nodes/Kosinkadink-ComfyUI-AnimateDiff-Evolved
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

In Create Raw Sigma Schedule, renamed lcm_zsnr to zsnr and changed logic to allow applying zsnr for non-lcm

Browse Source
This commit is contained in:
Jedrzej Kosinski
2024-08-23 04:43:47 -05:00
parent df8eb6fb90
commit ae7ae379cd
2 changed files with 6 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
animatediff/nodes_sigma_schedule.py
+5 -4
View File
@@ -44,7 +44,7 @@ class RawSigmaScheduleNode:
#"cosine_s": ("FLOAT", {"default": 8e-3, "min": 0.0, "max": 1.0, "step": 0.000001}),
"sampling": (ModelSamplingType._FULL_LIST,),
"lcm_original_timesteps": ("INT", {"default": 50, "min": 1, "max": 1000}),
"lcm_zsnr": ("BOOLEAN", {"default": False}),
"zsnr": ("BOOLEAN", {"default": False}),
}
}
@@ -53,14 +53,15 @@ class RawSigmaScheduleNode:
FUNCTION = "get_sigma_schedule"
def get_sigma_schedule(self, raw_beta_schedule: str, linear_start: float, linear_end: float,# cosine_s: float,
sampling: str, lcm_original_timesteps: int, lcm_zsnr: bool):
sampling: str, lcm_original_timesteps: int, zsnr: bool, lcm_zsnr: bool=None):
if lcm_zsnr is not None:
zsnr = lcm_zsnr
new_config = ModelSamplingConfig(beta_schedule=raw_beta_schedule, linear_start=linear_start, linear_end=linear_end)
if sampling != ModelSamplingType.LCM:
lcm_original_timesteps=None
lcm_zsnr=False
model_type = ModelSamplingType.from_alias(sampling)
new_model_sampling = BetaSchedules._to_model_sampling(alias=BetaSchedules.AUTOSELECT, model_type=model_type, config_override=new_config, original_timesteps=lcm_original_timesteps)
if lcm_zsnr:
if zsnr:
SigmaSchedule.apply_zsnr(new_model_sampling=new_model_sampling)
return (SigmaSchedule(model_sampling=new_model_sampling, model_type=model_type),)
animatediff/utils_model.py
+1 -1
View File
@@ -194,7 +194,7 @@ class BetaSchedules:
return ModelSamplingConfig(cls.to_name(alias), linear_start=linear_start, linear_end=linear_end)
@classmethod
def _to_model_sampling(cls, alias: str, model_type: ModelType, config_override: ModelSamplingConfig=None, original_timesteps: int=None):
def _to_model_sampling(cls, alias: str, model_type: ModelType, config_override: Union[ModelSamplingConfig,None]=None, original_timesteps: Union[int,None]=None):
if alias == cls.USE_EXISTING:
return None
elif config_override != None: