diff options
Diffstat (limited to 'extensions-builtin/Lora/networks.py')
| -rw-r--r-- | extensions-builtin/Lora/networks.py | 26 |
1 files changed, 20 insertions, 6 deletions
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py index 629bf853..72ebd624 100644 --- a/extensions-builtin/Lora/networks.py +++ b/extensions-builtin/Lora/networks.py @@ -1,3 +1,4 @@ +import gradio as gr
import logging
import os
import re
@@ -314,7 +315,12 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No emb_db.skipped_embeddings[name] = embedding
if failed_to_load_networks:
- sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
+ lora_not_found_message = f'Lora not found: {", ".join(failed_to_load_networks)}'
+ sd_hijack.model_hijack.comments.append(lora_not_found_message)
+ if shared.opts.lora_not_found_warning_console:
+ print(f'\n{lora_not_found_message}\n')
+ if shared.opts.lora_not_found_gradio_warning:
+ gr.Warning(lora_not_found_message)
purge_networks_from_memory()
@@ -389,18 +395,26 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn if module is not None and hasattr(self, 'weight'):
try:
with torch.no_grad():
- updown, ex_bias = module.calc_updown(self.weight)
+ if getattr(self, 'fp16_weight', None) is None:
+ weight = self.weight
+ bias = self.bias
+ else:
+ weight = self.fp16_weight.clone().to(self.weight.device)
+ bias = getattr(self, 'fp16_bias', None)
+ if bias is not None:
+ bias = bias.clone().to(self.bias.device)
+ updown, ex_bias = module.calc_updown(weight)
- if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+ if len(weight.shape) == 4 and weight.shape[1] == 9:
# inpainting model. zero pad updown to make channel[1] 4 to 9
updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
- self.weight += updown
+ self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
if ex_bias is not None and hasattr(self, 'bias'):
if self.bias is None:
- self.bias = torch.nn.Parameter(ex_bias)
+ self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
else:
- self.bias += ex_bias
+ self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype))
except RuntimeError as e:
logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
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