feat(diffusers): add builds for nvidia-l4t

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

.github/workflows/backend.yml

@@ -87,6 +87,18 @@ jobs:
             backend: "diffusers"
             dockerfile: "./backend/Dockerfile.python"
             context: "./backend"
+          - build-type: 'l4t'
+            cuda-major-version: "12"
+            cuda-minor-version: "0"
+            platforms: 'linux/amd64'
+            tag-latest: 'auto'
+            tag-suffix: '-gpu-nvidia-l4t-diffusers'
+            runs-on: 'ubuntu-24.04-arm'
+            base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
+            skip-drivers: 'true'
+            backend: "diffusers"
+            dockerfile: "./backend/Dockerfile.python"
+            context: "./backend"
           # CUDA 11 additional backends
           - build-type: 'cublas'
             cuda-major-version: "11"
@@ -313,7 +325,7 @@ jobs:
             platforms: 'linux/amd64'
             tag-latest: 'auto'
             tag-suffix: '-gpu-rocm-hipblas-transformers'
-            runs-on: 'ubuntu-latest'
+            runs-on: 'arc-runner-set'
             base-image: "rocm/dev-ubuntu-22.04:6.1"
             skip-drivers: 'false'
             backend: "transformers"
@@ -325,7 +337,7 @@ jobs:
             platforms: 'linux/amd64'
             tag-latest: 'auto'
             tag-suffix: '-gpu-rocm-hipblas-diffusers'
-            runs-on: 'ubuntu-latest'
+            runs-on: 'arc-runner-set'
             base-image: "rocm/dev-ubuntu-22.04:6.1"
             skip-drivers: 'false'
             backend: "diffusers"
@@ -338,7 +350,7 @@ jobs:
             platforms: 'linux/amd64'
             tag-latest: 'auto'
             tag-suffix: '-gpu-rocm-hipblas-kokoro'
-            runs-on: 'ubuntu-latest'
+            runs-on: 'arc-runner-set'
             base-image: "rocm/dev-ubuntu-22.04:6.1"
             skip-drivers: 'false'
             backend: "kokoro"
@@ -374,7 +386,7 @@ jobs:
             platforms: 'linux/amd64'
             tag-latest: 'auto'
             tag-suffix: '-gpu-rocm-hipblas-bark'
-            runs-on: 'ubuntu-latest'
+            runs-on: 'arc-runner-set'
             base-image: "rocm/dev-ubuntu-22.04:6.1"
             skip-drivers: 'false'
             backend: "bark"
@@ -920,6 +932,19 @@ jobs:
           #   backend: "rfdetr"
           #   dockerfile: "./backend/Dockerfile.python"
           #   context: "./backend"
+          # kitten-tts
+          - build-type: ''
+            cuda-major-version: ""
+            cuda-minor-version: ""
+            platforms: 'linux/amd64,linux/arm64'
+            tag-latest: 'auto'
+            tag-suffix: '-kitten-tts'
+            runs-on: 'ubuntu-latest'
+            base-image: "ubuntu:22.04"
+            skip-drivers: 'false'
+            backend: "kitten-tts"
+            dockerfile: "./backend/Dockerfile.python"
+            context: "./backend"
   llama-cpp-darwin:
     runs-on: macOS-14
     strategy:
@@ -955,6 +980,7 @@ jobs:
           path: build/llama-cpp.tar
   llama-cpp-darwin-publish:
     needs: llama-cpp-darwin
+    if: github.event_name != 'pull_request'
     runs-on: ubuntu-latest
     steps:
       - name: Download llama-cpp.tar
@@ -1043,6 +1069,7 @@ jobs:
           name: llama-cpp-tar-x86
           path: build/llama-cpp.tar
   llama-cpp-darwin-x86-publish:
+    if: github.event_name != 'pull_request'
     needs: llama-cpp-darwin-x86
     runs-on: ubuntu-latest
     steps:
@@ -1096,4 +1123,4 @@ jobs:
         run: |
           for tag in $(echo "${{ steps.quaymeta.outputs.tags }}" | tr ',' '\n'); do
             crane push llama-cpp.tar $tag
-          done
+          done

Dockerfile

@@ -9,7 +9,7 @@ ENV DEBIAN_FRONTEND=noninteractive
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
         ca-certificates curl wget espeak-ng libgomp1 \
-        python3 python-is-python3 ffmpeg && \
+        python3 python-is-python3 ffmpeg libopenblas-base libopenblas-dev && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 

Makefile

@@ -132,6 +132,9 @@ test: test-models/testmodel.ggml protogen-go
 	$(MAKE) test-tts
 	$(MAKE) test-stablediffusion
 
+backends/diffusers: docker-build-diffusers docker-save-diffusers build
+	./local-ai backends install "ocifile://$(abspath ./backend-images/diffusers.tar)"
+
 backends/llama-cpp: docker-build-llama-cpp docker-save-llama-cpp build
 	./local-ai backends install "ocifile://$(abspath ./backend-images/llama-cpp.tar)"
 
@@ -156,6 +159,12 @@ backends/huggingface: docker-build-huggingface docker-save-huggingface build
 backends/rfdetr: docker-build-rfdetr docker-save-rfdetr build
 	./local-ai backends install "ocifile://$(abspath ./backend-images/rfdetr.tar)"
 
+backends/kitten-tts: docker-build-kitten-tts docker-save-kitten-tts build
+	./local-ai backends install "ocifile://$(abspath ./backend-images/kitten-tts.tar)"
+
+backends/kokoro: docker-build-kokoro docker-save-kokoro build
+	./local-ai backends install "ocifile://$(abspath ./backend-images/kokoro.tar)"
+
 ########################################################
 ## AIO tests
 ########################################################
@@ -369,6 +378,18 @@ docker-build-huggingface:
 docker-build-rfdetr:
 	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:rfdetr -f backend/Dockerfile.python --build-arg BACKEND=rfdetr ./backend
 
+docker-build-kitten-tts:
+	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:kitten-tts -f backend/Dockerfile.python --build-arg BACKEND=kitten-tts ./backend
+
+docker-save-kitten-tts: backend-images
+	docker save local-ai-backend:kitten-tts -o backend-images/kitten-tts.tar
+
+docker-build-kokoro:
+	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:kokoro -f backend/Dockerfile.python --build-arg BACKEND=kokoro ./backend
+
+docker-save-kokoro: backend-images
+	docker save local-ai-backend:kokoro -o backend-images/kokoro.tar
+
 docker-save-rfdetr: backend-images
 	docker save local-ai-backend:rfdetr -o backend-images/rfdetr.tar
 
@@ -409,10 +430,10 @@ docker-build-transformers:
 	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:transformers -f backend/Dockerfile.python --build-arg BACKEND=transformers .
 
 docker-build-diffusers:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:diffusers -f backend/Dockerfile.python --build-arg BACKEND=diffusers .
+	docker build --progress=plain --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:diffusers -f backend/Dockerfile.python --build-arg BACKEND=diffusers ./backend
 
-docker-build-kokoro:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:kokoro -f backend/Dockerfile.python --build-arg BACKEND=kokoro .
+docker-save-diffusers: backend-images
+	docker save local-ai-backend:diffusers -o backend-images/diffusers.tar
 
 docker-build-whisper:
 	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:whisper -f backend/Dockerfile.golang --build-arg BACKEND=whisper  .

backend/cpp/llama-cpp/Makefile

@@ -1,5 +1,5 @@
 
-LLAMA_VERSION?=d31192b4ee1441bbbecd3cbf9e02633368bdc4f5
+LLAMA_VERSION?=a0552c8beef74e843bb085c8ef0c63f9ed7a2b27
 LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
 
 CMAKE_ARGS?=

backend/cpp/llama-cpp/grpc-server.cpp

@@ -313,9 +313,11 @@ static void params_parse(const backend::ModelOptions* request,
         params.pooling_type = LLAMA_POOLING_TYPE_RANK;
     }
 
+    
     if (request->ropescaling() == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
     else if (request->ropescaling() == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
-    else { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
+    else if (request->ropescaling() == "linear")   {  params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
+
     if ( request->yarnextfactor() != 0.0f ) {
         params.yarn_ext_factor = request->yarnextfactor();
     }

backend/go/whisper/Makefile

@@ -6,7 +6,7 @@ CMAKE_ARGS?=
 
 # whisper.cpp version
 WHISPER_REPO?=https://github.com/ggml-org/whisper.cpp
-WHISPER_CPP_VERSION?=0becabc8d68d9ffa6ddfba5240e38cd7a2642046
+WHISPER_CPP_VERSION?=4245c77b654cd384ad9f53a4a302be716b3e5861
 
 export WHISPER_CMAKE_ARGS?=-DBUILD_SHARED_LIBS=OFF
 export WHISPER_DIR=$(abspath ./sources/whisper.cpp)

backend/index.yaml

@@ -168,6 +168,7 @@
     nvidia: "cuda12-diffusers"
     intel: "intel-diffusers"
     amd: "rocm-diffusers"
+    nvidia-l4t: "nvidia-l4t-diffusers"
 - &exllama2
   name: "exllama2"
   urls:
@@ -294,6 +295,7 @@
   license: MIT
   icon: https://private-user-images.githubusercontent.com/660224/448166653-bd8c5f03-e91d-4ee5-b680-57355da204d1.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3NTAxOTE0MDAsIm5iZiI6MTc1MDE5MTEwMCwicGF0aCI6Ii82NjAyMjQvNDQ4MTY2NjUzLWJkOGM1ZjAzLWU5MWQtNGVlNS1iNjgwLTU3MzU1ZGEyMDRkMS5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjUwNjE3JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI1MDYxN1QyMDExNDBaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1hMmI1NGY3OGFiZTlhNGFkNTVlYTY4NTIwMWEzODRiZGE4YzdhNGQ5MGNhNzE3MDYyYTA2NDIxYTkyYzhiODkwJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.mR9kM9xX0TdzPuSpuspCllHYQiq79dFQ2rtuNvjrl6w
   name: "chatterbox"
+  alias: "chatterbox"
   capabilities:
     nvidia: "cuda12-chatterbox"
 - &piper
@@ -356,6 +358,24 @@
     - LLM
     - huggingface
   license: MIT
+- &kitten-tts
+  name: "kitten-tts"
+  uri: "quay.io/go-skynet/local-ai-backends:latest-kitten-tts"
+  mirrors:
+    - localai/localai-backends:latest-kitten-tts
+  urls:
+    - https://github.com/KittenML/KittenTTS
+  description: |
+    Kitten TTS is a text-to-speech model that can generate speech from text.
+  tags:
+    - text-to-speech
+    - TTS
+  license: apache-2.0
+- !!merge <<: *kitten-tts
+  name: "kitten-tts-development"
+  uri: "quay.io/go-skynet/local-ai-backends:master-kitten-tts"
+  mirrors:
+    - localai/localai-backends:master-kitten-tts
 - !!merge <<: *huggingface
   name: "huggingface-development"
   uri: "quay.io/go-skynet/local-ai-backends:master-huggingface"
@@ -834,6 +854,17 @@
     nvidia: "cuda12-diffusers-development"
     intel: "intel-diffusers-development"
     amd: "rocm-diffusers-development"
+    nvidia-l4t: "nvidia-l4t-diffusers-development"
+- !!merge <<: *diffusers
+  name: "nvidia-l4t-diffusers"
+  uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-l4t-diffusers"
+  mirrors:
+    - localai/localai-backends:latest-gpu-nvidia-l4t-diffusers
+- !!merge <<: *diffusers
+  name: "nvidia-l4t-diffusers-development"
+  uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-l4t-diffusers"
+  mirrors:
+    - localai/localai-backends:master-gpu-nvidia-l4t-diffusers
 - !!merge <<: *diffusers
   name: "cuda12-diffusers"
   uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-cuda-12-diffusers"

backend/python/common/libbackend.sh

@@ -57,6 +57,11 @@ function init() {
 # - hipblas
 # - intel
 function getBuildProfile() {
+    if [ "x${BUILD_TYPE}" == "xl4t" ]; then
+        echo "l4t"
+        return 0
+    fi
+
     # First check if we are a cublas build, and if so report the correct build profile
     if [ x"${BUILD_TYPE}" == "xcublas" ]; then
         if [ ! -z ${CUDA_MAJOR_VERSION} ]; then

backend/python/diffusers/backend.py

@@ -65,6 +65,19 @@ from diffusers.schedulers import (
     UniPCMultistepScheduler,
 )
 
+def is_float(s):
+    try:
+        float(s)
+        return True
+    except ValueError:
+        return False
+
+def is_int(s):
+    try:
+        int(s)
+        return True
+    except ValueError:
+        return False
 
 # The scheduler list mapping was taken from here: https://github.com/neggles/animatediff-cli/blob/6f336f5f4b5e38e85d7f06f1744ef42d0a45f2a7/src/animatediff/schedulers.py#L39
 # Credits to https://github.com/neggles
@@ -169,8 +182,24 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                 if ":" not in opt:
                     continue
                 key, value = opt.split(":")
+                # if value is a number, convert it to the appropriate type
+                if is_float(value):
+                    value = float(value)
+                elif is_int(value):
+                    value = int(value)
                 self.options[key] = value
 
+            # From options, extract if present "torch_dtype" and set it to the appropriate type
+            if "torch_dtype" in self.options:
+                if self.options["torch_dtype"] == "fp16":
+                    torchType = torch.float16
+                elif self.options["torch_dtype"] == "bf16":
+                    torchType = torch.bfloat16
+                elif self.options["torch_dtype"] == "fp32":
+                    torchType = torch.float32
+                # remove it from options
+                del self.options["torch_dtype"]
+
             print(f"Options: {self.options}", file=sys.stderr)
 
             local = False

backend/python/diffusers/requirements-cpu.txt

@@ -1,9 +1,9 @@
-diffusers
+git+https://github.com/huggingface/diffusers
 opencv-python
 transformers
 accelerate
 compel
 peft
 sentencepiece
-torch==2.4.1
+torch==2.7.1
 optimum-quanto

backend/python/diffusers/requirements-cublas11.txt

@@ -1,6 +1,6 @@
 --extra-index-url https://download.pytorch.org/whl/cu118
-torch==2.4.1+cu118
-diffusers
+torch==2.7.1+cu118
+git+https://github.com/huggingface/diffusers
 opencv-python
 transformers
 accelerate

backend/python/diffusers/requirements-cublas12.txt

@@ -1,5 +1,5 @@
-torch==2.4.1
-diffusers
+torch==2.7.1
+git+https://github.com/huggingface/diffusers
 opencv-python
 transformers
 accelerate

backend/python/diffusers/requirements-hipblas.txt

@@ -1,7 +1,7 @@
---extra-index-url https://download.pytorch.org/whl/rocm6.0
-torch==2.3.1+rocm6.0
-torchvision==0.18.1+rocm6.0
-diffusers
+--extra-index-url https://download.pytorch.org/whl/rocm6.3
+torch==2.7.1+rocm6.3
+torchvision==0.22.1+rocm6.3
+git+https://github.com/huggingface/diffusers
 opencv-python
 transformers
 accelerate

backend/python/diffusers/requirements-intel.txt

@@ -1,11 +1,11 @@
 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 intel-extension-for-pytorch==2.3.110+xpu
-torch==2.3.1+cxx11.abi
-torchvision==0.18.1+cxx11.abi
-oneccl_bind_pt==2.3.100+xpu
+torch==2.5.1+cxx11.abi
+torchvision==0.20.1+cxx11.abi
+oneccl_bind_pt==2.8.0+xpu
 optimum[openvino]
 setuptools
-diffusers
+git+https://github.com/huggingface/diffusers
 opencv-python
 transformers
 accelerate

backend/python/diffusers/requirements-l4t.txt

@@ -0,0 +1,9 @@
+--extra-index-url https://pypi.jetson-ai-lab.io/jp6/cu126/
+torch
+diffusers
+transformers
+accelerate
+compel
+peft
+optimum-quanto
+numpy<2

backend/python/kitten-tts/Makefile

@@ -0,0 +1,29 @@
+.PHONY: kitten-tts
+kitten-tts: protogen
+	bash install.sh
+
+.PHONY: run
+run: protogen
+	@echo "Running kitten-tts..."
+	bash run.sh
+	@echo "kitten-tts run."
+
+.PHONY: test
+test: protogen
+	@echo "Testing kitten-tts..."
+	bash test.sh
+	@echo "kitten-tts tested."
+
+.PHONY: protogen
+protogen: backend_pb2_grpc.py backend_pb2.py
+
+.PHONY: protogen-clean
+protogen-clean:
+	$(RM) backend_pb2_grpc.py backend_pb2.py
+
+backend_pb2_grpc.py backend_pb2.py:
+	python3 -m grpc_tools.protoc -I../.. -I./ --python_out=. --grpc_python_out=. backend.proto
+
+.PHONY: clean
+clean: protogen-clean
+	rm -rf venv __pycache__

backend/python/kitten-tts/backend.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""
+This is an extra gRPC server of LocalAI for Kitten TTS
+"""
+from concurrent import futures
+import time
+import argparse
+import signal
+import sys
+import os
+import backend_pb2
+import backend_pb2_grpc
+
+import torch
+from kittentts import KittenTTS
+import soundfile as sf
+
+import grpc
+
+
+_ONE_DAY_IN_SECONDS = 60 * 60 * 24
+
+# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
+MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+KITTEN_LANGUAGE = os.environ.get('KITTEN_LANGUAGE', None)
+
+# Implement the BackendServicer class with the service methods
+class BackendServicer(backend_pb2_grpc.BackendServicer):
+    """
+    BackendServicer is the class that implements the gRPC service
+    """
+    def Health(self, request, context):
+        return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
+    def LoadModel(self, request, context):
+
+        # Get device
+        # device = "cuda" if request.CUDA else "cpu"
+        if torch.cuda.is_available():
+            print("CUDA is available", file=sys.stderr)
+            device = "cuda"
+        else:
+            print("CUDA is not available", file=sys.stderr)
+            device = "cpu"
+
+        if not torch.cuda.is_available() and request.CUDA:
+            return backend_pb2.Result(success=False, message="CUDA is not available")
+
+        self.AudioPath = None
+        # List available KittenTTS models
+        print("Available KittenTTS voices: expr-voice-2-m, expr-voice-2-f, expr-voice-3-m, expr-voice-3-f, expr-voice-4-m, expr-voice-4-f, expr-voice-5-m, expr-voice-5-f")
+        if os.path.isabs(request.AudioPath):
+            self.AudioPath = request.AudioPath
+        elif request.AudioPath and request.ModelFile != "" and not os.path.isabs(request.AudioPath):
+            # get base path of modelFile
+            modelFileBase = os.path.dirname(request.ModelFile)
+            # modify LoraAdapter to be relative to modelFileBase
+            self.AudioPath = os.path.join(modelFileBase, request.AudioPath)
+
+        try:
+            print("Preparing KittenTTS model, please wait", file=sys.stderr)
+            # Use the model name from request.Model, defaulting to "KittenML/kitten-tts-nano-0.1" if not specified
+            model_name = request.Model if request.Model else "KittenML/kitten-tts-nano-0.1"
+            self.tts = KittenTTS(model_name)
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        # Implement your logic here for the LoadModel service
+        # Replace this with your desired response
+        return backend_pb2.Result(message="Model loaded successfully", success=True)
+
+    def TTS(self, request, context):
+        try:
+            # KittenTTS doesn't use language parameter like TTS, so we ignore it
+            # For multi-speaker models, use voice parameter
+            voice = request.voice if request.voice else "expr-voice-2-f"
+            
+            # Generate audio using KittenTTS
+            audio = self.tts.generate(request.text, voice=voice)
+            
+            # Save the audio using soundfile
+            sf.write(request.dst, audio, 24000)
+            
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(success=True)
+
+def serve(address):
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS),
+        options=[
+            ('grpc.max_message_length', 50 * 1024 * 1024),  # 50MB
+            ('grpc.max_send_message_length', 50 * 1024 * 1024),  # 50MB
+            ('grpc.max_receive_message_length', 50 * 1024 * 1024),  # 50MB
+        ])
+    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
+    server.add_insecure_port(address)
+    server.start()
+    print("Server started. Listening on: " + address, file=sys.stderr)
+
+    # Define the signal handler function
+    def signal_handler(sig, frame):
+        print("Received termination signal. Shutting down...")
+        server.stop(0)
+        sys.exit(0)
+
+    # Set the signal handlers for SIGINT and SIGTERM
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+
+    try:
+        while True:
+            time.sleep(_ONE_DAY_IN_SECONDS)
+    except KeyboardInterrupt:
+        server.stop(0)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the gRPC server.")
+    parser.add_argument(
+        "--addr", default="localhost:50051", help="The address to bind the server to."
+    )
+    args = parser.parse_args()
+
+    serve(args.addr)

backend/python/kitten-tts/install.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+set -e
+
+backend_dir=$(dirname $0)
+if [ -d $backend_dir/common ]; then
+    source $backend_dir/common/libbackend.sh
+else
+    source $backend_dir/../common/libbackend.sh
+fi
+
+# This is here because the Intel pip index is broken and returns 200 status codes for every package name, it just doesn't return any package links.
+# This makes uv think that the package exists in the Intel pip index, and by default it stops looking at other pip indexes once it finds a match.
+# We need uv to continue falling through to the pypi default index to find optimum[openvino] in the pypi index
+# the --upgrade actually allows us to *downgrade* torch to the version provided in the Intel pip index
+if [ "x${BUILD_PROFILE}" == "xintel" ]; then
+    EXTRA_PIP_INSTALL_FLAGS+=" --upgrade --index-strategy=unsafe-first-match"
+fi
+
+installRequirements

backend/python/kitten-tts/requirements.txt

@@ -0,0 +1,5 @@
+grpcio==1.71.0
+protobuf
+certifi
+packaging==24.1
+https://github.com/KittenML/KittenTTS/releases/download/0.1/kittentts-0.1.0-py3-none-any.whl

backend/python/kitten-tts/run.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+backend_dir=$(dirname $0)
+if [ -d $backend_dir/common ]; then
+    source $backend_dir/common/libbackend.sh
+else
+    source $backend_dir/../common/libbackend.sh
+fi
+
+startBackend $@

backend/python/kitten-tts/test.py

@@ -0,0 +1,82 @@
+"""
+A test script to test the gRPC service
+"""
+import unittest
+import subprocess
+import time
+import backend_pb2
+import backend_pb2_grpc
+
+import grpc
+
+
+class TestBackendServicer(unittest.TestCase):
+    """
+    TestBackendServicer is the class that tests the gRPC service
+    """
+    def setUp(self):
+        """
+        This method sets up the gRPC service by starting the server
+        """
+        self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
+        time.sleep(30)
+
+    def tearDown(self) -> None:
+        """
+        This method tears down the gRPC service by terminating the server
+        """
+        self.service.terminate()
+        self.service.wait()
+
+    def test_server_startup(self):
+        """
+        This method tests if the server starts up successfully
+        """
+        try:
+            self.setUp()
+            with grpc.insecure_channel("localhost:50051") as channel:
+                stub = backend_pb2_grpc.BackendStub(channel)
+                response = stub.Health(backend_pb2.HealthMessage())
+                self.assertEqual(response.message, b'OK')
+        except Exception as err:
+            print(err)
+            self.fail("Server failed to start")
+        finally:
+            self.tearDown()
+
+    def test_load_model(self):
+        """
+        This method tests if the model is loaded successfully
+        """
+        try:
+            self.setUp()
+            with grpc.insecure_channel("localhost:50051") as channel:
+                stub = backend_pb2_grpc.BackendStub(channel)
+                response = stub.LoadModel(backend_pb2.ModelOptions(Model="tts_models/en/vctk/vits"))
+                print(response)
+                self.assertTrue(response.success)
+                self.assertEqual(response.message, "Model loaded successfully")
+        except Exception as err:
+            print(err)
+            self.fail("LoadModel service failed")
+        finally:
+            self.tearDown()
+
+    def test_tts(self):
+        """
+        This method tests if the embeddings are generated successfully
+        """
+        try:
+            self.setUp()
+            with grpc.insecure_channel("localhost:50051") as channel:
+                stub = backend_pb2_grpc.BackendStub(channel)
+                response = stub.LoadModel(backend_pb2.ModelOptions(Model="tts_models/en/vctk/vits"))
+                self.assertTrue(response.success)
+                tts_request = backend_pb2.TTSRequest(text="80s TV news production music hit for tonight's biggest story")
+                tts_response = stub.TTS(tts_request)
+                self.assertIsNotNone(tts_response)
+        except Exception as err:
+            print(err)
+            self.fail("TTS service failed")
+        finally:
+            self.tearDown()

backend/python/kitten-tts/test.sh

@@ -8,4 +8,4 @@ else
     source $backend_dir/../common/libbackend.sh
 fi
 
-python3 -m grpc_tools.protoc -I../.. -I./ --python_out=. --grpc_python_out=. backend.proto
+runUnittests

backend/python/kokoro/Makefile

@@ -1,9 +1,18 @@
-.DEFAULT_GOAL := install
-
-.PHONY: install
-install:
+.PHONY: kokoro
+kokoro: protogen
 	bash install.sh
-	$(MAKE) protogen
+
+.PHONY: run
+run: protogen
+	@echo "Running kokoro..."
+	bash run.sh
+	@echo "kokoro run."
+
+.PHONY: test
+test: protogen
+	@echo "Testing kokoro..."
+	bash test.sh
+	@echo "kokoro tested."
 
