Pipeline to device (#210)

* Implement `pipeline.to(device)`

* DiffusionPipeline.to() decides best device on None.

* Breaking change: torch_device removed from __call__

`pipeline.to()` now has PyTorch semantics.

* Use kwargs and deprecation notice
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Apply torch_device compatibility to all pipelines.

* style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: anton-l <anton@huggingface.co>

src/diffusers/pipeline_utils.py

@@ -19,6 +19,8 @@ import inspect
 import os
 from typing import Optional, Union
 
+import torch
+
 from huggingface_hub import snapshot_download
 from PIL import Image
 
@@ -113,6 +115,26 @@ class DiffusionPipeline(ConfigMixin):
             save_method = getattr(sub_model, save_method_name)
             save_method(os.path.join(save_directory, pipeline_component_name))
 
+    def to(self, torch_device: Optional[Union[str, torch.device]] = None):
+        if torch_device is None:
+            return self
+
+        module_names, _ = self.extract_init_dict(dict(self.config))
+        for name in module_names.keys():
+            module = getattr(self, name)
+            if isinstance(module, torch.nn.Module):
+                module.to(torch_device)
+        return self
+
+    @property
+    def device(self) -> torch.device:
+        module_names, _ = self.extract_init_dict(dict(self.config))
+        for name in module_names.keys():
+            module = getattr(self, name)
+            if isinstance(module, torch.nn.Module):
+                return module.device
+        return torch.device("cpu")
+
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
         r"""

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 
 
+import warnings
+
 import torch
 
 from tqdm.auto import tqdm
@@ -28,21 +30,28 @@ class DDIMPipeline(DiffusionPipeline):
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(
-        self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50, output_type="pil"
-    ):
-        # eta corresponds to η in paper and should be between [0, 1]
-        if torch_device is None:
-            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+    def __call__(self, batch_size=1, generator=None, eta=0.0, num_inference_steps=50, output_type="pil", **kwargs):
+
+        if "torch_device" in kwargs:
+            device = kwargs.pop("torch_device")
+            warnings.warn(
+                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+                " Consider using `pipe.to(torch_device)` instead."
+            )
+
+            # Set device as before (to be removed in 0.3.0)
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.to(device)
 
-        self.unet.to(torch_device)
+        # eta corresponds to η in paper and should be between [0, 1]
 
         # Sample gaussian noise to begin loop
         image = torch.randn(
             (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
             generator=generator,
         )
-        image = image.to(torch_device)
+        image = image.to(self.device)
 
         # set step values
         self.scheduler.set_timesteps(num_inference_steps)

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 
 
+import warnings
+
 import torch
 
 from tqdm.auto import tqdm
@@ -28,18 +30,25 @@ class DDPMPipeline(DiffusionPipeline):
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, batch_size=1, generator=None, torch_device=None, output_type="pil"):
-        if torch_device is None:
-            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+    def __call__(self, batch_size=1, generator=None, output_type="pil", **kwargs):
+        if "torch_device" in kwargs:
+            device = kwargs.pop("torch_device")
+            warnings.warn(
+                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+                " Consider using `pipe.to(torch_device)` instead."
+            )
 
-        self.unet.to(torch_device)
+            # Set device as before (to be removed in 0.3.0)
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.to(device)
 
         # Sample gaussian noise to begin loop
         image = torch.randn(
             (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
             generator=generator,
         )
-        image = image.to(torch_device)
+        image = image.to(self.device)
 
         # set step values
         self.scheduler.set_timesteps(1000)

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

 import inspect
+import warnings
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -31,13 +32,22 @@ class LDMTextToImagePipeline(DiffusionPipeline):
         guidance_scale: Optional[float] = 1.0,
         eta: Optional[float] = 0.0,
         generator: Optional[torch.Generator] = None,
-        torch_device: Optional[Union[str, torch.device]] = None,
         output_type: Optional[str] = "pil",
+        **kwargs,
     ):
         # eta corresponds to η in paper and should be between [0, 1]
 
