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# CUDA_VISIBLE_DEVICES=0 python test_fatezero.py --config config/teaser/jeep_watercolor.yaml | |
pretrained_model_path: "FateZero/ckpt/stable-diffusion-v1-4" | |
train_dataset: | |
path: "FateZero/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 | |
validation_sample_logger_config: | |
use_train_latents: true | |
use_inversion_attention: true | |
guidance_scale: 7.5 | |
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",]] | |
masked_self_attention: True | |
# masked_latents: False # Directly copy the latents, performance not so good in our case | |
bend_th: [2, 2] | |
# preserve source structure of blend_words , [0, 1] | |
# default is bend_th: [2, 2] # replace full-resolution edit source with self-attention | |
# bend_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: "${..train_dataset.n_sample_frame}" | |
sample_seeds: [0] | |
num_inference_steps: 50 | |
prompt2prompt_edit: True | |
model_config: | |
lora: 160 | |
# temporal_downsample_time: 4 | |
SparseCausalAttention_index: ['mid'] | |
least_sc_channel: 640 | |
# least_sc_channel: 100000 | |
test_pipeline_config: | |
target: video_diffusion.pipelines.p2pDDIMSpatioTemporalPipeline.p2pDDIMSpatioTemporalPipeline | |
num_inference_steps: "${..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}" |