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-rw-r--r--modules/processing.py23
-rw-r--r--modules/shared_options.py1
2 files changed, 19 insertions, 5 deletions
diff --git a/modules/processing.py b/modules/processing.py
index 846e4796..f0656882 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -628,20 +628,33 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
sample = decode_first_stage(model, batch[i:i + 1])[0]
if check_for_nans:
+
try:
devices.test_for_nans(sample, "vae")
except devices.NansException as e:
- if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
+ if shared.opts.auto_vae_precision_bfloat16:
+ autofix_dtype = torch.bfloat16
+ autofix_dtype_text = "bfloat16"
+ autofix_dtype_setting = "Automatically convert VAE to bfloat16"
+ autofix_dtype_comment = ""
+ elif shared.opts.auto_vae_precision:
+ autofix_dtype = torch.float32
+ autofix_dtype_text = "32-bit float"
+ autofix_dtype_setting = "Automatically revert VAE to 32-bit floats"
+ autofix_dtype_comment = "\nTo always start with 32-bit VAE, use --no-half-vae commandline flag."
+ else:
+ raise e
+
+ if devices.dtype_vae == autofix_dtype:
raise e
errors.print_error_explanation(
"A tensor with all NaNs was produced in VAE.\n"
- "Web UI will now convert VAE into 32-bit float and retry.\n"
- "To disable this behavior, disable the 'Automatically revert VAE to 32-bit floats' setting.\n"
- "To always start with 32-bit VAE, use --no-half-vae commandline flag."
+ f"Web UI will now convert VAE into {autofix_dtype_text} and retry.\n"
+ f"To disable this behavior, disable the '{autofix_dtype_setting}' setting.{autofix_dtype_comment}"
)
- devices.dtype_vae = torch.float32
+ devices.dtype_vae = autofix_dtype
model.first_stage_model.to(devices.dtype_vae)
batch = batch.to(devices.dtype_vae)
diff --git a/modules/shared_options.py b/modules/shared_options.py
index ce06f022..e813546f 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -177,6 +177,7 @@ For img2img, VAE is used to process user's input image before the sampling, and
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list, infotext='VAE').info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
"sd_vae_overrides_per_model_preferences": OptionInfo(True, "Selected VAE overrides per-model preferences").info("you can set per-model VAE either by editing user metadata for checkpoints, or by making the VAE have same name as checkpoint"),
+ "auto_vae_precision_bfloat16": OptionInfo(False, "Automatically convert VAE to bfloat16").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image; if enabled, overrides the option below"),
"auto_vae_precision": OptionInfo(True, "Automatically revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
"sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Encoder').info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),
"sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Decoder').info("method to decode latent to image"),