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Billy
2025-03-06 09:11:00 +11:00
parent 551c78d9f3
commit f2689598c0
11 changed files with 64 additions and 60 deletions
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2
invokeai/app/invocations/baseinvocation.py
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@ -417,7 +417,7 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
ui_type = field.json_schema_extra.get("ui_type", None)
if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
logger.warn(f"\"UIType.{ui_type.split('_')[-1]}\" is deprecated, ignoring")
logger.warn(f'"UIType.{ui_type.split("_")[-1]}" is deprecated, ignoring')
field.json_schema_extra.pop("ui_type")
return None

2
invokeai/app/invocations/compel.py
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@ -513,7 +513,7 @@ def log_tokenization_for_text(
usedTokens += 1
if usedTokens > 0:
print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
print(f"\n>> [TOKENLOG] Tokens {display_label or ''} ({usedTokens}):")
print(f"{tokenized}\x1b[0m")
if discarded != "":

6
invokeai/app/invocations/segment_anything.py
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@ -185,9 +185,9 @@ class SegmentAnythingInvocation(BaseInvocation):
# Find the largest mask.
return [max(masks, key=lambda x: float(x.sum()))]
elif self.mask_filter == "highest_box_score":
assert (
bounding_boxes is not None
), "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
assert bounding_boxes is not None, (
"Bounding boxes must be provided to use the 'highest_box_score' mask filter."
)
assert len(masks) == len(bounding_boxes)
# Find the index of the bounding box with the highest score.
# Note that we fallback to -1.0 if the score is None. This is mainly to satisfy the type checker. In most

6
invokeai/app/services/config/config_default.py
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@ -476,9 +476,9 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
try:
# Meta is not included in the model fields, so we need to validate it separately
config = InvokeAIAppConfig.model_validate(loaded_config_dict)
assert (
config.schema_version == CONFIG_SCHEMA_VERSION
), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
assert config.schema_version == CONFIG_SCHEMA_VERSION, (
f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
)
return config
except Exception as e:
raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e

10
invokeai/backend/image_util/pngwriter.py
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@ -91,10 +91,10 @@ class PromptFormatter:
switches = []
switches.append(f'"{opt.prompt}"')
switches.append(f"-s{opt.steps or t2i.steps}")
switches.append(f"-W{opt.width or t2i.width}")
switches.append(f"-H{opt.height or t2i.height}")
switches.append(f"-C{opt.cfg_scale or t2i.cfg_scale}")
switches.append(f"-s{opt.steps or t2i.steps}")
switches.append(f"-W{opt.width or t2i.width}")
switches.append(f"-H{opt.height or t2i.height}")
switches.append(f"-C{opt.cfg_scale or t2i.cfg_scale}")
switches.append(f"-A{opt.sampler_name or t2i.sampler_name}")
# to do: put model name into the t2i object
# switches.append(f'--model{t2i.model_name}')
@ -109,7 +109,7 @@ class PromptFormatter:
if opt.gfpgan_strength:
switches.append(f"-G{opt.gfpgan_strength}")
if opt.upscale:
switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
switches.append(f"-U {' '.join([str(u) for u in opt.upscale])}")
if opt.variation_amount > 0:
switches.append(f"-v{opt.variation_amount}")
if opt.with_variations:

2
invokeai/backend/model_manager/load/memory_snapshot.py
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@ -70,7 +70,7 @@ def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: O
def get_msg_line(prefix: str, val1: int, val2: int) -> str:
diff = val2 - val1
return f"{prefix: <30} ({(diff/GB):+5.3f}): {(val1/GB):5.3f}GB -> {(val2/GB):5.3f}GB\n"
return f"{prefix: <30} ({(diff / GB):+5.3f}): {(val1 / GB):5.3f}GB -> {(val2 / GB):5.3f}GB\n"
msg = ""

30
invokeai/backend/model_manager/load/model_cache/model_cache.py
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@ -192,7 +192,7 @@ class ModelCache:
self._cached_models[key] = cache_record
self._cache_stack.append(key)
self._logger.debug(
f"Added model {key} (Type: {model.__class__.__name__}, Wrap mode: {wrapped_model.__class__.__name__}, Model size: {size/MB:.2f}MB)"
f"Added model {key} (Type: {model.__class__.__name__}, Wrap mode: {wrapped_model.__class__.__name__}, Model size: {size / MB:.2f}MB)"
)
@synchronized
@ -303,7 +303,7 @@ class ModelCache:
# 2. If the model can't fit fully into VRAM, then unload all other models and load as much of the model as
# possible.
vram_bytes_freed = self._offload_unlocked_models(model_vram_needed, working_mem_bytes)
self._logger.debug(f"Unloaded models (if necessary): vram_bytes_freed={(vram_bytes_freed/MB):.2f}MB")
self._logger.debug(f"Unloaded models (if necessary): vram_bytes_freed={(vram_bytes_freed / MB):.2f}MB")
# Check the updated vram_available after offloading.
vram_available = self._get_vram_available(working_mem_bytes)
@ -317,7 +317,7 @@ class ModelCache:
vram_bytes_freed_from_own_model = self._move_model_to_ram(cache_entry, -vram_available)
vram_available = self._get_vram_available(working_mem_bytes)
self._logger.debug(
f"Unloaded {vram_bytes_freed_from_own_model/MB:.2f}MB from the model being locked ({cache_entry.key})."
f"Unloaded {vram_bytes_freed_from_own_model / MB:.2f}MB from the model being locked ({cache_entry.key})."
)
# Move as much of the model as possible into VRAM.
@ -333,10 +333,12 @@ class ModelCache:
self._logger.info(
f"Loaded model '{cache_entry.key}' ({cache_entry.cached_model.model.__class__.__name__}) onto "
f"{self._execution_device.type} device in {(time.time() - start_time):.2f}s. "
f"Total model size: {model_total_bytes/MB:.2f}MB, "
f"VRAM: {model_cur_vram_bytes/MB:.2f}MB ({loaded_percent:.1%})"
f"Total model size: {model_total_bytes / MB:.2f}MB, "
f"VRAM: {model_cur_vram_bytes / MB:.2f}MB ({loaded_percent:.1%})"
)
self._logger.debug(
f"Loaded model onto execution device: model_bytes_loaded={(model_bytes_loaded / MB):.2f}MB, "
)
self._logger.debug(f"Loaded model onto execution device: model_bytes_loaded={(model_bytes_loaded/MB):.2f}MB, ")
self._logger.debug(
f"After loading: {self._get_vram_state_str(model_cur_vram_bytes, model_total_bytes, vram_available)}"
)
@ -495,10 +497,10 @@ class ModelCache:
"""Helper function for preparing a VRAM state log string."""
model_cur_vram_bytes_percent = model_cur_vram_bytes / model_total_bytes if model_total_bytes > 0 else 0
return (
f"model_total={model_total_bytes/MB:.0f} MB, "
+ f"model_vram={model_cur_vram_bytes/MB:.0f} MB ({model_cur_vram_bytes_percent:.1%} %), "
f"model_total={model_total_bytes / MB:.0f} MB, "
+ f"model_vram={model_cur_vram_bytes / MB:.0f} MB ({model_cur_vram_bytes_percent:.1%} %), "
# + f"vram_total={int(self._max_vram_cache_size * GB)/MB:.0f} MB, "
+ f"vram_available={(vram_available/MB):.0f} MB, "
+ f"vram_available={(vram_available / MB):.0f} MB, "
)
def _offload_unlocked_models(self, vram_bytes_required: int, working_mem_bytes: Optional[int] = None) -> int:
@ -509,7 +511,7 @@ class ModelCache:
int: The number of bytes freed based on believed model sizes. The actual change in VRAM may be different.
"""
self._logger.debug(
f"Offloading unlocked models with goal of making room for {vram_bytes_required/MB:.2f}MB of VRAM."
f"Offloading unlocked models with goal of making room for {vram_bytes_required / MB:.2f}MB of VRAM."
)
vram_bytes_freed = 0
# TODO(ryand): Give more thought to the offloading policy used here.
@ -527,7 +529,7 @@ class ModelCache:
cache_entry_bytes_freed = self._move_model_to_ram(cache_entry, vram_bytes_to_free)
if cache_entry_bytes_freed > 0:
self._logger.debug(
f"Unloaded {cache_entry.key} from VRAM to free {(cache_entry_bytes_freed/MB):.0f} MB."
f"Unloaded {cache_entry.key} from VRAM to free {(cache_entry_bytes_freed / MB):.0f} MB."
)
vram_bytes_freed += cache_entry_bytes_freed
@ -609,7 +611,7 @@ class ModelCache:
external references to the model, there's nothing that the cache can do about it, and those models will not be
garbage-collected.
"""
self._logger.debug(f"Making room for {bytes_needed/MB:.2f}MB of RAM.")
self._logger.debug(f"Making room for {bytes_needed / MB:.2f}MB of RAM.")
self._log_cache_state(title="Before dropping models:")
ram_bytes_available = self._get_ram_available()
@ -625,7 +627,7 @@ class ModelCache:
if not cache_entry.is_locked:
ram_bytes_freed += cache_entry.cached_model.total_bytes()
self._logger.debug(
f"Dropping {model_key} from RAM cache to free {(cache_entry.cached_model.total_bytes()/MB):.2f}MB."
f"Dropping {model_key} from RAM cache to free {(cache_entry.cached_model.total_bytes() / MB):.2f}MB."
)
self._delete_cache_entry(cache_entry)
del cache_entry
@ -650,7 +652,7 @@ class ModelCache:
gc.collect()
TorchDevice.empty_cache()
self._logger.debug(f"Dropped {models_cleared} models to free {ram_bytes_freed/MB:.2f}MB of RAM.")
self._logger.debug(f"Dropped {models_cleared} models to free {ram_bytes_freed / MB:.2f}MB of RAM.")
self._log_cache_state(title="After dropping models:")
def _delete_cache_entry(self, cache_entry: CacheRecord) -> None:

18
invokeai/backend/model_manager/merge.py
View File

@ -115,19 +115,19 @@ class ModelMerger(object):
base_models: Set[BaseModelType] = set()
variant = None if self._installer.app_config.precision == "float32" else "fp16"
assert (
len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference
), "When merging three models, only the 'add_difference' merge method is supported"
assert len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference, (
"When merging three models, only the 'add_difference' merge method is supported"
)
for key in model_keys:
info = store.get_model(key)
model_names.append(info.name)
assert isinstance(
info, MainDiffusersConfig
), f"{info.name} ({info.key}) is not a diffusers model. It must be optimized before merging"
assert info.variant == ModelVariantType(
"normal"
), f"{info.name} ({info.key}) is a {info.variant} model, which cannot currently be merged"
assert isinstance(info, MainDiffusersConfig), (
f"{info.name} ({info.key}) is not a diffusers model. It must be optimized before merging"
)
assert info.variant == ModelVariantType("normal"), (
f"{info.name} ({info.key}) is a {info.variant} model, which cannot currently be merged"
)
# tally base models used
base_models.add(info.base)

14
invokeai/backend/model_manager/util/libc_util.py
View File

@ -37,19 +37,21 @@ class Struct_mallinfo2(ctypes.Structure):
def __str__(self) -> str:
s = ""
s += f"{'arena': <10}= {(self.arena/2**30):15.5f} # Non-mmapped space allocated (GB) (uordblks + fordblks)\n"
s += (
f"{'arena': <10}= {(self.arena / 2**30):15.5f} # Non-mmapped space allocated (GB) (uordblks + fordblks)\n"
)
s += f"{'ordblks': <10}= {(self.ordblks): >15} # Number of free chunks\n"
s += f"{'smblks': <10}= {(self.smblks): >15} # Number of free fastbin blocks \n"
s += f"{'hblks': <10}= {(self.hblks): >15} # Number of mmapped regions \n"
s += f"{'hblkhd': <10}= {(self.hblkhd/2**30):15.5f} # Space allocated in mmapped regions (GB)\n"
s += f"{'hblkhd': <10}= {(self.hblkhd / 2**30):15.5f} # Space allocated in mmapped regions (GB)\n"
s += f"{'usmblks': <10}= {(self.usmblks): >15} # Unused\n"
s += f"{'fsmblks': <10}= {(self.fsmblks/2**30):15.5f} # Space in freed fastbin blocks (GB)\n"
s += f"{'fsmblks': <10}= {(self.fsmblks / 2**30):15.5f} # Space in freed fastbin blocks (GB)\n"
s += (
f"{'uordblks': <10}= {(self.uordblks/2**30):15.5f} # Space used by in-use allocations (non-mmapped)"
f"{'uordblks': <10}= {(self.uordblks / 2**30):15.5f} # Space used by in-use allocations (non-mmapped)"
" (GB)\n"
)
s += f"{'fordblks': <10}= {(self.fordblks/2**30):15.5f} # Space in free blocks (non-mmapped) (GB)\n"
s += f"{'keepcost': <10}= {(self.keepcost/2**30):15.5f} # Top-most, releasable space (GB)\n"
s += f"{'fordblks': <10}= {(self.fordblks / 2**30):15.5f} # Space in free blocks (non-mmapped) (GB)\n"
