chore(deps): bump github.com/onsi/gomega from 1.39.0 to 1.39.1 (#8357)

Bumps [github.com/onsi/gomega](https://github.com/onsi/gomega) from 1.39.0 to 1.39.1.
- [Release notes](https://github.com/onsi/gomega/releases)
- [Changelog](https://github.com/onsi/gomega/blob/master/CHANGELOG.md)
- [Commits](https://github.com/onsi/gomega/compare/v1.39.0...v1.39.1)

---
updated-dependencies:
- dependency-name: github.com/onsi/gomega
  dependency-version: 1.39.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

.env

@@ -32,15 +32,6 @@
 # Forces shutdown of the backends if busy (only if LOCALAI_SINGLE_ACTIVE_BACKEND is set)
 # LOCALAI_FORCE_BACKEND_SHUTDOWN=true
 
-## Specify a build type. Available: cublas, openblas, clblas.
-## cuBLAS: This is a GPU-accelerated version of the complete standard BLAS (Basic Linear Algebra Subprograms) library. It's provided by Nvidia and is part of their CUDA toolkit.
-## OpenBLAS: This is an open-source implementation of the BLAS library that aims to provide highly optimized code for various platforms. It includes support for multi-threading and can be compiled to use hardware-specific features for additional performance. OpenBLAS can run on many kinds of hardware, including CPUs from Intel, AMD, and ARM.
-## clBLAS:   This is an open-source implementation of the BLAS library that uses OpenCL, a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, and other processors. clBLAS is designed to take advantage of the parallel computing power of GPUs but can also run on any hardware that supports OpenCL. This includes hardware from different vendors like Nvidia, AMD, and Intel.
-# BUILD_TYPE=openblas
-
-## Uncomment and set to true to enable rebuilding from source
-# REBUILD=true
-
 ## Path where to store generated images
 # LOCALAI_IMAGE_PATH=/tmp/generated/images
 

.github/gallery-agent/agent.go

@@ -2,11 +2,16 @@ package main
 
 import (
 	"context"
+	"encoding/json"
 	"fmt"
+	"io"
+	"net/http"
 	"os"
+	"regexp"
 	"slices"
 	"strings"
 
+	"github.com/ghodss/yaml"
 	hfapi "github.com/mudler/LocalAI/pkg/huggingface-api"
 	cogito "github.com/mudler/cogito"
 
@@ -45,7 +50,12 @@ func cleanTextContent(text string) string {
 	}
 	// Remove trailing empty lines from the result
 	result := strings.Join(cleanedLines, "\n")
-	return strings.TrimRight(result, "\n")
+	return stripThinkingTags(strings.TrimRight(result, "\n"))
+}
+
+type galleryModel struct {
+	Name string   `yaml:"name"`
+	Urls []string `yaml:"urls"`
 }
 
 // isModelExisting checks if a specific model ID exists in the gallery using text search
@@ -56,9 +66,20 @@ func isModelExisting(modelID string) (bool, error) {
 		return false, fmt.Errorf("failed to read %s: %w", indexPath, err)
 	}
 
-	contentStr := string(content)
-	// Simple text search - if the model ID appears anywhere in the file, it exists
-	return strings.Contains(contentStr, modelID), nil
+	var galleryModels []galleryModel
+
+	err = yaml.Unmarshal(content, &galleryModels)
+	if err != nil {
+		return false, fmt.Errorf("failed to unmarshal %s: %w", indexPath, err)
+	}
+
+	for _, galleryModel := range galleryModels {
+		if slices.Contains(galleryModel.Urls, modelID) {
+			return true, nil
+		}
+	}
+
+	return false, nil
 }
 
 // filterExistingModels removes models that already exist in the gallery
@@ -92,6 +113,16 @@ func getGalleryIndexPath() string {
 	return "gallery/index.yaml"
 }
 
