wrap up and tidy

.gitattributes

@@ -51,3 +51,4 @@ model/Assets/lcm_ninja_30_7.5_2092464983.png filter=lfs diff=lfs merge=lfs -text
 *.rknn filter=lfs diff=lfs merge=lfs -text
 *.onnx_data filter=lfs diff=lfs merge=lfs -text
 *.png filter=lfs diff=lfs merge=lfs -text
+*.so filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.12-slim
+FROM python:3.12-slim-trixie
 
 # System deps (Pillow, RKNN, etc.)
 RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -6,10 +6,12 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     libsm6 \
     libxext6 \
     libxrender1 \
-    && rm -rf /var/lib/apt/lists/*
+    procps 
+#    && rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app
 
+
 # Python deps
 COPY requirements.txt /app/requirements.txt
 RUN python -m venv /venv && \
@@ -18,8 +20,9 @@ RUN python -m venv /venv && \
 
 # 👇 THIS copies *everything except what .dockerignore excludes*
 COPY . /app
+
+# Copy RKNN runtime shared library into image
 COPY librknnrt.so /usr/lib/librknnrt.so
-COPY librkllmrt.so  /usr/lib/librkllmrt.so 
 RUN ldconfig
 
 ENV PATH="/venv/bin:$PATH"

docker-compose.yml

@@ -1,5 +1,6 @@
 services:
   lcn-sd:
+    platform: linux/arm64
     container_name: rknn-lcm-sd
     build:
       context: .

lcm_server.py

@@ -104,6 +104,9 @@ class PipelineWorker:
         # Deterministic per-request random generator
         rng = np.random.RandomState(job.req.seed)
 
+        print("seed ", job.req.seed)
+        print("rng", rng)
+        
         result = self.pipe(
             prompt=job.req.prompt,
             height=h,

librknnrt.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73993ed4b440460825f21611731564503cc1d5a0c123746477da6cd574f34885
+size 7259064

rknnlcm.py

@@ -43,7 +43,7 @@ class RKNN2Model:
         *,
         core_mask: Optional[Union[str, int]] = None,
         multi_context: bool = True,
-        data_format: str = "nhwc",
+        data_format: str = "nchw",
         verbose_shapes: bool = False,
         runtime_kwargs: Optional[dict] = None,
         **_ignored: Any,
@@ -679,4 +679,4 @@ def generate_png_bytes(args):
     pil_image.save(buf, format="PNG")
     buf.seek(0)
 
-    return buf.getvalue()
+    return buf.getvalue()

run_rknn-lcm.py

@@ -56,6 +56,30 @@ class RKNN2Model:
         for res in results:
             print(f"output shape: {res.shape}")
         return results
+    # def __call__(self, **kwargs) -> List[np.ndarray]:
+    #     input_list = [value for _, value in kwargs.items()]
+
+    #     fixed = []
+    #     for x in input_list:
+    #         if isinstance(x, np.ndarray):
+    #             # RKNNLite generally expects float32/int32/int8/etc — never float64
+    #             if x.dtype == np.float64:
+    #                 x = x.astype(np.float32, copy=False)
+    #             # Also common: float16 -> float32 (safe default unless you KNOW model is fp16)
+    #             if x.dtype == np.float16:
+    #                 x = x.astype(np.float32, copy=False)
+
+    #             # Make contiguous (RKNN is happier)
+    #             if not x.flags["C_CONTIGUOUS"]:
+    #                 x = np.ascontiguousarray(x)
+
+    #             elif isinstance(x, (float, np.floating)):
+    #                 x = np.array(x, dtype=np.float32)
+
+    #         fixed.append(x)
+
+    #     results = self.rknnlite.inference(inputs=fixed, data_format='nchw')
+    #     return results
 
 class RKNN2LatentConsistencyPipeline(DiffusionPipeline):
 

sample-rest-call.sh

@@ -1,4 +1,4 @@
-curl -X POST http://127.0.0.1:4200/generate   -H "Content-Type: application/json"   -o out.png   -d '{
+curl -X POST http://node3.lan:4200/generate   -H "Content-Type: application/json"   -o out.png   -d '{
     "prompt": "a cinematic photograph of a futuristic city at sunset",
     "size": "512x512",
     "num_inference_steps": 5,