Optimize Stable Diffusion

docs/source/optimization/fp16.mdx

@@ -14,7 +14,64 @@ specific language governing permissions and limitations under the License.
 
 We present some techniques and ideas to optimize 🤗 Diffusers _inference_ for memory or speed.
 
-## CUDA `autocast`
+<table>
+<tr>
+ <td>
+ <td>Latency
+ <td>Speedup
+<tr>
+<tr>
+ <td>original
+ <td>9.50s
+ <td>x1
+<tr>
+<tr>
+ <td>cuDNN auto-tuner
+ <td>9.37s
+ <td>x1.01
+<tr>
+ <td>autocast (fp16)
+ <td>5.47s
+ <td>x1.91
+<tr>
+ <td>fp16
+ <td>3.61s
+ <td>x2.91
+<tr>
+ <td>channels last
+ <td>3.30s
+ <td>x2.87
+<tr>
+<tr>
+ <td>traced UNet
+ <td>3.21s
+ <td>x2.96
+</table>
+<em>obtained on NVIDIA TITAN RTX by generating a single image of size 512x512 from the prompt "a photo of an astronaut riding a horse on mars" with 50 DDIM steps.</em>
+
+## Enable cuDNN auto-tuner
+
+[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) supports many algorithms to compute a convolution. Autotuner runs a short benchmark and selects the kernel with the best performance on a given hardware for a given input size.
+
+Since we’re using **convolutional networks** (other types currently not supported), we can enable cuDNN autotuner before launching the inference by setting:
+
+```python
+import torch
+
+torch.backends.cudnn.benchmark = True
+```
+
+### Use tf32 instead of fp32 (on Ampere and later CUDA devices)
+
+On Ampere and later CUDA devices matrix multiplications and convolutions can use the TensorFloat32 (TF32) mode for faster but slightly less accurate computations. By default PyTorch enables TF32 mode for convolutions but not matrix multiplications, and unless a network requires full float32 precision we recommend enabling this setting for matrix multiplications, too. It can significantly speed up computations with typically negligible loss of numerical accuracy. You can read more about it [here](https://huggingface.co/docs/transformers/v4.18.0/en/performance#tf32). All you need to do is to add this before your inference:
+
+```python
+import torch
+
+torch.backends.cuda.matmul.allow_tf32 = True
+```
+
+## Automatic mixed precision (AMP)
 
 If you use a CUDA GPU, you can take advantage of `torch.autocast` to perform inference roughly twice as fast at the cost of slightly lower precision. All you need to do is put your inference call inside an `autocast` context manager. The following example shows how to do it using Stable Diffusion text-to-image generation as an example:
 
@@ -47,7 +104,7 @@ pipe = StableDiffusionPipeline.from_pretrained(
 
 ## Sliced attention for additional memory savings
 
-For even additional memory savings, you can use a sliced version of attention that performs the computation in steps instead of all at once. 
+For even additional memory savings, you can use a sliced version of attention that performs the computation in steps instead of all at once.
 
