[Onnx] support half-precision and fix bugs for onnx pipelines

scripts/convert_stable_diffusion_checkpoint_to_onnx.py

@@ -69,8 +69,15 @@ def onnx_export(
 
 
 @torch.no_grad()
-def convert_models(model_path: str, output_path: str, opset: int):
-    pipeline = StableDiffusionPipeline.from_pretrained(model_path)
+def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = False):
+    dtype = torch.float16 if fp16 else torch.float32
+    if fp16 and torch.cuda.is_available():
+        device = "cuda"
+    elif fp16 and not torch.cuda.is_available():
+        raise ValueError("`float16` model export is only supported on GPUs with CUDA")
+    else:
+        device = "cpu"
+    pipeline = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype).to(device)
     output_path = Path(output_path)
 
     # TEXT ENCODER
@@ -84,7 +91,7 @@ def convert_models(model_path: str, output_path: str, opset: int):
     onnx_export(
         pipeline.text_encoder,
         # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files
-        model_args=(text_input.input_ids.to(torch.int32)),
+        model_args=(text_input.input_ids.to(device=device, dtype=torch.int32)),
         output_path=output_path / "text_encoder" / "model.onnx",
         ordered_input_names=["input_ids"],
         output_names=["last_hidden_state", "pooler_output"],
@@ -100,9 +107,9 @@ def convert_models(model_path: str, output_path: str, opset: int):
     onnx_export(
         pipeline.unet,
         model_args=(
-            torch.randn(2, pipeline.unet.in_channels, 64, 64),
-            torch.LongTensor([0, 1]),
-            torch.randn(2, 77, 768),
+            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),
             False,
         ),
         output_path=unet_path,
@@ -139,7 +146,7 @@ def convert_models(model_path: str, output_path: str, opset: int):
     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), False),
+        model_args=(torch.randn(1, 3, 512, 512).to(device=device, dtype=dtype), False),
         output_path=output_path / "vae_encoder" / "model.onnx",
         ordered_input_names=["sample", "return_dict"],
         output_names=["latent_sample"],
@@ -155,7 +162,7 @@ def convert_models(model_path: str, output_path: str, opset: int):
     vae_decoder.forward = vae_encoder.decode
     onnx_export(
         vae_decoder,
-        model_args=(torch.randn(1, 4, 64, 64), False),
+        model_args=(torch.randn(1, 4, 64, 64).to(device=device, dtype=dtype), False),
         output_path=output_path / "vae_decoder" / "model.onnx",
         ordered_input_names=["latent_sample", "return_dict"],
         output_names=["sample"],
@@ -171,13 +178,16 @@ def convert_models(model_path: str, output_path: str, opset: int):
     safety_checker.forward = safety_checker.forward_onnx
     onnx_export(
         pipeline.safety_checker,
-        model_args=(torch.randn(1, 3, 224, 224), torch.randn(1, 512, 512, 3)),
+        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: "channels", 2: "height", 3: "width"},
+            "images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
         },
         opset=opset,
     )
@@ -221,7 +231,8 @@ def convert_models(model_path: str, output_path: str, opset: int):
         type=int,
         help="The version of the ONNX operator set to use.",
     )
+    parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
 
     args = parser.parse_args()
 
-    convert_models(args.model_path, args.output_path, args.opset)
+    convert_models(args.model_path, args.output_path, args.opset, args.fp16)

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py

@@ -55,7 +55,9 @@ def __call__(
         num_inference_steps: Optional[int] = 50,
         guidance_scale: Optional[float] = 7.5,
         negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
         eta: Optional[float] = 0.0,
+        generator: Optional[np.random.RandomState] = None,
         latents: Optional[np.ndarray] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
@@ -81,6 +83,9 @@ def __call__(
                 f" {type(callback_steps)}."
             )
 
+        if generator is None:
+            generator = np.random
+
         # get prompt text embeddings
         text_inputs = self.tokenizer(
             prompt,
@@ -98,6 +103,7 @@ def __call__(
             )
             text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
         text_embeddings = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
+        text_embeddings = np.repeat(text_embeddings, num_images_per_prompt, axis=0)
 
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
@@ -133,16 +139,18 @@ def __call__(
                 return_tensors="np",
             )
             uncond_embeddings = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
+            uncond_embeddings = np.repeat(uncond_embeddings, num_images_per_prompt, axis=0)
 
             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
 
         # get the initial random noise unless the user supplied it
-        latents_shape = (batch_size, 4, height // 8, width // 8)
+        latents_dtype = text_embeddings.dtype
+        latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
         if latents is None:
-            latents = np.random.randn(*latents_shape).astype(np.float32)
+            latents = generator.randn(*latents_shape).astype(latents_dtype)
         elif latents.shape != latents_shape:
             raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
 
@@ -185,13 +193,30 @@ def __call__(
                 callback(i, t, latents)
 
         latents = 1 / 0.18215 * latents
-        image = self.vae_decoder(latent_sample=latents)[0]
+        # image = self.vae_decoder(latent_sample=latents)[0]
+        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
+        image = np.concatenate(
+            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
+        )
 
         image = np.clip(image / 2 + 0.5, 0, 1)
         image = image.transpose((0, 2, 3, 1))
 
-        safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="np")
-        image, has_nsfw_concept = self.safety_checker(clip_input=safety_checker_input.pixel_values, images=image)
+        if self.safety_checker is not None:
+            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
+            images, has_nsfw_concept = [], []
+            for i in range(image.shape[0]):
+                image_i, has_nsfw_concept_i = self.safety_checker(
+                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
+                )
+                images.append(image_i)
+                has_nsfw_concept.append(has_nsfw_concept_i[0])
+            image = np.concatenate(images)
+        else:
+            has_nsfw_concept = None
 
         if output_type == "pil":
             image = self.numpy_to_pil(image)

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -121,6 +121,7 @@ def __call__(
         negative_prompt: Optional[Union[str, List[str]]] = None,
         num_images_per_prompt: Optional[int] = 1,
         eta: Optional[float] = 0.0,
+        generator: Optional[np.random.RandomState] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
@@ -159,6 +160,8 @@ def __call__(
             eta (`float`, *optional*, defaults to 0.0):
                 Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                 [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`np.random.RandomState`, *optional*):
+                A np.random.RandomState to make generation deterministic.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
@@ -197,6 +200,9 @@ def __call__(
                 f" {type(callback_steps)}."
             )
 
+        if generator is None:
+            generator = np.random
+
         # set timesteps
         self.scheduler.set_timesteps(num_inference_steps)
 
@@ -239,7 +245,7 @@ def __call__(
                     f" {type(prompt)}."
                 )
             elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
+                uncond_tokens = [negative_prompt] * batch_size
             elif batch_size != len(negative_prompt):
                 raise ValueError("The length of `negative_prompt` should be equal to batch_size.")
             else:
@@ -257,13 +263,15 @@ def __call__(
             uncond_embeddings = self.text_encoder(input_ids=uncond_input_ids.astype(np.int32))[0]
 
             # duplicate unconditional embeddings for each generation per prompt
-            uncond_embeddings = np.repeat(uncond_embeddings, batch_size * num_images_per_prompt, axis=0)
+            uncond_embeddings = np.repeat(uncond_embeddings, num_images_per_prompt, axis=0)
 
             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
 
+        latents_dtype = text_embeddings.dtype
+        init_image = init_image.astype(latents_dtype)
         # encode the init image into latents and scale the latents
         init_latents = self.vae_encoder(sample=init_image)[0]
         init_latents = 0.18215 * init_latents
@@ -297,7 +305,7 @@ def __call__(
         timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)
 
         # add noise to latents using the timesteps
-        noise = np.random.randn(*init_latents.shape).astype(np.float32)
+        noise = generator.randn(*init_latents.shape).astype(latents_dtype)
         init_latents = self.scheduler.add_noise(
             torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)
         )
@@ -341,14 +349,28 @@ def __call__(
                 callback(i, t, latents)
 
         latents = 1 / 0.18215 * latents
-        image = self.vae_decoder(latent_sample=latents)[0]
+        # image = self.vae_decoder(latent_sample=latents)[0]
+        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
+        image = np.concatenate(
+            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
+        )
 
         image = np.clip(image / 2 + 0.5, 0, 1)
         image = image.transpose((0, 2, 3, 1))
 
         if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="np")
-            image, has_nsfw_concept = self.safety_checker(clip_input=safety_checker_input.pixel_values, images=image)
+            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
+            images, has_nsfw_concept = [], []
+            for i in range(image.shape[0]):
+                image_i, has_nsfw_concept_i = self.safety_checker(
+                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
+                )
+                images.append(image_i)
+                has_nsfw_concept.append(has_nsfw_concept_i[0])
+            image = np.concatenate(images)
         else:
             has_nsfw_concept = None
 

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py

@@ -23,11 +23,11 @@
 
 
 def prepare_mask_and_masked_image(image, mask, latents_shape):
-    image = np.array(image.convert("RGB"))
+    image = np.array(image.convert("RGB").resize((latents_shape[1] * 8, latents_shape[0] * 8)))
     image = image[None].transpose(0, 3, 1, 2)
     image = image.astype(np.float32) / 127.5 - 1.0
 
-    image_mask = np.array(mask.convert("L"))
+    image_mask = np.array(mask.convert("L").resize((latents_shape[1] * 8, latents_shape[0] * 8)))
     masked_image = image * (image_mask < 127.5)
 
     mask = mask.resize((latents_shape[1], latents_shape[0]), PIL.Image.NEAREST)
@@ -138,6 +138,7 @@ def __call__(
         negative_prompt: Optional[Union[str, List[str]]] = None,
         num_images_per_prompt: Optional[int] = 1,
         eta: float = 0.0,
+        generator: Optional[np.random.RandomState] = None,
         latents: Optional[np.ndarray] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
@@ -180,6 +181,8 @@ def __call__(
             eta (`float`, *optional*, defaults to 0.0):
                 Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                 [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`np.random.RandomState`, *optional*):
+                A np.random.RandomState to make generation deterministic.
             latents (`np.ndarray`, *optional*):
                 Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                 generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
@@ -222,6 +225,9 @@ def __call__(
                 f" {type(callback_steps)}."
             )
 
+        if generator is None:
+            generator = np.random
+
         # set timesteps
         self.scheduler.set_timesteps(num_inference_steps)
 
@@ -261,7 +267,7 @@ def __call__(
                     f" {type(prompt)}."
                 )
             elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
+                uncond_tokens = [negative_prompt] * batch_size
             elif batch_size != len(negative_prompt):
                 raise ValueError(
                     f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
@@ -283,7 +289,7 @@ def __call__(
             uncond_embeddings = self.text_encoder(input_ids=uncond_input_ids.astype(np.int32))[0]
 
             # duplicate unconditional embeddings for each generation per prompt
-            uncond_embeddings = np.repeat(uncond_embeddings, batch_size * num_images_per_prompt, axis=0)
+            uncond_embeddings = np.repeat(uncond_embeddings, num_images_per_prompt, axis=0)
 
             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
@@ -294,7 +300,7 @@ def __call__(
         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 = np.random.randn(*latents_shape).astype(latents_dtype)
+            latents = generator.randn(*latents_shape).astype(latents_dtype)
         else:
             if latents.shape != latents_shape:
                 raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
@@ -307,6 +313,10 @@ def __call__(
         masked_image_latents = self.vae_encoder(sample=masked_image)[0]
         masked_image_latents = 0.18215 * masked_image_latents
 
+        # duplicate mask and masked_image_latents for each generation per prompt
+        mask = mask.repeat(batch_size * num_images_per_prompt, 0)
+        masked_image_latents = masked_image_latents.repeat(batch_size * num_images_per_prompt, 0)
+
         mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask
         masked_image_latents = (
             np.concatenate([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
@@ -367,14 +377,28 @@ def __call__(
                 callback(i, t, latents)
 
         latents = 1 / 0.18215 * latents
-        image = self.vae_decoder(latent_sample=latents)[0]
+        # image = self.vae_decoder(latent_sample=latents)[0]
+        # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
+        image = np.concatenate(
+            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
+        )
 
         image = np.clip(image / 2 + 0.5, 0, 1)
         image = image.transpose((0, 2, 3, 1))
 
         if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="np")
-            image, has_nsfw_concept = self.safety_checker(clip_input=safety_checker_input.pixel_values, images=image)
+            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
+            images, has_nsfw_concept = [], []
+            for i in range(image.shape[0]):
+                image_i, has_nsfw_concept_i = self.safety_checker(
+                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
+                )
+                images.append(image_i)
+                has_nsfw_concept.append(has_nsfw_concept_i[0])
+            image = np.concatenate(images)
         else:
             has_nsfw_concept = None