 .PHONY: protogen
 protogen: backend_pb2_grpc.py backend_pb2.py
@@ -13,7 +22,7 @@ protogen-clean:
 	$(RM) backend_pb2_grpc.py backend_pb2.py
 
 backend_pb2_grpc.py backend_pb2.py:
-	bash protogen.sh
+	python3 -m grpc_tools.protoc -I../.. -I./ --python_out=. --grpc_python_out=. backend.proto
 
 .PHONY: clean
 clean: protogen-clean

backend/python/kokoro/README.md

@@ -0,0 +1,23 @@
+# Kokoro TTS Backend for LocalAI
+
+This is a gRPC server backend for LocalAI that uses the Kokoro TTS pipeline.
+
+## Creating a separate environment for kokoro project
+
+```bash
+make kokoro
+```
+
+## Testing the gRPC server
+
+```bash
+make test
+```
+
+## Features
+
+- Lightweight TTS model with 82 million parameters
+- Apache-licensed weights
+- Fast and cost-efficient
+- Multi-language support
+- Multiple voice options

backend/python/kokoro/backend.py

@@ -1,101 +1,92 @@
 #!/usr/bin/env python3
 """
-Extra gRPC server for Kokoro models.
+This is an extra gRPC server of LocalAI for Kokoro TTS
 """
 from concurrent import futures
-
+import time
 import argparse
 import signal
 import sys
 import os
-import time
 import backend_pb2
 import backend_pb2_grpc
+
+import torch
+from kokoro import KPipeline
 import soundfile as sf
+
 import grpc
 
-from models import build_model
-from kokoro import generate
-import torch
 
-SAMPLE_RATE = 22050
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
 
 # If MAX_WORKERS are specified in the environment use it, otherwise default to 1
 MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+KOKORO_LANG_CODE = os.environ.get('KOKORO_LANG_CODE', 'a')
 
 # Implement the BackendServicer class with the service methods
 class BackendServicer(backend_pb2_grpc.BackendServicer):
     """
-    A gRPC servicer for the backend service.
-
-    This class implements the gRPC methods for the backend service, including Health, LoadModel, and Embedding.
+    BackendServicer is the class that implements the gRPC service
     """
     def Health(self, request, context):
-        """
-        A gRPC method that returns the health status of the backend service.
-
-        Args:
-            request: A HealthRequest object that contains the request parameters.
-            context: A grpc.ServicerContext object that provides information about the RPC.
-
-        Returns:
-            A Reply object that contains the health status of the backend service.
-        """
         return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
-
+    
     def LoadModel(self, request, context):
-        """
-        A gRPC method that loads a model into memory.
+        # Get device
+        if torch.cuda.is_available():
+            print("CUDA is available", file=sys.stderr)
+            device = "cuda"
+        else:
+            print("CUDA is not available", file=sys.stderr)
+            device = "cpu"
 
-        Args:
-            request: A LoadModelRequest object that contains the request parameters.
-            context: A grpc.ServicerContext object that provides information about the RPC.
+        if not torch.cuda.is_available() and request.CUDA:
+            return backend_pb2.Result(success=False, message="CUDA is not available")
 
-        Returns:
-            A Result object that contains the result of the LoadModel operation.
-        """
-        model_name = request.Model
         try:
-            device = "cuda:0" if torch.cuda.is_available() else "cpu"
-            self.MODEL = build_model(request.ModelFile, device)
+            print("Preparing Kokoro TTS pipeline, please wait", file=sys.stderr)
+            # empty dict
+            self.options = {}
             options = request.Options
-            # Find the voice from the options, options are a list of strings in this form optname:optvalue:
-            VOICE_NAME = None
+            # The options are a list of strings in this form optname:optvalue
+            # We are storing all the options in a dict so we can use it later when
+            # generating the images
             for opt in options:
-                if opt.startswith("voice:"):
-                    VOICE_NAME = opt.split(":")[1]
-                    break
-            if VOICE_NAME is None:
-                return backend_pb2.Result(success=False, message=f"No voice specified in options")
-            MODELPATH = request.ModelPath
-            # If voice name contains a plus, split it and load the two models and combine them
-            if "+" in VOICE_NAME:
-                voice1, voice2 = VOICE_NAME.split("+")
-                voice1 = torch.load(f'{MODELPATH}/{voice1}.pt', weights_only=True).to(device)
-                voice2 = torch.load(f'{MODELPATH}/{voice2}.pt', weights_only=True).to(device)
-                self.VOICEPACK = torch.mean(torch.stack([voice1, voice2]), dim=0)
-            else:
-                self.VOICEPACK = torch.load(f'{MODELPATH}/{VOICE_NAME}.pt', weights_only=True).to(device)
+                if ":" not in opt:
+                    continue
+                key, value = opt.split(":")
+                self.options[key] = value
 
-            self.VOICE_NAME = VOICE_NAME
-
-            print(f'Loaded voice: {VOICE_NAME}')
+            # Initialize Kokoro pipeline with language code
+            lang_code = self.options.get("lang_code", KOKORO_LANG_CODE)
+            self.pipeline = KPipeline(lang_code=lang_code)
+            print(f"Kokoro TTS pipeline loaded with language code: {lang_code}", file=sys.stderr)
         except Exception as err:
             return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
-
-        return backend_pb2.Result(message="Model loaded successfully", success=True)
+        
+        return backend_pb2.Result(message="Kokoro TTS pipeline loaded successfully", success=True)
 
     def TTS(self, request, context):
-        model_name = request.model
-        if model_name == "":
-            return backend_pb2.Result(success=False, message="request.model is required")
         try:
-            audio, out_ps = generate(self.MODEL, request.text, self.VOICEPACK, lang=self.VOICE_NAME)
-            print(out_ps)
-            sf.write(request.dst, audio, SAMPLE_RATE)
+            # Get voice from request, default to 'af_heart' if not specified
+            voice = request.voice if request.voice else 'af_heart'
+            
+            # Generate audio using Kokoro pipeline
+            generator = self.pipeline(request.text, voice=voice)
+            
+            # Get the first (and typically only) audio segment
+            for i, (gs, ps, audio) in enumerate(generator):
+                # Save audio to the destination file
+                sf.write(request.dst, audio, 24000)
+                print(f"Generated audio segment {i}: gs={gs}, ps={ps}", file=sys.stderr)
+                # For now, we only process the first segment
+                # If you need to handle multiple segments, you might want to modify this
+                break
+                
         except Exception as err:
             return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        
         return backend_pb2.Result(success=True)
 
 def serve(address):
@@ -108,11 +99,11 @@ def serve(address):
     backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
     server.add_insecure_port(address)
     server.start()
-    print("[Kokoro] Server started. Listening on: " + address, file=sys.stderr)
+    print("Server started. Listening on: " + address, file=sys.stderr)
 
     # Define the signal handler function
     def signal_handler(sig, frame):
-        print("[Kokoro] Received termination signal. Shutting down...")
+        print("Received termination signal. Shutting down...")
         server.stop(0)
         sys.exit(0)
 
@@ -132,5 +123,5 @@ if __name__ == "__main__":
         "--addr", default="localhost:50051", help="The address to bind the server to."
     )
     args = parser.parse_args()
-    print(f"[Kokoro] startup: {args}", file=sys.stderr)
+
     serve(args.addr)

backend/python/kokoro/istftnet.py

@@ -1,524 +0,0 @@
-# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/istftnet.py
-# https://github.com/yl4579/StyleTTS2/blob/main/Modules/istftnet.py
-from scipy.signal import get_window
-from torch.nn import Conv1d, ConvTranspose1d
-from torch.nn.utils import weight_norm, remove_weight_norm
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-# https://github.com/yl4579/StyleTTS2/blob/main/Modules/utils.py
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-LRELU_SLOPE = 0.1
-
-class AdaIN1d(nn.Module):
-    def __init__(self, style_dim, num_features):
-        super().__init__()
-        self.norm = nn.InstanceNorm1d(num_features, affine=False)
-        self.fc = nn.Linear(style_dim, num_features*2)
-
-    def forward(self, x, s):
-        h = self.fc(s)
-        h = h.view(h.size(0), h.size(1), 1)
-        gamma, beta = torch.chunk(h, chunks=2, dim=1)
-        return (1 + gamma) * self.norm(x) + beta
-
-class AdaINResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), style_dim=64):
-        super(AdaINResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-        
-        self.adain1 = nn.ModuleList([
-            AdaIN1d(style_dim, channels),
-            AdaIN1d(style_dim, channels),
-            AdaIN1d(style_dim, channels),
-        ])
-        
-        self.adain2 = nn.ModuleList([
-            AdaIN1d(style_dim, channels),
-            AdaIN1d(style_dim, channels),
-            AdaIN1d(style_dim, channels),
-        ])
-        
-        self.alpha1 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs1))])
-        self.alpha2 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs2))])
-
-
-    def forward(self, x, s):
-        for c1, c2, n1, n2, a1, a2 in zip(self.convs1, self.convs2, self.adain1, self.adain2, self.alpha1, self.alpha2):
-            xt = n1(x, s)
-            xt = xt + (1 / a1) * (torch.sin(a1 * xt) ** 2)  # Snake1D
-            xt = c1(xt)
-            xt = n2(xt, s)
-            xt = xt + (1 / a2) * (torch.sin(a2 * xt) ** 2)  # Snake1D
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-            
-class TorchSTFT(torch.nn.Module):
-    def __init__(self, filter_length=800, hop_length=200, win_length=800, window='hann'):
-        super().__init__()
-        self.filter_length = filter_length
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.window = torch.from_numpy(get_window(window, win_length, fftbins=True).astype(np.float32))
-
-    def transform(self, input_data):
-        forward_transform = torch.stft(
-            input_data,
-            self.filter_length, self.hop_length, self.win_length, window=self.window.to(input_data.device),
-            return_complex=True)
-
-        return torch.abs(forward_transform), torch.angle(forward_transform)
-
-    def inverse(self, magnitude, phase):
-        inverse_transform = torch.istft(
-            magnitude * torch.exp(phase * 1j),
-            self.filter_length, self.hop_length, self.win_length, window=self.window.to(magnitude.device))
-
-        return inverse_transform.unsqueeze(-2)  # unsqueeze to stay consistent with conv_transpose1d implementation
-
-    def forward(self, input_data):
-        self.magnitude, self.phase = self.transform(input_data)
-        reconstruction = self.inverse(self.magnitude, self.phase)
-        return reconstruction
-    
-class SineGen(torch.nn.Module):
-    """ Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(self, samp_rate, upsample_scale, harmonic_num=0,
-                 sine_amp=0.1, noise_std=0.003,
-                 voiced_threshold=0,
-                 flag_for_pulse=False):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-        self.flag_for_pulse = flag_for_pulse
-        self.upsample_scale = upsample_scale
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = (f0 > self.voiced_threshold).type(torch.float32)
-        return uv
-
-    def _f02sine(self, f0_values):
-        """ f0_values: (batchsize, length, dim)
-            where dim indicates fundamental tone and overtones
-        """
-        # convert to F0 in rad. The integer part n can be ignored
-        # because 2 * np.pi * n doesn't affect phase
-        rad_values = (f0_values / self.sampling_rate) % 1
-
-        # initial phase noise (no noise for fundamental component)
-        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
-                              device=f0_values.device)
-        rand_ini[:, 0] = 0
-        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-
-        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
-        if not self.flag_for_pulse:
-#             # for normal case
-
-#             # To prevent torch.cumsum numerical overflow,
-#             # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
-#             # Buffer tmp_over_one_idx indicates the time step to add -1.
-#             # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
-#             tmp_over_one = torch.cumsum(rad_values, 1) % 1
-#             tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
-#             cumsum_shift = torch.zeros_like(rad_values)
-#             cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-
-#             phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
-            rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2), 
-                                                         scale_factor=1/self.upsample_scale, 
-                                                         mode="linear").transpose(1, 2)
-    
-#             tmp_over_one = torch.cumsum(rad_values, 1) % 1
-#             tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
-#             cumsum_shift = torch.zeros_like(rad_values)
-#             cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-    
-            phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
-            phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale, 
-                                                    scale_factor=self.upsample_scale, mode="linear").transpose(1, 2)
-            sines = torch.sin(phase)
-            
-        else:
-            # If necessary, make sure that the first time step of every
-            # voiced segments is sin(pi) or cos(0)
-            # This is used for pulse-train generation
-
-            # identify the last time step in unvoiced segments
-            uv = self._f02uv(f0_values)
-            uv_1 = torch.roll(uv, shifts=-1, dims=1)
-            uv_1[:, -1, :] = 1
-            u_loc = (uv < 1) * (uv_1 > 0)
-
-            # get the instantanouse phase
-            tmp_cumsum = torch.cumsum(rad_values, dim=1)
-            # different batch needs to be processed differently
-            for idx in range(f0_values.shape[0]):
-                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
-                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
-                # stores the accumulation of i.phase within
-                # each voiced segments
-                tmp_cumsum[idx, :, :] = 0
-                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
-
-            # rad_values - tmp_cumsum: remove the accumulation of i.phase
-            # within the previous voiced segment.
-            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
-
-            # get the sines
-            sines = torch.cos(i_phase * 2 * np.pi)
-        return sines
-
-    def forward(self, f0):
-        """ sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
-                             device=f0.device)
-        # fundamental component
-        fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
-
-        # generate sine waveforms
-        sine_waves = self._f02sine(fn) * self.sine_amp
-
-        # generate uv signal
-        # uv = torch.ones(f0.shape)
-        # uv = uv * (f0 > self.voiced_threshold)
-        uv = self._f02uv(f0)
-
-        # noise: for unvoiced should be similar to sine_amp
-        #        std = self.sine_amp/3 -> max value ~ self.sine_amp
-        # .       for voiced regions is self.noise_std
-        noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-        noise = noise_amp * torch.randn_like(sine_waves)
-
-        # first: set the unvoiced part to 0 by uv
-        # then: additive noise
-        sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """ SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(sampling_rate, upsample_scale, harmonic_num,
-                                 sine_amp, add_noise_std, voiced_threshod)
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x):
-        """
-        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-        F0_sampled (batchsize, length, 1)
-        Sine_source (batchsize, length, 1)
-        noise_source (batchsize, length 1)
-        """
-        # source for harmonic branch
-        with torch.no_grad():
-            sine_wavs, uv, _ = self.l_sin_gen(x)
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-
-        # source for noise branch, in the same shape as uv
-        noise = torch.randn_like(uv) * self.sine_amp / 3
-        return sine_merge, noise, uv
-def padDiff(x):
-    return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
-
-    
-class Generator(torch.nn.Module):
-    def __init__(self, style_dim, resblock_kernel_sizes, upsample_rates, upsample_initial_channel, resblock_dilation_sizes, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size):
-        super(Generator, self).__init__()
-
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        resblock = AdaINResBlock1
-
-        self.m_source = SourceModuleHnNSF(
-                    sampling_rate=24000,
-                    upsample_scale=np.prod(upsample_rates) * gen_istft_hop_size,
-                    harmonic_num=8, voiced_threshod=10)
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * gen_istft_hop_size)
-        self.noise_convs = nn.ModuleList()
-        self.noise_res = nn.ModuleList()
-        
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes,resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d, style_dim))
-                
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            
-            if i + 1 < len(upsample_rates):  #
-                stride_f0 = np.prod(upsample_rates[i + 1:])
-                self.noise_convs.append(Conv1d(
-                    gen_istft_n_fft + 2, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2))
-                self.noise_res.append(resblock(c_cur, 7, [1,3,5], style_dim))
-            else:
-                self.noise_convs.append(Conv1d(gen_istft_n_fft + 2, c_cur, kernel_size=1))
-                self.noise_res.append(resblock(c_cur, 11, [1,3,5], style_dim))
-                
-                
-        self.post_n_fft = gen_istft_n_fft
-        self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-        self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
-        self.stft = TorchSTFT(filter_length=gen_istft_n_fft, hop_length=gen_istft_hop_size, win_length=gen_istft_n_fft)
-        
-        
-    def forward(self, x, s, f0):
-        with torch.no_grad():
-            f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
-
-            har_source, noi_source, uv = self.m_source(f0)
-            har_source = har_source.transpose(1, 2).squeeze(1)
-            har_spec, har_phase = self.stft.transform(har_source)
-            har = torch.cat([har_spec, har_phase], dim=1)
-        
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            x_source = self.noise_convs[i](har)
-            x_source = self.noise_res[i](x_source, s)
-
-            x = self.ups[i](x)
-            if i == self.num_upsamples - 1:
-                x = self.reflection_pad(x)
-
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x, s)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x, s)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :])
-        phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :])
-        return self.stft.inverse(spec, phase)
-    
-    def fw_phase(self, x, s):
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x, s)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x, s)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.reflection_pad(x)
-        x = self.conv_post(x)
-        spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :])
-        phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :])
-        return spec, phase
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-        
-class AdainResBlk1d(nn.Module):
-    def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2),
-                 upsample='none', dropout_p=0.0):
-        super().__init__()
-        self.actv = actv
-        self.upsample_type = upsample
-        self.upsample = UpSample1d(upsample)
-        self.learned_sc = dim_in != dim_out
-        self._build_weights(dim_in, dim_out, style_dim)
-        self.dropout = nn.Dropout(dropout_p)
-        
-        if upsample == 'none':
-            self.pool = nn.Identity()
-        else:
-            self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1))
-        
-        
-    def _build_weights(self, dim_in, dim_out, style_dim):
-        self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
-        self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
-        self.norm1 = AdaIN1d(style_dim, dim_in)
-        self.norm2 = AdaIN1d(style_dim, dim_out)
-        if self.learned_sc:
-            self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
-
-    def _shortcut(self, x):
-        x = self.upsample(x)
-        if self.learned_sc:
-            x = self.conv1x1(x)
-        return x
-
-    def _residual(self, x, s):
-        x = self.norm1(x, s)
-        x = self.actv(x)
-        x = self.pool(x)
-        x = self.conv1(self.dropout(x))
-        x = self.norm2(x, s)
-        x = self.actv(x)
-        x = self.conv2(self.dropout(x))
-        return x
-
-    def forward(self, x, s):
-        out = self._residual(x, s)
-        out = (out + self._shortcut(x)) / np.sqrt(2)
-        return out
-    
-class UpSample1d(nn.Module):
-    def __init__(self, layer_type):
-        super().__init__()
-        self.layer_type = layer_type
-
-    def forward(self, x):
-        if self.layer_type == 'none':
-            return x
-        else:
-            return F.interpolate(x, scale_factor=2, mode='nearest')
-
-class Decoder(nn.Module):
-    def __init__(self, dim_in=512, F0_channel=512, style_dim=64, dim_out=80, 
-                resblock_kernel_sizes = [3,7,11],
-                upsample_rates = [10, 6],
-                upsample_initial_channel=512,
-                resblock_dilation_sizes=[[1,3,5], [1,3,5], [1,3,5]],
-                upsample_kernel_sizes=[20, 12], 
-                gen_istft_n_fft=20, gen_istft_hop_size=5):
-        super().__init__()
-        
-        self.decode = nn.ModuleList()
-        
-        self.encode = AdainResBlk1d(dim_in + 2, 1024, style_dim)
-        
-        self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
-        self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
-        self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
-        self.decode.append(AdainResBlk1d(1024 + 2 + 64, 512, style_dim, upsample=True))
-
-        self.F0_conv = weight_norm(nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1))
-        
-        self.N_conv = weight_norm(nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1))
-        
-        self.asr_res = nn.Sequential(
-            weight_norm(nn.Conv1d(512, 64, kernel_size=1)),
-        )
-        
-        
-        self.generator = Generator(style_dim, resblock_kernel_sizes, upsample_rates, 
-                                   upsample_initial_channel, resblock_dilation_sizes, 
-                                   upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size)
-        
-    def forward(self, asr, F0_curve, N, s):
-        F0 = self.F0_conv(F0_curve.unsqueeze(1))
-        N = self.N_conv(N.unsqueeze(1))
-        
-        x = torch.cat([asr, F0, N], axis=1)
-        x = self.encode(x, s)
-        
-        asr_res = self.asr_res(asr)
-        
-        res = True
-        for block in self.decode:
-            if res:
-                x = torch.cat([x, asr_res, F0, N], axis=1)
-            x = block(x, s)
-            if block.upsample_type != "none":
-                res = False
-                
-        x = self.generator(x, s, F0_curve)
-        return x

