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Add cos xl (#710)

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* Add V_PREDICTION_EDM handing for CosXL models

Add V_PREDICTION_EDM handing for CosXL models

* Get correct sigmas from checkpoint.

* Round to 3 sig digs in order to make compatible with comfy implementation

* Add sigma data like ComfyUI has

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Co-authored-by: Gavin Chapman <gchapman@MAINPC>
This commit is contained in:
GavChap 2024-05-23 21:42:56 +01:00 committed by GitHub
parent 7eb5cbad01
commit 77bdb9208d
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2 changed files with 9 additions and 2 deletions
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6
ldm_patched/modules/model_sampling.py
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@ -107,9 +107,11 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
sigma_min = sampling_settings.get("sigma_min", 0.002)
sigma_max = sampling_settings.get("sigma_max", 120.0)
self.set_sigma_range(sigma_min, sigma_max)
sigma_data = sampling_settings.get("sigma_data", 1.0)
self.set_sigma_range(sigma_min, sigma_max, sigma_data)
def set_sigma_range(self, sigma_min, sigma_max):
def set_sigma_range(self, sigma_min, sigma_max, sigma_data):
self.sigma_data = sigma_data
sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers

5
ldm_patched/modules/supported_models.py
View File

@ -169,6 +169,11 @@ class SDXL(supported_models_base.BASE):
def model_type(self, state_dict, prefix=""):
if "v_pred" in state_dict:
return model_base.ModelType.V_PREDICTION
elif "edm_vpred.sigma_max" in state_dict:
self.sampling_settings["sigma_max"] = round(float(state_dict["edm_vpred.sigma_max"].item()),3)
if "edm_vpred.sigma_min" in state_dict:
self.sampling_settings["sigma_min"] = round(float(state_dict["edm_vpred.sigma_min"].item()),3)
return model_base.ModelType.V_PREDICTION_EDM
else:
return model_base.ModelType.EPS