+func stripThinkingTags(content string) string {
+	// Remove content between <thinking> and </thinking> (including multi-line)
+	content = regexp.MustCompile(`(?s)<thinking>.*?</thinking>`).ReplaceAllString(content, "")
+	// Remove content between <think> and </think> (including multi-line)
+	content = regexp.MustCompile(`(?s)<think>.*?</think>`).ReplaceAllString(content, "")
+	// Clean up any extra whitespace
+	content = strings.TrimSpace(content)
+	return content
+}
+
 func getRealReadme(ctx context.Context, repository string) (string, error) {
 	// Create a conversation fragment
 	fragment := cogito.NewEmptyFragment().
@@ -120,6 +151,11 @@ func getRealReadme(ctx context.Context, repository string) (string, error) {
 }
 
 func selectMostInterestingModels(ctx context.Context, searchResult *SearchResult) ([]ProcessedModel, error) {
+
+	if len(searchResult.Models) == 1 {
+		return searchResult.Models, nil
+	}
+
 	// Create a conversation fragment
 	fragment := cogito.NewEmptyFragment().
 		AddMessage("user",
@@ -218,71 +254,192 @@ Return your analysis and selection reasoning.`)
 	return filteredModels, nil
 }
 
-// ModelFamily represents a YAML anchor/family
-type ModelFamily struct {
-	Anchor string `json:"anchor"`
-	Name   string `json:"name"`
+// ModelMetadata represents extracted metadata from a model
+type ModelMetadata struct {
+	Tags    []string `json:"tags"`
+	License string   `json:"license"`
 }
 
-// selectModelFamily selects the appropriate model family/anchor for a given model
-func selectModelFamily(ctx context.Context, model ProcessedModel, availableFamilies []ModelFamily) (string, error) {
+// extractModelMetadata extracts tags and license from model README and documentation
+func extractModelMetadata(ctx context.Context, model ProcessedModel) ([]string, string, error) {
 	// Create a conversation fragment
 	fragment := cogito.NewEmptyFragment().
 		AddMessage("user",
-			`Your task is to select the most appropriate model family/anchor for a given AI model. You will be provided with:
-1. Information about the model (name, description, etc.)
-2. A list of available model families/anchors
+			`Your task is to extract metadata from an AI model's README and documentation. You will be provided with:
+1. Model information (ID, author, description)
+2. README content
 
-You need to select the family that best matches the model's architecture, capabilities, or characteristics. Consider:
-- Model architecture (e.g., Llama, Qwen, Mistral, etc.)
-- Model capabilities (e.g., vision, coding, chat, etc.)
-- Model size/type (e.g., small, medium, large)
-- Model purpose (e.g., general purpose, specialized, etc.)
+You need to extract:
+1. **Tags**: An array of relevant tags that describe the model. Use common tags from the gallery such as:
+   - llm, gguf, gpu, cpu, multimodal, image-to-text, text-to-text, text-to-speech, tts
+   - thinking, reasoning, chat, instruction-tuned, code, vision
+   - Model family names (e.g., llama, qwen, mistral, gemma) if applicable
+   - Any other relevant descriptive tags
+   Select 3-8 most relevant tags.
 
-Return the anchor name that best fits the model.`)
+2. **License**: The license identifier (e.g., "apache-2.0", "mit", "llama2", "gpl-3.0", "bsd", "cc-by-4.0").
+   If no license is found, return an empty string.
+
+Return the extracted metadata in a structured format.`)
 
 	// Add model information
 	modelInfo := "Model Information:\n"
 	modelInfo += fmt.Sprintf("  ID: %s\n", model.ModelID)
 	modelInfo += fmt.Sprintf("  Author: %s\n", model.Author)
 	modelInfo += fmt.Sprintf("  Downloads: %d\n", model.Downloads)
-	modelInfo += fmt.Sprintf("  Description: %s\n", model.ReadmeContentPreview)
-
-	fragment = fragment.AddMessage("user", modelInfo)
-
-	// Add available families
-	familiesInfo := "Available Model Families:\n"
-	for _, family := range availableFamilies {
-		familiesInfo += fmt.Sprintf("  - %s (%s)\n", family.Anchor, family.Name)
+	if model.ReadmeContent != "" {
+		modelInfo += fmt.Sprintf("  README Content:\n%s\n", model.ReadmeContent)
+	} else if model.ReadmeContentPreview != "" {
+		modelInfo += fmt.Sprintf("  README Preview: %s\n", model.ReadmeContentPreview)
 	}
 
-	fragment = fragment.AddMessage("user", familiesInfo)
-	fragment = fragment.AddMessage("user", "Select the most appropriate family anchor for this model. Return just the anchor name.")
+	fragment = fragment.AddMessage("user", modelInfo)
+	fragment = fragment.AddMessage("user", "Extract the tags and license from the model information. Return the metadata as a JSON object with 'tags' (array of strings) and 'license' (string).")
 