backend/python/kokoro/kokoro.py

@@ -1,166 +0,0 @@
-# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/kokoro.py
-import phonemizer
-import re
-import torch
-import numpy as np
-
-def split_num(num):
-    num = num.group()
-    if '.' in num:
-        return num
-    elif ':' in num:
-        h, m = [int(n) for n in num.split(':')]
-        if m == 0:
-            return f"{h} o'clock"
-        elif m < 10:
-            return f'{h} oh {m}'
-        return f'{h} {m}'
-    year = int(num[:4])
-    if year < 1100 or year % 1000 < 10:
-        return num
-    left, right = num[:2], int(num[2:4])
-    s = 's' if num.endswith('s') else ''
-    if 100 <= year % 1000 <= 999:
-        if right == 0:
-            return f'{left} hundred{s}'
-        elif right < 10:
-            return f'{left} oh {right}{s}'
-    return f'{left} {right}{s}'
-
-def flip_money(m):
-    m = m.group()
-    bill = 'dollar' if m[0] == '$' else 'pound'
-    if m[-1].isalpha():
-        return f'{m[1:]} {bill}s'
-    elif '.' not in m:
-        s = '' if m[1:] == '1' else 's'
-        return f'{m[1:]} {bill}{s}'
-    b, c = m[1:].split('.')
-    s = '' if b == '1' else 's'
-    c = int(c.ljust(2, '0'))
-    coins = f"cent{'' if c == 1 else 's'}" if m[0] == '$' else ('penny' if c == 1 else 'pence')
-    return f'{b} {bill}{s} and {c} {coins}'
-
-def point_num(num):
-    a, b = num.group().split('.')
-    return ' point '.join([a, ' '.join(b)])
-
-def normalize_text(text):
-    text = text.replace(chr(8216), "'").replace(chr(8217), "'")
-    text = text.replace('«', chr(8220)).replace('»', chr(8221))
-    text = text.replace(chr(8220), '"').replace(chr(8221), '"')
-    text = text.replace('(', '«').replace(')', '»')
-    for a, b in zip('、。！，：；？', ',.!,:;?'):
-        text = text.replace(a, b+' ')
-    text = re.sub(r'[^\S \n]', ' ', text)
-    text = re.sub(r'  +', ' ', text)
-    text = re.sub(r'(?<=\n) +(?=\n)', '', text)
-    text = re.sub(r'\bD[Rr]\.(?= [A-Z])', 'Doctor', text)
-    text = re.sub(r'\b(?:Mr\.|MR\.(?= [A-Z]))', 'Mister', text)
-    text = re.sub(r'\b(?:Ms\.|MS\.(?= [A-Z]))', 'Miss', text)
-    text = re.sub(r'\b(?:Mrs\.|MRS\.(?= [A-Z]))', 'Mrs', text)
-    text = re.sub(r'\betc\.(?! [A-Z])', 'etc', text)
-    text = re.sub(r'(?i)\b(y)eah?\b', r"\1e'a", text)
-    text = re.sub(r'\d*\.\d+|\b\d{4}s?\b|(?<!:)\b(?:[1-9]|1[0-2]):[0-5]\d\b(?!:)', split_num, text)
-    text = re.sub(r'(?<=\d),(?=\d)', '', text)
-    text = re.sub(r'(?i)[$£]\d+(?:\.\d+)?(?: hundred| thousand| (?:[bm]|tr)illion)*\b|[$£]\d+\.\d\d?\b', flip_money, text)
-    text = re.sub(r'\d*\.\d+', point_num, text)
-    text = re.sub(r'(?<=\d)-(?=\d)', ' to ', text)
-    text = re.sub(r'(?<=\d)S', ' S', text)
-    text = re.sub(r"(?<=[BCDFGHJ-NP-TV-Z])'?s\b", "'S", text)
-    text = re.sub(r"(?<=X')S\b", 's', text)
-    text = re.sub(r'(?:[A-Za-z]\.){2,} [a-z]', lambda m: m.group().replace('.', '-'), text)
-    text = re.sub(r'(?i)(?<=[A-Z])\.(?=[A-Z])', '-', text)
-    return text.strip()
-
-def get_vocab():
-    _pad = "$"
-    _punctuation = ';:,.!?¡¿—…"«»“” '
-    _letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
-    _letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
-    symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
-    dicts = {}
-    for i in range(len((symbols))):
-        dicts[symbols[i]] = i
-    return dicts
-
-VOCAB = get_vocab()
-def tokenize(ps):
-    return [i for i in map(VOCAB.get, ps) if i is not None]
-
-phonemizers = dict(
-    a=phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True),
-    b=phonemizer.backend.EspeakBackend(language='en-gb', preserve_punctuation=True, with_stress=True),
-)
-def phonemize(text, lang, norm=True):
-    if norm:
-        text = normalize_text(text)
-    ps = phonemizers[lang].phonemize([text])
-    ps = ps[0] if ps else ''
-    # https://en.wiktionary.org/wiki/kokoro#English
-    ps = ps.replace('kəkˈoːɹoʊ', 'kˈoʊkəɹoʊ').replace('kəkˈɔːɹəʊ', 'kˈəʊkəɹəʊ')
-    ps = ps.replace('ʲ', 'j').replace('r', 'ɹ').replace('x', 'k').replace('ɬ', 'l')
-    ps = re.sub(r'(?<=[a-zɹː])(?=hˈʌndɹɪd)', ' ', ps)
-    ps = re.sub(r' z(?=[;:,.!?¡¿—…"«»“” ]|$)', 'z', ps)
-    if lang == 'a':
-        ps = re.sub(r'(?<=nˈaɪn)ti(?!ː)', 'di', ps)
-    ps = ''.join(filter(lambda p: p in VOCAB, ps))
-    return ps.strip()
-
-def length_to_mask(lengths):
-    mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
-    mask = torch.gt(mask+1, lengths.unsqueeze(1))
-    return mask
-
-@torch.no_grad()
-def forward(model, tokens, ref_s, speed):
-    device = ref_s.device
-    tokens = torch.LongTensor([[0, *tokens, 0]]).to(device)
-    input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
-    text_mask = length_to_mask(input_lengths).to(device)
-    bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
-    d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
-    s = ref_s[:, 128:]
-    d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
-    x, _ = model.predictor.lstm(d)
-    duration = model.predictor.duration_proj(x)
-    duration = torch.sigmoid(duration).sum(axis=-1) / speed
-    pred_dur = torch.round(duration).clamp(min=1).long()
-    pred_aln_trg = torch.zeros(input_lengths, pred_dur.sum().item())
-    c_frame = 0
-    for i in range(pred_aln_trg.size(0)):
-        pred_aln_trg[i, c_frame:c_frame + pred_dur[0,i].item()] = 1
-        c_frame += pred_dur[0,i].item()
-    en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device)
-    F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
-    t_en = model.text_encoder(tokens, input_lengths, text_mask)
-    asr = t_en @ pred_aln_trg.unsqueeze(0).to(device)
-    return model.decoder(asr, F0_pred, N_pred, ref_s[:, :128]).squeeze().cpu().numpy()
-
-def generate(model, text, voicepack, lang='a', speed=1, ps=None):
-    ps = ps or phonemize(text, lang)
-    tokens = tokenize(ps)
-    if not tokens:
-        return None
-    elif len(tokens) > 510:
-        tokens = tokens[:510]
-        print('Truncated to 510 tokens')
-    ref_s = voicepack[len(tokens)]
-    out = forward(model, tokens, ref_s, speed)
-    ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens)
-    return out, ps
-
-def generate_full(model, text, voicepack, lang='a', speed=1, ps=None):
-    ps = ps or phonemize(text, lang)
-    tokens = tokenize(ps)
-    if not tokens:
-        return None
-    outs = []
-    loop_count = len(tokens)//510 + (1 if len(tokens) % 510 != 0 else 0)
-    for i in range(loop_count):
-        ref_s = voicepack[len(tokens[i*510:(i+1)*510])]
-        out = forward(model, tokens[i*510:(i+1)*510], ref_s, speed)
-        outs.append(out)
-    outs = np.concatenate(outs)
-    ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens)
-    return outs, ps

backend/python/kokoro/models.py

@@ -1,373 +0,0 @@
-# https://github.com/yl4579/StyleTTS2/blob/main/models.py
-# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/models.py
-from istftnet import AdaIN1d, Decoder
-from munch import Munch
-from pathlib import Path
-from plbert import load_plbert
-from torch.nn.utils import weight_norm, spectral_norm
-import json
-import numpy as np
-import os
-import os.path as osp
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-class LinearNorm(torch.nn.Module):
-    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
-        super(LinearNorm, self).__init__()
-        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.linear_layer.weight,
-            gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, x):
-        return self.linear_layer(x)
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-    
-class TextEncoder(nn.Module):
-    def __init__(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2)):
-        super().__init__()
-        self.embedding = nn.Embedding(n_symbols, channels)
-
-        padding = (kernel_size - 1) // 2
-        self.cnn = nn.ModuleList()
-        for _ in range(depth):
-            self.cnn.append(nn.Sequential(
-                weight_norm(nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding)),
-                LayerNorm(channels),
-                actv,
-                nn.Dropout(0.2),
-            ))
-        # self.cnn = nn.Sequential(*self.cnn)
-
-        self.lstm = nn.LSTM(channels, channels//2, 1, batch_first=True, bidirectional=True)
-
-    def forward(self, x, input_lengths, m):
-        x = self.embedding(x)  # [B, T, emb]
-        x = x.transpose(1, 2)  # [B, emb, T]
-        m = m.to(input_lengths.device).unsqueeze(1)
-        x.masked_fill_(m, 0.0)
-        
-        for c in self.cnn:
-            x = c(x)
-            x.masked_fill_(m, 0.0)
-            
-        x = x.transpose(1, 2)  # [B, T, chn]
-
-        input_lengths = input_lengths.cpu().numpy()
-        x = nn.utils.rnn.pack_padded_sequence(
-            x, input_lengths, batch_first=True, enforce_sorted=False)
-
-        self.lstm.flatten_parameters()
-        x, _ = self.lstm(x)
-        x, _ = nn.utils.rnn.pad_packed_sequence(
-            x, batch_first=True)
-                
-        x = x.transpose(-1, -2)
-        x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])
-
-        x_pad[:, :, :x.shape[-1]] = x
-        x = x_pad.to(x.device)
-        
-        x.masked_fill_(m, 0.0)
-        
-        return x
-
-    def inference(self, x):
-        x = self.embedding(x)
-        x = x.transpose(1, 2)
-        x = self.cnn(x)
-        x = x.transpose(1, 2)
-        self.lstm.flatten_parameters()
-        x, _ = self.lstm(x)
-        return x
-    
-    def length_to_mask(self, lengths):
-        mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
-        mask = torch.gt(mask+1, lengths.unsqueeze(1))
-        return mask
-
-
-class UpSample1d(nn.Module):
-    def __init__(self, layer_type):
-        super().__init__()
-        self.layer_type = layer_type
-
-    def forward(self, x):
-        if self.layer_type == 'none':
-            return x
-        else:
-            return F.interpolate(x, scale_factor=2, mode='nearest')
-
-class AdainResBlk1d(nn.Module):
-    def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2),
-                 upsample='none', dropout_p=0.0):
-        super().__init__()
-        self.actv = actv
-        self.upsample_type = upsample
-        self.upsample = UpSample1d(upsample)
-        self.learned_sc = dim_in != dim_out
-        self._build_weights(dim_in, dim_out, style_dim)
-        self.dropout = nn.Dropout(dropout_p)
-        
-        if upsample == 'none':
-            self.pool = nn.Identity()
-        else:
-            self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1))
-        
-        
-    def _build_weights(self, dim_in, dim_out, style_dim):
-        self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
-        self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
-        self.norm1 = AdaIN1d(style_dim, dim_in)
-        self.norm2 = AdaIN1d(style_dim, dim_out)
-        if self.learned_sc:
-            self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
-
-    def _shortcut(self, x):
-        x = self.upsample(x)
-        if self.learned_sc:
-            x = self.conv1x1(x)
-        return x
-
-    def _residual(self, x, s):
-        x = self.norm1(x, s)
-        x = self.actv(x)
-        x = self.pool(x)
-        x = self.conv1(self.dropout(x))
-        x = self.norm2(x, s)
-        x = self.actv(x)
-        x = self.conv2(self.dropout(x))
-        return x
-
-    def forward(self, x, s):
-        out = self._residual(x, s)
-        out = (out + self._shortcut(x)) / np.sqrt(2)
-        return out
-    
-class AdaLayerNorm(nn.Module):
-    def __init__(self, style_dim, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.fc = nn.Linear(style_dim, channels*2)
-
-    def forward(self, x, s):
-        x = x.transpose(-1, -2)
-        x = x.transpose(1, -1)
-                
-        h = self.fc(s)
-        h = h.view(h.size(0), h.size(1), 1)
-        gamma, beta = torch.chunk(h, chunks=2, dim=1)
-        gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1)
-        
-        
-        x = F.layer_norm(x, (self.channels,), eps=self.eps)
-        x = (1 + gamma) * x + beta
-        return x.transpose(1, -1).transpose(-1, -2)
-
-class ProsodyPredictor(nn.Module):
-
-    def __init__(self, style_dim, d_hid, nlayers, max_dur=50, dropout=0.1):
-        super().__init__() 
-        
-        self.text_encoder = DurationEncoder(sty_dim=style_dim, 
-                                            d_model=d_hid,
-                                            nlayers=nlayers, 
-                                            dropout=dropout)
-
-        self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
-        self.duration_proj = LinearNorm(d_hid, max_dur)
-        
-        self.shared = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
-        self.F0 = nn.ModuleList()
-        self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
-        self.F0.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
-        self.F0.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))
-
-        self.N = nn.ModuleList()
-        self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
-        self.N.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
-        self.N.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))
-        
-        self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
-        self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
-
-
-    def forward(self, texts, style, text_lengths, alignment, m):
-        d = self.text_encoder(texts, style, text_lengths, m)
-        
-        batch_size = d.shape[0]
-        text_size = d.shape[1]
-        
-        # predict duration
-        input_lengths = text_lengths.cpu().numpy()
-        x = nn.utils.rnn.pack_padded_sequence(
-            d, input_lengths, batch_first=True, enforce_sorted=False)
-        
-        m = m.to(text_lengths.device).unsqueeze(1)
-        
-        self.lstm.flatten_parameters()
-        x, _ = self.lstm(x)
-        x, _ = nn.utils.rnn.pad_packed_sequence(
-            x, batch_first=True)
-        
-        x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]])
-
-        x_pad[:, :x.shape[1], :] = x
-        x = x_pad.to(x.device)
-                
-        duration = self.duration_proj(nn.functional.dropout(x, 0.5, training=self.training))
-        
-        en = (d.transpose(-1, -2) @ alignment)
-
-        return duration.squeeze(-1), en
-    
-    def F0Ntrain(self, x, s):
-        x, _ = self.shared(x.transpose(-1, -2))
-        
-        F0 = x.transpose(-1, -2)
-        for block in self.F0:
-            F0 = block(F0, s)
-        F0 = self.F0_proj(F0)
-
-        N = x.transpose(-1, -2)
-        for block in self.N:
-            N = block(N, s)
-        N = self.N_proj(N)
-        
-        return F0.squeeze(1), N.squeeze(1)
-    
-    def length_to_mask(self, lengths):
-        mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
-        mask = torch.gt(mask+1, lengths.unsqueeze(1))
-        return mask
-
-class DurationEncoder(nn.Module):
-
-    def __init__(self, sty_dim, d_model, nlayers, dropout=0.1):
-        super().__init__()
-        self.lstms = nn.ModuleList()
-        for _ in range(nlayers):
-            self.lstms.append(nn.LSTM(d_model + sty_dim, 
-                                 d_model // 2, 
-                                 num_layers=1, 
-                                 batch_first=True, 
-                                 bidirectional=True, 
-                                 dropout=dropout))
-            self.lstms.append(AdaLayerNorm(sty_dim, d_model))
-        
-        
-        self.dropout = dropout
-        self.d_model = d_model
-        self.sty_dim = sty_dim
-
-    def forward(self, x, style, text_lengths, m):
-        masks = m.to(text_lengths.device)
-        
-        x = x.permute(2, 0, 1)
-        s = style.expand(x.shape[0], x.shape[1], -1)
-        x = torch.cat([x, s], axis=-1)
-        x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0)
-                
-        x = x.transpose(0, 1)
-        input_lengths = text_lengths.cpu().numpy()
-        x = x.transpose(-1, -2)
-        
-        for block in self.lstms:
-            if isinstance(block, AdaLayerNorm):
-                x = block(x.transpose(-1, -2), style).transpose(-1, -2)
-                x = torch.cat([x, s.permute(1, -1, 0)], axis=1)
-                x.masked_fill_(masks.unsqueeze(-1).transpose(-1, -2), 0.0)
-            else:
-                x = x.transpose(-1, -2)
-                x = nn.utils.rnn.pack_padded_sequence(
-                    x, input_lengths, batch_first=True, enforce_sorted=False)
-                block.flatten_parameters()
-                x, _ = block(x)
-                x, _ = nn.utils.rnn.pad_packed_sequence(
-                    x, batch_first=True)
-                x = F.dropout(x, p=self.dropout, training=self.training)
-                x = x.transpose(-1, -2)
-                
-                x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])
-
-                x_pad[:, :, :x.shape[-1]] = x
-                x = x_pad.to(x.device)
-        
-        return x.transpose(-1, -2)
-    
-    def inference(self, x, style):
-        x = self.embedding(x.transpose(-1, -2)) * np.sqrt(self.d_model)
-        style = style.expand(x.shape[0], x.shape[1], -1)
-        x = torch.cat([x, style], axis=-1)
-        src = self.pos_encoder(x)
-        output = self.transformer_encoder(src).transpose(0, 1)
-        return output
-    
-    def length_to_mask(self, lengths):
-        mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
-        mask = torch.gt(mask+1, lengths.unsqueeze(1))
-        return mask
-
-# https://github.com/yl4579/StyleTTS2/blob/main/utils.py
-def recursive_munch(d):
-    if isinstance(d, dict):
-        return Munch((k, recursive_munch(v)) for k, v in d.items())
-    elif isinstance(d, list):
-        return [recursive_munch(v) for v in d]
-    else:
-        return d
-
-def build_model(path, device):
-    config = Path(__file__).parent / 'config.json'
-    assert config.exists(), f'Config path incorrect: config.json not found at {config}'
-    with open(config, 'r') as r:
-        args = recursive_munch(json.load(r))
-    assert args.decoder.type == 'istftnet', f'Unknown decoder type: {args.decoder.type}'
-    decoder = Decoder(dim_in=args.hidden_dim, style_dim=args.style_dim, dim_out=args.n_mels,
-            resblock_kernel_sizes = args.decoder.resblock_kernel_sizes,
-            upsample_rates = args.decoder.upsample_rates,
-            upsample_initial_channel=args.decoder.upsample_initial_channel,
-            resblock_dilation_sizes=args.decoder.resblock_dilation_sizes,
-            upsample_kernel_sizes=args.decoder.upsample_kernel_sizes,
-            gen_istft_n_fft=args.decoder.gen_istft_n_fft, gen_istft_hop_size=args.decoder.gen_istft_hop_size)
-    text_encoder = TextEncoder(channels=args.hidden_dim, kernel_size=5, depth=args.n_layer, n_symbols=args.n_token)
-    predictor = ProsodyPredictor(style_dim=args.style_dim, d_hid=args.hidden_dim, nlayers=args.n_layer, max_dur=args.max_dur, dropout=args.dropout)
-    bert = load_plbert()
-    bert_encoder = nn.Linear(bert.config.hidden_size, args.hidden_dim)
-    for parent in [bert, bert_encoder, predictor, decoder, text_encoder]:
-        for child in parent.children():
-            if isinstance(child, nn.RNNBase):
-                child.flatten_parameters()
-    model = Munch(
-        bert=bert.to(device).eval(),
-        bert_encoder=bert_encoder.to(device).eval(),
-        predictor=predictor.to(device).eval(),
-        decoder=decoder.to(device).eval(),
-        text_encoder=text_encoder.to(device).eval(),
-    )
-    for key, state_dict in torch.load(path, map_location='cpu', weights_only=True)['net'].items():
-        assert key in model, key
-        try:
-            model[key].load_state_dict(state_dict)
-        except:
-            state_dict = {k[7:]: v for k, v in state_dict.items()}
-            model[key].load_state_dict(state_dict, strict=False)
-    return model

backend/python/kokoro/plbert.py

@@ -1,16 +0,0 @@
-# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/plbert.py
-# https://github.com/yl4579/StyleTTS2/blob/main/Utils/PLBERT/util.py
-from transformers import AlbertConfig, AlbertModel
-
-class CustomAlbert(AlbertModel):
-    def forward(self, *args, **kwargs):
-        # Call the original forward method
-        outputs = super().forward(*args, **kwargs)
-        # Only return the last_hidden_state
-        return outputs.last_hidden_state
-
-def load_plbert():
-    plbert_config = {'vocab_size': 178, 'hidden_size': 768, 'num_attention_heads': 12, 'intermediate_size': 2048, 'max_position_embeddings': 512, 'num_hidden_layers': 12, 'dropout': 0.1}
-    albert_base_configuration = AlbertConfig(**plbert_config)
-    bert = CustomAlbert(albert_base_configuration)
-    return bert

backend/python/kokoro/requirements-cpu.txt

@@ -1,2 +1,6 @@
-torch==2.4.1
-transformers
+--extra-index-url https://download.pytorch.org/whl/cpu
+transformers
+accelerate
+torch
+kokoro
+soundfile

backend/python/kokoro/requirements-cublas11.txt

@@ -1,3 +1,7 @@
 --extra-index-url https://download.pytorch.org/whl/cu118
-torch==2.4.1+cu118
-transformers
+torch==2.7.1+cu118
+torchaudio==2.7.1+cu118
+transformers
+accelerate
+kokoro
+soundfile

backend/python/kokoro/requirements-cublas12.txt

@@ -1,2 +1,6 @@
-torch==2.4.1
-transformers
+torch==2.7.1
+torchaudio==2.7.1
+transformers
+accelerate
+kokoro
+soundfile

backend/python/kokoro/requirements-hipblas.txt

@@ -1,3 +1,7 @@
---extra-index-url https://download.pytorch.org/whl/rocm6.0
-torch==2.4.1+rocm6.0
-transformers
+--extra-index-url https://download.pytorch.org/whl/rocm6.3
+torch==2.7.1+rocm6.3
+torchaudio==2.7.1+rocm6.3
+transformers
+accelerate
+kokoro
+soundfile

backend/python/kokoro/requirements-intel.txt

@@ -1,5 +1,11 @@
 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-intel-extension-for-pytorch==2.3.110+xpu
-torch==2.3.1+cxx11.abi
-oneccl_bind_pt==2.3.100+xpu
-transformers
+intel-extension-for-pytorch==2.8.10+xpu
+torch==2.5.1+cxx11.abi
+oneccl_bind_pt==2.8.0+xpu
+torchaudio==2.5.1+cxx11.abi
+optimum[openvino]
+setuptools
+transformers==4.48.3
+accelerate
+kokoro
+soundfile

backend/python/kokoro/requirements.txt

@@ -1,7 +1,6 @@
 grpcio==1.71.0
 protobuf
-phonemizer
-scipy
-munch
-setuptools
-soundfile
+certifi
+packaging==24.1
+pip
+chardet

backend/python/kokoro/test.py

@@ -0,0 +1,87 @@
+"""
+A test script to test the gRPC service
+"""
+import unittest
+import subprocess
+import time
+import backend_pb2
+import backend_pb2_grpc
+
+import grpc
+
+
+class TestBackendServicer(unittest.TestCase):
+    """
+    TestBackendServicer is the class that tests the gRPC service
+    """
+    def setUp(self):
+        """
+        This method sets up the gRPC service by starting the server
+        """
+        self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
+        time.sleep(30)
+
+    def tearDown(self) -> None:
+        """
+        This method tears down the gRPC service by terminating the server
+        """
+        self.service.terminate()
+        self.service.wait()
+
+    def test_server_startup(self):
+        """
+        This method tests if the server starts up successfully
+        """
+        try:
+            self.setUp()
+            with grpc.insecure_channel("localhost:50051") as channel:
+                stub = backend_pb2_grpc.BackendStub(channel)
+                response = stub.Health(backend_pb2.HealthMessage())
+                self.assertEqual(response.message, b'OK')
+        except Exception as err:
+            print(err)
+            self.fail("Server failed to start")
+        finally:
+            self.tearDown()
+
+    def test_load_model(self):
+        """
+        This method tests if the Kokoro pipeline is loaded successfully
+        """
+        try:
+            self.setUp()
+            with grpc.insecure_channel("localhost:50051") as channel:
+                stub = backend_pb2_grpc.BackendStub(channel)
+                response = stub.LoadModel(backend_pb2.ModelOptions(language="a"))
+                print(response)
+                self.assertTrue(response.success)
+                self.assertEqual(response.message, "Kokoro TTS pipeline loaded successfully")
+        except Exception as err:
+            print(err)
+            self.fail("LoadModel service failed")
+        finally:
+            self.tearDown()
+
+    def test_tts(self):
+        """
+        This method tests if the TTS generation works successfully
+        """
+        try:
+            self.setUp()
+            with grpc.insecure_channel("localhost:50051") as channel:
+                stub = backend_pb2_grpc.BackendStub(channel)
+                response = stub.LoadModel(backend_pb2.ModelOptions(language="a"))
+                self.assertTrue(response.success)
+                tts_request = backend_pb2.TTSRequest(
+                    text="Kokoro is an open-weight TTS model with 82 million parameters.",
+                    voice="af_heart",
+                    dst="test_output.wav"
+                )
+                tts_response = stub.TTS(tts_request)
+                self.assertIsNotNone(tts_response)
+                self.assertTrue(tts_response.success)
+        except Exception as err:
+            print(err)
+            self.fail("TTS service failed")
+        finally:
+            self.tearDown()

backend/python/transformers/backend.py

@@ -22,7 +22,7 @@ import torch.cuda
 
 XPU=os.environ.get("XPU", "0") == "1"
 from transformers import AutoTokenizer, AutoModel, set_seed, TextIteratorStreamer, StoppingCriteriaList, StopStringCriteria, MambaConfig, MambaForCausalLM
-from transformers import AutoProcessor, MusicgenForConditionalGeneration
+from transformers import AutoProcessor, MusicgenForConditionalGeneration, DiaForConditionalGeneration
 from scipy.io import wavfile
 import outetts
 from sentence_transformers import SentenceTransformer
@@ -90,6 +90,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         self.CUDA = torch.cuda.is_available()
         self.OV=False
         self.OuteTTS=False
+        self.DiaTTS=False
         self.SentenceTransformer = False
 
         device_map="cpu"
@@ -97,6 +98,30 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         quantization = None
         autoTokenizer = True
 
+        # Parse options from request.Options
+        self.options = {}
+        options = request.Options
+        
+        # The options are a list of strings in this form optname:optvalue
+        # We are storing all the options in a dict so we can use it later when generating
+        # Example options: ["max_new_tokens:3072", "guidance_scale:3.0", "temperature:1.8", "top_p:0.90", "top_k:45"]
+        for opt in options:
+            if ":" not in opt:
+                continue
+            key, value = opt.split(":", 1)
+            # if value is a number, convert it to the appropriate type
+            try:
+                if "." in value:
+                    value = float(value)
+                else:
+                    value = int(value)
+            except ValueError:
+                # Keep as string if conversion fails
+                pass
+            self.options[key] = value
+
+        print(f"Parsed options: {self.options}", file=sys.stderr)
+
         if self.CUDA:
             from transformers import BitsAndBytesConfig, AutoModelForCausalLM
             if request.MainGPU:
@@ -202,6 +227,16 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                 autoTokenizer = False
                 self.processor = AutoProcessor.from_pretrained(model_name)
                 self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
+            elif request.Type == "DiaForConditionalGeneration":
+                autoTokenizer = False
+                print("DiaForConditionalGeneration", file=sys.stderr)
+                self.processor = AutoProcessor.from_pretrained(model_name)
+                self.model = DiaForConditionalGeneration.from_pretrained(model_name)
+                if self.CUDA:
+                    self.model = self.model.to("cuda")
+                    self.processor = self.processor.to("cuda")
+                print("DiaForConditionalGeneration loaded", file=sys.stderr)
+                self.DiaTTS = True
             elif request.Type == "OuteTTS":
                 autoTokenizer = False
                 options = request.Options
@@ -262,7 +297,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             elif hasattr(self.model, 'config') and hasattr(self.model.config, 'max_position_embeddings'):
                 self.max_tokens = self.model.config.max_position_embeddings
             else:
-                self.max_tokens = 512
+                self.max_tokens = self.options.get("max_new_tokens", 512)
  
             if autoTokenizer:
                 self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
@@ -485,16 +520,15 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                     return_tensors="pt",
                 )
             
-            tokens = 256
             if request.HasField('duration'):
                 tokens = int(request.duration * 51.2) # 256 tokens = 5 seconds, therefore 51.2 tokens is one second
-            guidance = 3.0
+            guidance = self.options.get("guidance_scale", 3.0)
             if request.HasField('temperature'):
                 guidance = request.temperature
-            dosample = True
+            dosample = self.options.get("do_sample", True)
             if request.HasField('sample'):
                 dosample = request.sample
-            audio_values = self.model.generate(**inputs, do_sample=dosample, guidance_scale=guidance, max_new_tokens=tokens)
+            audio_values = self.model.generate(**inputs, do_sample=dosample, guidance_scale=guidance, max_new_tokens=self.max_tokens)
             print("[transformers-musicgen] SoundGeneration generated!", file=sys.stderr)
             sampling_rate = self.model.config.audio_encoder.sampling_rate
             wavfile.write(request.dst, rate=sampling_rate, data=audio_values[0, 0].numpy())
@@ -506,13 +540,59 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
         return backend_pb2.Result(success=True)
 
-    def OuteTTS(self, request, context):
+
+    def CallDiaTTS(self, request, context):
+        """
+        Generates dialogue audio using the Dia model.
+        
+        Args:
+            request: A TTSRequest containing text dialogue and generation parameters
+            context: The gRPC context
+            
+        Returns:
+            A Result object indicating success or failure
+        """
+        try:
+            print("[DiaTTS] generating dialogue audio", file=sys.stderr)
+            
+            # Prepare text input - expect dialogue format like [S1] ... [S2] ...
+            text = [request.text]
+            
+            # Process the input
+            inputs = self.processor(text=text, padding=True, return_tensors="pt")
+            
+            # Generate audio with parameters from options or defaults
+            generation_params = {
+                **inputs,
+                "max_new_tokens": self.max_tokens,
+                "guidance_scale": self.options.get("guidance_scale", 3.0),
+                "temperature": self.options.get("temperature", 1.8),
+                "top_p": self.options.get("top_p", 0.90),
+                "top_k": self.options.get("top_k", 45)
+            }
+            
+            outputs = self.model.generate(**generation_params)
+            
+            # Decode and save audio
+            outputs = self.processor.batch_decode(outputs)
+            self.processor.save_audio(outputs, request.dst)
+            
+            print("[DiaTTS] Generated dialogue audio", file=sys.stderr)
+            print("[DiaTTS] Audio saved to", request.dst, file=sys.stderr)
+            print("[DiaTTS] Dialogue generation done", file=sys.stderr)
+            
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(success=True)
+
+
+    def CallOuteTTS(self, request, context):
         try:
             print("[OuteTTS] generating TTS", file=sys.stderr)
             gen_cfg = outetts.GenerationConfig(
                 text="Speech synthesis is the artificial production of human speech.",
-                temperature=0.1,
-                repetition_penalty=1.1,
+                temperature=self.options.get("temperature", 0.1),
+                repetition_penalty=self.options.get("repetition_penalty", 1.1),
                 max_length=self.max_tokens,
                 speaker=self.speaker,
                 # voice_characteristics="upbeat enthusiasm, friendliness, clarity, professionalism, and trustworthiness"
@@ -528,7 +608,11 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
 # The TTS endpoint is older, and provides fewer features, but exists for compatibility reasons
     def TTS(self, request, context):
         if self.OuteTTS:
-            return self.OuteTTS(request, context)
+            return self.CallOuteTTS(request, context)
+        
+        if self.DiaTTS:
+            print("DiaTTS", file=sys.stderr)
+            return self.CallDiaTTS(request, context)
 
         model_name = request.model
         try:

backend/python/transformers/requirements-cpu.txt

@@ -1,9 +1,9 @@
-torch==2.4.1
+torch==2.7.1
 llvmlite==0.43.0
 numba==0.60.0
 accelerate
 transformers
 bitsandbytes
 outetts
-sentence-transformers==3.4.1
+sentence-transformers==5.0.0
 protobuf==6.31.0

backend/python/transformers/requirements-cublas11.txt

@@ -1,10 +1,10 @@
 --extra-index-url https://download.pytorch.org/whl/cu118
-torch==2.4.1+cu118
+torch==2.7.1+cu118
 llvmlite==0.43.0
 numba==0.60.0
 accelerate
 transformers
 bitsandbytes
 outetts
-sentence-transformers==4.1.0
+sentence-transformers==5.0.0
 protobuf==6.31.0

backend/python/transformers/requirements-cublas12.txt

@@ -1,9 +1,9 @@
-torch==2.4.1
+torch==2.7.1
 accelerate
 llvmlite==0.43.0
 numba==0.60.0
 transformers
 bitsandbytes
 outetts
-sentence-transformers==4.1.0
+sentence-transformers==5.0.0
 protobuf==6.31.0

backend/python/transformers/requirements-hipblas.txt

@@ -1,5 +1,5 @@
---extra-index-url https://download.pytorch.org/whl/rocm6.0
-torch==2.4.1+rocm6.0
+--extra-index-url https://download.pytorch.org/whl/rocm6.3
+torch==2.7.1+rocm6.3
 accelerate
 transformers
 llvmlite==0.43.0
@@ -7,5 +7,5 @@ numba==0.60.0
 bitsandbytes
 outetts
 bitsandbytes
-sentence-transformers==4.1.0
+sentence-transformers==5.0.0
 protobuf==6.31.0

backend/python/transformers/requirements-intel.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 intel-extension-for-pytorch==2.3.110+xpu
-torch==2.3.1+cxx11.abi
-oneccl_bind_pt==2.3.100+xpu
+torch==2.5.1+cxx11.abi
+oneccl_bind_pt==2.8.0+xpu
 optimum[openvino]
 llvmlite==0.43.0
 numba==0.60.0
@@ -9,5 +9,5 @@ transformers
 intel-extension-for-transformers
 bitsandbytes
 outetts
-sentence-transformers==4.1.0
+sentence-transformers==5.0.0
 protobuf==6.31.0

core/http/endpoints/openai/chat.go

@@ -305,7 +305,7 @@ func ChatEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, evaluat
 		// If we are using the tokenizer template, we don't need to process the messages
 		// unless we are processing functions
 		if !config.TemplateConfig.UseTokenizerTemplate || shouldUseFn {
-			predInput = evaluator.TemplateMessages(input.Messages, config, funcs, shouldUseFn)
+			predInput = evaluator.TemplateMessages(*input, input.Messages, config, funcs, shouldUseFn)
 
 			log.Debug().Msgf("Prompt (after templating): %s", predInput)
 			if config.Grammar != "" {

core/http/endpoints/openai/completion.go

@@ -109,8 +109,10 @@ func CompletionEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, e
 			predInput := config.PromptStrings[0]
 
 			templatedInput, err := evaluator.EvaluateTemplateForPrompt(templates.CompletionPromptTemplate, *config, templates.PromptTemplateData{
-				Input:        predInput,
-				SystemPrompt: config.SystemPrompt,
+				Input:           predInput,
+				SystemPrompt:    config.SystemPrompt,
+				ReasoningEffort: input.ReasoningEffort,
+				Metadata:        input.Metadata,
 			})
 			if err == nil {
 				predInput = templatedInput
@@ -160,8 +162,10 @@ func CompletionEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, e
 
 		for k, i := range config.PromptStrings {
 			templatedInput, err := evaluator.EvaluateTemplateForPrompt(templates.CompletionPromptTemplate, *config, templates.PromptTemplateData{
-				SystemPrompt: config.SystemPrompt,
-				Input:        i,
+				SystemPrompt:    config.SystemPrompt,
+				Input:           i,
+				ReasoningEffort: input.ReasoningEffort,
+				Metadata:        input.Metadata,
 			})
 			if err == nil {
 				i = templatedInput

core/http/endpoints/openai/edit.go

@@ -47,9 +47,11 @@ func EditEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, evaluat
 
 		for _, i := range config.InputStrings {
 			templatedInput, err := evaluator.EvaluateTemplateForPrompt(templates.EditPromptTemplate, *config, templates.PromptTemplateData{
-				Input:        i,
-				Instruction:  input.Instruction,
-				SystemPrompt: config.SystemPrompt,
+				Input:           i,
+				Instruction:     input.Instruction,
+				SystemPrompt:    config.SystemPrompt,
+				ReasoningEffort: input.ReasoningEffort,
+				Metadata:        input.Metadata,
 			})
 			if err == nil {
 				i = templatedInput

core/http/static/image.js

@@ -11,6 +11,7 @@ async function promptDallE(input) {
   document.getElementById("input").disabled = true;
 
   const model = document.getElementById("image-model").value;
+  const size = document.getElementById("image-size").value;
   const response = await fetch("v1/images/generations", {
     method: "POST",
     headers: {
@@ -21,7 +22,7 @@ async function promptDallE(input) {
       steps: 10,
       prompt: input,
       n: 1,
-      size: "512x512",
+      size: size,
     }),
   });
   const json = await response.json();
@@ -48,4 +49,13 @@ async function promptDallE(input) {
 
 document.getElementById("input").focus();
 document.getElementById("genimage").addEventListener("submit", genImage);
+
+// Handle Enter key press in the prompt input
+document.getElementById("input").addEventListener("keypress", function(event) {
+    if (event.key === "Enter") {
+        event.preventDefault();
+        genImage(event);
+    }
+});
+
 document.getElementById("loader").style.display = "none";

core/http/views/text2image.html

@@ -91,6 +91,30 @@
                             </svg>
                         </span>
                     </div>
+                    
+                    <!-- Size Selection -->
+                    <div class="mt-4">
+                        <label for="image-size" class="block text-sm font-medium text-gray-300 mb-2">
+                            <i class="fas fa-expand-arrows-alt mr-2"></i>Image Size:
+                        </label>
+                        <input
+                            type="text"
+                            id="image-size"
+                            value="256x256"
+                            placeholder="e.g., 256x256, 512x512, 1024x1024"
+                            class="bg-gray-900 text-white border border-gray-700 focus:border-blue-500 focus:ring focus:ring-blue-500 focus:ring-opacity-50 rounded-lg shadow-sm p-2.5 w-full max-w-xs transition-colors duration-200"
+                        />
+                    </div>
+                    
+                    <!-- Submit Button -->
+                    <div class="mt-6">
+                        <button
+                            type="submit"
+                            class="w-full bg-gradient-to-r from-blue-600 to-indigo-600 hover:from-blue-700 hover:to-indigo-700 text-white font-semibold py-3 px-6 rounded-lg transition duration-300 ease-in-out transform hover:scale-105 hover:shadow-lg focus:outline-none focus:ring-2 focus:ring-blue-500 focus:ring-opacity-50"
+                        >
+                            <i class="fas fa-magic mr-2"></i>Generate Image
+                        </button>
+                    </div>
                 </form>
                 
                 <!-- Image Results Container -->

core/schema/openai.go

@@ -183,6 +183,10 @@ type OpenAIRequest struct {
 	Backend string `json:"backend" yaml:"backend"`
 
 	ModelBaseName string `json:"model_base_name" yaml:"model_base_name"`
+
+	ReasoningEffort string `json:"reasoning_effort" yaml:"reasoning_effort"`
+
+	Metadata map[string]string `json:"metadata" yaml:"metadata"`
 }
 
 type ModelsDataResponse struct {

core/templates/evaluator.go

@@ -21,6 +21,8 @@ type PromptTemplateData struct {
 	Instruction          string
 	Functions            []functions.Function
 	MessageIndex         int
+	ReasoningEffort      string
+	Metadata             map[string]string
 }
 
 type ChatMessageTemplateData struct {
@@ -133,7 +135,7 @@ func (e *Evaluator) evaluateJinjaTemplateForPrompt(templateType TemplateType, te
 	return e.cache.evaluateJinjaTemplate(templateType, templateName, conversation)
 }
 
-func (e *Evaluator) TemplateMessages(messages []schema.Message, config *config.BackendConfig, funcs []functions.Function, shouldUseFn bool) string {
+func (e *Evaluator) TemplateMessages(input schema.OpenAIRequest, messages []schema.Message, config *config.BackendConfig, funcs []functions.Function, shouldUseFn bool) string {
 
 	if config.TemplateConfig.JinjaTemplate {
 		var messageData []ChatMessageTemplateData
@@ -283,6 +285,8 @@ func (e *Evaluator) TemplateMessages(messages []schema.Message, config *config.B
 		SuppressSystemPrompt: suppressConfigSystemPrompt,
 		Input:                predInput,
 		Functions:            funcs,
+		ReasoningEffort:      input.ReasoningEffort,
+		Metadata:             input.Metadata,
 	})
 	if err == nil {
 		predInput = templatedInput

core/templates/evaluator_test.go

@@ -219,7 +219,7 @@ var _ = Describe("Templates", func() {
 		for key := range chatMLTestMatch {
 			foo := chatMLTestMatch[key]
 			It("renders correctly `"+key+"`", func() {
-				templated := evaluator.TemplateMessages(foo["messages"].([]schema.Message), foo["config"].(*config.BackendConfig), foo["functions"].([]functions.Function), foo["shouldUseFn"].(bool))
+				templated := evaluator.TemplateMessages(schema.OpenAIRequest{}, foo["messages"].([]schema.Message), foo["config"].(*config.BackendConfig), foo["functions"].([]functions.Function), foo["shouldUseFn"].(bool))
 				Expect(templated).To(Equal(foo["expected"]), templated)
 			})
 		}
@@ -232,7 +232,7 @@ var _ = Describe("Templates", func() {
 		for key := range llama3TestMatch {
 			foo := llama3TestMatch[key]
 			It("renders correctly `"+key+"`", func() {
-				templated := evaluator.TemplateMessages(foo["messages"].([]schema.Message), foo["config"].(*config.BackendConfig), foo["functions"].([]functions.Function), foo["shouldUseFn"].(bool))
+				templated := evaluator.TemplateMessages(schema.OpenAIRequest{}, foo["messages"].([]schema.Message), foo["config"].(*config.BackendConfig), foo["functions"].([]functions.Function), foo["shouldUseFn"].(bool))
 				Expect(templated).To(Equal(foo["expected"]), templated)
 			})
 		}
@@ -245,7 +245,7 @@ var _ = Describe("Templates", func() {
 		for key := range jinjaTest {
 			foo := jinjaTest[key]
 			It("renders correctly `"+key+"`", func() {
-				templated := evaluator.TemplateMessages(foo["messages"].([]schema.Message), foo["config"].(*config.BackendConfig), foo["functions"].([]functions.Function), foo["shouldUseFn"].(bool))
+				templated := evaluator.TemplateMessages(schema.OpenAIRequest{}, foo["messages"].([]schema.Message), foo["config"].(*config.BackendConfig), foo["functions"].([]functions.Function), foo["shouldUseFn"].(bool))
 				Expect(templated).To(Equal(foo["expected"]), templated)
 			})
 		}

