Update nodes.py

nodes.py

@@ -277,7 +277,7 @@ class OptimizerConfigProdigy:
 
     def create_config(self, weight_decay, decouple, min_snr_gamma, use_bias_correction, extra_optimizer_args, **kwargs):
         kwargs["optimizer_type"] = "prodigy"
-        extra_args = [arg.strip() for arg in extra_optimizer_args.strip().split(',') if arg.strip()]
+        extra_args = [arg.strip() for arg in extra_optimizer_args.strip().split('|') if arg.strip()]
         node_args = [
                 f"weight_decay={weight_decay}",
                 f"decouple={decouple}",
@@ -1068,6 +1068,7 @@ class FluxKohyaInferenceSampler:
             "guidance_scale": ("FLOAT", {"default": 3.5, "min": 1.0, "max": 32.0, "step": 0.05, "tooltip": "guidance scale"}),
             "seed": ("INT", {"default": 42,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
             "use_fp8": ("BOOLEAN", {"default": True, "tooltip": "use fp8 weights"}),
+            "apply_t5_attn_mask": ("BOOLEAN", {"default": True, "tooltip": "use t5 attention mask"}),
             "prompt": ("STRING", {"multiline": True, "default": "illustration of a kitten", "tooltip": "prompt"}),
           
             },
@@ -1078,7 +1079,7 @@ class FluxKohyaInferenceSampler:
     FUNCTION = "sample"
     CATEGORY = "FluxTrainer"
 
-    def sample(self, flux_models, lora_name, steps, width, height, guidance_scale, seed, prompt, use_fp8, lora_method):
+    def sample(self, flux_models, lora_name, steps, width, height, guidance_scale, seed, prompt, use_fp8, lora_method, apply_t5_attn_mask):
 
         from .library import flux_utils as flux_utils
         from .library import strategy_flux as strategy_flux
@@ -1091,7 +1092,7 @@ class FluxKohyaInferenceSampler:
         import gc
 
         device = "cuda"
-        apply_t5_attn_mask = True
+        
 
         if use_fp8:
             accelerator = accelerate.Accelerator(mixed_precision="bf16")
@@ -1284,6 +1285,7 @@ class FluxKohyaInferenceSampler:
             flux_dtype: torch.dtype,
         ):
             timesteps = get_schedule(num_steps, img.shape[1], shift=not is_schnell)
+            print(timesteps)
 
             # denoise initial noise
             if accelerator: