feat(scripts): add env debug script (#191)
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@ -373,7 +373,7 @@ have added your own.
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### Test the models
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### Test the models
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You should verify that all of the steps up to this point have worked correctly by attempting to run the
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You should verify that all of the steps up to this point have worked correctly by attempting to run the
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`api/test-diffusers.py` script, which is a slight variation on the original txt2img script.
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`api/scripts/test-diffusers.py` script, which is a slight variation on the original txt2img script.
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If the script works, there will be an image of an astronaut in `outputs/test.png`.
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If the script works, there will be an image of an astronaut in `outputs/test.png`.
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@ -95,7 +95,9 @@ def blend_textual_inversions(
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# add the tokens to the tokenizer
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# add the tokens to the tokenizer
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logger.debug(
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logger.debug(
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"found embeddings for %s tokens: %s", len(embeds.keys()), list(embeds.keys())
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"found embeddings for %s tokens: %s",
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len(embeds.keys()),
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list(embeds.keys()),
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)
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)
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num_added_tokens = tokenizer.add_tokens(list(embeds.keys()))
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num_added_tokens = tokenizer.add_tokens(list(embeds.keys()))
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if num_added_tokens == 0:
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if num_added_tokens == 0:
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@ -0,0 +1,62 @@
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from importlib.metadata import version
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from typing import List
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REQUIRED_MODULES = ["onnx", "diffusers", "safetensors", "torch"]
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REQUIRED_RUNTIME = [
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"onnxruntime",
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"onnxruntime_gpu",
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"onnxruntime_rocm",
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"onnxruntime_training",
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]
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def check_modules() -> List[str]:
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results = []
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for name in REQUIRED_MODULES:
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try:
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__import__(name)
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module_version = version(name)
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results.append(
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f"required module {name} is present at version {module_version}"
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)
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except ImportError as e:
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results.append(f"unable to import required module {name}: {e}")
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return results
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def check_providers() -> List[str]:
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results = []
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try:
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import onnxruntime
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import torch
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available = onnxruntime.get_available_providers()
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for provider in onnxruntime.get_all_providers():
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if provider in available:
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results.append(f"onnxruntime provider {provider} is available")
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else:
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results.append(f"onnxruntime provider {provider} is missing")
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except Exception as e:
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results.append(f"unable to check runtime providers: {e}")
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return results
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ALL_CHECKS = [
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check_modules,
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check_providers,
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]
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def check_all():
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results = []
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for check in ALL_CHECKS:
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results.extend(check())
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print(results)
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if __name__ == "__main__":
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check_all()
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@ -0,0 +1,124 @@
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from collections import Counter
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from logging import getLogger
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from os import path
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from sys import argv
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from typing import Callable, List
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import torch
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from onnx import load_model
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from safetensors import safe_open
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import onnx_web
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logger = getLogger(__name__)
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CheckFunc = Callable[[str], List[str]]
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def check_file_extension(filename: str) -> List[str]:
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"""
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Check the file extension
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"""
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_name, ext = path.splitext(filename)
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ext = ext.removeprefix(".")
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if ext != "":
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return [f"format:{ext}"]
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return []
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def check_file_diffusers(filename: str) -> List[str]:
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"""
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Check for a diffusers directory with model_index.json
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"""
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if path.isdir(filename) and path.exists(path.join(filename, "model_index.json")):
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return ["model:diffusion"]
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return []
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def check_parser_safetensor(filename: str) -> List[str]:
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"""
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Attempt to load as a safetensor
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"""
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try:
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if path.isfile(filename):
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# only try to parse files
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safe_open(filename, framework="pt")
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return ["format:safetensor"]
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except Exception as e:
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logger.debug("error parsing as safetensor: %s", e)
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return []
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def check_parser_torch(filename: str) -> List[str]:
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"""
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Attempt to load as a torch tensor
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"""
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try:
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if path.isfile(filename):
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# only try to parse files
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torch.load(filename)
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return ["format:torch"]
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except Exception as e:
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logger.debug("error parsing as torch tensor: %s", e)
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return []
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def check_parser_onnx(filename: str) -> List[str]:
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"""
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Attempt to load as an ONNX model
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"""
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try:
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if path.isfile(filename):
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load_model(filename)
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return ["format:onnx"]
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except Exception as e:
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logger.debug("error parsing as ONNX model: %s", e)
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return []
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def check_network_lora(filename: str) -> List[str]:
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"""
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TODO: Check for LoRA keys
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"""
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return []
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def check_network_inversion(filename: str) -> List[str]:
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"""
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TODO: Check for Textual Inversion keys
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"""
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return []
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ALL_CHECKS: List[CheckFunc] = [
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check_file_diffusers,
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check_file_extension,
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check_network_inversion,
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check_network_lora,
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check_parser_onnx,
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check_parser_safetensor,
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check_parser_torch,
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]
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def check_file(filename: str) -> Counter:
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logger.info("checking file: %s", filename)
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counts = Counter()
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for check in ALL_CHECKS:
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logger.info("running check: %s", check.__name__)
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counts.update(check(filename))
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common = counts.most_common()
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logger.info("file %s is most likely: %s", filename, common)
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if __name__ == "__main__":
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for file in argv[1:]:
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check_file(file)
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@ -1,114 +0,0 @@
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from collections import Counter
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from logging import getLogger
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from onnx import load_model
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from os import path
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from safetensors import safe_open
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from sys import argv
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from typing import Callable, List
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import onnx_web
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import torch
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logger = getLogger(__name__)
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CheckFunc = Callable[[str], List[str]]
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def check_file_extension(filename: str) -> List[str]:
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"""
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Check the file extension
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"""
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_name, ext = path.splitext(filename)
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ext = ext.removeprefix(".")
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if ext != "":
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return [f"format:{ext}"]
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return []
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def check_file_diffusers(filename: str) -> List[str]:
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"""
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Check for a diffusers directory with model_index.json
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"""
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if path.isdir(filename) and path.exists(path.join(filename, "model_index.json")):
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return ["model:diffusion"]
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return []
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def check_parser_safetensor(filename: str) -> List[str]:
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"""
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Attempt to load as a safetensor
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"""
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try:
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if path.isfile(filename):
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# only try to parse files
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safe_open(filename, framework="pt")
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return ["format:safetensor"]
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except Exception as e:
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logger.debug("error parsing as safetensor: %s", e)
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return []
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def check_parser_torch(filename: str) -> List[str]:
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"""
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Attempt to load as a torch tensor
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"""
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try:
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if path.isfile(filename):
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# only try to parse files
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torch.load(filename)
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return ["format:torch"]
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except Exception as e:
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logger.debug("error parsing as torch tensor: %s", e)
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return []
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def check_parser_onnx(filename: str) -> List[str]:
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"""
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Attempt to load as an ONNX model
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"""
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try:
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if path.isfile(filename):
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load_model(filename)
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return ["format:onnx"]
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except Exception as e:
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logger.debug("error parsing as ONNX model: %s", e)
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return []
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def check_network_lora(filename: str) -> List[str]:
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"""
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TODO: Check for LoRA keys
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"""
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return []
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def check_network_inversion(filename: str) -> List[str]:
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"""
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TODO: Check for Textual Inversion keys
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"""
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return []
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ALL_CHECKS: List[CheckFunc] = [
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check_file_diffusers,
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check_file_extension,
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check_network_inversion,
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check_network_lora,
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check_parser_onnx,
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check_parser_safetensor,
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check_parser_torch,
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]
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def check_file(filename: str) -> Counter:
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logger.info("checking file: %s", filename)
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counts = Counter()
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for check in ALL_CHECKS:
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logger.info("running check: %s", check.__name__)
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counts.update(check(filename))
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common = counts.most_common()
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logger.info("file %s is most likely: %s", filename, common)
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if __name__ == "__main__":
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for file in argv[1:]:
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check_file(file)
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@ -12,9 +12,9 @@ steps = 22
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height = 512
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height = 512
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width = 512
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width = 512
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model = path.join('..', 'models', 'stable-diffusion-onnx-v1-5')
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model = path.join('..', '..', 'models', 'stable-diffusion-onnx-v1-5')
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prompt = 'an astronaut eating a hamburger'
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prompt = 'an astronaut eating a hamburger'
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output = path.join('..', 'outputs', 'test.png')
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output = path.join('..', '..', 'outputs', 'test.png')
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print('generating test image...')
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print('generating test image...')
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pipe = OnnxStableDiffusionPipeline.from_pretrained(model, provider='DmlExecutionProvider', safety_checker=None)
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pipe = OnnxStableDiffusionPipeline.from_pretrained(model, provider='DmlExecutionProvider', safety_checker=None)
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@ -11,9 +11,9 @@ steps = 22
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height = 512
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height = 512
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width = 512
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width = 512
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esrgan = path.join('..', 'models', 'RealESRGAN_x4plus.onnx')
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esrgan = path.join('..', '..', 'models', 'RealESRGAN_x4plus.onnx')
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output = path.join('..', 'outputs', 'test.png')
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output = path.join('..', '..', 'outputs', 'test.png')
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upscale = path.join('..', 'outputs', 'test-large.png')
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upscale = path.join('..', '..', 'outputs', 'test-large.png')
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print('upscaling test image...')
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print('upscaling test image...')
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session = ort.InferenceSession(esrgan, providers=['DmlExecutionProvider'])
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session = ort.InferenceSession(esrgan, providers=['DmlExecutionProvider'])
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