docs/content/docs/advanced/advanced-usage.md

@@ -95,7 +95,7 @@ Specifying a `config-file` via CLI allows to declare models in a single file as
    chat: chat
 ```
 
-See also [chatbot-ui](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) as an example on how to use config files.
+See also [chatbot-ui](https://github.com/mudler/LocalAI-examples/tree/main/chatbot-ui) as an example on how to use config files.
 
 It is possible to specify a full URL or a short-hand URL to a YAML model configuration file and use it on start with local-ai, for example to use phi-2:
 
@@ -341,7 +341,7 @@ Below is an instruction that describes a task, paired with an input that provide
 
 Instead of installing models manually, you can use the LocalAI API endpoints and a model definition to install programmatically via API models in runtime.
 
-A curated collection of model files is in the [model-gallery](https://github.com/go-skynet/model-gallery) (work in progress!). The files of the model gallery are different from the model files used to configure LocalAI models. The model gallery files contains information about the model setup, and the files necessary to run the model locally.
+A curated collection of model files is in the [model-gallery](https://github.com/mudler/LocalAI/tree/master/gallery). The files of the model gallery are different from the model files used to configure LocalAI models. The model gallery files contains information about the model setup, and the files necessary to run the model locally.
 
 To install for example `lunademo`, you can send a POST call to the `/models/apply` endpoint with the model definition url (`url`) and the name of the model should have in LocalAI (`name`, optional):
 

docs/content/docs/faq.md

@@ -46,7 +46,7 @@ There is the availability of localai-webui and chatbot-ui in the examples sectio
 
 ### Does it work with AutoGPT? 
 
-Yes, see the [examples](https://github.com/go-skynet/LocalAI/tree/master/examples/)!
+Yes, see the [examples](https://github.com/mudler/LocalAI-examples)!
 
 ### How can I troubleshoot when something is wrong?
 

docs/content/docs/features/embeddings.md

@@ -75,4 +75,4 @@ curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json
 
 ## 💡 Examples
 
-- Example that uses LLamaIndex and LocalAI as embedding: [here](https://github.com/go-skynet/LocalAI/tree/master/examples/query_data/).
+- Example that uses LLamaIndex and LocalAI as embedding: [here](https://github.com/mudler/LocalAI-examples/tree/main/query_data).

docs/content/docs/features/openai-functions.md

@@ -263,4 +263,4 @@ Grammars and function tools can be used as well in conjunction with vision APIs:
 
 ## 💡 Examples
 
-A full e2e example with `docker-compose` is available [here](https://github.com/go-skynet/LocalAI/tree/master/examples/functions).
+A full e2e example with `docker-compose` is available [here](https://github.com/mudler/LocalAI-examples/tree/main/functions).

docs/content/docs/getting-started/models.md

@@ -207,4 +207,4 @@ For instructions on building LocalAI from source, see the [Build Section]({{% re
 {{% /tab %}}
 {{< /tabs >}}
 
-For more model configurations, visit the [Examples Section](https://github.com/mudler/LocalAI/tree/master/examples/configurations).
+For more model configurations, visit the [Examples Section](https://github.com/mudler/LocalAI-examples/tree/main/configurations).

docs/content/docs/whats-new.md

@@ -99,8 +99,8 @@ Thanks to the community efforts now we have a new [how-to website](https://io.mi
 
 #### 💡 More examples!
 
-- Open source autopilot? See the new addition by {{< github "gruberdev" >}} in our [examples](https://github.com/go-skynet/LocalAI/tree/master/examples/continue) on how to use Continue with LocalAI!
-- Want to try LocalAI with Insomnia? Check out the new [Insomnia example](https://github.com/go-skynet/LocalAI/tree/master/examples/insomnia) by {{< github "dave-gray101" >}}!
+- Open source autopilot? See the new addition by {{< github "gruberdev" >}} in our [examples](https://github.com/mudler/LocalAI-examples/tree/main/continue) on how to use Continue with LocalAI!
+- Want to try LocalAI with Insomnia? Check out the new [Insomnia example](https://github.com/mudler/LocalAI-examples/tree/main/insomnia) by {{< github "dave-gray101" >}}!
 
 #### LocalAGI in discord!
 
@@ -258,7 +258,7 @@ And here when it actually picks to reply to the user instead of using functions!
 
 Note: functions are supported only with `llama.cpp`-compatible models.
 
-A full example is available here: https://github.com/go-skynet/LocalAI/tree/master/examples/functions
+A full example is available here: https://github.com/mudler/LocalAI-examples/tree/main/functions
 
 ### gRPC backends
 
@@ -377,9 +377,9 @@ We now support a vast variety of models, while being backward compatible with pr
 
 ### Examples
 
-- 💡 [AutoGPT](https://github.com/go-skynet/LocalAI/tree/master/examples/autoGPT) example ( [mudler](https://github.com/mudler) )
-- 💡 [PrivateGPT](https://github.com/go-skynet/LocalAI/tree/master/examples/privateGPT) example ( [mudler](https://github.com/mudler) )
-- 💡 [Flowise](https://github.com/go-skynet/LocalAI/tree/master/examples/flowise) example ( [mudler](https://github.com/mudler) )
+- 💡 [AutoGPT](https://github.com/mudler/LocalAI-examples/tree/main/autoGPT) example ( [mudler](https://github.com/mudler) )
+- 💡 [PrivateGPT](https://github.com/mudler/LocalAI-examples/tree/main/privateGPT) example ( [mudler](https://github.com/mudler) )
+- 💡 [Flowise](https://github.com/mudler/LocalAI-examples/tree/main/flowise) example ( [mudler](https://github.com/mudler) )
 
 Two new projects offer now direct integration with LocalAI!
 
@@ -449,7 +449,7 @@ Now LocalAI can generate images too:
 
 - 14-05-2023: __v1.11.1__ released! `rwkv` backend patch release
 - 13-05-2023: __v1.11.0__ released! 🔥 Updated `llama.cpp` bindings: This update includes a breaking change in the model files ( https://github.com/ggerganov/llama.cpp/pull/1405 ) - old models should still work with the `gpt4all-llama` backend.
-- 12-05-2023: __v1.10.0__ released! 🔥🔥 Updated `gpt4all` bindings. Added support for GPTNeox (experimental), RedPajama (experimental), Starcoder (experimental), Replit (experimental), MosaicML MPT. Also now `embeddings` endpoint supports tokens arrays. See the [langchain-chroma](https://github.com/go-skynet/LocalAI/tree/master/examples/langchain-chroma) example! Note - this update does NOT include https://github.com/ggerganov/llama.cpp/pull/1405 which makes models incompatible.
+- 12-05-2023: __v1.10.0__ released! 🔥🔥 Updated `gpt4all` bindings. Added support for GPTNeox (experimental), RedPajama (experimental), Starcoder (experimental), Replit (experimental), MosaicML MPT. Also now `embeddings` endpoint supports tokens arrays. See the [langchain-chroma](https://github.com/mudler/LocalAI-examples/tree/main/langchain-chroma) example! Note - this update does NOT include https://github.com/ggerganov/llama.cpp/pull/1405 which makes models incompatible.
 - 11-05-2023: __v1.9.0__ released! 🔥 Important whisper updates ( {{< pr "233" >}} {{< pr "229" >}} ) and extended gpt4all model families support ( {{< pr "232" >}} ). Redpajama/dolly experimental ( {{< pr "214" >}} )
 - 10-05-2023: __v1.8.0__ released! 🔥 Added support for fast and accurate embeddings with `bert.cpp` ( {{< pr "222" >}} )
 - 09-05-2023: Added experimental support for transcriptions endpoint ( {{< pr "211" >}} )

docs/data/version.json

@@ -1,3 +1,3 @@
 {
-  "version": "v3.3.1"
+  "version": "v3.3.2"
 }

gallery/harmony.yaml

@@ -0,0 +1,69 @@
+---
+name: "harmony"
+
+config_file: |
+  mmap: true
+  backend: "llama-cpp"
+  template:
+    chat_message: |-
+      <|start|>{{ if .FunctionCall -}}functions.{{ .FunctionCall.Name }} to=assistant{{ else if eq .RoleName "assistant"}}assistant<|channel|>final<|message|>{{else}}{{ .RoleName }}{{end}}<|message|>
+      {{- if .Content -}}
+      {{- .Content -}}
+      {{- end -}}
+      {{- if .FunctionCall -}}
+      {{- toJson .FunctionCall -}}
+      {{- end -}}<|end|>
+    function: |-
+      <|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
+      Knowledge cutoff: 2024-06
+      Current date: {{ now | date "Mon Jan 2 15:04:05 MST 2006" }}
+
+      Reasoning: {{if eq .ReasoningEffort ""}}medium{{else}}{{.ReasoningEffort}}{{end}}
+
+      # {{with .Metadata}}{{ if ne .system_prompt "" }}{{ .system_prompt }}{{ end }}{{else}}You are a friendly and helpful assistant.{{ end }}<|end|>{{- .Input -}}<|start|>assistant
+
+      # Tools
+
+      ## functions
+
+      namespace functions {
+      {{-range .Functions}}
+      {{if .Description }}
+      // {{ .Description }}
+      {{- end }}
+      {{- if and .Parameters.Properties (gt (len .Parameters.Properties) 0) }}
+      type {{ .Name }} = (_: {
+      {{- range $name, $prop := .Parameters.Properties }}
+      {{- if $prop.Description }}
+        // {{ $prop.Description }}
+      {{- end }}
+        {{ $name }}: {{ if gt (len $prop.Type) 1 }}{{ range $i, $t := $prop.Type }}{{ if $i }} | {{ end }}{{ $t }}{{ end }}{{ else }}{{ index $prop.Type 0 }}{{ end }},
+      {{- end }}
+      }) => any;
+      {{- else }}
+      type {{ .Function.Name }} = () => any;
+      {{- end }}
+      {{- end }}{{/* end of range .Functions */}}
+      } // namespace functions
+
+      # Instructions
+
+      <|end|>{{.Input -}}<|start|>assistant
+    chat: |-
+      <|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
+      Knowledge cutoff: 2024-06
+      Current date: {{ now | date "Mon Jan 2 15:04:05 MST 2006" }}
+
+      Reasoning: {{if eq .ReasoningEffort ""}}medium{{else}}{{.ReasoningEffort}}{{end}}
+
+      # {{with .Metadata}}{{ if ne .system_prompt "" }}{{ .system_prompt }}{{ end }}{{else}}You are a friendly and helpful assistant.{{ end }}<|end|>{{- .Input -}}<|start|>assistant
+    completion: |
+      {{.Input}}
+  context_size: 8192
+  f16: true
+  stopwords:
+  - '<|im_end|>'
+  - '<dummy32000>'
+  - '</s>'
+  - '<|endoftext|>'
+  - '<|return|>'

gallery/index.yaml

@@ -1,4 +1,253 @@
 ---
+- name: "kokoro"
+  url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
+  urls:
+    - https://github.com/hexgrad/kokoro
+  license: apache-2.0
+  tags:
+    - tts
+    - kokoro
+    - gpu
+    - cpu
+    - text-to-speech
+  description: |
+    Kokoro is an open-weight TTS model with 82 million parametrs. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects.
+  overrides:
+    backend: "kokoro"
+    name: "kokoro"
+    description: "Kokoro is an open-weight TTS model with 82 million parametrs. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects."
+    parameters:
+      voice: "af_heart"
+    options:
+      # this is for american
+      # 🇺🇸 'a' => American English, 🇬🇧 'b' => British English
+      # 🇪🇸 'e' => Spanish es
+      # 🇫🇷 'f' => French fr-fr
+      # 🇮🇳 'h' => Hindi hi
+      # 🇮🇹 'i' => Italian it
+      # 🇯🇵 'j' => Japanese: pip install misaki[ja]
+      # 🇧🇷 'p' => Brazilian Portuguese pt-br
+      # 🇨🇳 'z' => Mandarin Chinese: pip install misaki[zh]
+      - lang_code:a
+    known_usecases:
+      - tts
+- name: "kitten-tts"
+  url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
+  urls:
+    - https://github.com/KittenML/KittenTTS
+  license: apache-2.0
+  tags:
+    - tts
+    - kitten-tts
+    - gpu
+    - cpu
+    - text-to-speech
+  description: |
+    Kitten TTS is an open-source realistic text-to-speech model with just 15 million parameters, designed for lightweight deployment and high-quality voice synthesis.
+  overrides:
+    backend: "kitten-tts"
+    name: "kitten-tts"
+    description: "Kitten TTS is a text-to-speech model that can generate speech from text."
+    parameters:
+      model: "KittenML/kitten-tts-nano-0.1"
+      voice: "expr-voice-5-f"
+    known_usecases:
+      - tts
+- &qwenimage
+  name: "qwen-image"
+  url: "github:mudler/LocalAI/gallery/qwen-image.yaml@master"
+  urls:
+    - https://huggingface.co/Qwen/Qwen-Image
+  icon: https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_logo.png
+  license: apache-2.0
+  tags:
+    - qwen-image
+    - gpu
+    - text-to-image
+  description: |
+    We are thrilled to release Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.
+- &gptoss
+  name: "gpt-oss-20b"
+  url: "github:mudler/LocalAI/gallery/harmony.yaml@master"
+  license: apache-2.0
+  tags:
+    - gguf
+    - gpu
+    - cpu
+    - gguf
+    - openai
+  icon: https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg
+  urls:
+    - https://huggingface.co/openai/gpt-oss-20b
+    - https://huggingface.co/ggml-org/gpt-oss-20b-GGUF
+  description: |
+    Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.
+
+    We’re releasing two flavors of the open models:
+
+        gpt-oss-120b — for production, general purpose, high reasoning use cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters)
+        gpt-oss-20b — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
+
+    Both models were trained on our harmony response format and should only be used with the harmony format as it will not work correctly otherwise.
+
+        This model card is dedicated to the smaller gpt-oss-20b model. Check out gpt-oss-120b for the larger model.
+
+    Highlights
+
+        Permissive Apache 2.0 license: Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
+        Configurable reasoning effort: Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
+        Full chain-of-thought: Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
+        Fine-tunable: Fully customize models to your specific use case through parameter fine-tuning.
+        Agentic capabilities: Use the models’ native capabilities for function calling, web browsing, Python code execution, and Structured Outputs.
+        Native MXFP4 quantization: The models are trained with native MXFP4 precision for the MoE layer, making gpt-oss-120b run on a single H100 GPU and the gpt-oss-20b model run within 16GB of memory.
+  overrides:
+    parameters:
+      model: gpt-oss-20b-mxfp4.gguf
+  files:
+    - filename: gpt-oss-20b-mxfp4.gguf
+      sha256: 52f57ab7d3df3ba9173827c1c6832e73375553a846f3e32b49f1ae2daad688d4
+      uri: huggingface://ggml-org/gpt-oss-20b-GGUF/gpt-oss-20b-mxfp4.gguf
+- !!merge <<: *gptoss
+  name: "gpt-oss-120b"
+  url: "github:mudler/LocalAI/gallery/harmony.yaml@master"
+  icon: https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-120b.svg
+  urls:
+    - https://huggingface.co/openai/gpt-oss-120b
+    - https://huggingface.co/ggml-org/gpt-oss-120b-GGUF
+  overrides:
+    parameters:
+      model: gpt-oss-120b-mxfp4-00001-of-00003.gguf
+  files:
+    - filename: gpt-oss-120b-mxfp4-00001-of-00003.gguf
+      sha256: 40b630223b9fc43820fa0aae5d0ab61020f5858d1719642357753dca9e7df29f
+      uri: huggingface://ggml-org/gpt-oss-120b-GGUF/gpt-oss-120b-mxfp4-00001-of-00003.gguf
+    - filename: gpt-oss-120b-mxfp4-00002-of-00003.gguf
+      sha256: fbdb8cdec70edb82c53bfc69cc0f54a34759a23d317fa0771a63be6571907b38
+      uri: huggingface://ggml-org/gpt-oss-120b-GGUF/gpt-oss-120b-mxfp4-00002-of-00003.gguf
+    - filename: gpt-oss-120b-mxfp4-00003-of-00003.gguf
+      sha256: b326bfd8ac696c4b9a14e9e84d5529b2bb86847aea0e65443cbf075accba8b71
+      uri: huggingface://ggml-org/gpt-oss-120b-GGUF/gpt-oss-120b-mxfp4-00003-of-00003.gguf
+- !!merge <<: *gptoss
+  name: "openai_gpt-oss-20b-neo"
+  icon: https://huggingface.co/DavidAU/Openai_gpt-oss-20b-NEO-GGUF/resolve/main/matrix1.gif
+  urls:
+    - https://huggingface.co/DavidAU/Openai_gpt-oss-20b-NEO-GGUF
+  description: |
+    These are NEO Imatrix GGUFs, NEO dataset by DavidAU.
+
+    NEO dataset improves overall performance, and is for all use cases.
+
+    Example output below (creative), using settings below.
+
+    Model also passed "hard" coding test too (6 experts); no issues (IQ4_NL).
+
+    (Forcing the model to create code with no dependencies and limits of coding short cuts, with multiple loops, and in real time with no blocking in a language that does not support it normally.)
+
+    Due to quanting issues with this model (which result in oddball quant sizes / mixtures), only TESTED quants will be uploaded (at the moment).
+  overrides:
+    parameters:
+      model: OpenAI-20B-NEO-MXFP4_MOE4.gguf
+  files:
+    - filename: OpenAI-20B-NEO-MXFP4_MOE4.gguf
+      sha256: 066c84a0844b1f1f4515e5c64095fe4c67e86d5eb70db4e368e283b1134d9c1e
+      uri: huggingface://DavidAU/Openai_gpt-oss-20b-NEO-GGUF/OpenAI-20B-NEO-MXFP4_MOE4.gguf
+- !!merge <<: *gptoss
+  name: "huihui-ai_huihui-gpt-oss-20b-bf16-abliterated"
+  urls:
+    - https://huggingface.co/huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated
+    - https://huggingface.co/bartowski/huihui-ai_Huihui-gpt-oss-20b-BF16-abliterated-GGUF
+  description: |
+    This is an uncensored version of unsloth/gpt-oss-20b-BF16 created with abliteration (see remove-refusals-with-transformers to know more about it).
+  overrides:
+    parameters:
+      model: huihui-ai_Huihui-gpt-oss-20b-BF16-abliterated-MXFP4_MOE.gguf
+  files:
+    - filename: huihui-ai_Huihui-gpt-oss-20b-BF16-abliterated-MXFP4_MOE.gguf
+      sha256: abca50d1bd95c49d71db36aad0f38090ea5465ce148634c496a48bc87030bdd9
+      uri: huggingface://bartowski/huihui-ai_Huihui-gpt-oss-20b-BF16-abliterated-GGUF/huihui-ai_Huihui-gpt-oss-20b-BF16-abliterated-MXFP4_MOE.gguf
+- !!merge <<: *gptoss
+  name: "openai-gpt-oss-20b-abliterated-uncensored-neo-imatrix"
+  icon: https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-abliterated-uncensored-NEO-Imatrix-gguf/resolve/main/power-the-matrix.gif
+  urls:
+    - https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-abliterated-uncensored-NEO-Imatrix-gguf
+  description: |
+    These are NEO Imatrix GGUFs, NEO dataset by DavidAU.
+
+    NEO dataset improves overall performance, and is for all use cases.
+
+    This model uses Huihui-gpt-oss-20b-BF16-abliterated as a base which DE-CENSORS the model and removes refusals.
+
+    Example output below (creative; IQ4_NL), using settings below.
+
+    This model can be a little rough around the edges (due to abliteration) ; make sure you see the settings below for best operation.
+
+    It can also be creative, off the shelf crazy and rational too.
+
+    Enjoy!
+  overrides:
+    parameters:
+      model: OpenAI-20B-NEOPlus-Uncensored-IQ4_NL.gguf
+  files:
+    - filename: OpenAI-20B-NEOPlus-Uncensored-IQ4_NL.gguf
+      sha256: 274ffaaf0783270c071006842ffe60af73600fc63c2b6153c0701b596fc3b122
+      uri: huggingface://DavidAU/OpenAi-GPT-oss-20b-abliterated-uncensored-NEO-Imatrix-gguf/OpenAI-20B-NEOPlus-Uncensored-IQ4_NL.gguf
+- name: "chatterbox"
+  url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
+  icon: https://private-user-images.githubusercontent.com/660224/448166653-bd8c5f03-e91d-4ee5-b680-57355da204d1.png
+  license: "mit"
+  urls:
+    - https://github.com/resemble-ai/chatterbox
+  tags:
+    - tts
+    - dia
+    - gpu
+    - text-to-speech
+  description: |
+    Chatterbox, Resemble AI's first production-grade open source TTS model. Licensed under MIT, Chatterbox has been benchmarked against leading closed-source systems like ElevenLabs, and is consistently preferred in side-by-side evaluations.
+  overrides:
+    backend: "chatterbox"
+    name: "chatterbox"
+    known_usecases:
+      - tts
+- name: "dia"
+  url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
+  icon: https://github.com/nari-labs/dia/raw/main/dia/static/images/banner.png
+  urls:
+    - https://github.com/nari-labs/dia
+    - https://huggingface.co/nari-labs/Dia-1.6B-0626
+  license: apache-2.0
+  tags:
+    - tts
+    - dia
+    - gpu
+    - text-to-speech
+  overrides:
+    backend: "transformers"
+    name: "dia"
+    description: "Dia is a 1.6B parameter text to speech model created by Nari Labs."
+    parameters:
+      model: nari-labs/Dia-1.6B-0626
+    type: DiaForConditionalGeneration
+    known_usecases:
+      - tts
+- name: "outetts"
+  url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
+  urls:
+    - https://github.com/edwko/OuteTTS
+  license: apache-2.0
+  tags:
+    - tts
+    - gpu
+    - text-to-speech
+  overrides:
+    backend: "transformers"
+    name: "outetts"
+    description: "OuteTTS is a 1.6B parameter text to speech model created by OuteAI."
+    parameters:
+      model: OuteAI/OuteTTS-0.3-1B
+    type: OuteTTS
+    known_usecases:
+      - tts
 - &afm
   name: "arcee-ai_afm-4.5b"
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master"
@@ -1943,6 +2192,45 @@
     - filename: Qwen_Qwen3-30B-A3B-Thinking-2507-Q4_K_M.gguf
       sha256: 1359aa08e2f2dfe7ce4b5ff88c4c996e6494c9d916b1ebacd214bb74bbd5a9db
       uri: huggingface://bartowski/Qwen_Qwen3-30B-A3B-Thinking-2507-GGUF/Qwen_Qwen3-30B-A3B-Thinking-2507-Q4_K_M.gguf
+- !!merge <<: *qwen3
+  name: "qwen_qwen3-4b-instruct-2507"
+  urls:
+    - https://huggingface.co/bartowski/Qwen_Qwen3-4B-Instruct-2507-GGUF
+    - https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507
+  description: |
+    We introduce the updated version of the Qwen3-4B non-thinking mode, named Qwen3-4B-Instruct-2507, featuring the following key enhancements:
+
+    Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
+    Substantial gains in long-tail knowledge coverage across multiple languages.
+    Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
+    Enhanced capabilities in 256K long-context understanding.
+  overrides:
+    parameters:
+      model: Qwen_Qwen3-4B-Instruct-2507-Q8_0.gguf
+  files:
+    - filename: Qwen_Qwen3-4B-Instruct-2507-Q8_0.gguf
+      sha256: 260b5b5b6ad73e44df81a43ea1f5c11c37007b6bac18eb3cd2016e8667c19662
+      uri: huggingface://bartowski/Qwen_Qwen3-4B-Instruct-2507-GGUF/Qwen_Qwen3-4B-Instruct-2507-Q8_0.gguf
+- !!merge <<: *qwen3
+  name: "qwen_qwen3-4b-thinking-2507"
+  urls:
+    - https://huggingface.co/bartowski/Qwen_Qwen3-4B-Thinking-2507-GGUF
+    - https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507
+  description: |
+    Over the past three months, we have continued to scale the thinking capability of Qwen3-4B, improving both the quality and depth of reasoning. We are pleased to introduce Qwen3-4B-Thinking-2507, featuring the following key enhancements:
+
+        Significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise.
+        Markedly better general capabilities, such as instruction following, tool usage, text generation, and alignment with human preferences.
