[Community Pipeline] lpw_stable_diffusion: add...

[Community Pipeline] lpw_stable_diffusion: add xformers_memory_efficient_attention and sequential_cpu_offload  (#1130)

lpw_stable_diffusion: xformers and cpu_offload

examples/community/lpw_stable_diffusion.py

@@ -12,7 +12,7 @@ from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from diffusers.utils import deprecate, logging
+from diffusers.utils import deprecate, is_accelerate_available, logging
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 
@@ -340,13 +340,15 @@ def get_weighted_text_embeddings(
     # assign weights to the prompts and normalize in the sense of mean
     # TODO: should we normalize by chunk or in a whole (current implementation)?
     if (not skip_parsing) and (not skip_weighting):
-        previous_mean = text_embeddings.mean(axis=[-2, -1])
+        previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
         text_embeddings *= prompt_weights.unsqueeze(-1)
-        text_embeddings *= (previous_mean / text_embeddings.mean(axis=[-2, -1])).unsqueeze(-1).unsqueeze(-1)
+        current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
+        text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
         if uncond_prompt is not None:
-            previous_mean = uncond_embeddings.mean(axis=[-2, -1])
+            previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
             uncond_embeddings *= uncond_weights.unsqueeze(-1)
-            uncond_embeddings *= (previous_mean / uncond_embeddings.mean(axis=[-2, -1])).unsqueeze(-1).unsqueeze(-1)
+            current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
+            uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
 
     if uncond_prompt is not None:
         return text_embeddings, uncond_embeddings
@@ -431,6 +433,19 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
             new_config["steps_offset"] = 1
             scheduler._internal_dict = FrozenDict(new_config)
 
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` 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 `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
         if safety_checker is None:
             logger.warn(
                 f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
@@ -451,6 +466,24 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
             feature_extractor=feature_extractor,
         )
 
+    def enable_xformers_memory_efficient_attention(self):
+        r"""
+        Enable memory efficient attention as implemented in xformers.
+
+        When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
+        time. Speed up at training time is not guaranteed.
+
+        Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
+        is used.
+        """
+        self.unet.set_use_memory_efficient_attention_xformers(True)
+
+    def disable_xformers_memory_efficient_attention(self):
+        r"""
+        Disable memory efficient attention as implemented in xformers.
+        """
+        self.unet.set_use_memory_efficient_attention_xformers(False)
+
     def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
         r"""
         Enable sliced attention computation.
@@ -478,6 +511,23 @@ class StableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
         # set slice_size = `None` to disable `attention slicing`
         self.enable_attention_slicing(None)
 
+    def enable_sequential_cpu_offload(self):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = self.device
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
     @torch.no_grad()
     def __call__(
         self,

examples/community/lpw_stable_diffusion_onnx.py

@@ -701,7 +701,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(DiffusionPipeline):
                     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)
+                has_nsfw_concept.append(has_nsfw_concept_i[0])
             image = np.concatenate(images)
         else:
             has_nsfw_concept = None