[ONNX] Improve ONNXPipeline scheduler compatibility, fix safety_checker

scripts/convert_stable_diffusion_checkpoint_to_onnx.py

@@ -81,6 +81,8 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
     output_path = Path(output_path)
 
     # TEXT ENCODER
+    num_tokens = pipeline.text_encoder.config.max_position_embeddings
+    text_hidden_size = pipeline.text_encoder.config.hidden_size
     text_input = pipeline.tokenizer(
         "A sample prompt",
         padding="max_length",
@@ -103,13 +105,15 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
     del pipeline.text_encoder
 
     # UNET
+    unet_in_channels = pipeline.unet.config.in_channels
+    unet_sample_size = pipeline.unet.config.sample_size
     unet_path = output_path / "unet" / "model.onnx"
     onnx_export(
         pipeline.unet,
         model_args=(
-            torch.randn(2, pipeline.unet.in_channels, 64, 64).to(device=device, dtype=dtype),
-            torch.LongTensor([0, 1]).to(device=device),
-            torch.randn(2, 77, 768).to(device=device, dtype=dtype),
+            torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
+            torch.randn(2).to(device=device, dtype=dtype),
+            torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
             False,
         ),
         output_path=unet_path,
@@ -142,11 +146,16 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
 
     # VAE ENCODER
     vae_encoder = pipeline.vae
+    vae_in_channels = vae_encoder.config.in_channels
+    vae_sample_size = vae_encoder.config.sample_size
     # need to get the raw tensor output (sample) from the encoder
     vae_encoder.forward = lambda sample, return_dict: vae_encoder.encode(sample, return_dict)[0].sample()
     onnx_export(
         vae_encoder,
-        model_args=(torch.randn(1, 3, 512, 512).to(device=device, dtype=dtype), False),
+        model_args=(
+            torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype),
+            False,
+        ),
         output_path=output_path / "vae_encoder" / "model.onnx",
         ordered_input_names=["sample", "return_dict"],
         output_names=["latent_sample"],
@@ -158,11 +167,16 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
 
     # VAE DECODER
     vae_decoder = pipeline.vae
+    vae_latent_channels = vae_decoder.config.latent_channels
+    vae_out_channels = vae_decoder.config.out_channels
     # forward only through the decoder part
     vae_decoder.forward = vae_encoder.decode
     onnx_export(
         vae_decoder,
-        model_args=(torch.randn(1, 4, 64, 64).to(device=device, dtype=dtype), False),
+        model_args=(
+            torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
+            False,
+        ),
         output_path=output_path / "vae_decoder" / "model.onnx",
         ordered_input_names=["latent_sample", "return_dict"],
         output_names=["sample"],
@@ -174,24 +188,35 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
     del pipeline.vae
 
     # SAFETY CHECKER
-    safety_checker = pipeline.safety_checker
-    safety_checker.forward = safety_checker.forward_onnx
-    onnx_export(
-        pipeline.safety_checker,
-        model_args=(
-            torch.randn(1, 3, 224, 224).to(device=device, dtype=dtype),
-            torch.randn(1, 512, 512, 3).to(device=device, dtype=dtype),
-        ),
-        output_path=output_path / "safety_checker" / "model.onnx",
-        ordered_input_names=["clip_input", "images"],
-        output_names=["out_images", "has_nsfw_concepts"],
-        dynamic_axes={
-            "clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
-            "images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
-        },
-        opset=opset,
-    )
-    del pipeline.safety_checker
+    if pipeline.safety_checker is not None:
+        safety_checker = pipeline.safety_checker
+        clip_num_channels = safety_checker.config.vision_config.num_channels
+        clip_image_size = safety_checker.config.vision_config.image_size
+        safety_checker.forward = safety_checker.forward_onnx
+        onnx_export(
+            pipeline.safety_checker,
+            model_args=(
+                torch.randn(
+                    1,
+                    clip_num_channels,
+                    clip_image_size,
+                    clip_image_size,
+                ).to(device=device, dtype=dtype),
+                torch.randn(1, vae_sample_size, vae_sample_size, vae_out_channels).to(device=device, dtype=dtype),
+            ),
+            output_path=output_path / "safety_checker" / "model.onnx",
+            ordered_input_names=["clip_input", "images"],
+            output_names=["out_images", "has_nsfw_concepts"],
+            dynamic_axes={
+                "clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
+                "images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
+            },
+            opset=opset,
+        )
+        del pipeline.safety_checker
+        safety_checker = OnnxRuntimeModel.from_pretrained(output_path / "safety_checker")
+    else:
+        safety_checker = None
 
     onnx_pipeline = OnnxStableDiffusionPipeline(
         vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"),
@@ -200,7 +225,7 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
         tokenizer=pipeline.tokenizer,
         unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),
         scheduler=pipeline.scheduler,
-        safety_checker=OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"),
+        safety_checker=safety_checker,
         feature_extractor=pipeline.feature_extractor,
     )
 

src/diffusers/onnx_utils.py

@@ -24,7 +24,7 @@
 
 from huggingface_hub import hf_hub_download
 
-from .utils import ONNX_WEIGHTS_NAME, is_onnx_available, logging
+from .utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
 
 
 if is_onnx_available():
@@ -33,13 +33,28 @@
 
 logger = logging.get_logger(__name__)
 
+ORT_TO_NP_TYPE = {
+    "tensor(bool)": np.bool_,
+    "tensor(int8)": np.int8,
+    "tensor(uint8)": np.uint8,
+    "tensor(int16)": np.int16,
+    "tensor(uint16)": np.uint16,
+    "tensor(int32)": np.int32,
+    "tensor(uint32)": np.uint32,
+    "tensor(int64)": np.int64,
+    "tensor(uint64)": np.uint64,
+    "tensor(float16)": np.float16,
+    "tensor(float)": np.float32,
+    "tensor(double)": np.float64,
+}
+
 
 class OnnxRuntimeModel:
     def __init__(self, model=None, **kwargs):
         logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.")
         self.model = model
         self.model_save_dir = kwargs.get("model_save_dir", None)
-        self.latest_model_name = kwargs.get("latest_model_name", "model.onnx")
+        self.latest_model_name = kwargs.get("latest_model_name", ONNX_WEIGHTS_NAME)
 
     def __call__(self, **kwargs):
         inputs = {k: np.array(v) for k, v in kwargs.items()}
@@ -84,6 +99,15 @@ def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional
         except shutil.SameFileError:
             pass
 
+        # copy external weights (for models >2GB)
+        src_path = self.model_save_dir.joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
+        if src_path.exists():
+            dst_path = Path(save_directory).joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
+            try:
+                shutil.copyfile(src_path, dst_path)
+            except shutil.SameFileError:
+                pass
+
     def save_pretrained(
         self,
         save_directory: Union[str, os.PathLike],

src/diffusers/pipeline_utils.py

@@ -541,7 +541,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             # if the model is in a pipeline module, then we load it from the pipeline
             if name in passed_class_obj:
                 # 1. check that passed_class_obj has correct parent class
-                if not is_pipeline_module:
+                if not is_pipeline_module and passed_class_obj[name] is not None:
                     library = importlib.import_module(library_name)
                     class_obj = getattr(library, class_name)
                     importable_classes = LOADABLE_CLASSES[library_name]

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py

@@ -2,11 +2,12 @@
 from typing import Callable, List, Optional, Union
 
 import numpy as np
+import torch
 
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
-from ...onnx_utils import OnnxRuntimeModel
+from ...onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
 from ...pipeline_utils import DiffusionPipeline
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from ...utils import deprecate, logging
@@ -186,7 +187,7 @@ def __call__(
         # set timesteps
         self.scheduler.set_timesteps(num_inference_steps)
 
-        latents = latents * self.scheduler.init_noise_sigma
+        latents = latents * np.float(self.scheduler.init_noise_sigma)
 
         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
@@ -197,15 +198,20 @@ def __call__(
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
+        timestep_dtype = next(
+            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
+        )
+        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
+
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+            latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
+            latent_model_input = latent_model_input.cpu().numpy()
 
             # predict the noise residual
-            noise_pred = self.unet(
-                sample=latent_model_input, timestep=np.array([t]), encoder_hidden_states=text_embeddings
-            )
+            timestep = np.array([t], dtype=timestep_dtype)
+            noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=text_embeddings)
             noise_pred = noise_pred[0]
 
             # perform guidance
@@ -214,7 +220,7 @@ def __call__(
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+            latents = self.scheduler.step(noise_pred, t, torch.from_numpy(latents), **extra_step_kwargs).prev_sample
             latents = np.array(latents)
 