 <Tip>
 Attention slicing is useful even if a batch size of just 1 is used - as long as the model uses more than one attention head. If there is more than one attention head the *QK^T* attention matrix can be computed sequentially for each head which can save a significant amount of memory.
@@ -73,4 +130,139 @@ with torch.autocast("cuda"):
     image = pipe(prompt).images[0]  
 ```
 
-There's a small performance penalty of about 10% slower inference times, but this method allows you to use Stable Diffusion in as little as 3.2 GB of VRAM! 
+There's a small performance penalty of about 10% slower inference times, but this method allows you to use Stable Diffusion in as little as 3.2 GB of VRAM!
+
+## Using Channels Last memory format
+
+Channels last memory format is an alternative way of ordering NCHW tensors in memory preserving dimensions ordering. Channels last tensors ordered in such a way that channels become the densest dimension (aka storing images pixel-per-pixel). Since not all operators currently support channels last format it may result in a worst performance, so it's better to try it and see if it works for your model.
+
+For example, in order to set the UNet model in our pipeline to use channels last format, we can use the following:
+
+```python
+print(pipe.unet.conv_out.state_dict()["weight"].stride())  # (2880, 9, 3, 1)
+pipe.unet.to(memory_format=torch.channels_last)  # in-place operation
+print(
+    pipe.unet.conv_out.state_dict()["weight"].stride()
+)  # (2880, 1, 960, 320) haveing a stride of 1 for the 2nd dimension proves that it works
+```
+
+## Tracing
+
+Tracing runs an example input tensor through your model, and captures the operations that are invoked as that input makes its way through the model's layers so that an executable or `ScriptFunction` is returned that will be optimized using just-in-time compilation.
+
+To trace our UNet model, we can use the following:
+
+```python
+import time
+import torch
+from diffusers import StableDiffusionPipeline
+import functools
+
+# torch disable grad
+torch.set_grad_enabled(False)
+
+# set variables
+n_experiments = 2
+unet_runs_per_experiment = 50
+
+# load inputs
+def generate_inputs():
+    sample = torch.randn(2, 4, 64, 64).half().cuda()
+    timestep = torch.rand(1).half().cuda() * 999
+    encoder_hidden_states = torch.randn(2, 77, 768).half().cuda()
+    return sample, timestep, encoder_hidden_states
+
+
+pipe = StableDiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    # scheduler=scheduler,
+    use_auth_token=True,
+    revision="fp16",
+    torch_dtype=torch.float16,
+).to("cuda")
+unet = pipe.unet
+unet.eval()
+unet.to(memory_format=torch.channels_last)  # use channels_last memory format
+unet.forward = functools.partial(unet.forward, return_dict=False)  # set return_dict=False as default
+
+# warmup
+for _ in range(3):
+    with torch.inference_mode():
+        inputs = generate_inputs()
+        orig_output = unet(*inputs)
+
+# trace
+print("tracing..")
+unet_traced = torch.jit.trace(unet, inputs)
+unet_traced.eval()
+print("done tracing")
+
+
+# warmup and optimize graph
+for _ in range(5):
+    with torch.inference_mode():
+        inputs = generate_inputs()
+        orig_output = unet_traced(*inputs)
+
+
+# benchmarking
+with torch.inference_mode():
+    for _ in range(n_experiments):
+        torch.cuda.synchronize()
+        start_time = time.time()
+        for _ in range(unet_runs_per_experiment):
+            orig_output = unet_traced(*inputs)
+        torch.cuda.synchronize()
+        print(f"unet traced inference took {time.time() - start_time:.2f} seconds")
+    for _ in range(n_experiments):
+        torch.cuda.synchronize()
+        start_time = time.time()
+        for _ in range(unet_runs_per_experiment):
+            orig_output = unet(*inputs)
+        torch.cuda.synchronize()
+        print(f"unet inference took {time.time() - start_time:.2f} seconds")
+
+# save the model
+unet_traced.save("unet_traced.pt")
+```
+
+Then we can replace the `unet` attribute of the pipeline with the traced model like the following
+
+```python
+from diffusers import StableDiffusionPipeline
+import torch
+from dataclasses import dataclass
+
+
+@dataclass
+class UNet2DConditionOutput:
+    sample: torch.FloatTensor
+
+
+pipe = StableDiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    # scheduler=scheduler,
+    use_auth_token=True,
+    revision="fp16",
+    torch_dtype=torch.float16,
+).to("cuda")
+
+# use jitted unet
+unet_traced = torch.jit.load("unet_traced.pt")
+# del pipe.unet
+class TracedUNet(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.in_channels = pipe.unet.in_channels
+        self.device = pipe.unet.device
+
+    def forward(self, latent_model_input, t, encoder_hidden_states):
+        sample = unet_traced(latent_model_input, t, encoder_hidden_states)[0]
+        return UNet2DConditionOutput(sample=sample)
+
+
+pipe.unet = TracedUNet()
+
+with torch.inference_mode():
+    image = pipe([prompt] * 1, num_inference_steps=50).images[0]
+```

src/diffusers/models/attention.py

@@ -72,8 +72,7 @@ def forward(self, hidden_states):
 
         # get scores
         scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads))
-
-        attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)
+        attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)  # TODO: use baddmm
         attention_probs = torch.softmax(attention_scores.float(), dim=-1).type(attention_scores.dtype)
 
         # compute attention output
@@ -275,7 +274,13 @@ def forward(self, hidden_states, context=None, mask=None):
         return self.to_out(hidden_states)
 
     def _attention(self, query, key, value):
-        attention_scores = torch.matmul(query, key.transpose(-1, -2)) * self.scale
+        attention_scores = torch.baddbmm(
+            torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
+            query,
+            key.transpose(-1, -2),
+            beta=0,
+            alpha=self.scale,
+        )
         attention_probs = attention_scores.softmax(dim=-1)
