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Adding a gradio demo of SVD to be run locally (#144)
* Adding a gradio demo of SVD to be run locally * Update gradio_app.py * Create svd_xt_1_1.yaml * Update pt2.txt --------- Co-authored-by: Sumith Kulal <sumith1896@gmail.com>
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146
scripts/sampling/configs/svd_xt_1_1.yaml
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146
scripts/sampling/configs/svd_xt_1_1.yaml
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model:
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target: sgm.models.diffusion.DiffusionEngine
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params:
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scale_factor: 0.18215
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disable_first_stage_autocast: True
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ckpt_path: checkpoints/svd_xt_1_1.safetensors
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denoiser_config:
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target: sgm.modules.diffusionmodules.denoiser.Denoiser
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params:
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scaling_config:
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target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
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network_config:
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target: sgm.modules.diffusionmodules.video_model.VideoUNet
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params:
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adm_in_channels: 768
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num_classes: sequential
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use_checkpoint: True
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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_head_channels: 64
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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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spatial_transformer_attn_type: softmax-xformers
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extra_ff_mix_layer: True
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use_spatial_context: True
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merge_strategy: learned_with_images
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video_kernel_size: [3, 1, 1]
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conditioner_config:
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target: sgm.modules.GeneralConditioner
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params:
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emb_models:
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- is_trainable: False
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input_key: cond_frames_without_noise
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target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
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params:
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n_cond_frames: 1
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n_copies: 1
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open_clip_embedding_config:
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target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
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params:
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freeze: True
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- input_key: fps_id
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is_trainable: False
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target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
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params:
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outdim: 256
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- input_key: motion_bucket_id
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is_trainable: False
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target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
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params:
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outdim: 256
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- input_key: cond_frames
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is_trainable: False
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target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
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params:
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disable_encoder_autocast: True
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n_cond_frames: 1
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n_copies: 1
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is_ae: True
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encoder_config:
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target: sgm.models.autoencoder.AutoencoderKLModeOnly
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params:
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embed_dim: 4
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monitor: val/rec_loss
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ddconfig:
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attn_type: vanilla-xformers
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double_z: True
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult: [1, 2, 4, 4]
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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- input_key: cond_aug
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is_trainable: False
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target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
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params:
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outdim: 256
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first_stage_config:
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target: sgm.models.autoencoder.AutoencodingEngine
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params:
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loss_config:
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target: torch.nn.Identity
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regularizer_config:
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target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
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encoder_config:
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target: sgm.modules.diffusionmodules.model.Encoder
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params:
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attn_type: vanilla
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double_z: True
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult: [1, 2, 4, 4]
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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decoder_config:
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target: sgm.modules.autoencoding.temporal_ae.VideoDecoder
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params:
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attn_type: vanilla
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double_z: True
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult: [1, 2, 4, 4]
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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video_kernel_size: [3, 1, 1]
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sampler_config:
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target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
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params:
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discretization_config:
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target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
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params:
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sigma_max: 700.0
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guider_config:
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target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
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params:
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max_scale: 3.0
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min_scale: 1.5
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