+        Enhanced 256K long-context understanding capabilities.
+
+    NOTE: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks.
+  overrides:
+    parameters:
+      model: Qwen_Qwen3-4B-Thinking-2507-Q8_0.gguf
+  files:
+    - filename: Qwen_Qwen3-4B-Thinking-2507-Q8_0.gguf
+      sha256: 2c08db093bc57c2c77222d27ffe8d41cb0b5648e66ba84e5fb9ceab429f6735c
+      uri: huggingface://bartowski/Qwen_Qwen3-4B-Thinking-2507-GGUF/Qwen_Qwen3-4B-Thinking-2507-Q8_0.gguf
 - &gemma3
   url: "github:mudler/LocalAI/gallery/gemma.yaml@master"
   name: "gemma-3-27b-it"
@@ -11641,6 +11929,29 @@
     - filename: ockerman0_AnubisLemonade-70B-v1.1-Q4_K_M.gguf
       sha256: e217b2c39d4fae8499ca2a24ff8c7025ec93cd16883aa57f43ac9240222c4754
       uri: huggingface://bartowski/ockerman0_AnubisLemonade-70B-v1.1-GGUF/ockerman0_AnubisLemonade-70B-v1.1-Q4_K_M.gguf
+- !!merge <<: *llama31
+  name: "tarek07_nomad-llama-70b"
+  icon: https://cdn-uploads.huggingface.co/production/uploads/64909c086073a0cd172d0411/5F7S8kdO8NTMua6iCRTUO.png
+  urls:
+    - https://huggingface.co/Tarek07/Nomad-LLaMa-70B
+    - https://huggingface.co/bartowski/Tarek07_Nomad-LLaMa-70B-GGUF
+  description: |
+    I decided to make a simple model for a change, with some models I was curious to see work together.
+    models:
+      - model: ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large
+      - model: TheDrummer/Anubis-70B-v1.1
+      - model: Mawdistical/Vulpine-Seduction-70B
+      - model: Darkhn/L3.3-70B-Animus-V5-Pro
+      - model: zerofata/L3.3-GeneticLemonade-Unleashed-v3-70B
+      - model: Sao10K/Llama-3.3-70B-Vulpecula-r1
+    base_model: nbeerbower/Llama-3.1-Nemotron-lorablated-70B
+  overrides:
+    parameters:
+      model: Tarek07_Nomad-LLaMa-70B-Q4_K_M.gguf
+  files:
+    - filename: Tarek07_Nomad-LLaMa-70B-Q4_K_M.gguf
+      sha256: 734c7042a84cd6c059c4ddd3ffb84b23752aeaaf670c5cbb0031f8128ec5ffc8
+      uri: huggingface://bartowski/Tarek07_Nomad-LLaMa-70B-GGUF/Tarek07_Nomad-LLaMa-70B-Q4_K_M.gguf
 - &deepseek
   url: "github:mudler/LocalAI/gallery/deepseek.yaml@master" ## Deepseek
   name: "deepseek-coder-v2-lite-instruct"

gallery/qwen-image.yaml

@@ -0,0 +1,19 @@
+---
+name: "qwen-image"
+
+config_file: |
+    backend: diffusers
+    cfg_scale: 0
+    diffusers:
+      cuda: true
+      enable_parameters: num_inference_steps
+      pipeline_type: DiffusionPipeline
+    f16: true
+    low_vram: true
+    name: qwen-image
+    parameters:
+      model: Qwen/Qwen-Image
+    step: 50
+    options:
+      - true_cfg_scale:4.0
+      - torch_dtype:bf16

go.mod

@@ -1,8 +1,8 @@
 module github.com/mudler/LocalAI
 
-go 1.23.0
+go 1.23.8
 
-toolchain go1.23.1
+toolchain go1.24.5
 
 require (
 	dario.cat/mergo v1.0.1
@@ -28,19 +28,19 @@ require (
 	github.com/ipfs/go-log v1.0.5
 	github.com/jaypipes/ghw v0.12.0
 	github.com/joho/godotenv v1.5.1
-	github.com/klauspost/cpuid/v2 v2.2.9
-	github.com/libp2p/go-libp2p v0.40.0
+	github.com/klauspost/cpuid/v2 v2.2.10
+	github.com/libp2p/go-libp2p v0.43.0
 	github.com/mholt/archiver/v3 v3.5.1
 	github.com/microcosm-cc/bluemonday v1.0.26
-	github.com/mudler/edgevpn v0.30.2
+	github.com/mudler/edgevpn v0.31.0
 	github.com/mudler/go-processmanager v0.0.0-20240820160718-8b802d3ecf82
 	github.com/nikolalohinski/gonja/v2 v2.3.2
-	github.com/onsi/ginkgo/v2 v2.22.2
+	github.com/onsi/ginkgo/v2 v2.23.3
 	github.com/onsi/gomega v1.36.2
 	github.com/otiai10/copy v1.14.1
 	github.com/otiai10/openaigo v1.7.0
 	github.com/phayes/freeport v0.0.0-20220201140144-74d24b5ae9f5
-	github.com/prometheus/client_golang v1.21.0
+	github.com/prometheus/client_golang v1.22.0
 	github.com/rs/zerolog v1.33.0
 	github.com/russross/blackfriday v1.6.0
 	github.com/sashabaranov/go-openai v1.26.2
@@ -52,12 +52,12 @@ require (
 	github.com/testcontainers/testcontainers-go v0.35.0
 	github.com/tmc/langchaingo v0.1.12
 	github.com/valyala/fasthttp v1.55.0
-	go.opentelemetry.io/otel v1.34.0
+	go.opentelemetry.io/otel v1.35.0
 	go.opentelemetry.io/otel/exporters/prometheus v0.50.0
-	go.opentelemetry.io/otel/metric v1.34.0
+	go.opentelemetry.io/otel/metric v1.35.0
 	go.opentelemetry.io/otel/sdk/metric v1.28.0
 	google.golang.org/grpc v1.67.1
-	google.golang.org/protobuf v1.36.5
+	google.golang.org/protobuf v1.36.6
 	gopkg.in/yaml.v2 v2.4.0
 	gopkg.in/yaml.v3 v3.0.1
 	oras.land/oras-go/v2 v2.5.0
@@ -72,7 +72,7 @@ require (
 	github.com/felixge/httpsnoop v1.0.4 // indirect
 	github.com/go-task/slim-sprig/v3 v3.0.0 // indirect
 	github.com/json-iterator/go v1.1.12 // indirect
-	github.com/libp2p/go-yamux/v5 v5.0.0 // indirect
+	github.com/libp2p/go-yamux/v5 v5.0.1 // indirect
 	github.com/magiconair/properties v1.8.7 // indirect
 	github.com/moby/docker-image-spec v1.3.1 // indirect
 	github.com/moby/patternmatcher v0.6.0 // indirect
@@ -84,31 +84,31 @@ require (
 	github.com/otiai10/mint v1.6.3 // indirect
 	github.com/pion/datachannel v1.5.10 // indirect
 	github.com/pion/dtls/v2 v2.2.12 // indirect
-	github.com/pion/dtls/v3 v3.0.4 // indirect
-	github.com/pion/ice/v4 v4.0.6 // indirect
-	github.com/pion/interceptor v0.1.37 // indirect
+	github.com/pion/dtls/v3 v3.0.6 // indirect
+	github.com/pion/ice/v4 v4.0.10 // indirect
+	github.com/pion/interceptor v0.1.40 // indirect
 	github.com/pion/logging v0.2.3 // indirect
 	github.com/pion/mdns/v2 v2.0.7 // indirect
 	github.com/pion/randutil v0.1.0 // indirect
 	github.com/pion/rtcp v1.2.15 // indirect
-	github.com/pion/rtp v1.8.11 // indirect
-	github.com/pion/sctp v1.8.35 // indirect
-	github.com/pion/sdp/v3 v3.0.10 // indirect
-	github.com/pion/srtp/v3 v3.0.4 // indirect
+	github.com/pion/rtp v1.8.19 // indirect
+	github.com/pion/sctp v1.8.39 // indirect
+	github.com/pion/sdp/v3 v3.0.13 // indirect
+	github.com/pion/srtp/v3 v3.0.6 // indirect
 	github.com/pion/stun v0.6.1 // indirect
 	github.com/pion/stun/v3 v3.0.0 // indirect
 	github.com/pion/transport/v2 v2.2.10 // indirect
 	github.com/pion/transport/v3 v3.0.7 // indirect
-	github.com/pion/turn/v4 v4.0.0 // indirect
-	github.com/pion/webrtc/v4 v4.0.9 // indirect
+	github.com/pion/turn/v4 v4.0.2 // indirect
+	github.com/pion/webrtc/v4 v4.1.2 // indirect
 	github.com/rs/dnscache v0.0.0-20230804202142-fc85eb664529 // indirect
 	github.com/savsgio/gotils v0.0.0-20240303185622-093b76447511 // indirect
 	github.com/shirou/gopsutil/v4 v4.24.7 // indirect
 	github.com/wlynxg/anet v0.0.5 // indirect
 	go.opentelemetry.io/auto/sdk v1.1.0 // indirect
 	go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.56.0 // indirect
-	go.uber.org/mock v0.5.0 // indirect
-	golang.org/x/time v0.8.0 // indirect
+	go.uber.org/mock v0.5.2 // indirect
+	golang.org/x/time v0.12.0 // indirect
 )
 
 require (
@@ -133,7 +133,6 @@ require (
 	github.com/containerd/errdefs v0.1.0 // indirect
 	github.com/containerd/log v0.1.0 // indirect
 	github.com/containerd/stargz-snapshotter/estargz v0.14.3 // indirect
-	github.com/coreos/go-systemd/v22 v22.5.0 // indirect
 	github.com/creachadair/otp v0.5.0 // indirect
 	github.com/davecgh/go-spew v1.1.1 // indirect
 	github.com/davidlazar/go-crypto v0.0.0-20200604182044-b73af7476f6c // indirect
@@ -146,7 +145,6 @@ require (
 	github.com/docker/go-connections v0.5.0
 	github.com/docker/go-units v0.5.0 // indirect
 	github.com/dsnet/compress v0.0.2-0.20210315054119-f66993602bf5 // indirect
-	github.com/elastic/gosigar v0.14.3 // indirect
 	github.com/flynn/noise v1.1.0 // indirect
 	github.com/francoispqt/gojay v1.2.13 // indirect
 	github.com/ghodss/yaml v1.0.0 // indirect
@@ -159,7 +157,6 @@ require (
 	github.com/go-openapi/jsonreference v0.21.0 // indirect
 	github.com/go-openapi/spec v0.21.0 // indirect
 	github.com/go-openapi/swag v0.23.0 // indirect
-	github.com/godbus/dbus/v5 v5.1.0 // indirect
 	github.com/gofiber/contrib/fiberzerolog v1.0.2
 	github.com/gofiber/template v1.8.3 // indirect
 	github.com/gofiber/utils v1.1.0 // indirect
@@ -167,42 +164,38 @@ require (
 	github.com/golang/groupcache v0.0.0-20210331224755-41bb18bfe9da // indirect
 	github.com/golang/snappy v0.0.4 // indirect
 	github.com/google/btree v1.1.3 // indirect
-	github.com/google/go-cmp v0.6.0 // indirect
+	github.com/google/go-cmp v0.7.0 // indirect
 	github.com/google/gopacket v1.1.19 // indirect
 	github.com/google/pprof v0.0.0-20250208200701-d0013a598941 // indirect
 	github.com/gorilla/css v1.0.1 // indirect
 	github.com/gorilla/websocket v1.5.3 // indirect
-	github.com/hashicorp/errwrap v1.1.0 // indirect
-	github.com/hashicorp/go-multierror v1.1.1 // indirect
 	github.com/hashicorp/golang-lru v1.0.2 // indirect
 	github.com/hashicorp/golang-lru/v2 v2.0.7 // indirect
 	github.com/henvic/httpretty v0.1.4 // indirect
 	github.com/huandu/xstrings v1.5.0 // indirect
 	github.com/huin/goupnp v1.3.0 // indirect
-	github.com/ipfs/boxo v0.27.4 // indirect
+	github.com/ipfs/boxo v0.30.0 // indirect
 	github.com/ipfs/go-cid v0.5.0 // indirect
-	github.com/ipfs/go-datastore v0.7.0 // indirect
-	github.com/ipfs/go-log/v2 v2.5.1 // indirect
+	github.com/ipfs/go-datastore v0.8.2 // indirect
+	github.com/ipfs/go-log/v2 v2.6.0 // indirect
 	github.com/ipld/go-ipld-prime v0.21.0 // indirect
 	github.com/jackpal/go-nat-pmp v1.0.2 // indirect
 	github.com/jaypipes/pcidb v1.0.0 // indirect
 	github.com/jbenet/go-temp-err-catcher v0.1.0 // indirect
-	github.com/jbenet/goprocess v0.1.4 // indirect
 	github.com/josharian/intern v1.0.0 // indirect
-	github.com/klauspost/compress v1.17.11 // indirect
+	github.com/klauspost/compress v1.18.0 // indirect
 	github.com/klauspost/pgzip v1.2.5 // indirect
-	github.com/koron/go-ssdp v0.0.5 // indirect
+	github.com/koron/go-ssdp v0.0.6 // indirect
 	github.com/libp2p/go-buffer-pool v0.1.0 // indirect
 	github.com/libp2p/go-cidranger v1.1.0 // indirect
 	github.com/libp2p/go-flow-metrics v0.2.0 // indirect
 	github.com/libp2p/go-libp2p-asn-util v0.4.1 // indirect
-	github.com/libp2p/go-libp2p-kad-dht v0.29.1 // indirect
-	github.com/libp2p/go-libp2p-kbucket v0.6.5 // indirect
-	github.com/libp2p/go-libp2p-pubsub v0.13.0 // indirect
+	github.com/libp2p/go-libp2p-kad-dht v0.33.1 // indirect
+	github.com/libp2p/go-libp2p-kbucket v0.7.0 // indirect
+	github.com/libp2p/go-libp2p-pubsub v0.14.2 // indirect
 	github.com/libp2p/go-libp2p-record v0.3.1 // indirect
-	github.com/libp2p/go-libp2p-routing-helpers v0.7.4 // indirect
+	github.com/libp2p/go-libp2p-routing-helpers v0.7.5 // indirect
 	github.com/libp2p/go-msgio v0.3.0 // indirect
-	github.com/libp2p/go-nat v0.2.0 // indirect
 	github.com/libp2p/go-netroute v0.2.2 // indirect
 	github.com/libp2p/go-reuseport v0.4.0 // indirect
 	github.com/libp2p/zeroconf/v2 v2.2.0 // indirect
@@ -213,7 +206,7 @@ require (
 	github.com/mattn/go-colorable v0.1.14 // indirect
 	github.com/mattn/go-isatty v0.0.20 // indirect
 	github.com/mattn/go-runewidth v0.0.15 // indirect
-	github.com/miekg/dns v1.1.63 // indirect
+	github.com/miekg/dns v1.1.66 // indirect
 	github.com/mikioh/tcpinfo v0.0.0-20190314235526-30a79bb1804b // indirect
 	github.com/mikioh/tcpopt v0.0.0-20190314235656-172688c1accc // indirect
 	github.com/minio/sha256-simd v1.0.1 // indirect
@@ -225,24 +218,23 @@ require (
 	github.com/moby/term v0.5.0 // indirect
 	github.com/mr-tron/base58 v1.2.0 // indirect
 	github.com/mudler/go-piper v0.0.0-20241023091659-2494246fd9fc
-	github.com/mudler/water v0.0.0-20221010214108-8c7313014ce0 // indirect
+	github.com/mudler/water v0.0.0-20250808092830-dd90dcf09025 // indirect
 	github.com/muesli/reflow v0.3.0 // indirect
 	github.com/muesli/termenv v0.15.2 // indirect
 	github.com/multiformats/go-base32 v0.1.0 // indirect
 	github.com/multiformats/go-base36 v0.2.0 // indirect
-	github.com/multiformats/go-multiaddr v0.14.0
+	github.com/multiformats/go-multiaddr v0.16.0
 	github.com/multiformats/go-multiaddr-dns v0.4.1 // indirect
 	github.com/multiformats/go-multiaddr-fmt v0.1.0 // indirect
 	github.com/multiformats/go-multibase v0.2.0 // indirect
-	github.com/multiformats/go-multicodec v0.9.0 // indirect
+	github.com/multiformats/go-multicodec v0.9.1 // indirect
 	github.com/multiformats/go-multihash v0.2.3 // indirect
-	github.com/multiformats/go-multistream v0.6.0 // indirect
+	github.com/multiformats/go-multistream v0.6.1 // indirect
 	github.com/multiformats/go-varint v0.0.7 // indirect
 	github.com/nwaples/rardecode v1.1.0 // indirect
 	github.com/olekukonko/tablewriter v0.0.5 // indirect
 	github.com/opencontainers/go-digest v1.0.0 // indirect
 	github.com/opencontainers/image-spec v1.1.0
-	github.com/opencontainers/runtime-spec v1.2.0 // indirect
 	github.com/opentracing/opentracing-go v1.2.0 // indirect
 	github.com/pbnjay/memory v0.0.0-20210728143218-7b4eea64cf58 // indirect
 	github.com/peterbourgon/diskv v2.0.1+incompatible // indirect
@@ -253,13 +245,12 @@ require (
 	github.com/pmezard/go-difflib v1.0.0 // indirect
 	github.com/polydawn/refmt v0.89.0 // indirect
 	github.com/power-devops/perfstat v0.0.0-20240221224432-82ca36839d55 // indirect
-	github.com/prometheus/client_model v0.6.1 // indirect
-	github.com/prometheus/common v0.62.0 // indirect
-	github.com/prometheus/procfs v0.15.1 // indirect
+	github.com/prometheus/client_model v0.6.2 // indirect
+	github.com/prometheus/common v0.64.0 // indirect
+	github.com/prometheus/procfs v0.16.1 // indirect
 	github.com/quic-go/qpack v0.5.1 // indirect
-	github.com/quic-go/quic-go v0.49.0 // indirect
-	github.com/quic-go/webtransport-go v0.8.1-0.20241018022711-4ac2c9250e66 // indirect
-	github.com/raulk/go-watchdog v1.3.0 // indirect
+	github.com/quic-go/quic-go v0.54.0 // indirect
+	github.com/quic-go/webtransport-go v0.9.0 // indirect
 	github.com/rivo/uniseg v0.4.7 // indirect
 	github.com/shoenig/go-m1cpu v0.1.6 // indirect
 	github.com/shopspring/decimal v1.4.0 // indirect
@@ -285,27 +276,27 @@ require (
 	github.com/yusufpapurcu/wmi v1.2.4 // indirect
 	go.opencensus.io v0.24.0 // indirect
 	go.opentelemetry.io/otel/sdk v1.31.0 // indirect
-	go.opentelemetry.io/otel/trace v1.34.0 // indirect
-	go.uber.org/dig v1.18.0 // indirect
-	go.uber.org/fx v1.23.0 // indirect
+	go.opentelemetry.io/otel/trace v1.35.0 // indirect
+	go.uber.org/dig v1.19.0 // indirect
+	go.uber.org/fx v1.24.0 // indirect
 	go.uber.org/multierr v1.11.0 // indirect
 	go.uber.org/zap v1.27.0 // indirect
-	golang.org/x/crypto v0.33.0 // indirect
-	golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa // indirect
-	golang.org/x/mod v0.23.0 // indirect
-	golang.org/x/net v0.35.0 // indirect
-	golang.org/x/sync v0.11.0 // indirect
-	golang.org/x/sys v0.30.0 // indirect
-	golang.org/x/term v0.29.0 // indirect
-	golang.org/x/text v0.22.0 // indirect
-	golang.org/x/tools v0.30.0 // indirect
+	golang.org/x/crypto v0.39.0 // indirect
+	golang.org/x/exp v0.0.0-20250606033433-dcc06ee1d476 // indirect
+	golang.org/x/mod v0.25.0 // indirect
+	golang.org/x/net v0.41.0 // indirect
+	golang.org/x/sync v0.15.0 // indirect
+	golang.org/x/sys v0.33.0 // indirect
+	golang.org/x/term v0.32.0 // indirect
+	golang.org/x/text v0.26.0 // indirect
+	golang.org/x/tools v0.34.0 // indirect
 	golang.zx2c4.com/wintun v0.0.0-20230126152724-0fa3db229ce2 // indirect
-	golang.zx2c4.com/wireguard v0.0.0-20231211153847-12269c276173 // indirect
+	golang.zx2c4.com/wireguard v0.0.0-20250521234502-f333402bd9cb // indirect
 	golang.zx2c4.com/wireguard/windows v0.5.3 // indirect
-	gonum.org/v1/gonum v0.15.1 // indirect
+	gonum.org/v1/gonum v0.16.0 // indirect
 	google.golang.org/genproto/googleapis/rpc v0.0.0-20241007155032-5fefd90f89a9 // indirect
 	gopkg.in/fsnotify.v1 v1.4.7 // indirect
 	gopkg.in/tomb.v1 v1.0.0-20141024135613-dd632973f1e7 // indirect
 	howett.net/plist v1.0.0 // indirect
-	lukechampine.com/blake3 v1.3.0 // indirect
+	lukechampine.com/blake3 v1.4.1 // indirect
 )

go.sum

@@ -47,8 +47,6 @@ github.com/aymanbagabas/go-osc52/v2 v2.0.1 h1:HwpRHbFMcZLEVr42D4p7XBqjyuxQH5SMiE
 github.com/aymanbagabas/go-osc52/v2 v2.0.1/go.mod h1:uYgXzlJ7ZpABp8OJ+exZzJJhRNQ2ASbcXHWsFqH8hp8=
 github.com/aymerick/douceur v0.2.0 h1:Mv+mAeH1Q+n9Fr+oyamOlAkUNPWPlA8PPGR0QAaYuPk=
 github.com/aymerick/douceur v0.2.0/go.mod h1:wlT5vV2O3h55X9m7iVYN0TBM0NH/MmbLnd30/FjWUq4=
-github.com/benbjohnson/clock v1.1.0/go.mod h1:J11/hYXuz8f4ySSvYwY0FKfm+ezbsZBKZxNJlLklBHA=
-github.com/benbjohnson/clock v1.3.0/go.mod h1:J11/hYXuz8f4ySSvYwY0FKfm+ezbsZBKZxNJlLklBHA=
 github.com/benbjohnson/clock v1.3.5 h1:VvXlSJBzZpA/zum6Sj74hxwYI2DIxRWuNIoXAzHZz5o=
 github.com/benbjohnson/clock v1.3.5/go.mod h1:J11/hYXuz8f4ySSvYwY0FKfm+ezbsZBKZxNJlLklBHA=
 github.com/beorn7/perks v0.0.0-20180321164747-3a771d992973/go.mod h1:Dwedo/Wpr24TaqPxmxbtue+5NUziq4I4S80YR8gNf3Q=
@@ -67,10 +65,8 @@ github.com/charmbracelet/glamour v0.7.0 h1:2BtKGZ4iVJCDfMF229EzbeR1QRKLWztO9dMtj
 github.com/charmbracelet/glamour v0.7.0/go.mod h1:jUMh5MeihljJPQbJ/wf4ldw2+yBP59+ctV36jASy7ps=
 github.com/chasefleming/elem-go v0.26.0 h1:RB20oElG4J8W2aQx6jfUuESPQJ52KvC37eLEAPxwJDA=
 github.com/chasefleming/elem-go v0.26.0/go.mod h1:hz73qILBIKnTgOujnSMtEj20/epI+f6vg71RUilJAA4=
-github.com/cilium/ebpf v0.2.0/go.mod h1:To2CFviqOWL/M0gIMsvSMlqe7em/l1ALkX1PyjrX2Qs=
 github.com/client9/misspell v0.3.4/go.mod h1:qj6jICC3Q7zFZvVWo7KLAzC3yx5G7kyvSDkc90ppPyw=
 github.com/cncf/udpa/go v0.0.0-20191209042840-269d4d468f6f/go.mod h1:M8M6+tZqaGXZJjfX53e64911xZQV5JYwmTeXPW+k8Sc=
-github.com/containerd/cgroups v0.0.0-20201119153540-4cbc285b3327/go.mod h1:ZJeTFisyysqgcCdecO57Dj79RfL0LNeGiFUqLYQRYLE=
 github.com/containerd/cgroups v1.1.0 h1:v8rEWFl6EoqHB+swVNjVoCJE8o3jX7e8nqBGPLaDFBM=
 github.com/containerd/cgroups v1.1.0/go.mod h1:6ppBcbh/NOOUU+dMKrykgaBnK9lCIBxHqJDGwsa1mIw=
 github.com/containerd/containerd v1.7.19 h1:/xQ4XRJ0tamDkdzrrBAUy/LE5nCcxFKdBm4EcPrSMEE=
@@ -86,13 +82,10 @@ github.com/containerd/platforms v0.2.1/go.mod h1:XHCb+2/hzowdiut9rkudds9bE5yJ7np
 github.com/containerd/stargz-snapshotter/estargz v0.14.3 h1:OqlDCK3ZVUO6C3B/5FSkDwbkEETK84kQgEeFwDC+62k=
 github.com/containerd/stargz-snapshotter/estargz v0.14.3/go.mod h1:KY//uOCIkSuNAHhJogcZtrNHdKrA99/FCCRjE3HD36o=
 github.com/coreos/go-systemd v0.0.0-20181012123002-c6f51f82210d/go.mod h1:F5haX7vjVVG0kc13fIWeqUViNPyEJxv/OmvnBo0Yme4=
-github.com/coreos/go-systemd/v22 v22.1.0/go.mod h1:xO0FLkIi5MaZafQlIrOotqXZ90ih+1atmu1JpKERPPk=
-github.com/coreos/go-systemd/v22 v22.5.0 h1:RrqgGjYQKalulkV8NGVIfkXQf6YYmOyiJKk8iXXhfZs=
 github.com/coreos/go-systemd/v22 v22.5.0/go.mod h1:Y58oyj3AT4RCenI/lSvhwexgC+NSVTIJ3seZv2GcEnc=
 github.com/cpuguy83/dockercfg v0.3.2 h1:DlJTyZGBDlXqUZ2Dk2Q3xHs/FtnooJJVaad2S9GKorA=
 github.com/cpuguy83/dockercfg v0.3.2/go.mod h1:sugsbF4//dDlL/i+S+rtpIWp+5h0BHJHfjj5/jFyUJc=
 github.com/cpuguy83/go-md2man/v2 v2.0.0-20190314233015-f79a8a8ca69d/go.mod h1:maD7wRr/U5Z6m/iR4s+kqSMx2CaBsrgA7czyZG/E6dU=
-github.com/cpuguy83/go-md2man/v2 v2.0.0/go.mod h1:maD7wRr/U5Z6m/iR4s+kqSMx2CaBsrgA7czyZG/E6dU=
 github.com/cpuguy83/go-md2man/v2 v2.0.2/go.mod h1:tgQtvFlXSQOSOSIRvRPT7W67SCa46tRHOmNcaadrF8o=
 github.com/creachadair/mds v0.21.3 h1:RRgEAPIb52cU0q7UxGyN+13QlCVTZIL4slRr0cYYQfA=
 github.com/creachadair/mds v0.21.3/go.mod h1:1ltMWZd9yXhaHEoZwBialMaviWVUpRPvMwVP7saFAzM=
@@ -125,7 +118,6 @@ github.com/docker/docker-credential-helpers v0.7.0 h1:xtCHsjxogADNZcdv1pKUHXryef
 github.com/docker/docker-credential-helpers v0.7.0/go.mod h1:rETQfLdHNT3foU5kuNkFR1R1V12OJRRO5lzt2D1b5X0=
 github.com/docker/go-connections v0.5.0 h1:USnMq7hx7gwdVZq1L49hLXaFtUdTADjXGp+uj1Br63c=
 github.com/docker/go-connections v0.5.0/go.mod h1:ov60Kzw0kKElRwhNs9UlUHAE/F9Fe6GLaXnqyDdmEXc=
-github.com/docker/go-units v0.4.0/go.mod h1:fgPhTUdO+D/Jk86RDLlptpiXQzgHJF7gydDDbaIK4Dk=
 github.com/docker/go-units v0.5.0 h1:69rxXcBk27SvSaaxTtLh/8llcHD8vYHT7WSdRZ/jvr4=
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@@ -134,9 +126,6 @@ github.com/dsnet/golib v0.0.0-20171103203638-1ea166775780/go.mod h1:Lj+Z9rebOhdf
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@@ -189,10 +178,7 @@ github.com/go-skynet/go-llama.cpp v0.0.0-20240314183750-6a8041ef6b46/go.mod h1:i
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@@ -210,7 +196,6 @@ github.com/gofiber/websocket/v2 v2.2.1/go.mod h1:Ao/+nyNnX5u/hIFPuHl28a+NIkrqK7P
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@@ -241,11 +226,11 @@ github.com/google/go-cmp v0.3.0/go.mod h1:8QqcDgzrUqlUb/G2PQTWiueGozuR1884gddMyw
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@@ -277,11 +262,6 @@ github.com/grpc-ecosystem/grpc-gateway v1.5.0 h1:WcmKMm43DR7RdtlkEXQJyo5ws8iTp98
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@@ -296,14 +276,14 @@ github.com/huandu/xstrings v1.5.0 h1:2ag3IFq9ZDANvthTwTiqSSZLjDc+BedvHPAp5tJy2TI
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@@ -311,10 +291,10 @@ github.com/ipfs/go-ipfs-util v0.0.3/go.mod h1:LHzG1a0Ig4G+iZ26UUOMjHd+lfM84LZCrn
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@@ -323,11 +303,8 @@ github.com/jaypipes/ghw v0.12.0 h1:xU2/MDJfWmBhJnujHY9qwXQLs3DBsf0/Xa9vECY0Tho=
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@@ -341,20 +318,19 @@ github.com/jstemmer/go-junit-report v0.0.0-20190106144839-af01ea7f8024/go.mod h1
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@@ -372,32 +348,30 @@ github.com/libp2p/go-cidranger v1.1.0 h1:ewPN8EZ0dd1LSnrtuwd4709PXVcITVeuwbag38y
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@@ -415,7 +389,6 @@ github.com/marten-seemann/tcp v0.0.0-20210406111302-dfbc87cc63fd/go.mod h1:QuCEs
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@@ -431,8 +404,8 @@ github.com/microcosm-cc/bluemonday v1.0.1/go.mod h1:hsXNsILzKxV+sX77C5b8FSuKF00v
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@@ -474,14 +447,14 @@ github.com/morikuni/aec v1.0.0/go.mod h1:BbKIizmSmc5MMPqRYbxO4ZU0S0+P200+tUnFx7P
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@@ -557,12 +527,12 @@ github.com/pion/datachannel v1.5.10/go.mod h1:p/jJfC9arb29W7WrxyKbepTU20CFgyx5oL
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@@ -572,14 +542,14 @@ github.com/pion/randutil v0.1.0 h1:CFG1UdESneORglEsnimhUjf33Rwjubwj6xfiOXBa3mA=
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@@ -590,10 +560,10 @@ github.com/pion/transport/v2 v2.2.10 h1:ucLBLE8nuxiHfvkFKnkDQRYWYfp8ejf4YBOPfaQp
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@@ -606,26 +576,24 @@ github.com/polydawn/refmt v0.89.0/go.mod h1:/zvteZs/GwLtCgZ4BL6CBsk9IKIlexP43ObX
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@@ -683,7 +651,6 @@ github.com/shurcooL/sanitized_anchor_name v0.0.0-20170918181015-86672fcb3f95/go.
