Upload src/pipeline.py with huggingface_hub

src/pipeline.py

@@ -30,9 +30,10 @@ REVISION = "5ef0012f11a863e5111ec56540302a023bc8587b"
 TinyVAE = "madebyollin/taef1"
 TinyVAE_REV = "2d552378e58c9c94201075708d7de4e1163b2689"
 
-
+# @torch.inference_mode()
 def load_pipeline() -> Pipeline:
-    path = os.path.join(HF_HUB_CACHE, "models--jokerbit--flux.1-schnell-Robert-int8wo/snapshots/5ef0012f11a863e5111ec56540302a023bc8587b/transformer")
+    # path = os.path.join(HF_HUB_CACHE, "models--jokerbit--flux.1-schnell-Robert-int8wo/snapshots/5ef0012f11a863e5111ec56540302a023bc8587b/transformer")
+    path = os.path.join(HF_HUB_CACHE, "models--RobertML--FLUX.1-schnell-int8wo/snapshots/307e0777d92df966a3c0f99f31a6ee8957a9857a")
     transformer = FluxTransformer2DModel.from_pretrained(
             path, 
             use_safetensors=False,
@@ -57,10 +58,10 @@ def load_pipeline() -> Pipeline:
   
     for _ in range(4):
         pipeline(prompt="onomancy, aftergo, spirantic, Platyhelmia, modificator, drupaceous, jobbernowl, hereness", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256)
-
+    torch.cuda.empty_cache()
     return pipeline
 
-@torch.inference_mode()
+# @torch.inference_mode()
 def infer(request: TextToImageRequest, pipeline: Pipeline, generator: torch.Generator) -> Image:
 
     return pipeline(