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auto branching fix

This commit is contained in:
Johannes Stelzer 2022-11-24 11:24:23 +01:00
parent 78505be6da
commit 4ad098a20a
1 changed files with 17 additions and 29 deletions
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46
latent_blending.py
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@ -1029,9 +1029,9 @@ def get_time(resolution=None):
def get_branching( def get_branching(
quality: str = 'medium', quality: str = 'medium',
depth: str = 'medium', deepth_strength: float = 0.65,
strength_injection_first: float = 0.65,
nmb_frames: int = 360, nmb_frames: int = 360,
nmb_mindist: int = 3,
): ):
r""" r"""
Helper function to set up the branching structure automatically. Helper function to set up the branching structure automatically.
@ -1040,51 +1040,38 @@ def get_branching(
quality: str quality: str
Determines how many diffusion steps are being made + how many branches in total. Determines how many diffusion steps are being made + how many branches in total.
Choose: fast, medium, high, ultra Choose: fast, medium, high, ultra
quality: depth deepth_strength: float = 0.65,
Determines how deep the first injection will happen. Determines how deep the first injection will happen.
Deeper injections will cause (unwanted) formation of new structures, Deeper injections will cause (unwanted) formation of new structures,
more shallow values will go into alpha-blendy land more shallow values will go into alpha-blendy land.
Choose: verydeep, deep, medium, shallow, veryshallow
strength_injection_first: float = 0.65,
...
nmb_frames: int = 360, nmb_frames: int = 360,
total number of frames
nmb_mindist: int = 3
minimum distance in terms of diffusion iteratinos between subsequent injections
""" """
nmb_mindist = 3 #minimum distance between injections
depth = 'override'
#FIXME: XXX nmb frames last has to be enforced. avoid weird cases where no injection...
if depth == 'verydeep':
strength_injection_first = 0.35
elif depth == 'deep':
strength_injection_first = 0.45
elif depth == 'medium':
strength_injection_first = 0.6
elif depth == 'shallow':
strength_injection_first = 0.8
elif depth == 'veryshallow':
strength_injection_first = 0.9
if quality == 'superfast': if quality == 'lowest':
num_inference_steps = 8 num_inference_steps = 12
nmb_branches_final = 5 nmb_branches_final = 5
elif quality == 'fast': elif quality == 'low':
num_inference_steps = 15 num_inference_steps = 15
nmb_branches_final = nmb_frames//30 nmb_branches_final = nmb_frames//16
elif quality == 'medium': elif quality == 'medium':
num_inference_steps = 30 num_inference_steps = 30
nmb_branches_final = nmb_frames//10 nmb_branches_final = nmb_frames//8
elif quality == 'high': elif quality == 'high':
num_inference_steps = 60 num_inference_steps = 60
nmb_branches_final = nmb_frames//3 nmb_branches_final = nmb_frames//4
elif quality == 'ultra': elif quality == 'ultra':
num_inference_steps = 100 num_inference_steps = 100
nmb_branches_final = nmb_frames nmb_branches_final = nmb_frames//2
else: else:
raise ValueError("quality = '{quality}' not supported") raise ValueError("quality = '{quality}' not supported")
idx_injection_first = int(np.round(num_inference_steps*strength_injection_first)) idx_injection_first = int(np.round(num_inference_steps*deepth_strength))
idx_injection_last = num_inference_steps - 3 idx_injection_last = num_inference_steps - 3
nmb_injections = int(np.floor(num_inference_steps/5)) - 1 nmb_injections = int(np.floor(num_inference_steps/5)) - 1
@ -1110,6 +1097,7 @@ def get_branching(
return num_inference_steps, list_injection_idx_clean, list_nmb_branches_clean return num_inference_steps, list_injection_idx_clean, list_nmb_branches_clean
#%% le main #%% le main
if __name__ == "__main__": if __name__ == "__main__":