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swan_duck_flamingo.yaml
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# CUDA_VISIBLE_DEVICES=1 python test_fatezero.py --config config/shape/swan_duck_flamingo.yaml
pretrained_model_path: "./ckpt/swan_150"
dataset_config:
path: "./data/attribute/swan_swarov"
prompt: "a black swan with a red beak swimming in a river near a wall and bushes,"
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: [
# source prompt
a black swan with a red beak swimming in a river near a wall and bushes,
# foreground color and species
a white duck with a yellow beak swimming in a river near a wall and bushes,
a pink flamingo with a red beak swimming in a river near a wall and bushes,
]
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_: 0.8
# Source background structure preserving, in [0, 1].
# e.g., =0.6 Replace the first 60% steps self-attention
self_replace_steps: 0.6
# 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",]]
# blend_self_attention: True
# # blend_latents: False # performance not so good in our case, need debug
# blend_th: [2, 2]
# preserve source structure of blend_words , [0, 1]
# default is blend_th: [2, 2] # preserve all source self-attention
# blend_th : [0.0, 0.0], mask -> 1, use more target self-attention att_replace, more generated attention, less source acttention
1:
is_replace_controller: False
cross_replace_steps:
default_: 0.0
self_replace_steps: 0.5
# eq_params:
# words: ["duck"]
# values: [5] # amplify attention to the word "tiger" by *2
use_inversion_attention: True
blend_words: [['swan',], ["cat",]]
blend_self_attention: True
# blend_latents: False # performance not so good in our case, need debug
blend_th: [0.2, 0.2]
2:
is_replace_controller: False
cross_replace_steps:
default_: 0.3
self_replace_steps: 0.5
# eq_params:
# words: ["duck"]
# values: [5] # amplify attention to the word "tiger" by *2
use_inversion_attention: True
blend_words: [['swan',], ["cat",]]
blend_self_attention: True
# blend_latents: False # performance not so good in our case, need debug
blend_th: [0.2, 0.2]
clip_length: "${..dataset_config.n_sample_frame}"
sample_seeds: [0]
num_inference_steps: 30
prompt2prompt_edit: True
model_config:
lora: 160
# temporal_downsample_time: 4
# SparseCausalAttention_index: ['mid']
# least_sc_channel: 1280
# least_sc_channel: 100000
test_pipeline_config:
target: video_diffusion.pipelines.p2p_ddim_spatial_temporal.P2pDDIMSpatioTemporalPipeline
num_inference_steps: "${..validation_sample_logger.num_inference_steps}"
seed: 0