mirror of
https://github.com/vladmandic/automatic
synced 2026-09-18 16:54:33 +02:00
Fix onnx model downloading & more fixes.
This commit is contained in:
Seunghoon Lee
+20
-3
@@ -33,7 +33,7 @@ def preprocess_pipeline(p, refiner_enabled: bool):
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shared.log.warning(f"Unsupported pipeline for 'olive-ai' compile backend: {shared.opts.diffusers_pipeline}. You should select one of the ONNX pipelines.")
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return
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if shared.opts.cuda_compile_backend == "olive-ai":
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if shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "olive-ai":
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compile_height = p.height
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compile_width = p.width
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if (shared.compiled_model_state is None or
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@@ -62,12 +62,29 @@ def initialize():
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if initialized:
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return
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from modules.onnx_pipelines import do_diffusers_hijack
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from modules import onnx_pipelines as pipelines
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# OnnxRuntimeModel Hijack.
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OnnxRuntimeModel.__module__ = 'diffusers'
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diffusers.OnnxRuntimeModel = OnnxRuntimeModel
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do_diffusers_hijack()
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diffusers.OnnxStableDiffusionPipeline = pipelines.OnnxStableDiffusionPipeline
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diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionPipeline
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diffusers.OnnxStableDiffusionImg2ImgPipeline = pipelines.OnnxStableDiffusionImg2ImgPipeline
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diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionImg2ImgPipeline
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diffusers.OnnxStableDiffusionInpaintPipeline = pipelines.OnnxStableDiffusionInpaintPipeline
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diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionInpaintPipeline
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diffusers.OnnxStableDiffusionXLPipeline = pipelines.OnnxStableDiffusionXLPipeline
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diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion-xl"] = diffusers.OnnxStableDiffusionXLPipeline
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diffusers.OnnxStableDiffusionXLImg2ImgPipeline = pipelines.OnnxStableDiffusionXLImg2ImgPipeline
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diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion-xl"] = diffusers.OnnxStableDiffusionXLImg2ImgPipeline
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# Huggingface model compatibility
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diffusers.ORTStableDiffusionXLPipeline = diffusers.OnnxStableDiffusionXLPipeline
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diffusers.ORTStableDiffusionXLImg2ImgPipeline = diffusers.OnnxStableDiffusionXLImg2ImgPipeline
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initialized = True
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+24
-30
@@ -22,7 +22,7 @@ from modules.sd_models import CheckpointInfo
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from modules.processing import StableDiffusionProcessing
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from modules.olive import config
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from modules.onnx import OnnxFakeModule, submodels_sd, submodels_sdxl, submodels_sdxl_refiner
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from modules.onnx_utils import check_pipeline_sdxl, load_init_dict, load_submodel, load_submodels, load_pipeline, get_sess_options, patch_kwargs, construct_refiner_pipeline
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from modules.onnx_utils import check_pipeline_sdxl, check_cache_onnx, load_init_dict, load_submodel, load_submodels, load_pipeline, get_sess_options, patch_kwargs, construct_refiner_pipeline
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from modules.onnx_ep import ExecutionProvider, EP_TO_NAME, get_provider
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@@ -80,8 +80,8 @@ class OnnxRawPipeline(OnnxPipelineBase):
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self.init_dict = load_init_dict(constructor, path)
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self.scheduler = load_submodel(self.path, None, "scheduler", self.init_dict["scheduler"])
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else:
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cls = diffusers.StableDiffusionXLPipeline if self._is_sdxl else diffusers.StableDiffusionPipeline
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try:
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cls = diffusers.StableDiffusionXLPipeline if self._is_sdxl else diffusers.StableDiffusionPipeline
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pipeline = cls.from_single_file(path)
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self.scheduler = pipeline.scheduler
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if os.path.isdir(shared.opts.onnx_temp_dir):
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@@ -92,7 +92,9 @@ class OnnxRawPipeline(OnnxPipelineBase):
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self.is_refiner = self._is_sdxl and "Img2Img" in diffusers.DiffusionPipeline.load_config(shared.opts.onnx_temp_dir)["_class_name"]
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self.init_dict = load_init_dict(constructor, shared.opts.onnx_temp_dir)
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except Exception:
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log.error('Failed to load pipeline to optimize.')
