minimal stable diffusion GPU memory usage with accelerate hooks

.github/workflows/pr_tests.yml

@@ -34,6 +34,7 @@ jobs:
         python -m pip install --upgrade pip
         python -m pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
         python -m pip install -e .[quality,test]
+        python -m pip install git+https://github.com/huggingface/accelerate
 
     - name: Environment
       run: |
@@ -80,6 +81,7 @@ jobs:
         ${CONDA_RUN} python -m pip install --upgrade pip
         ${CONDA_RUN} python -m pip install -e .[quality,test]
         ${CONDA_RUN} python -m pip install --pre torch==${MPS_TORCH_VERSION} --extra-index-url https://download.pytorch.org/whl/test/cpu
+        ${CONDA_RUN} python -m pip install git+https://github.com/huggingface/accelerate
 
     - name: Environment
       shell: arch -arch arm64 bash {0}

.github/workflows/push_tests.yml

@@ -36,6 +36,7 @@ jobs:
         python -m pip uninstall -y torch torchvision torchtext
         python -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu116
         python -m pip install -e .[quality,test]
+        python -m pip install git+https://github.com/huggingface/accelerate
 
     - name: Environment
       run: |
@@ -58,8 +59,6 @@ jobs:
         name: torch_test_reports
         path: reports
 
-
-
   run_examples_single_gpu:
     name: Examples tests
     runs-on: [ self-hosted, docker-gpu, single-gpu ]
@@ -83,6 +82,7 @@ jobs:
         python -m pip uninstall -y torch torchvision torchtext
         python -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu116
         python -m pip install -e .[quality,test,training]
+        python -m pip install git+https://github.com/huggingface/accelerate
 
     - name: Environment
       run: |

src/diffusers/pipeline_utils.py

@@ -209,6 +209,8 @@ def device(self) -> torch.device:
         for name in module_names.keys():
             module = getattr(self, name)
             if isinstance(module, torch.nn.Module):
+                if module.device == torch.device("meta"):
+                    return torch.device("cpu")
                 return module.device
         return torch.device("cpu")
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -3,6 +3,7 @@
 
 import torch
 
+from diffusers.utils import is_accelerate_available
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict
@@ -118,6 +119,18 @@ def disable_attention_slicing(self):
         # set slice_size = `None` to disable `attention slicing`
         self.enable_attention_slicing(None)
 
+    def cuda_with_minimal_gpu_usage(self):
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device("cuda")
+        self.enable_attention_slicing(1)
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
+            cpu_offload(cpu_offloaded_model, device)
+
     @torch.no_grad()
     def __call__(
         self,

tests/test_pipelines.py

@@ -535,3 +535,23 @@ def test_stable_diffusion_accelerate_load_reduces_memory_footprint(self):
         tracemalloc.stop()
 
         assert peak_accelerate < peak_normal
+
+    @slow
+    @unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU")
+    def test_stable_diffusion_pipeline_with_unet_on_gpu_only(self):
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()
+
+        pipeline_id = "CompVis/stable-diffusion-v1-4"
+        prompt = "Andromeda galaxy in a bottle"
+
+        pipeline = StableDiffusionPipeline.from_pretrained(
+            pipeline_id, revision="fp16", torch_dtype=torch.float32, use_auth_token=True
+        )
+        pipeline.cuda_with_minimal_gpu_usage()
+
+        _ = pipeline(prompt)
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+        # make sure that less than 0.8 GB is allocated
+        assert mem_bytes < 0.8 * 10**9