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@@ -738,7 +705,6 @@ github.com/tmc/langchaingo v0.1.12/go.mod h1:cd62xD6h+ouk8k/QQFhOsjRYBSA1JJ5UVKX
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@@ -767,7 +733,6 @@ github.com/xi2/xz v0.0.0-20171230120015-48954b6210f8 h1:nIPpBwaJSVYIxUFsDv3M8ofm
 github.com/xi2/xz v0.0.0-20171230120015-48954b6210f8/go.mod h1:HUYIGzjTL3rfEspMxjDjgmT5uz5wzYJKVo23qUhYTos=
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@@ -783,42 +748,40 @@ go.opentelemetry.io/auto/sdk v1.1.0 h1:cH53jehLUN6UFLY71z+NDOiNJqDdPRaXzTel0sJyS
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 go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.56.0/go.mod h1:qxuZLtbq5QDtdeSHsS7bcf6EH6uO6jUAgk764zd3rhM=
-go.opentelemetry.io/otel v1.34.0 h1:zRLXxLCgL1WyKsPVrgbSdMN4c0FMkDAskSTQP+0hdUY=
-go.opentelemetry.io/otel v1.34.0/go.mod h1:OWFPOQ+h4G8xpyjgqo4SxJYdDQ/qmRH+wivy7zzx9oI=
+go.opentelemetry.io/otel v1.35.0 h1:xKWKPxrxB6OtMCbmMY021CqC45J+3Onta9MqjhnusiQ=
+go.opentelemetry.io/otel v1.35.0/go.mod h1:UEqy8Zp11hpkUrL73gSlELM0DupHoiq72dR+Zqel/+Y=
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 go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp v1.31.0/go.mod h1:2HpZxxQurfGxJlJDblybejHB6RX6pmExPNe517hREw4=
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 go.opentelemetry.io/otel/exporters/prometheus v0.50.0/go.mod h1:pMm5PkUo5YwbLiuEf7t2xg4wbP0/eSJrMxIMxKosynY=
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-go.opentelemetry.io/otel/metric v1.34.0/go.mod h1:CEDrp0fy2D0MvkXE+dPV7cMi8tWZwX3dmaIhwPOaqHE=
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 go.opentelemetry.io/otel/sdk/metric v1.28.0 h1:OkuaKgKrgAbYrrY0t92c+cC+2F6hsFNnCQArXCKlg08=
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 go.uber.org/atomic v1.7.0/go.mod h1:fEN4uk6kAWBTFdckzkM89CLk9XfWZrxpCo0nPH17wJc=
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 go.uber.org/multierr v1.11.0 h1:blXXJkSxSSfBVBlC76pxqeO+LN3aDfLQo+309xJstO0=
 go.uber.org/multierr v1.11.0/go.mod h1:20+QtiLqy0Nd6FdQB9TLXag12DsQkrbs3htMFfDN80Y=
 go.uber.org/tools v0.0.0-20190618225709-2cfd321de3ee/go.mod h1:vJERXedbb3MVM5f9Ejo0C68/HhF8uaILCdgjnY+goOA=
 go.uber.org/zap v1.16.0/go.mod h1:MA8QOfq0BHJwdXa996Y4dYkAqRKB8/1K1QMMZVaNZjQ=
-go.uber.org/zap v1.19.1/go.mod h1:j3DNczoxDZroyBnOT1L/Q79cfUMGZxlv/9dzN7SM1rI=
 go.uber.org/zap v1.27.0 h1:aJMhYGrd5QSmlpLMr2MftRKl7t8J8PTZPA732ud/XR8=
 go.uber.org/zap v1.27.0/go.mod h1:GB2qFLM7cTU87MWRP2mPIjqfIDnGu+VIO4V/SdhGo2E=
 go4.org v0.0.0-20180809161055-417644f6feb5/go.mod h1:MkTOUMDaeVYJUOUsaDXIhWPZYa1yOyC1qaOBpL57BhE=
@@ -836,11 +799,11 @@ golang.org/x/crypto v0.0.0-20210921155107-089bfa567519/go.mod h1:GvvjBRRGRdwPK5y
 golang.org/x/crypto v0.8.0/go.mod h1:mRqEX+O9/h5TFCrQhkgjo2yKi0yYA+9ecGkdQoHrywE=
 golang.org/x/crypto v0.12.0/go.mod h1:NF0Gs7EO5K4qLn+Ylc+fih8BSTeIjAP05siRnAh98yw=
 golang.org/x/crypto v0.18.0/go.mod h1:R0j02AL6hcrfOiy9T4ZYp/rcWeMxM3L6QYxlOuEG1mg=
-golang.org/x/crypto v0.33.0 h1:IOBPskki6Lysi0lo9qQvbxiQ+FvsCC/YWOecCHAixus=
-golang.org/x/crypto v0.33.0/go.mod h1:bVdXmD7IV/4GdElGPozy6U7lWdRXA4qyRVGJV57uQ5M=
+golang.org/x/crypto v0.39.0 h1:SHs+kF4LP+f+p14esP5jAoDpHU8Gu/v9lFRK6IT5imM=
+golang.org/x/crypto v0.39.0/go.mod h1:L+Xg3Wf6HoL4Bn4238Z6ft6KfEpN0tJGo53AAPC632U=
 golang.org/x/exp v0.0.0-20190121172915-509febef88a4/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
-golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa h1:t2QcU6V556bFjYgu4L6C+6VrCPyJZ+eyRsABUPs1mz4=
-golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa/go.mod h1:BHOTPb3L19zxehTsLoJXVaTktb06DFgmdW6Wb9s8jqk=
+golang.org/x/exp v0.0.0-20250606033433-dcc06ee1d476 h1:bsqhLWFR6G6xiQcb+JoGqdKdRU6WzPWmK8E0jxTjzo4=
+golang.org/x/exp v0.0.0-20250606033433-dcc06ee1d476/go.mod h1:3//PLf8L/X+8b4vuAfHzxeRUl04Adcb341+IGKfnqS8=
 golang.org/x/lint v0.0.0-20180702182130-06c8688daad7/go.mod h1:UVdnD1Gm6xHRNCYTkRU2/jEulfH38KcIWyp/GAMgvoE=
 golang.org/x/lint v0.0.0-20181026193005-c67002cb31c3/go.mod h1:UVdnD1Gm6xHRNCYTkRU2/jEulfH38KcIWyp/GAMgvoE=
 golang.org/x/lint v0.0.0-20190227174305-5b3e6a55c961/go.mod h1:wehouNa3lNwaWXcvxsM5YxQ5yQlVC4a0KAMCusXpPoU=
@@ -851,12 +814,11 @@ golang.org/x/mod v0.0.0-20190513183733-4bf6d317e70e/go.mod h1:mXi4GBBbnImb6dmsKG
 golang.org/x/mod v0.1.1-0.20191105210325-c90efee705ee/go.mod h1:QqPTAvyqsEbceGzBzNggFXnrqF1CaUcvgkdR5Ot7KZg=
 golang.org/x/mod v0.2.0/go.mod h1:s0Qsj1ACt9ePp/hMypM3fl4fZqREWJwdYDEqhRiZZUA=
 golang.org/x/mod v0.3.0/go.mod h1:s0Qsj1ACt9ePp/hMypM3fl4fZqREWJwdYDEqhRiZZUA=
-golang.org/x/mod v0.4.2/go.mod h1:s0Qsj1ACt9ePp/hMypM3fl4fZqREWJwdYDEqhRiZZUA=
 golang.org/x/mod v0.6.0-dev.0.20220419223038-86c51ed26bb4/go.mod h1:jJ57K6gSWd91VN4djpZkiMVwK6gcyfeH4XE8wZrZaV4=
 golang.org/x/mod v0.7.0/go.mod h1:iBbtSCu2XBx23ZKBPSOrRkjjQPZFPuis4dIYUhu/chs=
 golang.org/x/mod v0.8.0/go.mod h1:iBbtSCu2XBx23ZKBPSOrRkjjQPZFPuis4dIYUhu/chs=
-golang.org/x/mod v0.23.0 h1:Zb7khfcRGKk+kqfxFaP5tZqCnDZMjC5VtUBs87Hr6QM=
-golang.org/x/mod v0.23.0/go.mod h1:6SkKJ3Xj0I0BrPOZoBy3bdMptDDU9oJrpohJ3eWZ1fY=
+golang.org/x/mod v0.25.0 h1:n7a+ZbQKQA/Ysbyb0/6IbB1H/X41mKgbhfv7AfG/44w=
+golang.org/x/mod v0.25.0/go.mod h1:IXM97Txy2VM4PJ3gI61r1YEk/gAj6zAHN3AdZt6S9Ww=
 golang.org/x/net v0.0.0-20180724234803-3673e40ba225/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
 golang.org/x/net v0.0.0-20180826012351-8a410e7b638d/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
 golang.org/x/net v0.0.0-20180906233101-161cd47e91fd/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
@@ -873,7 +835,6 @@ golang.org/x/net v0.0.0-20201021035429-f5854403a974/go.mod h1:sp8m0HH+o8qH0wwXwY
 golang.org/x/net v0.0.0-20201110031124-69a78807bb2b/go.mod h1:sp8m0HH+o8qH0wwXwYZr8TS3Oi6o0r6Gce1SSxlDquU=
 golang.org/x/net v0.0.0-20210119194325-5f4716e94777/go.mod h1:m0MpNAwzfU5UDzcl9v0D8zg8gWTRqZa9RBIspLL5mdg=
 golang.org/x/net v0.0.0-20210226172049-e18ecbb05110/go.mod h1:m0MpNAwzfU5UDzcl9v0D8zg8gWTRqZa9RBIspLL5mdg=
-golang.org/x/net v0.0.0-20210405180319-a5a99cb37ef4/go.mod h1:p54w0d4576C0XHj96bSt6lcn1PtDYWL6XObtHCRCNQM=
 golang.org/x/net v0.0.0-20210423184538-5f58ad60dda6/go.mod h1:OJAsFXCWl8Ukc7SiCT/9KSuxbyM7479/AVlXFRxuMCk=
 golang.org/x/net v0.0.0-20220722155237-a158d28d115b/go.mod h1:XRhObCWvk6IyKnWLug+ECip1KBveYUHfp+8e9klMJ9c=
 golang.org/x/net v0.3.0/go.mod h1:MBQ8lrhLObU/6UmLb4fmbmk5OcyYmqtbGd/9yIeKjEE=
@@ -882,8 +843,8 @@ golang.org/x/net v0.9.0/go.mod h1:d48xBJpPfHeWQsugry2m+kC02ZBRGRgulfHnEXEuWns=
 golang.org/x/net v0.10.0/go.mod h1:0qNGK6F8kojg2nk9dLZ2mShWaEBan6FAoqfSigmmuDg=
 golang.org/x/net v0.14.0/go.mod h1:PpSgVXXLK0OxS0F31C1/tv6XNguvCrnXIDrFMspZIUI=
 golang.org/x/net v0.20.0/go.mod h1:z8BVo6PvndSri0LbOE3hAn0apkU+1YvI6E70E9jsnvY=
-golang.org/x/net v0.35.0 h1:T5GQRQb2y08kTAByq9L4/bz8cipCdA8FbRTXewonqY8=
-golang.org/x/net v0.35.0/go.mod h1:EglIi67kWsHKlRzzVMUD93VMSWGFOMSZgxFjparz1Qk=
+golang.org/x/net v0.41.0 h1:vBTly1HeNPEn3wtREYfy4GZ/NECgw2Cnl+nK6Nz3uvw=
+golang.org/x/net v0.41.0/go.mod h1:B/K4NNqkfmg07DQYrbwvSluqCJOOXwUjeb/5lOisjbA=
 golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
 golang.org/x/oauth2 v0.0.0-20181017192945-9dcd33a902f4/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
 golang.org/x/oauth2 v0.0.0-20181203162652-d668ce993890/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
@@ -899,9 +860,8 @@ golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJ
 golang.org/x/sync v0.0.0-20210220032951-036812b2e83c/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20220722155255-886fb9371eb4/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.1.0/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
-golang.org/x/sync v0.11.0 h1:GGz8+XQP4FvTTrjZPzNKTMFtSXH80RAzG+5ghFPgK9w=
-golang.org/x/sync v0.11.0/go.mod h1:Czt+wKu1gCyEFDUtn0jG5QVvpJ6rzVqr5aXyt9drQfk=
-golang.org/x/sys v0.0.0-20180810173357-98c5dad5d1a0/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
+golang.org/x/sync v0.15.0 h1:KWH3jNZsfyT6xfAfKiz6MRNmd46ByHDYaZ7KSkCtdW8=
+golang.org/x/sync v0.15.0/go.mod h1:1dzgHSNfp02xaA81J2MS99Qcpr2w7fw1gpm99rleRqA=
 golang.org/x/sys v0.0.0-20180830151530-49385e6e1522/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20180909124046-d0be0721c37e/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20181029174526-d69651ed3497/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
@@ -909,20 +869,15 @@ golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5h
 golang.org/x/sys v0.0.0-20190316082340-a2f829d7f35f/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20190412213103-97732733099d/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20190916202348-b4ddaad3f8a3/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
-golang.org/x/sys v0.0.0-20191026070338-33540a1f6037/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
-golang.org/x/sys v0.0.0-20200124204421-9fbb57f87de9/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20200602225109-6fdc65e7d980/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20200930185726-fdedc70b468f/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20201119102817-f84b799fce68/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20201204225414-ed752295db88/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20210303074136-134d130e1a04/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
-golang.org/x/sys v0.0.0-20210330210617-4fbd30eecc44/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20210423082822-04245dca01da/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20210426080607-c94f62235c83/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
-golang.org/x/sys v0.0.0-20210510120138-977fb7262007/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20210615035016-665e8c7367d1/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20210616094352-59db8d763f22/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20220520151302-bc2c85ada10a/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20220715151400-c0bba94af5f8/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20220722155257-8c9f86f7a55f/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
@@ -940,8 +895,8 @@ golang.org/x/sys v0.11.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.12.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.16.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
 golang.org/x/sys v0.20.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
-golang.org/x/sys v0.30.0 h1:QjkSwP/36a20jFYWkSue1YwXzLmsV5Gfq7Eiy72C1uc=
-golang.org/x/sys v0.30.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
+golang.org/x/sys v0.33.0 h1:q3i8TbbEz+JRD9ywIRlyRAQbM0qF7hu24q3teo2hbuw=
+golang.org/x/sys v0.33.0/go.mod h1:BJP2sWEmIv4KK5OTEluFJCKSidICx8ciO85XgH3Ak8k=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
 golang.org/x/term v0.0.0-20210927222741-03fcf44c2211/go.mod h1:jbD1KX2456YbFQfuXm/mYQcufACuNUgVhRMnK/tPxf8=
 golang.org/x/term v0.3.0/go.mod h1:q750SLmJuPmVoN1blW3UFBPREJfb1KmY3vwxfr+nFDA=
@@ -951,8 +906,8 @@ golang.org/x/term v0.8.0/go.mod h1:xPskH00ivmX89bAKVGSKKtLOWNx2+17Eiy94tnKShWo=
 golang.org/x/term v0.11.0/go.mod h1:zC9APTIj3jG3FdV/Ons+XE1riIZXG4aZ4GTHiPZJPIU=
 golang.org/x/term v0.16.0/go.mod h1:yn7UURbUtPyrVJPGPq404EukNFxcm/foM+bV/bfcDsY=
 golang.org/x/term v0.20.0/go.mod h1:8UkIAJTvZgivsXaD6/pH6U9ecQzZ45awqEOzuCvwpFY=
-golang.org/x/term v0.29.0 h1:L6pJp37ocefwRRtYPKSWOWzOtWSxVajvz2ldH/xi3iU=
-golang.org/x/term v0.29.0/go.mod h1:6bl4lRlvVuDgSf3179VpIxBF0o10JUpXWOnI7nErv7s=
+golang.org/x/term v0.32.0 h1:DR4lr0TjUs3epypdhTOkMmuF5CDFJ/8pOnbzMZPQ7bg=
+golang.org/x/term v0.32.0/go.mod h1:uZG1FhGx848Sqfsq4/DlJr3xGGsYMu/L5GW4abiaEPQ=
 golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
 golang.org/x/text v0.3.1-0.20180807135948-17ff2d5776d2/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
 golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
@@ -963,16 +918,15 @@ golang.org/x/text v0.7.0/go.mod h1:mrYo+phRRbMaCq/xk9113O4dZlRixOauAjOtrjsXDZ8=
 golang.org/x/text v0.9.0/go.mod h1:e1OnstbJyHTd6l/uOt8jFFHp6TRDWZR/bV3emEE/zU8=
 golang.org/x/text v0.12.0/go.mod h1:TvPlkZtksWOMsz7fbANvkp4WM8x/WCo/om8BMLbz+aE=
 golang.org/x/text v0.14.0/go.mod h1:18ZOQIKpY8NJVqYksKHtTdi31H5itFRjB5/qKTNYzSU=
-golang.org/x/text v0.22.0 h1:bofq7m3/HAFvbF51jz3Q9wLg3jkvSPuiZu/pD1XwgtM=
-golang.org/x/text v0.22.0/go.mod h1:YRoo4H8PVmsu+E3Ou7cqLVH8oXWIHVoX0jqUWALQhfY=
+golang.org/x/text v0.26.0 h1:P42AVeLghgTYr4+xUnTRKDMqpar+PtX7KWuNQL21L8M=
+golang.org/x/text v0.26.0/go.mod h1:QK15LZJUUQVJxhz7wXgxSy/CJaTFjd0G+YLonydOVQA=
 golang.org/x/time v0.0.0-20180412165947-fbb02b2291d2/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
 golang.org/x/time v0.0.0-20181108054448-85acf8d2951c/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
-golang.org/x/time v0.8.0 h1:9i3RxcPv3PZnitoVGMPDKZSq1xW1gK1Xy3ArNOGZfEg=
-golang.org/x/time v0.8.0/go.mod h1:3BpzKBy/shNhVucY/MWOyx10tF3SFh9QdLuxbVysPQM=
+golang.org/x/time v0.12.0 h1:ScB/8o8olJvc+CQPWrK3fPZNfh7qgwCrY0zJmoEQLSE=
+golang.org/x/time v0.12.0/go.mod h1:CDIdPxbZBQxdj6cxyCIdrNogrJKMJ7pr37NYpMcMDSg=
 golang.org/x/tools v0.0.0-20180828015842-6cd1fcedba52/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20181030000716-a0a13e073c7b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
-golang.org/x/tools v0.0.0-20181030221726-6c7e314b6563/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20190114222345-bf090417da8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20190226205152-f727befe758c/go.mod h1:9Yl7xja0Znq3iFh3HoIrodX9oNMXvdceNzlUR8zjMvY=
 golang.org/x/tools v0.0.0-20190311212946-11955173bddd/go.mod h1:LCzVGOaR6xXOjkQ3onu1FJEFr0SW1gC7cKk1uF8kGRs=
@@ -985,24 +939,23 @@ golang.org/x/tools v0.0.0-20191119224855-298f0cb1881e/go.mod h1:b+2E5dAYhXwXZwtn
 golang.org/x/tools v0.0.0-20200130002326-2f3ba24bd6e7/go.mod h1:TB2adYChydJhpapKDTa4BR/hXlZSLoq2Wpct/0txZ28=
 golang.org/x/tools v0.0.0-20200619180055-7c47624df98f/go.mod h1:EkVYQZoAsY45+roYkvgYkIh4xh/qjgUK9TdY2XT94GE=
 golang.org/x/tools v0.0.0-20210106214847-113979e3529a/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
-golang.org/x/tools v0.1.5/go.mod h1:o0xws9oXOQQZyjljx8fwUC0k7L1pTE6eaCbjGeHmOkk=
 golang.org/x/tools v0.1.12/go.mod h1:hNGJHUnrk76NpqgfD5Aqm5Crs+Hm0VOH/i9J2+nxYbc=
 golang.org/x/tools v0.4.0/go.mod h1:UE5sM2OK9E/d67R0ANs2xJizIymRP5gJU295PvKXxjQ=
 golang.org/x/tools v0.6.0/go.mod h1:Xwgl3UAJ/d3gWutnCtw505GrjyAbvKui8lOU390QaIU=
-golang.org/x/tools v0.30.0 h1:BgcpHewrV5AUp2G9MebG4XPFI1E2W41zU1SaqVA9vJY=
-golang.org/x/tools v0.30.0/go.mod h1:c347cR/OJfw5TI+GfX7RUPNMdDRRbjvYTS0jPyvsVtY=
+golang.org/x/tools v0.34.0 h1:qIpSLOxeCYGg9TrcJokLBG4KFA6d795g0xkBkiESGlo=
+golang.org/x/tools v0.34.0/go.mod h1:pAP9OwEaY1CAW3HOmg3hLZC5Z0CCmzjAF2UQMSqNARg=
 golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191011141410-1b5146add898/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.zx2c4.com/wintun v0.0.0-20230126152724-0fa3db229ce2 h1:B82qJJgjvYKsXS9jeunTOisW56dUokqW/FOteYJJ/yg=
 golang.zx2c4.com/wintun v0.0.0-20230126152724-0fa3db229ce2/go.mod h1:deeaetjYA+DHMHg+sMSMI58GrEteJUUzzw7en6TJQcI=
-golang.zx2c4.com/wireguard v0.0.0-20231211153847-12269c276173 h1:/jFs0duh4rdb8uIfPMv78iAJGcPKDeqAFnaLBropIC4=
-golang.zx2c4.com/wireguard v0.0.0-20231211153847-12269c276173/go.mod h1:tkCQ4FQXmpAgYVh++1cq16/dH4QJtmvpRv19DWGAHSA=
+golang.zx2c4.com/wireguard v0.0.0-20250521234502-f333402bd9cb h1:whnFRlWMcXI9d+ZbWg+4sHnLp52d5yiIPUxMBSt4X9A=
+golang.zx2c4.com/wireguard v0.0.0-20250521234502-f333402bd9cb/go.mod h1:rpwXGsirqLqN2L0JDJQlwOboGHmptD5ZD6T2VmcqhTw=
 golang.zx2c4.com/wireguard/windows v0.5.3 h1:On6j2Rpn3OEMXqBq00QEDC7bWSZrPIHKIus8eIuExIE=
 golang.zx2c4.com/wireguard/windows v0.5.3/go.mod h1:9TEe8TJmtwyQebdFwAkEWOPr3prrtqm+REGFifP60hI=
-gonum.org/v1/gonum v0.15.1 h1:FNy7N6OUZVUaWG9pTiD+jlhdQ3lMP+/LcTpJ6+a8sQ0=
-gonum.org/v1/gonum v0.15.1/go.mod h1:eZTZuRFrzu5pcyjN5wJhcIhnUdNijYxX1T2IcrOGY0o=
+gonum.org/v1/gonum v0.16.0 h1:5+ul4Swaf3ESvrOnidPp4GZbzf0mxVQpDCYUQE7OJfk=
+gonum.org/v1/gonum v0.16.0/go.mod h1:fef3am4MQ93R2HHpKnLk4/Tbh/s0+wqD5nfa6Pnwy4E=
 google.golang.org/api v0.0.0-20180910000450-7ca32eb868bf/go.mod h1:4mhQ8q/RsB7i+udVvVy5NUi08OU8ZlA0gRVgrF7VFY0=
 google.golang.org/api v0.0.0-20181030000543-1d582fd0359e/go.mod h1:4mhQ8q/RsB7i+udVvVy5NUi08OU8ZlA0gRVgrF7VFY0=
 google.golang.org/api v0.1.0/go.mod h1:UGEZY7KEX120AnNLIHFMKIo4obdJhkp2tPbaPlQx13Y=
@@ -1041,8 +994,8 @@ google.golang.org/protobuf v1.22.0/go.mod h1:EGpADcykh3NcUnDUJcl1+ZksZNG86OlYog2
 google.golang.org/protobuf v1.23.0/go.mod h1:EGpADcykh3NcUnDUJcl1+ZksZNG86OlYog2l/sGQquU=
 google.golang.org/protobuf v1.23.1-0.20200526195155-81db48ad09cc/go.mod h1:EGpADcykh3NcUnDUJcl1+ZksZNG86OlYog2l/sGQquU=
 google.golang.org/protobuf v1.25.0/go.mod h1:9JNX74DMeImyA3h4bdi1ymwjUzf21/xIlbajtzgsN7c=
-google.golang.org/protobuf v1.36.5 h1:tPhr+woSbjfYvY6/GPufUoYizxw1cF/yFoxJ2fmpwlM=
-google.golang.org/protobuf v1.36.5/go.mod h1:9fA7Ob0pmnwhb644+1+CVWFRbNajQ6iRojtC/QF5bRE=
+google.golang.org/protobuf v1.36.6 h1:z1NpPI8ku2WgiWnf+t9wTPsn6eP1L7ksHUlkfLvd9xY=
+google.golang.org/protobuf v1.36.6/go.mod h1:jduwjTPXsFjZGTmRluh+L6NjiWu7pchiJ2/5YcXBHnY=
 gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
 gopkg.in/check.v1 v1.0.0-20180628173108-788fd7840127/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
 gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c h1:Hei/4ADfdWqJk1ZMxUNpqntNwaWcugrBjAiHlqqRiVk=
@@ -1056,18 +1009,16 @@ gopkg.in/tomb.v1 v1.0.0-20141024135613-dd632973f1e7/go.mod h1:dt/ZhP58zS4L8KSrWD
 gopkg.in/yaml.v1 v1.0.0-20140924161607-9f9df34309c0/go.mod h1:WDnlLJ4WF5VGsH/HVa3CI79GS0ol3YnhVnKP89i0kNg=
 gopkg.in/yaml.v2 v2.2.1/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI=
 gopkg.in/yaml.v2 v2.2.2/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI=
-gopkg.in/yaml.v2 v2.2.8/go.mod h1:hI93XBmqTisBFMUTm0b8Fm+jr3Dg1NNxqwp+5A1VGuI=
 gopkg.in/yaml.v2 v2.4.0 h1:D8xgwECY7CYvx+Y2n4sBz93Jn9JRvxdiyyo8CTfuKaY=
 gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ=
 gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
-gopkg.in/yaml.v3 v3.0.0-20210107192922-496545a6307b/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
 gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
 gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
 gotest.tools/v3 v3.5.1 h1:EENdUnS3pdur5nybKYIh2Vfgc8IUNBjxDPSjtiJcOzU=
 gotest.tools/v3 v3.5.1/go.mod h1:isy3WKz7GK6uNw/sbHzfKBLvlvXwUyV06n6brMxxopU=
 grpc.go4.org v0.0.0-20170609214715-11d0a25b4919/go.mod h1:77eQGdRu53HpSqPFJFmuJdjuHRquDANNeA4x7B8WQ9o=
-gvisor.dev/gvisor v0.0.0-20230927004350-cbd86285d259 h1:TbRPT0HtzFP3Cno1zZo7yPzEEnfu8EjLfl6IU9VfqkQ=
-gvisor.dev/gvisor v0.0.0-20230927004350-cbd86285d259/go.mod h1:AVgIgHMwK63XvmAzWG9vLQ41YnVHN0du0tEC46fI7yY=
+gvisor.dev/gvisor v0.0.0-20250503011706-39ed1f5ac29c h1:m/r7OM+Y2Ty1sgBQ7Qb27VgIMBW8ZZhT4gLnUyDIhzI=
+gvisor.dev/gvisor v0.0.0-20250503011706-39ed1f5ac29c/go.mod h1:3r5CMtNQMKIvBlrmM9xWUNamjKBYPOWyXOjmg5Kts3g=
 honnef.co/go/tools v0.0.0-20180728063816-88497007e858/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
 honnef.co/go/tools v0.0.0-20190102054323-c2f93a96b099/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
 honnef.co/go/tools v0.0.0-20190106161140-3f1c8253044a/go.mod h1:rf3lG4BRIbNafJWhAfAdb/ePZxsR/4RtNHQocxwk9r4=
@@ -1075,8 +1026,8 @@ honnef.co/go/tools v0.0.0-20190523083050-ea95bdfd59fc/go.mod h1:rf3lG4BRIbNafJWh
 honnef.co/go/tools v0.0.1-2019.2.3/go.mod h1:a3bituU0lyd329TUQxRnasdCoJDkEUEAqEt0JzvZhAg=
 howett.net/plist v1.0.0 h1:7CrbWYbPPO/PyNy38b2EB/+gYbjCe2DXBxgtOOZbSQM=
 howett.net/plist v1.0.0/go.mod h1:lqaXoTrLY4hg8tnEzNru53gicrbv7rrk+2xJA/7hw9g=
-lukechampine.com/blake3 v1.3.0 h1:sJ3XhFINmHSrYCgl958hscfIa3bw8x4DqMP3u1YvoYE=
-lukechampine.com/blake3 v1.3.0/go.mod h1:0OFRp7fBtAylGVCO40o87sbupkyIGgbpv1+M1k1LM6k=
+lukechampine.com/blake3 v1.4.1 h1:I3Smz7gso8w4/TunLKec6K2fn+kyKtDxr/xcQEN84Wg=
+lukechampine.com/blake3 v1.4.1/go.mod h1:QFosUxmjB8mnrWFSNwKmvxHpfY72bmD2tQ0kBMM3kwo=
 oras.land/oras-go/v2 v2.5.0 h1:o8Me9kLY74Vp5uw07QXPiitjsw7qNXi8Twd+19Zf02c=
 oras.land/oras-go/v2 v2.5.0/go.mod h1:z4eisnLP530vwIOUOJeBIj0aGI0L1C3d53atvCBqZHg=
 sigs.k8s.io/yaml v1.3.0 h1:a2VclLzOGrwOHDiV8EfBGhvjHvP46CtW5j6POvhYGGo=

swagger/docs.go

@@ -1535,6 +1535,12 @@ const docTemplate = `{
                         "$ref": "#/definitions/schema.Message"
                     }
                 },
+                "metadata": {
+                    "type": "object",
+                    "additionalProperties": {
+                        "type": "string"
+                    }
+                },
                 "mode": {
                     "description": "Image (not supported by OpenAI)",
                     "type": "integer"
@@ -1567,6 +1573,9 @@ const docTemplate = `{
                 "quality": {
                     "type": "string"
                 },
+                "reasoning_effort": {
+                    "type": "string"
+                },
                 "ref_images": {
                     "description": "Reference images for models that support them (e.g., Flux Kontext)",
                     "type": "array",

swagger/swagger.json

@@ -1528,6 +1528,12 @@
                         "$ref": "#/definitions/schema.Message"
                     }
                 },
+                "metadata": {
+                    "type": "object",
+                    "additionalProperties": {
+                        "type": "string"
+                    }
+                },
                 "mode": {
                     "description": "Image (not supported by OpenAI)",
                     "type": "integer"
@@ -1560,6 +1566,9 @@
                 "quality": {
                     "type": "string"
                 },
+                "reasoning_effort": {
+                    "type": "string"
+                },
                 "ref_images": {
                     "description": "Reference images for models that support them (e.g., Flux Kontext)",
                     "type": "array",

swagger/swagger.yaml

@@ -491,6 +491,10 @@ definitions:
         items:
           $ref: '#/definitions/schema.Message'
         type: array
+      metadata:
+        additionalProperties:
+          type: string
+        type: object
       mode:
         description: Image (not supported by OpenAI)
         type: integer
@@ -514,6 +518,8 @@ definitions:
         description: Prompt is read only by completion/image API calls
       quality:
         type: string
+      reasoning_effort:
+        type: string
       ref_images:
         description: Reference images for models that support them (e.g., Flux Kontext)
         items: