Training FLUX in ComfyUI. A brief workflow, didn't add regularization image. v2.0 release note: Fix Flux-Trainer VisualizeLoss can't record global loss bug. v1.0 workflow just show current loop loss and can't auto flash loss data, so it always show first loop loss.v2.0 workflow fix this problem. But you should modify Flux-Trainer code. <pre><code>class VisualizeLoss:
 @classmethod

 ...
 
 # change output types
 RETURN_TYPES = ("NETWORKTRAINER", "IMAGE", "FLOAT",)
 RETURN_NAMES = ("network_trainer", "plot", "loss_list",)
 FUNCTION = "draw"
 CATEGORY = "FluxTrainer"

 def draw(self, network_trainer, window_size, plot_style, normalize_y, width, height, log_scale):
 ...
 
 # add code
 training_loop = network_trainer["training_loop"]
 network_trainer = network_trainer["network_trainer"]

 ...
 
 # It will make loss list become a list of all the same constants, I don't know how to modify it, temporarily shut down it.
 # 
 # if window_size &gt; 0:
 # loss_values = moving_average(loss_values, window_size)

 ...
 
 
 trainer = {
 "network_trainer": network_trainer,
 "training_loop": training_loop,
 }
 
 # change output 
 return (trainer, image_tensor, loss_values,)</code></pre>

FLUX Trainer Workflow

Fix Flux-Trainer VisualizeLoss can't record global loss bug. v1.0 workflow just show current loop loss and can't auto flash loss data, so it always show first loop loss. v2.0 workflow fix this problem. But you should modify Flux-Trainer code. <pre><code>class VisualizeLoss:
 @classmethod

 ...
 
 # change output types
 RETURN_TYPES = ("NETWORKTRAINER", "IMAGE", "FLOAT",)
 RETURN_NAMES = ("network_trainer", "plot", "loss_list",)
 FUNCTION = "draw"
 CATEGORY = "FluxTrainer"

 def draw(self, network_trainer, window_size, plot_style, normalize_y, width, height, log_scale):
 ...
 
 # add code
 training_loop = network_trainer["training_loop"]
 network_trainer = network_trainer["network_trainer"]

 ...
 
 # It will make loss list become a list of all the same constants, I don't know how to modify it, temporarily shut down it.
 # 
 # if window_size &gt; 0:
 # loss_values = moving_average(loss_values, window_size)

 ...
 
 
 trainer = {
 "network_trainer": network_trainer,
 "training_loop": training_loop,
 }
 
 # change output 
 return (trainer, image_tensor, loss_values,)</code></pre>

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