             # call the callback, if provided
@@ -235,6 +241,9 @@ def __call__(
             safety_checker_input = self.feature_extractor(
                 self.numpy_to_pil(image), return_tensors="np"
             ).pixel_values.astype(image.dtype)
+
+            image, has_nsfw_concepts = self.safety_checker(clip_input=safety_checker_input, images=image)
+
             # There will throw an error if use safety_checker batchsize>1
             images, has_nsfw_concept = [], []
             for i in range(image.shape[0]):

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -8,7 +8,7 @@
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
-from ...onnx_utils import OnnxRuntimeModel
+from ...onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
 from ...pipeline_utils import DiffusionPipeline
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from ...utils import deprecate, logging
@@ -338,14 +338,21 @@ def __call__(
         t_start = max(num_inference_steps - init_timestep + offset, 0)
         timesteps = self.scheduler.timesteps[t_start:].numpy()
 
+        timestep_dtype = next(
+            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
+        )
+        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
+
         for i, t in enumerate(self.progress_bar(timesteps)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+            latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
+            latent_model_input = latent_model_input.cpu().numpy()
 
             # predict the noise residual
+            timestep = np.array([t], dtype=timestep_dtype)
             noise_pred = self.unet(
-                sample=latent_model_input, timestep=np.array([t]), encoder_hidden_states=text_embeddings
+                sample=latent_model_input, timestep=timestep, encoder_hidden_states=text_embeddings
             )[0]
 
             # perform guidance
@@ -354,7 +361,7 @@ def __call__(
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+            latents = self.scheduler.step(noise_pred, t, torch.from_numpy(latents), **extra_step_kwargs).prev_sample
             latents = latents.numpy()
 
             # call the callback, if provided
@@ -375,7 +382,7 @@ def __call__(
             safety_checker_input = self.feature_extractor(
                 self.numpy_to_pil(image), return_tensors="np"
             ).pixel_values.astype(image.dtype)
-            # There will throw an error if use safety_checker batchsize>1
+            # safety_checker does not support batched inputs yet
             images, has_nsfw_concept = [], []
             for i in range(image.shape[0]):
                 image_i, has_nsfw_concept_i = self.safety_checker(

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py

@@ -8,7 +8,7 @@
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
-from ...onnx_utils import OnnxRuntimeModel
+from ...onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
 from ...pipeline_utils import DiffusionPipeline
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from ...utils import deprecate, logging
@@ -352,7 +352,7 @@ def __call__(
         self.scheduler.set_timesteps(num_inference_steps)
 
         # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
+        latents = latents * np.float(self.scheduler.init_noise_sigma)
 
         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
@@ -363,17 +363,23 @@ def __call__(
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
+        timestep_dtype = next(
+            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
+        )
+        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
+
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
             # concat latents, mask, masked_image_latnets in the channel dimension
             latent_model_input = np.concatenate([latent_model_input, mask, masked_image_latents], axis=1)
             latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
-            latent_model_input = latent_model_input.numpy()
+            latent_model_input = latent_model_input.cpu().numpy()
 
             # predict the noise residual
+            timestep = np.array([t], dtype=timestep_dtype)
             noise_pred = self.unet(
-                sample=latent_model_input, timestep=np.array([t]), encoder_hidden_states=text_embeddings
+                sample=latent_model_input, timestep=timestep, encoder_hidden_states=text_embeddings
             )[0]
 
             # perform guidance
@@ -382,7 +388,7 @@ def __call__(
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+            latents = self.scheduler.step(noise_pred, t, torch.from_numpy(latents), **extra_step_kwargs).prev_sample
             latents = latents.numpy()
 
             # call the callback, if provided
@@ -403,7 +409,7 @@ def __call__(
             safety_checker_input = self.feature_extractor(
                 self.numpy_to_pil(image), return_tensors="np"
             ).pixel_values.astype(image.dtype)
-            # There will throw an error if use safety_checker batchsize>1
+            # safety_checker does not support batched inputs yet
             images, has_nsfw_concept = [], []
             for i in range(image.shape[0]):
                 image_i, has_nsfw_concept_i = self.safety_checker(

src/diffusers/utils/__init__.py

@@ -67,6 +67,7 @@
 WEIGHTS_NAME = "diffusion_pytorch_model.bin"
 FLAX_WEIGHTS_NAME = "diffusion_flax_model.msgpack"
 ONNX_WEIGHTS_NAME = "model.onnx"
+ONNX_EXTERNAL_WEIGHTS_NAME = "weights.pb"
 HUGGINGFACE_CO_RESOLVE_ENDPOINT = "https://huggingface.co"
 DIFFUSERS_CACHE = default_cache_path
 DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"