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add lora_load_gpu setting

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Vladimir Mandic
2024-09-25 17:38:40 -04:00
parent 3198a19899
commit e7e79c4870
5 changed files with 22 additions and 15 deletions
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CHANGELOG.md
+3 -1
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@@ -54,7 +54,8 @@
- loras are no longer filtered per detected type vs loaded model type as its unreliable
- loras display in networks now shows possible version in top-left corner
- correct using of `extra_networks_default_multiplier` if not scale is specified
- always keep lora on gpu
- setting `lora_load_gpu` to load LoRA directly to GPU
*default*: true unless lovwram
- **huggingface**:
- force logout/login on token change
- unified handling of cache folder: set via `HF_HUB` or `HF_HUB_CACHE` or via settings -> system paths
@@ -78,6 +79,7 @@
- modularize main process loop
- massive log cleanup
- full lint pass
- improve inference mode handling
## Update for 2024-09-13
extensions-builtin/Lora/network_lora.py
+4 -1
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@@ -13,6 +13,7 @@ class ModuleTypeLora(network.ModuleType):
class NetworkModuleLora(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.up_model = self.create_module(weights.w, "lora_up.weight")
@@ -21,6 +22,7 @@ class NetworkModuleLora(network.NetworkModule):
self.dim = weights.w["lora_down.weight"].shape[0]
def create_module(self, weights, key, none_ok=False):
from modules.shared import opts
weight = weights.get(key)
if weight is None and none_ok:
return None
@@ -47,7 +49,8 @@ class NetworkModuleLora(network.NetworkModule):
if weight.shape != module.weight.shape:
weight = weight.reshape(module.weight.shape)
module.weight.copy_(weight)
module = module.to(device=devices.device, dtype=devices.dtype)
if opts.lora_load_gpu:
module = module.to(device=devices.device, dtype=devices.dtype)
module.weight.requires_grad_(False)
return module
javascript/sdnext.css
+1 -1
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@@ -224,7 +224,7 @@ table.settings-value-table td { padding: 0.4em; border: 1px solid #ccc; max-widt
.extra-network-cards .card .overlay .tag { padding: 2px; margin: 2px; background: rgba(70, 70, 70, 0.60); font-size: var(--text-md); cursor: pointer; display: inline-block; }
.extra-network-cards .card .actions>span { padding: 4px; font-size: 34px !important; }
.extra-network-cards .card .actions>span:hover { color: var(--highlight-color); }
.extra-network-cards .card .version { position: absolute; top: 0; left: 0; padding: 2px; font-weight: bolder; text-shadow: 1px 1px black; text-transform: uppercase; font-size: 0.8rem; background: gray; opacity: 75%; margin: 2px; line-height: 0.9rem; }
.extra-network-cards .card .version { position: absolute; top: 0; left: 0; padding: 2px; font-weight: bolder; text-shadow: 1px 1px black; text-transform: uppercase; background: gray; opacity: 75%; margin: 4px; line-height: 0.9rem; }
.extra-network-cards .card:hover .actions { display: block; }
.extra-network-cards .card:hover .overlay .tags { display: block; }
.extra-network-cards .card:has(>img[src*="card-no-preview.png"])::before { content: ''; position: absolute; width: 100%; height: 100%; mix-blend-mode: multiply; background-color: var(--data-color); }
modules/shared.py
+1
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@@ -868,6 +868,7 @@ options_templates.update(options_section(('extra_networks', "Networks"), {
"lora_apply_tags": OptionInfo(0, "LoRA auto-apply tags", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
"lora_in_memory_limit": OptionInfo(0, "LoRA memory cache", gr.Slider, {"minimum": 0, "maximum": 24, "step": 1}),
"lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox, { "visible": False }),
"lora_load_gpu": OptionInfo(True if not cmd_opts.lowvram else False, "Load LoRA directly to GPU"),
"hypernetwork_enabled": OptionInfo(False, "Enable Hypernetwork support"),
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, { "choices": ["None"], "visible": False }),
"wildcards_enabled": OptionInfo(True, "Enable file wildcards support"),
modules/textual_inversion/textual_inversion.py
+13 -12
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@@ -134,18 +134,18 @@ def insert_vectors(embedding, tokenizers, text_encoders, hiddensizes):
Future warning, if another text encoder becomes available with embedding dimensions in [768,1280,4096]
this may cause collisions.
"""
for vector, size in zip(embedding.vec, embedding.vector_sizes):
if size not in hiddensizes:
continue
idx = hiddensizes.index(size)
unk_token_id = tokenizers[idx].convert_tokens_to_ids(tokenizers[idx].unk_token)
if text_encoders[idx].get_input_embeddings().weight.data.shape[0] != len(tokenizers[idx]):
text_encoders[idx].resize_token_embeddings(len(tokenizers[idx]))
for token, v in zip(embedding.tokens, vector.unbind()):
token_id = tokenizers[idx].convert_tokens_to_ids(token)
if token_id > unk_token_id:
text_encoders[idx].get_input_embeddings().weight.data[token_id] = v
with devices.inference_context():
for vector, size in zip(embedding.vec, embedding.vector_sizes):
if size not in hiddensizes:
continue
idx = hiddensizes.index(size)
unk_token_id = tokenizers[idx].convert_tokens_to_ids(tokenizers[idx].unk_token)
if text_encoders[idx].get_input_embeddings().weight.data.shape[0] != len(tokenizers[idx]):
text_encoders[idx].resize_token_embeddings(len(tokenizers[idx]))
for token, v in zip(embedding.tokens, vector.unbind()):
token_id = tokenizers[idx].convert_tokens_to_ids(token)
if token_id > unk_token_id:
text_encoders[idx].get_input_embeddings().weight.data[token_id] = v
class Embedding:
@@ -310,6 +310,7 @@ class EmbeddingDatabase:
self.register_embedding(embedding, shared.sd_model)
except Exception as e:
shared.log.error(f'Load embedding: name="{embedding.name}" file="{embedding.filename}" {e}')
errors.display(e, f'Load embedding: name="{embedding.name}" file="{embedding.filename}"')
return
def load_from_file(self, path, filename):