__/__init__.cpython-38.pyc +0 -0
- ldm/modules/diffusionmodules/__pycache__/model.cpython-38.pyc +0 -0
- ldm/modules/diffusionmodules/__pycache__/openaimodel.cpython-38.pyc +0 -0
- ldm/modules/diffusionmodules/__pycache__/util.cpython-38.pyc +0 -0
- ldm/modules/discriminator.py +0 -97
- ldm/modules/distributions/__pycache__/__init__.cpython-38.pyc +0 -0
- ldm/modules/distributions/__pycache__/distributions.cpython-38.pyc +0 -0
- ldm/modules/encoders/__pycache__/__init__.cpython-38.pyc +0 -0
- ldm/modules/encoders/__pycache__/modules.cpython-38.pyc +0 -0
- out/express/DRAGON-R.jpg +0 -0
- out/express/samples/0000.png +0 -0
- out/express/samples/0001.png +0 -0
- out/express/samples/0002.png +0 -0
- out/express/samples/0003.png +0 -0
- txt2img.py +0 -4
__pycache__/categories.cpython-38.pyc
CHANGED
Binary files a/__pycache__/categories.cpython-38.pyc and b/__pycache__/categories.cpython-38.pyc differ
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configs/finetune/finetune_bert.yaml
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model:
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base_learning_rate: 1.0e-5
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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: "image"
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cond_stage_key: "caption"
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image_size: 32
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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
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weight_disc: 0.01
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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
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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: 1280
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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.BERTEmbedder
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params:
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n_embed: 1280
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n_layer: 32
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device: "cuda"
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discriminator_config:
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target: ldm.modules.discriminator.Discriminator
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params:
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bnorm: True
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leakyparam: 0.2
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bias: False
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generic: False
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 1
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num_workers: 32
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wrap: false
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train:
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target: ldm.data.rasterizer.Rasterizer
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params:
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img_size: 256
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text: "R"
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style_word: "DRAGON"
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data_path: "data/cat"
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alternate_glyph: None
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num_samples: 2001
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make_black: False
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one_font: False
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full_word: False
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font_name: "Garuda-Bold.ttf"
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just_use_style: false
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use_alt: False
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validation:
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target: ldm.data.rasterizer.Rasterizer
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params:
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img_size: 256
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text: "R"
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style_word: "DRAGON"
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data_path: "data/cat"
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alternate_glyph: None
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num_samples: 5
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make_black: False
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one_font: False
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full_word: False
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font_name: "Garuda-Bold.ttf"
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just_use_style: false
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use_alt: False
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lightning:
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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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image_logger:
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target: main.ImageLogger
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params:
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batch_frequency: 1000
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max_images: 1
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increase_log_steps: False
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trainer:
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benchmark: True
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max_steps: 500
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configs/finetune/finetune_clip.yaml
DELETED
@@ -1,118 +0,0 @@
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model:
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base_learning_rate: 1.0e-5 #1e-4
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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: "image"
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cond_stage_key: "caption"
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image_size: 64 # 32
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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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weight_disc: 0.01
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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: 64 # 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 # 1280
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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: 512 #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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discriminator_config:
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target: ldm.modules.discriminator.Discriminator64
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 1
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num_workers: 32
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wrap: false
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train:
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target: ldm.data.rasterizer.Rasterizer
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params:
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img_size: 256
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text: "R"
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style_word: "DRAGON"
|
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data_path: "data/cat"
|
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alternate_glyph: None
|
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num_samples: 2001
|
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-
make_black: False
|
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-
one_font: False
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-
full_word: False
|
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font_name: "Garuda-Bold.ttf"
|
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just_use_style: false
|
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use_alt: False
|
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validation:
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target: ldm.data.rasterizer.Rasterizer
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params:
|
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img_size: 256
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text: "R"
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style_word: "DRAGON"
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data_path: "data/cat"
|
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alternate_glyph: None
|
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num_samples: 5
|
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-
make_black: False
|
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-
one_font: False
|
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full_word: False
|
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-
font_name: "Garuda-Bold.ttf"
|
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-
just_use_style: false
|
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-
use_alt: False
|
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-
|
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-
lightning:
|
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-
modelcheckpoint:
|
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params:
|
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-
every_n_train_steps: 200
|
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-
callbacks:
|
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-
image_logger:
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target: main.ImageLogger
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params:
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batch_frequency: 100
|
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max_images: 1
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increase_log_steps: False
|
115 |
-
|
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trainer:
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benchmark: True
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max_steps: 1001
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configs/finetune/finetune_generic.yaml
CHANGED
@@ -65,13 +65,13 @@ model:
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n_layer: 32
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device: "cuda"
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discriminator_config:
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data:
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n_layer: 32
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device: "cuda"
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+
# discriminator_config:
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# target: ldm.modules.discriminator.Discriminator
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# params:
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# bnorm: True
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# leakyparam: 0.2
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# bias: False
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# generic: True
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data:
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configs/finetune/finetune_multi_bert.yaml
DELETED
@@ -1,127 +0,0 @@
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model:
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base_learning_rate: 1.0e-5 #1e-4
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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: "image"
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cond_stage_key: "caption"
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image_size: 32 # 32
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channels: 4
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-
cond_stage_trainable: False # Note: different from the one we trained before
|
15 |
-
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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-
weight_disc: 0.01
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20 |
-
|
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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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27 |
-
model_channels: 320
|
28 |
-
attention_resolutions: [ 4, 2, 1 ]
|
29 |
-
num_res_blocks: 2
|
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channel_mult: [ 1, 2, 4, 4 ]
|
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-
num_heads: 8
|
32 |
-
use_spatial_transformer: True
|
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transformer_depth: 1
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context_dim: 1280 # 1280
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use_checkpoint: True
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36 |
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legacy: False
|
37 |
-
|
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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
|
42 |
-
monitor: val/rec_loss
|
43 |
-
ddconfig:
|
44 |
-
double_z: true
|
45 |
-
z_channels: 4
|
46 |
-
resolution: 256 #256
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47 |
-
in_channels: 3
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-
out_ch: 3
|
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-
ch: 128
|
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-
ch_mult:
|
51 |
-
- 1
|
52 |
-
- 2
|
53 |
-
- 4
|
54 |
-
- 4
|
55 |
-
num_res_blocks: 2
|
56 |
-
attn_resolutions: []
|
57 |
-
dropout: 0.0
|
58 |
-
lossconfig:
|
59 |
-
target: torch.nn.Identity
|
60 |
-
|
61 |
-
cond_stage_config:
|
62 |
-
target: ldm.modules.encoders.modules.BERTEmbedder
|
63 |
-
params:
|
64 |
-
n_embed: 1280
|
65 |
-
n_layer: 32
|
66 |
-
|
67 |
-
discriminator_config:
|
68 |
-
target: ldm.modules.discriminator.Discriminator
|
69 |
-
params:
|
70 |
-
bnorm: True
|
71 |
-
leakyparam: 0.2
|
72 |
-
bias: False
|
73 |
-
generic: False
|
74 |
-
|
75 |
-
|
76 |
-
data:
|
77 |
-
target: main.DataModuleFromConfig
|
78 |
-
params:
|
79 |
-
batch_size: 1
|
80 |
-
num_workers: 32
|
81 |
-
wrap: false
|
82 |
-
train:
|
83 |
-
target: ldm.data.rasterizer.Rasterizer
|
84 |
-
params:
|
85 |
-
img_size: 256
|
86 |
-
text: "R"
|
87 |
-
style_word: "DRAGON"
|
88 |
-
data_path: "data/cat"
|
89 |
-
alternate_glyph: None
|
90 |
-
num_samples: 2001
|
91 |
-
make_black: False
|
92 |
-
one_font: False
|
93 |
-
full_word: False
|
94 |
-
font_name: "Garuda-Bold.ttf"
|
95 |
-
just_use_style: false
|
96 |
-
use_alt: False
|
97 |
-
validation:
|
98 |
-
target: ldm.data.rasterizer.Rasterizer
|
99 |
-
params:
|
100 |
-
img_size: 256
|
101 |
-
text: "R"
|
102 |
-
style_word: "DRAGON"
|
103 |
-
data_path: "data/cat"
|
104 |
-
alternate_glyph: None
|
105 |
-
num_samples: 5
|
106 |
-
make_black: False
|
107 |
-
one_font: False
|
108 |
-
full_word: False
|
109 |
-
font_name: "Garuda-Bold.ttf"
|
110 |
-
just_use_style: false
|
111 |
-
use_alt: False
|
112 |
-
|
113 |
-
lightning:
|
114 |
-
modelcheckpoint:
|
115 |
-
params:
|
116 |
-
every_n_train_steps: 2000
|
117 |
-
callbacks:
|
118 |
-
image_logger:
|
119 |
-
target: main.ImageLogger
|
120 |
-
params:
|
121 |
-
batch_frequency: 5000
|
122 |
-
max_images: 1
|
123 |
-
increase_log_steps: False
|
124 |
-
|
125 |
-
trainer:
|
126 |
-
benchmark: True
|
127 |
-
max_steps: 800
|
|
|
|
|
|
|
|
|
|
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|
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|
|
configs/finetune/finetune_multi_clip.yaml
DELETED
@@ -1,118 +0,0 @@
|
|
1 |
-
model:
|
2 |
-
base_learning_rate: 1.0e-5 #1e-4
|
3 |
-
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
4 |
-
params:
|
5 |
-
linear_start: 0.00085
|
6 |
-
linear_end: 0.0120
|
7 |
-
num_timesteps_cond: 1
|
8 |
-
log_every_t: 200
|
9 |
-
timesteps: 1000
|
10 |
-
first_stage_key: "image"
|
11 |
-
cond_stage_key: "caption"
|
12 |
-
image_size: 64 # 32
|
13 |
-
channels: 4
|
14 |
-
cond_stage_trainable: False # Note: different from the one we trained before
|
15 |
-
conditioning_key: crossattn
|
16 |
-
monitor: val/loss_simple_ema
|
17 |
-
scale_factor: 0.18215
|
18 |
-
use_ema: False
|
19 |
-
weight_disc: 0.01
|
20 |
-
|
21 |
-
unet_config:
|
22 |
-
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
23 |
-
params:
|
24 |
-
image_size: 64 # unused
|
25 |
-
in_channels: 4
|
26 |
-
out_channels: 4
|
27 |
-
model_channels: 320
|
28 |
-
attention_resolutions: [ 4, 2, 1 ]
|
29 |
-
num_res_blocks: 2
|
30 |
-
channel_mult: [ 1, 2, 4, 4 ]
|
31 |
-
num_heads: 8
|
32 |
-
use_spatial_transformer: True
|
33 |
-
transformer_depth: 1
|
34 |
-
context_dim: 768 # 1280
|
35 |
-
use_checkpoint: True
|
36 |
-
legacy: False
|
37 |
-
|
38 |
-
first_stage_config:
|
39 |
-
target: ldm.models.autoencoder.AutoencoderKL
|
40 |
-
params:
|
41 |
-
embed_dim: 4
|
42 |
-
monitor: val/rec_loss
|
43 |
-
ddconfig:
|
44 |
-
double_z: true
|
45 |
-
z_channels: 4
|
46 |
-
resolution: 512 #256
|
47 |
-
in_channels: 3
|
48 |
-
out_ch: 3
|
49 |
-
ch: 128
|
50 |
-
ch_mult:
|
51 |
-
- 1
|
52 |
-
- 2
|
53 |
-
- 4
|
54 |
-
- 4
|
55 |
-
num_res_blocks: 2
|
56 |
-
attn_resolutions: []
|
57 |
-
dropout: 0.0
|
58 |
-
lossconfig:
|
59 |
-
target: torch.nn.Identity
|
60 |
-
|
61 |
-
cond_stage_config:
|
62 |
-
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
|
63 |
-
|
64 |
-
discriminator_config:
|
65 |
-
target: ldm.modules.discriminator.Discriminator64
|
66 |
-
|
67 |
-
data:
|
68 |
-
target: main.DataModuleFromConfig
|
69 |
-
params:
|
70 |
-
batch_size: 1
|
71 |
-
num_workers: 32
|
72 |
-
wrap: false
|
73 |
-
train:
|
74 |
-
target: ldm.data.rasterizer.Rasterizer
|
75 |
-
params:
|
76 |
-
img_size: 256
|
77 |
-
text: "R"
|
78 |
-
style_word: "DRAGON"
|
79 |
-
data_path: "data/cat"
|
80 |
-
alternate_glyph: None
|
81 |
-
num_samples: 2001
|
82 |
-
make_black: False
|
83 |
-
one_font: False
|
84 |
-
full_word: False
|
85 |
-
font_name: "Garuda-Bold.ttf"
|
86 |
-
just_use_style: false
|
87 |
-
use_alt: False
|
88 |
-
validation:
|
89 |
-
target: ldm.data.rasterizer.Rasterizer
|
90 |
-
params:
|
91 |
-
img_size: 256
|
92 |
-
text: "R"
|
93 |
-
style_word: "DRAGON"
|
94 |
-
data_path: "data/cat"
|
95 |
-
alternate_glyph: None
|
96 |
-
num_samples: 5
|
97 |
-
make_black: False
|
98 |
-
one_font: False
|
99 |
-
full_word: False
|
100 |
-
font_name: "Garuda-Bold.ttf"
|
101 |
-
just_use_style: false
|
102 |
-
use_alt: False
|
103 |
-
|
104 |
-
lightning:
|
105 |
-
modelcheckpoint:
|
106 |
-
params:
|
107 |
-
every_n_train_steps: 200
|
108 |
-
callbacks:
|
109 |
-
image_logger:
|
110 |
-
target: main.ImageLogger
|
111 |
-
params:
|
112 |
-
batch_frequency: 100
|
113 |
-
max_images: 1
|
114 |
-
increase_log_steps: False
|
115 |
-
|
116 |
-
trainer:
|
117 |
-
benchmark: True
|
118 |
-
max_steps: 1501
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
ldm/__pycache__/util.cpython-38.pyc
CHANGED
Binary files a/ldm/__pycache__/util.cpython-38.pyc and b/ldm/__pycache__/util.cpython-38.pyc differ
|
|
ldm/models/__pycache__/autoencoder.cpython-38.pyc
CHANGED
Binary files a/ldm/models/__pycache__/autoencoder.cpython-38.pyc and b/ldm/models/__pycache__/autoencoder.cpython-38.pyc differ
|
|
ldm/models/diffusion/__pycache__/__init__.cpython-38.pyc
CHANGED
Binary files a/ldm/models/diffusion/__pycache__/__init__.cpython-38.pyc and b/ldm/models/diffusion/__pycache__/__init__.cpython-38.pyc differ
|
|
ldm/models/diffusion/__pycache__/ddim.cpython-38.pyc
CHANGED
Binary files a/ldm/models/diffusion/__pycache__/ddim.cpython-38.pyc and b/ldm/models/diffusion/__pycache__/ddim.cpython-38.pyc differ
|
|
ldm/models/diffusion/__pycache__/ddpm.cpython-38.pyc
CHANGED
Binary files a/ldm/models/diffusion/__pycache__/ddpm.cpython-38.pyc and b/ldm/models/diffusion/__pycache__/ddpm.cpython-38.pyc differ
|
|
ldm/models/diffusion/__pycache__/plms.cpython-38.pyc
CHANGED
Binary files a/ldm/models/diffusion/__pycache__/plms.cpython-38.pyc and b/ldm/models/diffusion/__pycache__/plms.cpython-38.pyc differ
|
|
ldm/models/diffusion/ddpm.py
CHANGED
@@ -485,7 +485,7 @@ class LatentDiffusion(DDPM):
|
|
485 |
self.init_from_ckpt(ckpt_path, ignore_keys)
|
486 |
self.restarted_from_ckpt = True
|
487 |
|
488 |
-
self.discriminator = instantiate_from_config(discriminator_config)
|
489 |
self.weight_disc = weight_disc
|
490 |
self.iter = 0
|
491 |
|
@@ -919,84 +919,6 @@ class LatentDiffusion(DDPM):
|
|
919 |
return z_C, alpha
|
920 |
|
921 |
|
922 |
-
def discriminator_loss(self, batch, optimizer_idx =0):
|
923 |
-
#
|
924 |
-
criterion = nn.BCELoss()
|
925 |
-
|
926 |
-
real_label = 1.
|
927 |
-
fake_label = 0.
|
928 |
-
|
929 |
-
caption = batch["cond"]
|
930 |
-
with torch.no_grad():
|
931 |
-
cond = self.get_learned_conditioning(caption)
|
932 |
-
|
933 |
-
img1 = rearrange(batch["style"]["image"], 'b h w c -> b c h w')
|
934 |
-
save_image(img1, "img_style.png")
|
935 |
-
img1_base = img1.to(memory_format=torch.contiguous_format).float()
|
936 |
-
img1 = self.encode_first_stage(img1_base)
|
937 |
-
z_S = self.get_first_stage_encoding(img1).detach()
|
938 |
-
|
939 |
-
img2 = rearrange(batch["base"]["image"], 'b h w c -> b c h w')
|
940 |
-
save_image(img2, "img_base.png")
|
941 |
-
img2_base = img2.to(memory_format=torch.contiguous_format).float()
|
942 |
-
img2 = self.encode_first_stage(img2_base)
|
943 |
-
z_R = self.get_first_stage_encoding(img2).detach()
|
944 |
-
|
945 |
-
x_start = z_S
|
946 |
-
real_x = z_R
|
947 |
-
|
948 |
-
t = torch.randint(0, self.num_timesteps, (z_S.shape[0],), device=self.device).long()
|
949 |
-
logvar_t = self.logvar[t.cpu()].to(self.device)
|
950 |
-
|
951 |
-
noise = default(None, lambda: torch.randn_like(z_S))
|
952 |
-
|
953 |
-
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
|
954 |
-
|
955 |
-
letter = batch["number"][0].cpu().detach().numpy()
|
956 |
-
|
957 |
-
#update generator
|
958 |
-
if optimizer_idx == 0:
|
959 |
-
|
960 |
-
noise1 = self.apply_model(x_noisy, t, cond)
|
961 |
-
z_theta = self.predict_start_from_noise(x_noisy,t,noise1)
|
962 |
-
fake_x = z_theta
|
963 |
-
|
964 |
-
loss_diff = self.get_loss(noise1, noise, mean=False).mean([1,2,3])
|
965 |
-
loss_diff = loss_diff / torch.exp(logvar_t) + logvar_t
|
966 |
-
loss_diff = self.l_simple_weight * loss_diff.mean()
|
967 |
-
|
968 |
-
label = torch.full((1,), real_label, dtype=torch.float, device=self.device)
|
969 |
-
|
970 |
-
output = self.discriminator(fake_x, letter).view(-1)
|
971 |
-
loss_disc = criterion(output, label)
|
972 |
-
|
973 |
-
return loss_diff, loss_disc
|
974 |
-
|
975 |
-
#update discriminator
|
976 |
-
if optimizer_idx == 1:
|
977 |
-
|
978 |
-
noise1 = self.apply_model(x_noisy, t, cond)
|
979 |
-
z_theta = self.predict_start_from_noise(x_noisy,t,noise1)
|
980 |
-
fake_x = z_theta
|
981 |
-
|
982 |
-
loss_diff = self.get_loss(noise1, noise, mean=False).mean([1,2,3])
|
983 |
-
loss_diff = loss_diff / torch.exp(logvar_t) + logvar_t
|
984 |
-
loss_diff = self.l_simple_weight * loss_diff.mean()
|
985 |
-
|
986 |
-
label = torch.full((1,), real_label, dtype=torch.float, device=self.device)
|
987 |
-
output = self.discriminator(real_x, letter).view(-1)
|
988 |
-
loss1 = criterion(output, label)
|
989 |
-
|
990 |
-
|
991 |
-
label = torch.full((1,), fake_label, dtype=torch.float, device=self.device)
|
992 |
-
output = self.discriminator(fake_x, letter).view(-1)
|
993 |
-
loss2 = criterion(output, label)
|
994 |
-
|
995 |
-
loss_disc = (loss1+loss2)/2
|
996 |
-
|
997 |
-
return loss_diff, loss_disc
|
998 |
-
|
999 |
-
|
1000 |
def make_images(self, batch):
|
1001 |
batch = batch["base"]
|
1002 |
use_ddim = 50
|
@@ -1073,32 +995,6 @@ class LatentDiffusion(DDPM):
|
|
1073 |
Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join("out_cur/", f"{base_count:04}.png"))
|
1074 |
base_count += 1
|
1075 |
|
1076 |
-
def training_step(self, batch,batch_idx, optimizer_idx=None):
|
1077 |
-
|
1078 |
-
loss_diff, loss_disc = self.discriminator_loss(batch, optimizer_idx=optimizer_idx)
|
1079 |
-
loss =loss_diff+ self.weight_disc*loss_disc
|
1080 |
-
self.iter+=1
|
1081 |
-
|
1082 |
-
if (self.iter-1) % 100 == 0:
|
1083 |
-
self.log_view(batch)
|
1084 |
-
|
1085 |
-
# if self.iter == batch["epochs"]:
|
1086 |
-
# self.last_step_run(batch)
|
1087 |
-
|
1088 |
-
|
1089 |
-
return loss
|
1090 |
-
|
1091 |
-
@torch.no_grad()
|
1092 |
-
def validation_step(self, batch,optimizer_idx):
|
1093 |
-
|
1094 |
-
return None
|
1095 |
-
cap = batch["cond"]
|
1096 |
-
batch = batch["base"]
|
1097 |
-
batch["caption"] = cap
|
1098 |
-
|
1099 |
-
loss, loss_dict_no_ema = self.shared_step(batch)
|
1100 |
-
self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
|
1101 |
-
|
1102 |
def forward(self, x, c, *args, **kwargs):
|
1103 |
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
|
1104 |
if self.model.conditioning_key is not None:
|
|
|
485 |
self.init_from_ckpt(ckpt_path, ignore_keys)
|
486 |
self.restarted_from_ckpt = True
|
487 |
|
488 |
+
# self.discriminator = instantiate_from_config(discriminator_config)
|
489 |
self.weight_disc = weight_disc
|
490 |
self.iter = 0
|
491 |
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|
919 |
return z_C, alpha
|
920 |
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921 |
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922 |
def make_images(self, batch):
|
923 |
batch = batch["base"]
|
924 |
use_ddim = 50
|
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|
995 |
Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join("out_cur/", f"{base_count:04}.png"))
|
996 |
base_count += 1
|
997 |
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|
998 |
def forward(self, x, c, *args, **kwargs):
|
999 |
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
|
1000 |
if self.model.conditioning_key is not None:
|
ldm/modules/__pycache__/attention.cpython-38.pyc
CHANGED
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ldm/modules/__pycache__/discriminator.cpython-38.pyc
CHANGED
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ldm/modules/__pycache__/ema.cpython-38.pyc
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|
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ldm/modules/__pycache__/x_transformer.cpython-38.pyc
CHANGED
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|
