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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Run python black, point out that onnx is an alpha feature in the installer
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parent
af4fd328a6
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aeac557c41
@ -168,7 +168,7 @@ def graphical_accelerator():
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"cuda",
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)
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nvidia_with_dml = (
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"an [gold1 b]NVIDIA[/] GPU (using CUDA™, and DirectML™ for ONNX)",
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"an [gold1 b]NVIDIA[/] GPU (using CUDA™, and DirectML™ for ONNX) -- ALPHA",
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"cuda_and_dml",
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)
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amd = (
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@ -438,7 +438,11 @@ class ModelInstall(object):
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for filename in files:
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filePath = Path(filename)
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p = hf_download_with_resume(
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repo_id, model_dir=location / filePath.parent, model_name=filePath.name, access_token=self.access_token, subfolder=filePath.parent
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repo_id,
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model_dir=location / filePath.parent,
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model_name=filePath.name,
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access_token=self.access_token,
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subfolder=filePath.parent,
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)
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if p:
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paths.append(p)
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@ -54,7 +54,9 @@ class ModelProbe(object):
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}
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@classmethod
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def register_probe(cls, format: Literal["diffusers", "checkpoint", "onnx"], model_type: ModelType, probe_class: ProbeBase):
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def register_probe(
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cls, format: Literal["diffusers", "checkpoint", "onnx"], model_type: ModelType, probe_class: ProbeBase
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):
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cls.PROBES[format][model_type] = probe_class
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@classmethod
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@ -96,7 +98,7 @@ class ModelProbe(object):
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if format_type == "diffusers"
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else cls.get_model_type_from_checkpoint(model_path, model)
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)
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format_type = 'onnx' if model_type == ModelType.ONNX else format_type
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format_type = "onnx" if model_type == ModelType.ONNX else format_type
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probe_class = cls.PROBES[format_type].get(model_type)
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if not probe_class:
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return None
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@ -170,7 +172,7 @@ class ModelProbe(object):
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if model:
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class_name = model.__class__.__name__
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else:
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if (folder_path / 'unet/model.onnx').exists():
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if (folder_path / "unet/model.onnx").exists():
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return ModelType.ONNX
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if (folder_path / "learned_embeds.bin").exists():
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return ModelType.TextualInversion
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@ -474,6 +476,7 @@ class ONNXFolderProbe(FolderProbeBase):
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def get_variant_type(self) -> ModelVariantType:
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return ModelVariantType.Normal
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class ControlNetFolderProbe(FolderProbeBase):
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def get_base_type(self) -> BaseModelType:
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config_file = self.folder_path / "config.json"
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