Inference and fine-tuning error reporting for image synthesis

[luoan7248]

Are there any special conventions for setting image size parameters for fine-tuning and reasoning.

When lora is trained, the height setting of 1024 and the width setting of 560 report an error, and the inference is also set in the same way, and the same error is reported.

But if I set 1024 at the same time, I don't get an error.

The specific information is as follows：

Epoch 0: 0%| | 0/250 [00:00<?, ?it/s]Traceback (most recent call last): File "examples/train/kolors/train_kolors_lora.py", line 77, in launch_training_task(model, args) File "/workspace/luoan/DiffSynth-Studio-main/diffsynth/trainers/text_to_image.py", line 244, in launch_training_task trainer.fit(model=model, train_dataloaders=train_loader) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 543, in fit call._call_and_handle_interrupt( File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py", line 43, in _call_and_handle_interrupt return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/launchers/subprocess_script.py", line 105, in launch return function(*args, *kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 579, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 986, in _run results = self._run_stage() File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 1030, in _run_stage self.fit_loop.run() File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/fit_loop.py", line 205, in run self.advance() File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/fit_loop.py", line 363, in advance self.epoch_loop.run(self._data_fetcher) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 140, in run self.advance(data_fetcher) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 250, in advance batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 190, in run self._optimizer_step(batch_idx, closure) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 268, in _optimizer_step call._call_lightning_module_hook( File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py", line 159, in _call_lightning_module_hook output = fn(args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/core/module.py", line 1308, in optimizer_step optimizer.step(closure=optimizer_closure) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/core/optimizer.py", line 153, in step step_output = self._strategy.optimizer_step(self._optimizer, closure, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/ddp.py", line 270, in optimizer_step optimizer_output = super().optimizer_step(optimizer, closure, model, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 238, in optimizer_step return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/plugins/precision/amp.py", line 77, in optimizer_step closure_result = closure() File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 144, in call self._result = self.closure(*args, *kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 129, in closure step_output = self._step_fn() File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 317, in _training_step training_step_output = call._call_strategy_hook(trainer, "training_step", kwargs.values()) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py", line 311, in _call_strategy_hook output = fn(args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 389, in training_step return self._forward_redirection(self.model, self.lightning_module, "training_step", *args, *kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 640, in call wrapper_output = wrapper_module(args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1156, in forward output = self._run_ddp_forward(*inputs, *kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1110, in _run_ddp_forward return module_to_run(inputs[0], kwargs[0]) # type: ignore[index] File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 633, in wrapped_forward out = method(*_args, *_kwargs) File "/workspace/luoan/DiffSynth-Studio-main/diffsynth/trainers/text_to_image.py", line 64, in training_step noise_pred = self.pipe.denoising_model()( File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(args, kwargs) File "/workspace/luoan/DiffSynth-Studio-main/diffsynth/models/sdxl_unet.py", line 126, in forward hidden_states, time_emb, text_emb, res_stack = block( File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, kwargs) File "/workspace/luoan/DiffSynth-Studio-main/diffsynth/models/sd_unet.py", line 226, in forward hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 36 but got size 35 for tensor number 1 in the list. Traceback (most recent call last): File "/workspace/luoan/DiffSynth-Studio-main/examples/train/kolors/train_kolors_lora.py", line 77, in launch_training_task(model, args) File "/workspace/luoan/DiffSynth-Studio-main/diffsynth/trainers/text_to_image.py", line 244, in launch_training_task trainer.fit(model=model, train_dataloaders=train_loader) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 543, in fit call._call_and_handle_interrupt( File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py", line 43, in _call_and_handle_interrupt return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, *kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/launchers/subprocess_script.py", line 105, in launch return function(args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 579, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 986, in _run results = self._run_stage() File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/trainer.py", line 1030, in _run_stage self.fit_loop.run() File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/fit_loop.py", line 205, in run self.advance() File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/fit_loop.py", line 363, in advance self.epoch_loop.run(self._data_fetcher) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 140, in run self.advance(data_fetcher) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 250, in advance batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 190, in run self._optimizer_step(batch_idx, closure) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 268, in _optimizer_step call._call_lightning_module_hook( File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py", line 159, in _call_lightning_module_hook output = fn(args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/core/module.py", line 1308, in optimizer_step optimizer.step(closure=optimizer_closure) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/core/optimizer.py", line 153, in step step_output = self._strategy.optimizer_step(self._optimizer, closure, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/ddp.py", line 270, in optimizer_step optimizer_output = super().optimizer_step(optimizer, closure, model, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 238, in optimizer_step return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/plugins/precision/amp.py", line 77, in optimizer_step closure_result = closure() File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 144, in call self._result = self.closure(args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 129, in closure step_output = self._step_fn() File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/loops/optimization/automatic.py", line 317, in _training_step training_step_output = call._call_strategy_hook(trainer, "training_step", kwargs.values()) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/trainer/call.py", line 311, in _call_strategy_hook output = fn(args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 389, in training_step return self._forward_redirection(self.model, self.lightning_module, "training_step", *args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 640, in call wrapper_output = wrapper_module(*args, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, *kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1156, in forward output = self._run_ddp_forward(inputs, kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1110, in _run_ddp_forward return module_to_run(*inputs[0], kwargs[0]) # type: ignore[index] File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, *kwargs) File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/lightning/pytorch/strategies/strategy.py", line 633, in wrapped_forward out = method(_args, _kwargs) File "/workspace/luoan/DiffSynth-Studio-main/diffsynth/trainers/text_to_image.py", line 64, in training_step noise_pred = self.pipe.denoising_model()( File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, *kwargs) File "/workspace/luoan/DiffSynth-Studio-main/diffsynth/models/sdxl_unet.py", line 126, in forward hidden_states, time_emb, text_emb, res_stack = block( File "/mnt/10101/staryea/anaconda3/envs/scepter/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(args, **kwargs) File "/workspace/luoan/DiffSynth-Studio-main/diffsynth/models/sd_unet.py", line 226, in forward hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 36 but got size 35 for tensor number 1 in the list.

[MahouShoujoMivutilde]

It's not.

Do NOT run it.

It's malware that'll steal your account if executed, to spread further spamming the same message elsewhere, like happened to this person.

There are a lot of comments like that

https://github.com/search?q=is%3Aissue+%22In+the+installer+menu%2C+select+%5C%22gcc.%5C%22%22+AND+%22password%3A+changeme%22&type=issues&s=created&o=desc

(and this is how I found this issue)

[Artiprocher]

The height and width can only be set to a number divisible by 64.