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ldm/modules/diffusionmodules/__pycache__/__init__.cpython-38.pyc
CHANGED
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ldm/modules/diffusionmodules/__pycache__/model.cpython-38.pyc
CHANGED
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ldm/modules/diffusionmodules/__pycache__/openaimodel.cpython-38.pyc
CHANGED
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ldm/modules/diffusionmodules/__pycache__/util.cpython-38.pyc
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|
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ldm/modules/discriminator.py
DELETED
@@ -1,97 +0,0 @@
|
|
1 |
-
from torch import nn
|
2 |
-
import pdb
|
3 |
-
import torch
|
4 |
-
|
5 |
-
# to use with clip
|
6 |
-
class Discriminator64(nn.Module):
|
7 |
-
def __init__(self, bnorm=True, leakyparam=0.0, bias=False, generic=False):
|
8 |
-
super(Discriminator64, self).__init__()
|
9 |
-
|
10 |
-
self.bnorm = bnorm
|
11 |
-
self.generic = generic
|
12 |
-
|
13 |
-
self.relu = nn.LeakyReLU(leakyparam, inplace=True)
|
14 |
-
|
15 |
-
self.bn2 = nn.BatchNorm2d(128)
|
16 |
-
self.bn3 = nn.BatchNorm2d(256)
|
17 |
-
self.bn4 = nn.BatchNorm2d(512)
|
18 |
-
|
19 |
-
self.layer1 = nn.Conv2d(4, 64, 4, 2, 1, bias=bias)
|
20 |
-
self.layer2 = nn.Conv2d(64, 128, 4, 2, 1, bias=bias)
|
21 |
-
self.layer3 = nn.Conv2d(128, 256, 4, 2, 1, bias=bias)
|
22 |
-
self.layer4 = nn.Conv2d(256, 512, 4, 2, 1, bias=bias)
|
23 |
-
if generic:
|
24 |
-
self.layer5 = nn.Conv2d(512, 26, 4, 1, 0, bias=bias)
|
25 |
-
else:
|
26 |
-
self.layer5 = nn.Conv2d(512, 1, 4, 1, 0, bias=bias)
|
27 |
-
self.sig = nn.Sigmoid()
|
28 |
-
|
29 |
-
|
30 |
-
def forward(self, input, letter):
|
31 |
-
out1 = self.relu(self.layer1(input))
|
32 |
-
|
33 |
-
if self.bnorm:
|
34 |
-
out2 = self.relu(self.bn2(self.layer2(out1)))
|
35 |
-
out3 = self.relu(self.bn3(self.layer3(out2)))
|
36 |
-
out4= self.relu(self.bn4(self.layer4(out3)))
|
37 |
-
else:
|
38 |
-
out2 = self.relu(self.layer2(out1))
|
39 |
-
out3 = self.relu(self.layer3(out2))
|
40 |
-
out4= self.relu(self.layer4(out3))
|
41 |
-
|
42 |
-
out5 = self.sig(self.layer5(out4))
|
43 |
-
out5 = out5.flatten()
|
44 |
-
|
45 |
-
if self.generic:
|
46 |
-
out5 = out5[letter].mean()
|
47 |
-
else:
|
48 |
-
out5 = out5.mean()
|
49 |
-
|
50 |
-
return out5
|
51 |
-
|
52 |
-
|
53 |
-
# to use with bert
|
54 |
-
class Discriminator(nn.Module):
|
55 |
-
def __init__(self, bnorm=True, leakyparam=0.0, bias=False, generic=False):
|
56 |
-
super(Discriminator, self).__init__()
|
57 |
-
|
58 |
-
self.bnorm = bnorm
|
59 |
-
self.generic = generic
|
60 |
-
|
61 |
-
self.relu = nn.LeakyReLU(leakyparam, inplace=True)
|
62 |
-
self.sig = nn.Sigmoid()
|
63 |
-
self.bn2 = nn.BatchNorm2d(128)
|
64 |
-
self.bn3 = nn.BatchNorm2d(256)
|
65 |
-
self.bn4 = nn.BatchNorm2d(512)
|
66 |
-
|
67 |
-
