jeep_watercolor.yaml

# CUDA_VISIBLE_DEVICES=7 python test_fatezero.py --config config/teaser/jeep_watercolor.yaml

pretrained_model_path: "./ckpt/stable-diffusion-v1-4"

dataset_config:
    path: "data/teaser_car-turn"
    prompt: "a silver jeep driving down a curvy road in the countryside"
    n_sample_frame: 8
    sampling_rate: 1
    stride: 80
    offset: 
        left: 0
        right: 0
        top: 0
        bottom: 0


editing_config:
    use_invertion_latents: true
    use_inversion_attention: true
    guidance_scale: 7.5
    editing_prompts: [
        a silver jeep driving down a curvy road in the countryside,
        watercolor painting of a silver jeep driving down a curvy road in the countryside,
    ]
    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 layout preserving. High steps, replace more cross attention to preserve semantic layout
            cross_replace_steps: 
                default_: 0.8

            # Source background structure preserving, in [0, 1]. 
            # e.g., =0.6 Replace the first 60% steps self-attention
            self_replace_steps: 0.9

            
            # Amplify the target-words cross attention, larger value, more close to target
            # eq_params: 
            #     words: ["", ""]
            #     values: [10,10] 
            
            # 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: [['jeep',], ["car",]] 
            blend_self_attention:  True
            # blend_latents: False   #  Directly copy the latents, performance not so good in our case      
            blend_th: [2, 2]
            # preserve source structure of blend_words , [0, 1]
            # default is blend_th: [2, 2]  # replace full-resolution edit source with self-attention 
            # blend_th-> [0.0, 0.0], mask -> 1, use more edit self-attention, more generated shape, less source acttention
            

        1:
            cross_replace_steps: 
                default_: 0.8
            self_replace_steps: 0.8
            
            eq_params: 
                words: ["watercolor"]
                values: [10] # amplify attention to the word "tiger" by *2 
            use_inversion_attention: True
            is_replace_controller: False

            
    clip_length: "${..dataset_config.n_sample_frame}"
    sample_seeds: [0]

    num_inference_steps: 50
    prompt2prompt_edit: True

    
model_config:
    lora: 160
    # temporal_downsample_time: 4
    SparseCausalAttention_index: ['mid'] 
    least_sc_channel: 640
    # least_sc_channel: 100000

test_pipeline_config:
    target: video_diffusion.pipelines.p2p_ddim_spatial_temporal.P2pDDIMSpatioTemporalPipeline
    num_inference_steps: "${..validation_sample_logger.num_inference_steps}"



seed: 0