s += f"{'keepcost': <10}= {(self.keepcost / 2**30):15.5f} # Top-most, releasable space (GB)\n"
return s

22
invokeai/backend/patches/lora_conversions/sdxl_lora_conversion_utils.py
View File

@ -73,36 +73,36 @@ def _make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
for j in range(2):
# loop over resnets/attentions for downblocks
hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
# if i > 0: commentout for sdxl
# no attention layers in up_blocks.0
hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
sd_upsample_prefix = f"output_blocks.{3*i + 2}.{2}." # change for sdxl
sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{2}." # change for sdxl
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
hf_mid_atn_prefix = "mid_block.attentions.0."
@ -111,7 +111,7 @@ def _make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
for j in range(2):
hf_mid_res_prefix = f"mid_block.resnets.{j}."
sd_mid_res_prefix = f"middle_block.{2*j}."
sd_mid_res_prefix = f"middle_block.{2 * j}."
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
unet_conversion_map_resnet = [
@ -133,13 +133,13 @@ def _make_sdxl_unet_conversion_map() -> List[Tuple[str, str]]:
unet_conversion_map.append((sd, hf))
for j in range(2):
hf_time_embed_prefix = f"time_embedding.linear_{j+1}."
sd_time_embed_prefix = f"time_embed.{j*2}."
hf_time_embed_prefix = f"time_embedding.linear_{j + 1}."
sd_time_embed_prefix = f"time_embed.{j * 2}."
unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix))
for j in range(2):
hf_label_embed_prefix = f"add_embedding.linear_{j+1}."
sd_label_embed_prefix = f"label_emb.0.{j*2}."
hf_label_embed_prefix = f"add_embedding.linear_{j + 1}."
sd_label_embed_prefix = f"label_emb.0.{j * 2}."
unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix))
unet_conversion_map.append(("input_blocks.0.0.", "conv_in."))

12
tests/app/services/download/test_download_queue.py
View File

@ -212,12 +212,12 @@ def test_multifile_download(tmp_path: Path, mm2_session: Session) -> None:
assert job.bytes > 0, "expected download bytes to be positive"
assert job.bytes == job.total_bytes, "expected download bytes to equal total bytes"
assert job.download_path == tmp_path / "sdxl-turbo"
assert Path(
tmp_path, "sdxl-turbo/model_index.json"
).exists(), f"expected {tmp_path}/sdxl-turbo/model_inded.json to exist"
assert Path(
tmp_path, "sdxl-turbo/text_encoder/config.json"
).exists(), f"expected {tmp_path}/sdxl-turbo/text_encoder/config.json to exist"
assert Path(tmp_path, "sdxl-turbo/model_index.json").exists(), (
f"expected {tmp_path}/sdxl-turbo/model_inded.json to exist"
)
assert Path(tmp_path, "sdxl-turbo/text_encoder/config.json").exists(), (
f"expected {tmp_path}/sdxl-turbo/text_encoder/config.json to exist"
)
assert events == {DownloadJobStatus.RUNNING, DownloadJobStatus.COMPLETED}
queue.stop()