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log.error(f'Failed to load pipeline to optimize: is_sdxl={self._is_sdxl}')
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log.warn('Model load failed. Please check Diffusers pipeline in Compute Settings.')
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return
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if "vae" in self.init_dict:
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del self.init_dict["vae"]
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@@ -111,7 +113,9 @@ class OnnxRawPipeline(OnnxPipelineBase):
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ort.set_default_logger_severity(3)
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out_dir = os.path.join(shared.opts.onnx_cached_models_path, self.original_filename)
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if os.path.isdir(out_dir): # already converted (cached)
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if (self.from_huggingface_cache and check_cache_onnx(self.path)):
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return self.path
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if os.path.isdir(out_dir): # if model is ONNX format or had already converted.
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return out_dir
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try:
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@@ -357,7 +361,7 @@ class OnnxRawPipeline(OnnxPipelineBase):
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return self.derive_properties(load_pipeline(diffusers.StableDiffusionXLPipeline if self._is_sdxl else diffusers.StableDiffusionPipeline, self.path, **kwargs))
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out_dir = converted_dir
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if shared.opts.cuda_compile_backend == "olive-ai":
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if shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "olive-ai":
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log.warning("Olive implementation is experimental. It contains potentially an issue and is subject to change at any time.")
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if p.width != p.height:
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log.warning("Olive detected different width and height. The quality of the result is not guaranteed.")
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@@ -413,6 +417,9 @@ def prepare_latents(
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class OnnxStableDiffusionPipeline(diffusers.OnnxStableDiffusionPipeline, OnnxPipelineBase):
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__module__ = 'diffusers'
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__name__ = 'OnnxStableDiffusionPipeline'
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def __init__(
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self,
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vae_encoder: diffusers.OnnxRuntimeModel,
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@@ -570,6 +577,9 @@ class OnnxStableDiffusionPipeline(diffusers.OnnxStableDiffusionPipeline, OnnxPip
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class OnnxStableDiffusionImg2ImgPipeline(diffusers.OnnxStableDiffusionImg2ImgPipeline, OnnxPipelineBase):
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__module__ = 'diffusers'
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__name__ = 'OnnxStableDiffusionImg2ImgPipeline'
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image_processor: VaeImageProcessor
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def __init__(
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@@ -762,6 +772,9 @@ class OnnxStableDiffusionImg2ImgPipeline(diffusers.OnnxStableDiffusionImg2ImgPip
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class OnnxStableDiffusionInpaintPipeline(diffusers.OnnxStableDiffusionInpaintPipeline, OnnxPipelineBase):
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__module__ = 'diffusers'
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__name__ = 'OnnxStableDiffusionInpaintPipeline'
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def __init__(
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self,
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vae_encoder: diffusers.OnnxRuntimeModel,
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@@ -969,6 +982,9 @@ class OnnxStableDiffusionInpaintPipeline(diffusers.OnnxStableDiffusionInpaintPip
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class OnnxStableDiffusionXLPipeline(OnnxPipelineBase, optimum.onnxruntime.ORTStableDiffusionXLPipeline):
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__module__ = 'optimum.onnxruntime.modeling_diffusion'
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__name__ = 'ORTStableDiffusionXLPipeline'
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def __init__(
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self,
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vae_decoder,
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@@ -1159,6 +1175,9 @@ class OnnxStableDiffusionXLPipeline(OnnxPipelineBase, optimum.onnxruntime.ORTSta
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class OnnxStableDiffusionXLImg2ImgPipeline(OnnxPipelineBase, optimum.onnxruntime.ORTStableDiffusionXLImg2ImgPipeline):
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__module__ = 'optimum.onnxruntime.modeling_diffusion'
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__name__ = 'ORTStableDiffusionXLImg2ImgPipeline'