self.layer1 = nn.Conv2d(4, 64, 4, 2, 1, bias=bias)
|
68 |
-
self.layer2 = nn.Conv2d(64, 128, 4, 2, 1, bias=bias)
|
69 |
-
self.layer3 = nn.Conv2d(128, 256, 4, 2, 1, bias=bias)
|
70 |
-
self.layer4 = nn.Conv2d(256, 512, 4, 2, 1, bias=bias)
|
71 |
-
if generic:
|
72 |
-
self.layer5 = nn.Conv2d(512, 26, 2, 1, 0, bias=bias)
|
73 |
-
else:
|
74 |
-
self.layer5 = nn.Conv2d(512, 1, 2, 1, 0, bias=bias)
|
75 |
-
|
76 |
-
def forward(self, input, letter):
|
77 |
-
|
78 |
-
out1 = self.relu(self.layer1(input))
|
79 |
-
|
80 |
-
if self.bnorm:
|
81 |
-
out2 = self.relu(self.bn2(self.layer2(out1)))
|
82 |
-
out3 = self.relu(self.bn3(self.layer3(out2)))
|
83 |
-
out4= self.relu(self.bn4(self.layer4(out3)))
|
84 |
-
else:
|
85 |
-
out2 = self.relu(self.layer2(out1))
|
86 |
-
out3 = self.relu(self.layer3(out2))
|
87 |
-
out4= self.relu(self.layer4(out3))
|
88 |
-
|
89 |
-
out5 = self.sig(self.layer5(out4))
|
90 |
-
out5 = out5.flatten()
|
91 |
-
|
92 |
-
if self.generic:
|
93 |
-
out5 = out5[letter].mean()
|
94 |
-
else:
|
95 |
-
out5 = out5.mean()
|
96 |
-
|
97 |
-
return out5
|
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ldm/modules/distributions/__pycache__/__init__.cpython-38.pyc
CHANGED
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ldm/modules/distributions/__pycache__/distributions.cpython-38.pyc
CHANGED
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ldm/modules/encoders/__pycache__/__init__.cpython-38.pyc
CHANGED
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ldm/modules/encoders/__pycache__/modules.cpython-38.pyc
CHANGED
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|
|
out/express/DRAGON-R.jpg
CHANGED
out/express/samples/0000.png
CHANGED
out/express/samples/0001.png
CHANGED
out/express/samples/0002.png
CHANGED
out/express/samples/0003.png
CHANGED
txt2img.py
CHANGED
@@ -132,10 +132,6 @@ if __name__ == "__main__":
|
|
132 |
seed_everything(seed)
|
133 |
|
134 |
# config = OmegaConf.load("configs/latent-diffusion/txt2img-1p4B-eval_with_tokens.yaml") # TODO: Optionally download from same location as ckpt and chnage this logic
|
135 |
-
if opt.H == 512:
|
136 |
-
config = OmegaConf.load("configs/finetune/finetune_clip.yaml")
|
137 |
-
else:
|
138 |
-
config = OmegaConf.load("configs/finetune/finetune_bert.yaml")
|
139 |
|
140 |
config = OmegaConf.load("configs/finetune/finetune_generic.yaml")
|
141 |
# config = OmegaConf.load("configs/latent-diffusion/txt2img-1p4B-finetune2.yaml")
|
|
|
132 |
seed_everything(seed)
|
133 |
|
134 |
# config = OmegaConf.load("configs/latent-diffusion/txt2img-1p4B-eval_with_tokens.yaml") # TODO: Optionally download from same location as ckpt and chnage this logic
|
|
|
|
|
|
|
|
|
135 |
|
136 |
config = OmegaConf.load("configs/finetune/finetune_generic.yaml")
|
137 |
# config = OmegaConf.load("configs/latent-diffusion/txt2img-1p4B-finetune2.yaml")
|