diff --git a/scripts/convert_stable_diffusion_checkpoint_to_onnx.py b/scripts/convert_stable_diffusion_checkpoint_to_onnx.py index f0e0b178..26d3d561 100644 --- a/scripts/convert_stable_diffusion_checkpoint_to_onnx.py +++ b/scripts/convert_stable_diffusion_checkpoint_to_onnx.py @@ -215,8 +215,10 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F ) del pipeline.safety_checker safety_checker = OnnxRuntimeModel.from_pretrained(output_path / "safety_checker") + feature_extractor = pipeline.feature_extractor else: safety_checker = None + feature_extractor = None onnx_pipeline = OnnxStableDiffusionPipeline( vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"), @@ -226,7 +228,8 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"), scheduler=pipeline.scheduler, safety_checker=safety_checker, - feature_extractor=pipeline.feature_extractor, + feature_extractor=feature_extractor, + requires_safety_checker=safety_checker is not None, ) onnx_pipeline.save_pretrained(output_path) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py index 3caab834..6cb2c8ba 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py @@ -18,7 +18,6 @@ from typing import Callable, List, Optional, Union import numpy as np import torch -from packaging import version from transformers import CLIPFeatureExtractor, CLIPTokenizer from ...configuration_utils import FrozenDict @@ -42,6 +41,8 @@ class OnnxStableDiffusionPipeline(DiffusionPipeline): safety_checker: OnnxRuntimeModel feature_extractor: CLIPFeatureExtractor + _optional_components = ["safety_checker", "feature_extractor"] + def __init__( self, vae_encoder: OnnxRuntimeModel, @@ -99,27 +100,6 @@ class OnnxStableDiffusionPipeline(DiffusionPipeline): " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) - is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( - version.parse(unet.config._diffusers_version).base_version - ) < version.parse("0.9.0.dev0") - is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 - if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: - deprecation_message = ( - "The configuration file of the unet has set the default `sample_size` to smaller than" - " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" - " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" - " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" - " in the config might lead to incorrect results in future versions. If you have downloaded this" - " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" - " the `unet/config.json` file" - ) - deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(unet.config) - new_config["sample_size"] = 64 - unet._internal_dict = FrozenDict(new_config) - self.register_modules( vae_encoder=vae_encoder, vae_decoder=vae_decoder, @@ -130,7 +110,6 @@ class OnnxStableDiffusionPipeline(DiffusionPipeline): safety_checker=safety_checker, feature_extractor=feature_extractor, ) - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.register_to_config(requires_safety_checker=requires_safety_checker) def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt): @@ -213,8 +192,8 @@ class OnnxStableDiffusionPipeline(DiffusionPipeline): def __call__( self, prompt: Union[str, List[str]], - height: Optional[int] = None, - width: Optional[int] = None, + height: Optional[int] = 512, + width: Optional[int] = 512, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, @@ -228,10 +207,6 @@ class OnnxStableDiffusionPipeline(DiffusionPipeline): callback_steps: Optional[int] = 1, **kwargs, ): - # 0. Default height and width to unet - height = height or self.unet.config.sample_size * self.vae_scale_factor - width = width or self.unet.config.sample_size * self.vae_scale_factor - if isinstance(prompt, str): batch_size = 1 elif isinstance(prompt, list): @@ -264,12 +239,7 @@ class OnnxStableDiffusionPipeline(DiffusionPipeline): # get the initial random noise unless the user supplied it latents_dtype = text_embeddings.dtype - latents_shape = ( - batch_size * num_images_per_prompt, - 4, - height // self.vae_scale_factor, - width // self.vae_scale_factor, - ) + latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8) if latents is None: latents = generator.randn(*latents_shape).astype(latents_dtype) elif latents.shape != latents_shape: diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py index 4d422016..949ef94b 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py @@ -19,7 +19,6 @@ import numpy as np import torch import PIL -from packaging import version from transformers import CLIPFeatureExtractor, CLIPTokenizer from ...configuration_utils import FrozenDict @@ -78,6 +77,8 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline): safety_checker: OnnxRuntimeModel feature_extractor: CLIPFeatureExtractor + _optional_components = ["safety_checker", "feature_extractor"] + def __init__( self, vae_encoder: OnnxRuntimeModel, @@ -135,27 +136,6 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline): " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) - is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( - version.parse(unet.config._diffusers_version).base_version - ) < version.parse("0.9.0.dev0") - is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 - if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: - deprecation_message = ( - "The configuration file of the unet has set the default `sample_size` to smaller than" - " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" - " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" - " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" - " in the config might lead to incorrect results in future versions. If you have downloaded this" - " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" - " the `unet/config.json` file" - ) - deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(unet.config) - new_config["sample_size"] = 64 - unet._internal_dict = FrozenDict(new_config) - self.register_modules( vae_encoder=vae_encoder, vae_decoder=vae_decoder, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py index 863f7b7a..0a8f7a5f 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py @@ -19,7 +19,6 @@ import numpy as np import torch import PIL -from packaging import version from transformers import CLIPFeatureExtractor, CLIPTokenizer from ...configuration_utils import FrozenDict @@ -91,6 +90,8 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline): safety_checker: OnnxRuntimeModel feature_extractor: CLIPFeatureExtractor + _optional_components = ["safety_checker", "feature_extractor"] + def __init__( self, vae_encoder: OnnxRuntimeModel, @@ -149,27 +150,6 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline): " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) - is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( - version.parse(unet.config._diffusers_version).base_version - ) < version.parse("0.9.0.dev0") - is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 - if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: - deprecation_message = ( - "The configuration file of the unet has set the default `sample_size` to smaller than" - " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" - " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" - " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" - " in the config might lead to incorrect results in future versions. If you have downloaded this" - " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" - " the `unet/config.json` file" - ) - deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(unet.config) - new_config["sample_size"] = 64 - unet._internal_dict = FrozenDict(new_config) - self.register_modules( vae_encoder=vae_encoder, vae_decoder=vae_decoder, @@ -180,7 +160,6 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline): safety_checker=safety_checker, feature_extractor=feature_extractor, ) - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_onnx_stable_diffusion.OnnxStableDiffusionPipeline._encode_prompt @@ -267,8 +246,8 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline): prompt: Union[str, List[str]], image: PIL.Image.Image, mask_image: PIL.Image.Image, - height: Optional[int] = None, - width: Optional[int] = None, + height: Optional[int] = 512, + width: Optional[int] = 512, num_inference_steps: int = 50, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, @@ -296,9 +275,9 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline): repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. - height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. - width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the @@ -343,9 +322,6 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline): list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ - # 0. Default height and width to unet - height = height or self.unet.config.sample_size * self.vae_scale_factor - width = width or self.unet.config.sample_size * self.vae_scale_factor if isinstance(prompt, str): batch_size = 1 @@ -381,12 +357,7 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline): ) num_channels_latents = NUM_LATENT_CHANNELS - latents_shape = ( - batch_size * num_images_per_prompt, - num_channels_latents, - height // self.vae_scale_factor, - width // self.vae_scale_factor, - ) + latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8) latents_dtype = text_embeddings.dtype if latents is None: latents = generator.randn(*latents_shape).astype(latents_dtype)