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| pretrained_model_path: "./ckpt/stable-diffusion-v1-4" | |
| train_dataset: | |
| path: "data/style/red_water_lily_opening" | |
| prompt: "a pink water lily" | |
| start_sample_frame: 1 | |
| n_sample_frame: 8 | |
| # n_sample_frame: 22 | |
| sampling_rate: 20 | |
| stride: 8000 | |
| # offset: | |
| # left: 300 | |
| # right: 0 | |
| # top: 0 | |
| # bottom: 0 | |
| validation_sample_logger_config: | |
| use_train_latents: True | |
| use_inversion_attention: True | |
| guidance_scale: 7.5 | |
| prompts: [ | |
| a pink water lily, | |
| Claude Monet painting of a pink water lily, | |
| ] | |
| p2p_config: | |
| 0: | |
| # Whether to directly copy the cross attention from source | |
| # True: directly copy, better for object replacement | |
| # False: keep source attention, better for style | |
| is_replace_controller: False | |
| # Semantic preserving and replacement Debug me | |
| cross_replace_steps: | |
| default_: 0.7 | |
| # Source background structure preserving, in [0, 1]. | |
| # e.g., =0.6 Replace the first 60% steps self-attention | |
| self_replace_steps: 0.7 | |
| # Amplify the target-words cross attention, larger value, more close to target | |
| eq_params: | |
| words: ["silver", "sculpture"] | |
| values: [2,2] | |
| # Target structure-divergence hyperparames | |
| # If you change the shape of object better to use all three line, otherwise, no need. | |
| # Without following three lines, all self-attention will be replaced | |
| blend_words: [['cat',], ["cat",]] | |
| masked_self_attention: True | |
| # masked_latents: False # performance not so good in our case, need debug | |
| bend_th: [2, 2] | |
| # preserve source structure of blend_words , [0, 1] | |
| # default is bend_th: [2, 2] # preserve all source self-attention | |
| # bend_th : [0.0, 0.0], mask -> 1, use more att_replace, more generated attention, less source acttention | |
| 1: | |
| is_replace_controller: False | |
| cross_replace_steps: | |
| default_: 0.5 | |
| self_replace_steps: 0.5 | |
| eq_params: | |
| words: ["Monet"] | |
| values: [10] | |
| clip_length: "${..train_dataset.n_sample_frame}" | |
| sample_seeds: [0] | |
| val_all_frames: False | |
| num_inference_steps: 50 | |
| prompt2prompt_edit: True | |
| model_config: | |
| lora: 160 | |
| # temporal_downsample_time: 4 | |
| SparseCausalAttention_index: ['mid'] | |
| least_sc_channel: 1280 | |
| # least_sc_channel: 100000 | |
| test_pipeline_config: | |
| target: video_diffusion.pipelines.p2pDDIMSpatioTemporalPipeline.p2pDDIMSpatioTemporalPipeline | |
| num_inference_steps: "${..validation_sample_logger.num_inference_steps}" | |
| epsilon: 1e-5 | |
| train_steps: 10 | |
| seed: 0 | |
| learning_rate: 1e-5 | |
| train_temporal_conv: False | |
| guidance_scale: "${validation_sample_logger_config.guidance_scale}" |