         # compute attention output
         hidden_states = torch.matmul(attention_probs, value)
@@ -292,7 +297,9 @@ def _sliced_attention(self, query, key, value, sequence_length, dim):
         for i in range(hidden_states.shape[0] // slice_size):
             start_idx = i * slice_size
             end_idx = (i + 1) * slice_size
-            attn_slice = torch.matmul(query[start_idx:end_idx], key[start_idx:end_idx].transpose(1, 2)) * self.scale
+            attn_slice = (
+                torch.matmul(query[start_idx:end_idx], key[start_idx:end_idx].transpose(1, 2)) * self.scale
+            )  # TODO: use baddbmm for better performance
             attn_slice = attn_slice.softmax(dim=-1)
             attn_slice = torch.matmul(attn_slice, value[start_idx:end_idx])
 

src/diffusers/models/embeddings.py

@@ -37,10 +37,12 @@ def get_timestep_embedding(
     assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
 
     half_dim = embedding_dim // 2
-    exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32)
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+    )
     exponent = exponent / (half_dim - downscale_freq_shift)
 
-    emb = torch.exp(exponent).to(device=timesteps.device)
+    emb = torch.exp(exponent)
     emb = timesteps[:, None].float() * emb[None, :]
 
     # scale embeddings

src/diffusers/models/resnet.py

@@ -331,7 +331,7 @@ def forward(self, x, temb):
 
         # make sure hidden states is in float32
         # when running in half-precision
-        hidden_states = self.norm1(hidden_states).type(hidden_states.dtype)
+        hidden_states = self.norm1(hidden_states)
         hidden_states = self.nonlinearity(hidden_states)
 
         if self.upsample is not None:
@@ -349,7 +349,7 @@ def forward(self, x, temb):
 
         # make sure hidden states is in float32
         # when running in half-precision
-        hidden_states = self.norm2(hidden_states).type(hidden_states.dtype)
+        hidden_states = self.norm2(hidden_states)
         hidden_states = self.nonlinearity(hidden_states)
 
         hidden_states = self.dropout(hidden_states)

src/diffusers/models/unet_2d_condition.py

@@ -230,16 +230,16 @@ def forward(
         # 1. time
         timesteps = timestep
         if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
         elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
-            timesteps = timesteps.to(dtype=torch.float32)
-            timesteps = timesteps[None].to(device=sample.device)
+            timesteps = timesteps[None].to(sample.device)
 
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         timesteps = timesteps.expand(sample.shape[0])
 
         t_emb = self.time_proj(timesteps)
-        emb = self.time_embedding(t_emb)
+        emb = self.time_embedding(t_emb.to(self.dtype))
 
         # 2. pre-process
         sample = self.conv_in(sample)
@@ -279,7 +279,7 @@ def forward(
         # 6. post-process
         # make sure hidden states is in float32
         # when running in half-precision
-        sample = self.conv_norm_out(sample.float()).type(sample.dtype)
+        sample = self.conv_norm_out(sample)
         sample = self.conv_act(sample)
         sample = self.conv_out(sample)
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -225,15 +225,23 @@ def __call__(
                 latents_shape,
                 generator=generator,
                 device=latents_device,
+                dtype=text_embeddings.dtype,
             )
         else:
             if latents.shape != latents_shape:
                 raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-        latents = latents.to(self.device)
+            latents = latents.to(latents_device)
 
         # set timesteps
         self.scheduler.set_timesteps(num_inference_steps)
 
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimzed to move all timesteps to correct device beforehand
+        if torch.is_tensor(self.scheduler.timesteps):
+            timesteps_tensor = self.scheduler.timesteps.to(self.device)
+        else:
+            timesteps_tensor = torch.tensor(self.scheduler.timesteps.copy(), device=self.device)
+
         # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
         if isinstance(self.scheduler, LMSDiscreteScheduler):
             latents = latents * self.scheduler.sigmas[0]
@@ -247,7 +255,7 @@ def __call__(
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
             if isinstance(self.scheduler, LMSDiscreteScheduler):
@@ -278,7 +286,9 @@ def __call__(
 
         # run safety checker
         safety_checker_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_checker_input.pixel_values)
+        image, has_nsfw_concept = self.safety_checker(
+            images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
+        )
 
         if output_type == "pil":
             image = self.numpy_to_pil(image)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -265,7 +265,11 @@ def __call__(
         latents = init_latents
 
         t_start = max(num_inference_steps - init_timestep + offset, 0)
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps[t_start:])):
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimzed to move all timesteps to correct device beforehand
+        timesteps_tensor = torch.tensor(self.scheduler.timesteps[t_start:], device=self.device)
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
             t_index = t_start + i
 
             # expand the latents if we are doing classifier free guidance

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -298,7 +298,11 @@ def __call__(
 
         latents = init_latents
         t_start = max(num_inference_steps - init_timestep + offset, 0)
-        for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimzed to move all timesteps to correct device beforehand
+        timesteps_tensor = torch.tensor(self.scheduler.timesteps[t_start:], device=self.device)
+
+        for i, t in tqdm(enumerate(timesteps_tensor)):
             t_index = t_start + i
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents