feat(attn_procs): initial URL work (see notes)

TODO:

* test S3 for single files (should work, http works)
* test archives (totally untested, all archive code is new)

api/app.py

@@ -21,7 +21,10 @@
 import traceback
 from precision import MODEL_REVISION, MODEL_PRECISION
 from device import device, device_id, device_name
-from diffusers.models.cross_attention import CrossAttnProcessor, LoRACrossAttnProcessor
+from diffusers.models.cross_attention import CrossAttnProcessor
+from utils import Storage
+from hashlib import sha256
+
 
 RUNTIME_DOWNLOADS = os.getenv("RUNTIME_DOWNLOADS") == "1"
 USE_DREAMBOOTH = os.getenv("USE_DREAMBOOTH") == "1"
@@ -278,6 +281,18 @@ def inference(all_inputs: dict) -> dict:
     if attn_procs is not last_attn_procs:
         last_attn_procs = attn_procs
         if attn_procs:
+            storage = Storage(attn_procs, no_raise=True)
+            if storage:
+                fname = storage.url.split("/").pop()
+                hash = sha256(attn_procs.encode("utf-8")).hexdigest()
+                if True:
+                    # TODO, way to specify explicit name
+                    path = os.path.join(
+                        MODELS_DIR, "attn_proc--url_" + hash[:7] + "--" + fname
+                    )
+                attn_procs = path
+                if not os.path.exists(path):
+                    storage.download_and_extract(path)
             print("Load attn_procs " + attn_procs)
             pipeline.unet.load_attn_procs(attn_procs)
         else:
@@ -286,7 +301,7 @@ def inference(all_inputs: dict) -> dict:
 
     # TODO, generalize
     cross_attention_kwargs = model_inputs.get("cross_attention_kwargs", None)
-    if cross_attention_kwargs:
+    if isinstance(cross_attention_kwargs, str):
         model_inputs["cross_attention_kwargs"] = json.loads(cross_attention_kwargs)
 
     # Parse out your arguments

tests/integration/test_attn_procs.py

@@ -5,23 +5,31 @@
 
 
 class TestAttnProcs:
-    def test_hf_download(self):
-        """
-        Make sure when switching models we release VRAM afterwards.
-        """
-        dda = getDDA(
+    def setup_class(self):
+        print("setup_class")
+        # self.minio = minio = getMinio("global")
+
+        self.dda = dda = getDDA(
+            # minio=minio
             stream_logs=True,
         )
         print(dda)
 
-        TEST_ARGS = {"test_url": dda.url}
+        self.TEST_ARGS = {"test_url": dda.url}
 
-        mem_usage = list()
+    def teardown_class(self):
+        print("teardown_class")
+        # self.minio.stop() - leave global up
+        self.dda.stop()
 
+    def test_hf_download(self):
+        """
+        Download user/repo from HuggingFace.
+        """
         # fp32 model is obviously bigger
         result = runTest(
             "txt2img",
-            TEST_ARGS,
+            self.TEST_ARGS,
             {
                 "MODEL_ID": "runwayml/stable-diffusion-v1-5",
                 "MODEL_REVISION": "fp16",
@@ -37,4 +45,28 @@ def test_hf_download(self):
         )
 
         assert result["image_base64"]
-        dda.stop()
+
+    def test_http_download_diffusers_archive(self):
+        """
+        Download user/repo from HuggingFace.
+        """
+
+        # fp32 model is obviously bigger
+        result = runTest(
+            "txt2img",
+            self.TEST_ARGS,
+            {
+                "MODEL_ID": "runwayml/stable-diffusion-v1-5",
+                "MODEL_REVISION": "fp16",
+                "MODEL_PRECISION": "fp16",
+                "attn_procs": "https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example/resolve/main/pytorch_lora_weights.bin",
+            },
+            {
+                "num_inference_steps": 1,
+                "prompt": "A picture of a sks dog in a bucket",
+                "seed": 1,
+                "cross_attention_kwargs": {"scale": 0.5},
+            },
+        )
+
+        assert result["image_base64"]