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def __init__(
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self,
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vae_decoder,
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@@ -1344,28 +1363,3 @@ class OnnxStableDiffusionXLImg2ImgPipeline(OnnxPipelineBase, optimum.onnxruntime
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return (image,)
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return StableDiffusionXLPipelineOutput(images=image)
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def do_diffusers_hijack():
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diffusers.OnnxStableDiffusionPipeline = OnnxStableDiffusionPipeline
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diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionPipeline
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OnnxStableDiffusionImg2ImgPipeline.__module__ = 'diffusers'
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OnnxStableDiffusionImg2ImgPipeline.__name__ = 'OnnxStableDiffusionImg2ImgPipeline'
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diffusers.OnnxStableDiffusionImg2ImgPipeline = OnnxStableDiffusionImg2ImgPipeline
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diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionImg2ImgPipeline
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OnnxStableDiffusionInpaintPipeline.__module__ = 'diffusers'
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OnnxStableDiffusionInpaintPipeline.__name__ = 'OnnxStableDiffusionInpaintPipeline'
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diffusers.OnnxStableDiffusionInpaintPipeline = OnnxStableDiffusionInpaintPipeline
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diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["onnx-stable-diffusion"] = diffusers.OnnxStableDiffusionInpaintPipeline
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OnnxStableDiffusionXLPipeline.__module__ = 'optimum.onnxruntime.modeling_diffusion'
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OnnxStableDiffusionXLPipeline.__name__ = 'ORTStableDiffusionXLPipeline'
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diffusers.OnnxStableDiffusionXLPipeline = OnnxStableDiffusionXLPipeline
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diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion-xl"] = diffusers.OnnxStableDiffusionXLPipeline
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OnnxStableDiffusionXLImg2ImgPipeline.__module__ = 'optimum.onnxruntime.modeling_diffusion'
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OnnxStableDiffusionXLImg2ImgPipeline.__name__ = 'ORTStableDiffusionXLImg2ImgPipeline'
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diffusers.OnnxStableDiffusionXLImg2ImgPipeline = OnnxStableDiffusionXLImg2ImgPipeline
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diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["onnx-stable-diffusion-xl"] = diffusers.OnnxStableDiffusionXLImg2ImgPipeline
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@@ -44,6 +44,20 @@ def check_pipeline_sdxl(cls: Type[diffusers.DiffusionPipeline]) -> bool:
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return 'XL' in cls.__name__
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def check_cache_onnx(path: os.PathLike) -> bool:
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if not os.path.isdir(path):
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return False
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init_dict_path = os.path.join(path, "model_index.json")
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if not os.path.isfile(init_dict_path):
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return False
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init_dict = None
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with open(init_dict_path, "r") as file:
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init_dict = file.read()
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if "OnnxRuntimeModel" not in init_dict:
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return False
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return True
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def load_submodel(path: os.PathLike, is_sdxl: bool, submodel_name: str, item: List[Union[str, None]], **kwargs_ort):
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lib, atr = item
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if lib is None or atr is None:
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@@ -147,7 +147,6 @@ def list_models():
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model_list = list(modelloader.load_models(model_path=model_path, model_url=None, command_path=shared.opts.ckpt_dir, ext_filter=ext_filter, download_name=None, ext_blacklist=[".vae.ckpt", ".vae.safetensors"]))
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if shared.backend == shared.Backend.DIFFUSERS:
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model_list += modelloader.load_diffusers_models(model_path=os.path.join(models_path, 'Diffusers'), command_path=shared.opts.diffusers_dir, clear=True)
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model_list += modelloader.load_diffusers_models(model_path=shared.opts.onnx_sideloaded_models_path, command_path=shared.opts.onnx_sideloaded_models_path, clear=False)
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for filename in sorted(model_list, key=str.lower):
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checkpoint_info = CheckpointInfo(filename)
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if checkpoint_info.name is not None:
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@@ -471,7 +471,6 @@ options_templates.update(options_section(('system-paths', "System Paths"), {
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"ldsr_models_path": OptionInfo(os.path.join(paths.models_path, 'LDSR'), "Folder with LDSR models", folder=True),
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"clip_models_path": OptionInfo(os.path.join(paths.models_path, 'CLIP'), "Folder with CLIP models", folder=True),
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"onnx_cached_models_path": OptionInfo(os.path.join(paths.models_path, 'ONNX', 'cache'), "Folder with ONNX cached models", folder=True),
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"onnx_sideloaded_models_path": OptionInfo(os.path.join(paths.models_path, 'ONNX', 'sideloaded'), "Folder with ONNX models from huggingface", folder=True),
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"other_paths_sep_options": OptionInfo("<h2>Other paths</h2>", "", gr.HTML),
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"openvino_cache_path": OptionInfo('cache', "Directory for OpenVINO cache", folder=True),
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@@ -373,10 +373,10 @@ def create_ui():
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def hf_select(evt: gr.SelectData, data):
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return data[evt.index[0]][0]
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def hf_download_model(hub_id: str, token, variant, revision, mirror, is_onnx, custom_pipeline):
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def hf_download_model(hub_id: str, token, variant, revision, mirror, custom_pipeline):
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from modules.modelloader import download_diffusers_model
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download_diffusers_model(hub_id, cache_dir=opts.onnx_sideloaded_models_path if is_onnx else opts.diffusers_dir, token=token, variant=variant, revision=revision, mirror=mirror, custom_pipeline=custom_pipeline)
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from modules.sd_models import list_models # pylint: disable=W0621
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download_diffusers_model(hub_id, cache_dir=opts.diffusers_dir, token=token, variant=variant, revision=revision, mirror=mirror, custom_pipeline=custom_pipeline)
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from modules.sd_models import list_models # pylint: disable=W0621
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list_models()
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log.info(f'Diffuser model downloaded: model="{hub_id}"')
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return f'Diffuser model downloaded: model="{hub_id}"'
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@@ -392,9 +392,8 @@ def create_ui():
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hf_selected = gr.Textbox('', label='Select model', placeholder='select model from search results or enter model name manually')
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with gr.Column(scale=1):
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with gr.Row():
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hf_variant = gr.Textbox(opts.cuda_dtype.lower(), label = 'Specify model variant', placeholder='')
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hf_revision = gr.Textbox('', label = 'Specify model revision', placeholder='')
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hf_onnx = gr.Checkbox(False, label = 'ONNX model')
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hf_variant = gr.Textbox(opts.cuda_dtype.lower(), label='Specify model variant', placeholder='')
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hf_revision = gr.Textbox('', label='Specify model revision', placeholder='')
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with gr.Row():
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hf_token = gr.Textbox('', label='Huggingface token', placeholder='optional access token for private or gated models')
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hf_mirror = gr.Textbox('', label='Huggingface mirror', placeholder='optional mirror site for downloads')
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@@ -411,7 +410,7 @@ def create_ui():
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hf_search_text.submit(fn=hf_search, inputs=[hf_search_text], outputs=[hf_results])
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hf_search_btn.click(fn=hf_search, inputs=[hf_search_text], outputs=[hf_results])
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hf_results.select(fn=hf_select, inputs=[hf_results], outputs=[hf_selected])
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hf_download_model_btn.click(fn=hf_download_model, inputs=[hf_selected, hf_token, hf_variant, hf_revision, hf_mirror, hf_onnx, hf_custom_pipeline], outputs=[models_outcome])
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hf_download_model_btn.click(fn=hf_download_model, inputs=[hf_selected, hf_token, hf_variant, hf_revision, hf_mirror, hf_custom_pipeline], outputs=[models_outcome])
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with gr.Tab(label="CivitAI"):
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data = []
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