-        if torch_device is None:
-            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+        if "torch_device" in kwargs:
+            device = kwargs.pop("torch_device")
+            warnings.warn(
+                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+                " Consider using `pipe.to(torch_device)` instead."
+            )
+
+            # Set device as before (to be removed in 0.3.0)
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.to(device)
 
         if isinstance(prompt, str):
             batch_size = 1
@@ -49,24 +59,20 @@ class LDMTextToImagePipeline(DiffusionPipeline):
         if height % 8 != 0 or width % 8 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
 
-        self.unet.to(torch_device)
-        self.vqvae.to(torch_device)
-        self.bert.to(torch_device)
-
         # get unconditional embeddings for classifier free guidance
         if guidance_scale != 1.0:
             uncond_input = self.tokenizer([""] * batch_size, padding="max_length", max_length=77, return_tensors="pt")
-            uncond_embeddings = self.bert(uncond_input.input_ids.to(torch_device))[0]
+            uncond_embeddings = self.bert(uncond_input.input_ids.to(self.device))[0]
 
         # get prompt text embeddings
         text_input = self.tokenizer(prompt, padding="max_length", max_length=77, return_tensors="pt")
-        text_embeddings = self.bert(text_input.input_ids.to(torch_device))[0]
+        text_embeddings = self.bert(text_input.input_ids.to(self.device))[0]
 
         latents = torch.randn(
             (batch_size, self.unet.in_channels, height // 8, width // 8),
             generator=generator,
         )
-        latents = latents.to(torch_device)
+        latents = latents.to(self.device)
 
         self.scheduler.set_timesteps(num_inference_steps)
 

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

 import inspect
+import warnings
 
 import torch
 
@@ -14,22 +15,26 @@ class LDMPipeline(DiffusionPipeline):
         self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(
-        self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50, output_type="pil"
-    ):
+    def __call__(self, batch_size=1, generator=None, eta=0.0, num_inference_steps=50, output_type="pil", **kwargs):
         # eta corresponds to η in paper and should be between [0, 1]
 
-        if torch_device is None:
-            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+        if "torch_device" in kwargs:
+            device = kwargs.pop("torch_device")
+            warnings.warn(
+                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+                " Consider using `pipe.to(torch_device)` instead."
+            )
 
-        self.unet.to(torch_device)
-        self.vqvae.to(torch_device)
+            # Set device as before (to be removed in 0.3.0)
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.to(device)
 
         latents = torch.randn(
             (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
             generator=generator,
         )
-        latents = latents.to(torch_device)
+        latents = latents.to(self.device)
 
         self.scheduler.set_timesteps(num_inference_steps)
 

src/diffusers/pipelines/pndm/pipeline_pndm.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 
 
+import warnings
+
 import torch
 
 from tqdm.auto import tqdm
@@ -28,20 +30,28 @@ class PNDMPipeline(DiffusionPipeline):
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, batch_size=1, generator=None, torch_device=None, num_inference_steps=50, output_type="pil"):
+    def __call__(self, batch_size=1, generator=None, num_inference_steps=50, output_type="pil", **kwargs):
         # For more information on the sampling method you can take a look at Algorithm 2 of
         # the official paper: https://arxiv.org/pdf/2202.09778.pdf
-        if torch_device is None:
-            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
 
-        self.unet.to(torch_device)
+        if "torch_device" in kwargs:
+            device = kwargs.pop("torch_device")
+            warnings.warn(
+                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+                " Consider using `pipe.to(torch_device)` instead."
+            )
+
+            # Set device as before (to be removed in 0.3.0)
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.to(device)
 
         # Sample gaussian noise to begin loop
         image = torch.randn(
             (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
             generator=generator,
         )
-        image = image.to(torch_device)
+        image = image.to(self.device)
 
         self.scheduler.set_timesteps(num_inference_steps)
         for t in tqdm(self.scheduler.timesteps):

src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py

 #!/usr/bin/env python3
+import warnings
+
 import torch
 
 from diffusers import DiffusionPipeline
@@ -11,24 +13,32 @@ class ScoreSdeVePipeline(DiffusionPipeline):
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, batch_size=1, num_inference_steps=2000, generator=None, torch_device=None, output_type="pil"):
-
-        if torch_device is None:
-            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+    def __call__(self, batch_size=1, num_inference_steps=2000, generator=None, output_type="pil", **kwargs):
+        if "torch_device" in kwargs:
+            device = kwargs.pop("torch_device")
+            warnings.warn(
+                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+                " Consider using `pipe.to(torch_device)` instead."
+            )
+
+            # Set device as before (to be removed in 0.3.0)
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.to(device)
 
         img_size = self.unet.config.sample_size
         shape = (batch_size, 3, img_size, img_size)
 
-        model = self.unet.to(torch_device)
+        model = self.unet
 
         sample = torch.randn(*shape) * self.scheduler.config.sigma_max
-        sample = sample.to(torch_device)
+        sample = sample.to(self.device)
 
         self.scheduler.set_timesteps(num_inference_steps)
         self.scheduler.set_sigmas(num_inference_steps)
 
         for i, t in tqdm(enumerate(self.scheduler.timesteps)):
-            sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=torch_device)
+            sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device)
 
             # correction step
             for _ in range(self.scheduler.correct_steps):

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

 import inspect
+import warnings
 from typing import List, Optional, Union
 
 import torch
@@ -45,11 +46,20 @@ class StableDiffusionPipeline(DiffusionPipeline):
         guidance_scale: Optional[float] = 7.5,
         eta: Optional[float] = 0.0,
         generator: Optional[torch.Generator] = None,
-        torch_device: Optional[Union[str, torch.device]] = None,
         output_type: Optional[str] = "pil",
+        **kwargs,
     ):
-        if torch_device is None:
-            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+        if "torch_device" in kwargs:
+            device = kwargs.pop("torch_device")
+            warnings.warn(
+                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+                " Consider using `pipe.to(torch_device)` instead."
+            )
+
+            # Set device as before (to be removed in 0.3.0)
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.to(device)
 
         if isinstance(prompt, str):
             batch_size = 1
@@ -61,11 +71,6 @@ class StableDiffusionPipeline(DiffusionPipeline):
         if height % 8 != 0 or width % 8 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
 
-        self.unet.to(torch_device)
-        self.vae.to(torch_device)
-        self.text_encoder.to(torch_device)
-        self.safety_checker.to(torch_device)
-
         # get prompt text embeddings
         text_input = self.tokenizer(
             prompt,
@@ -74,7 +79,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
             truncation=True,
             return_tensors="pt",
         )
-        text_embeddings = self.text_encoder(text_input.input_ids.to(torch_device))[0]
+        text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[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`
@@ -86,7 +91,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
             uncond_input = self.tokenizer(
                 [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
             )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(torch_device))[0]
+            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
 
             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
@@ -97,7 +102,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         latents = torch.randn(
             (batch_size, self.unet.in_channels, height // 8, width // 8),
             generator=generator,
-            device=torch_device,
+            device=self.device,
         )
 
         # set timesteps
@@ -150,7 +155,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         image = image.cpu().permute(0, 2, 3, 1).numpy()
 
         # run safety checker
-        safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(torch_device)
+        safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
         image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
 
         if output_type == "pil":

src/diffusers/pipelines/stochatic_karras_ve/pipeline_stochastic_karras_ve.py

 #!/usr/bin/env python3
+import warnings
+
 import torch
 
 from tqdm.auto import tqdm
@@ -27,18 +29,27 @@ class KarrasVePipeline(DiffusionPipeline):
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, batch_size=1, num_inference_steps=50, generator=None, torch_device=None, output_type="pil"):
-        if torch_device is None:
-            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+    def __call__(self, batch_size=1, num_inference_steps=50, generator=None, output_type="pil", **kwargs):
+        if "torch_device" in kwargs:
+            device = kwargs.pop("torch_device")
+            warnings.warn(
+                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
+                " Consider using `pipe.to(torch_device)` instead."
+            )
+
+            # Set device as before (to be removed in 0.3.0)
+            if device is None:
+                device = "cuda" if torch.cuda.is_available() else "cpu"
+            self.to(device)
 
         img_size = self.unet.config.sample_size
         shape = (batch_size, 3, img_size, img_size)
 
-        model = self.unet.to(torch_device)
+        model = self.unet
 
         # sample x_0 ~ N(0, sigma_0^2 * I)
         sample = torch.randn(*shape) * self.scheduler.config.sigma_max
-        sample = sample.to(torch_device)
+        sample = sample.to(self.device)
 
         self.scheduler.set_timesteps(num_inference_steps)