diff options
| -rw-r--r-- | extensions-builtin/Lora/extra_networks_lora.py | 12 | ||||
| -rw-r--r-- | extensions-builtin/Lora/networks.py | 61 | ||||
| -rw-r--r-- | extensions-builtin/Lora/scripts/lora_script.py | 6 | ||||
| -rwxr-xr-x | modules/processing.py | 13 |
4 files changed, 58 insertions, 34 deletions
diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py index ba2945c6..32e32cab 100644 --- a/extensions-builtin/Lora/extra_networks_lora.py +++ b/extensions-builtin/Lora/extra_networks_lora.py @@ -1,4 +1,4 @@ -from modules import extra_networks, shared
+from modules import extra_networks, shared, sd_hijack
import networks
@@ -6,9 +6,14 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork): def __init__(self):
super().__init__('lora')
+ self.errors = {}
+ """mapping of network names to the number of errors the network had during operation"""
+
def activate(self, p, params_list):
additional = shared.opts.sd_lora
+ self.errors.clear()
+
if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
@@ -56,4 +61,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork): p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)
def deactivate(self, p):
- pass
+ if self.errors:
+ p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
+
+ self.errors.clear()
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py index ba621139..c252ed9e 100644 --- a/extensions-builtin/Lora/networks.py +++ b/extensions-builtin/Lora/networks.py @@ -1,3 +1,4 @@ +import logging
import os
import re
@@ -194,7 +195,7 @@ def load_network(name, network_on_disk): net.modules[key] = net_module
if keys_failed_to_match:
- print(f"Failed to match keys when loading network {network_on_disk.filename}: {keys_failed_to_match}")
+ logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
return net
@@ -207,7 +208,6 @@ def purge_networks_from_memory(): devices.torch_gc()
-
def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
already_loaded = {}
@@ -248,7 +248,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No if net is None:
failed_to_load_networks.append(name)
- print(f"Couldn't find network with name {name}")
+ logging.info(f"Couldn't find network with name {name}")
continue
net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
@@ -257,7 +257,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No loaded_networks.append(net)
if failed_to_load_networks:
- sd_hijack.model_hijack.comments.append("Failed to find networks: " + ", ".join(failed_to_load_networks))
+ sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
purge_networks_from_memory()
@@ -314,17 +314,22 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn for net in loaded_networks:
module = net.modules.get(network_layer_name, None)
if module is not None and hasattr(self, 'weight'):
- with torch.no_grad():
- updown, ex_bias = module.calc_updown(self.weight)
+ try:
+ with torch.no_grad():
+ updown, ex_bias = module.calc_updown(self.weight)
- if len(self.weight.shape) == 4 and self.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))
+ if len(self.weight.shape) == 4 and self.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
- if ex_bias is not None and getattr(self, 'bias', None) is not None:
- self.bias += ex_bias
- continue
+ self.weight += updown
+ if ex_bias is not None and getattr(self, 'bias', None) is not None:
+ self.bias += ex_bias
+ 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
+
+ continue
module_q = net.modules.get(network_layer_name + "_q_proj", None)
module_k = net.modules.get(network_layer_name + "_k_proj", None)
@@ -332,21 +337,28 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn module_out = net.modules.get(network_layer_name + "_out_proj", None)
if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
- with torch.no_grad():
- updown_q = module_q.calc_updown(self.in_proj_weight)
- updown_k = module_k.calc_updown(self.in_proj_weight)
- updown_v = module_v.calc_updown(self.in_proj_weight)
- updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
- updown_out = module_out.calc_updown(self.out_proj.weight)
-
- self.in_proj_weight += updown_qkv
- self.out_proj.weight += updown_out
- continue
+ try:
+ with torch.no_grad():
+ updown_q = module_q.calc_updown(self.in_proj_weight)
+ updown_k = module_k.calc_updown(self.in_proj_weight)
+ updown_v = module_v.calc_updown(self.in_proj_weight)
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+ updown_out = module_out.calc_updown(self.out_proj.weight)
+
+ self.in_proj_weight += updown_qkv
+ self.out_proj.weight += updown_out
+
+ 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
+
+ continue
if module is None:
continue
- print(f'failed to calculate network weights for layer {network_layer_name}')
+ logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
self.network_current_names = wanted_names
@@ -519,6 +531,7 @@ def infotext_pasted(infotext, params): if added:
params["Prompt"] += "\n" + "".join(added)
+extra_network_lora = None
available_networks = {}
available_network_aliases = {}
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index dc307f8c..4c6e774a 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -23,9 +23,9 @@ def unload(): def before_ui():
ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
- extra_network = extra_networks_lora.ExtraNetworkLora()
- extra_networks.register_extra_network(extra_network)
- extra_networks.register_extra_network_alias(extra_network, "lyco")
+ networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora()
+ extra_networks.register_extra_network(networks.extra_network_lora)
+ extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco")
if not hasattr(torch.nn, 'Linear_forward_before_network'):
diff --git a/modules/processing.py b/modules/processing.py index 007a4e05..10749aa2 100755 --- a/modules/processing.py +++ b/modules/processing.py @@ -157,6 +157,7 @@ class StableDiffusionProcessing: cached_uc = [None, None]
cached_c = [None, None]
+ comments: dict = None
sampler: sd_samplers_common.Sampler | None = field(default=None, init=False)
is_using_inpainting_conditioning: bool = field(default=False, init=False)
paste_to: tuple | None = field(default=None, init=False)
@@ -196,6 +197,8 @@ class StableDiffusionProcessing: if self.sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
+ self.comments = {}
+
self.sampler_noise_scheduler_override = None
self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
@@ -226,6 +229,9 @@ class StableDiffusionProcessing: def sd_model(self, value):
pass
+ def comment(self, text):
+ self.comments[text] = 1
+
def txt2img_image_conditioning(self, x, width=None, height=None):
self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
@@ -429,7 +435,7 @@ class Processed: self.subseed = subseed
self.subseed_strength = p.subseed_strength
self.info = info
- self.comments = comments
+ self.comments = "".join(f"{comment}\n" for comment in p.comments)
self.width = p.width
self.height = p.height
self.sampler_name = p.sampler_name
@@ -720,8 +726,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: modules.sd_hijack.model_hijack.apply_circular(p.tiling)
modules.sd_hijack.model_hijack.clear_comments()
- comments = {}
-
p.setup_prompts()
if type(seed) == list:
@@ -801,7 +805,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: p.setup_conds()
for comment in model_hijack.comments:
- comments[comment] = 1
+ p.comment(comment)
p.extra_generation_params.update(model_hijack.extra_generation_params)
@@ -930,7 +934,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: images_list=output_images,
seed=p.all_seeds[0],
info=infotexts[0],
- comments="".join(f"{comment}\n" for comment in comments),
subseed=p.all_subseeds[0],
index_of_first_image=index_of_first_image,
infotexts=infotexts,
|