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comfy/supported_models.py
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@ -1036,20 +1036,20 @@ class ZImage(Lumina2):
return supported_models_base.ClipTarget(comfy.text_encoders.z_image.ZImageTokenizer, comfy.text_encoders.z_image.te(**hunyuan_detect)) return supported_models_base.ClipTarget(comfy.text_encoders.z_image.ZImageTokenizer, comfy.text_encoders.z_image.te(**hunyuan_detect))
class NewBieImageModel(supported_models_base.BASE): class NewBieImageModel(supported_models_base.BASE):
unet_config = { unet_config = {
"image_model": "NewBieImage", "image_model": "NewBieImage",
"model_type": "newbie_dit", "model_type": "newbie_dit",
} }
sampling_settings = { sampling_settings = {
"multiplier": 1.0, "multiplier": 1.0,
"shift": 6.0, "shift": 6.0,
} }
memory_usage_factor = 1.5 memory_usage_factor = 1.5
unet_extra_config = {} unet_extra_config = {}
latent_format = latent_formats.Flux latent_format = latent_formats.Flux
supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
vae_key_prefix = ["vae."] vae_key_prefix = ["vae."]
text_encoder_key_prefix = ["text_encoders."] text_encoder_key_prefix = ["text_encoders."]
def get_model(self, state_dict, prefix="", device=None): def get_model(self, state_dict, prefix="", device=None):
out = model_base.NewBieImage(self, device=device) out = model_base.NewBieImage(self, device=device)