 	// Get a response
 	newFragment, err := llm.Ask(ctx, fragment)
 	if err != nil {
-		return "", err
+		return nil, "", err
 	}
 
-	// Extract the selected family
-	selectedFamily := strings.TrimSpace(newFragment.LastMessage().Content)
+	// Extract structured metadata
+	metadata := ModelMetadata{}
 
-	// Validate that the selected family exists in our list
-	for _, family := range availableFamilies {
-		if family.Anchor == selectedFamily {
-			return selectedFamily, nil
-		}
+	s := structures.Structure{
+		Schema: jsonschema.Definition{
+			Type:                 jsonschema.Object,
+			AdditionalProperties: false,
+			Properties: map[string]jsonschema.Definition{
+				"tags": {
+					Type:        jsonschema.Array,
+					Items:       &jsonschema.Definition{Type: jsonschema.String},
+					Description: "Array of relevant tags describing the model",
+				},
+				"license": {
+					Type:        jsonschema.String,
+					Description: "License identifier (e.g., apache-2.0, mit, llama2). Empty string if not found.",
+				},
+			},
+			Required: []string{"tags", "license"},
+		},
+		Object: &metadata,
 	}
 
-	// If no exact match, try to find a close match
-	for _, family := range availableFamilies {
-		if strings.Contains(strings.ToLower(family.Anchor), strings.ToLower(selectedFamily)) ||
-			strings.Contains(strings.ToLower(selectedFamily), strings.ToLower(family.Anchor)) {
-			return family.Anchor, nil
-		}
+	err = newFragment.ExtractStructure(ctx, llm, s)
+	if err != nil {
+		return nil, "", err
 	}
 
-	// Default fallback
-	return "llama3", nil
+	return metadata.Tags, metadata.License, nil
+}
+
+// extractIconFromReadme scans the README content for image URLs and returns the first suitable icon URL found
+func extractIconFromReadme(readmeContent string) string {
+	if readmeContent == "" {
+		return ""
+	}
+
+	// Regular expressions to match image URLs in various formats (case-insensitive)
+	// Match markdown image syntax: ![alt](url) - case insensitive extensions
+	markdownImageRegex := regexp.MustCompile(`(?i)!\[[^\]]*\]\(([^)]+\.(png|jpg|jpeg|svg|webp|gif))\)`)
+	// Match HTML img tags: <img src="url">
+	htmlImageRegex := regexp.MustCompile(`(?i)<img[^>]+src=["']([^"']+\.(png|jpg|jpeg|svg|webp|gif))["']`)
+	// Match plain URLs ending with image extensions
+	plainImageRegex := regexp.MustCompile(`(?i)https?://[^\s<>"']+\.(png|jpg|jpeg|svg|webp|gif)`)
+
+	// Try markdown format first
+	matches := markdownImageRegex.FindStringSubmatch(readmeContent)
+	if len(matches) > 1 && matches[1] != "" {
+		url := strings.TrimSpace(matches[1])
+		// Prefer HuggingFace CDN URLs or absolute URLs
+		if strings.HasPrefix(strings.ToLower(url), "http") {
+			return url
+		}
+	}
+
+	// Try HTML img tags
+	matches = htmlImageRegex.FindStringSubmatch(readmeContent)
+	if len(matches) > 1 && matches[1] != "" {
+		url := strings.TrimSpace(matches[1])
+		if strings.HasPrefix(strings.ToLower(url), "http") {
+			return url
+		}
+	}
+
+	// Try plain URLs
+	matches = plainImageRegex.FindStringSubmatch(readmeContent)
+	if len(matches) > 0 {
+		url := strings.TrimSpace(matches[0])
+		if strings.HasPrefix(strings.ToLower(url), "http") {
+			return url
+		}
+	}
+
+	return ""
+}
+
+// getHuggingFaceAvatarURL attempts to get the HuggingFace avatar URL for a user
+func getHuggingFaceAvatarURL(author string) string {
+	if author == "" {
+		return ""
+	}
+
+	// Try to fetch user info from HuggingFace API
+	// HuggingFace API endpoint: https://huggingface.co/api/users/{username}
+	baseURL := "https://huggingface.co"
+	userURL := fmt.Sprintf("%s/api/users/%s", baseURL, author)
+
+	req, err := http.NewRequest("GET", userURL, nil)
+	if err != nil {
+		return ""
+	}
+
+	client := &http.Client{}
+	resp, err := client.Do(req)
+	if err != nil {
+		return ""
+	}
+	defer resp.Body.Close()
+
+	if resp.StatusCode != http.StatusOK {
+		return ""
+	}
+
+	// Parse the response to get avatar URL
+	var userInfo map[string]interface{}
+	body, err := io.ReadAll(resp.Body)
+	if err != nil {
+		return ""
+	}
+
+	if err := json.Unmarshal(body, &userInfo); err != nil {
+		return ""
+	}
+
+	// Try to extract avatar URL from response
+	if avatar, ok := userInfo["avatarUrl"].(string); ok && avatar != "" {
+		return avatar
+	}
+	if avatar, ok := userInfo["avatar"].(string); ok && avatar != "" {
+		return avatar
+	}
+
+	return ""
+}
+
+// extractModelIcon extracts icon URL from README or falls back to HuggingFace avatar
+func extractModelIcon(model ProcessedModel) string {
+	// First, try to extract icon from README
+	if icon := extractIconFromReadme(model.ReadmeContent); icon != "" {
+		return icon
+	}
+
+	// Fallback: Try to get HuggingFace user avatar
+	if model.Author != "" {
+		if avatar := getHuggingFaceAvatarURL(model.Author); avatar != "" {
+			return avatar
+		}
+	}
+
+	return ""
 }

.github/gallery-agent/gallery.go

@@ -2,13 +2,61 @@ package main
 
 import (
 	"context"
+	"encoding/json"
 	"fmt"
 	"os"
 	"strings"
+
+	"github.com/ghodss/yaml"
+	"github.com/mudler/LocalAI/core/gallery/importers"
 )
 
+func formatTextContent(text string) string {
+	return formatTextContentWithIndent(text, 4, 6)
+}
+
+// formatTextContentWithIndent formats text content with specified base and list item indentation
+func formatTextContentWithIndent(text string, baseIndent int, listItemIndent int) string {
+	var formattedLines []string
+	lines := strings.Split(text, "\n")
+	for _, line := range lines {
+		trimmed := strings.TrimRight(line, " \t\r")
+		if trimmed == "" {
+			// Keep empty lines as empty (no indentation)
+			formattedLines = append(formattedLines, "")
+		} else {
+			// Preserve relative indentation from yaml.Marshal output
+			// Count existing leading spaces to preserve relative structure
+			leadingSpaces := len(trimmed) - len(strings.TrimLeft(trimmed, " \t"))
+			trimmedStripped := strings.TrimLeft(trimmed, " \t")
+
+			var totalIndent int
+			if strings.HasPrefix(trimmedStripped, "-") {
+				// List items: use listItemIndent (ignore existing leading spaces)
+				totalIndent = listItemIndent
+			} else {
+				// Regular lines: use baseIndent + preserve relative indentation
+				// This handles both top-level keys (leadingSpaces=0) and nested properties (leadingSpaces>0)
+				totalIndent = baseIndent + leadingSpaces
+			}
+
+			indentStr := strings.Repeat(" ", totalIndent)
+			formattedLines = append(formattedLines, indentStr+trimmedStripped)
+		}
+	}
+	formattedText := strings.Join(formattedLines, "\n")
+	// Remove any trailing spaces from the formatted description
+	formattedText = strings.TrimRight(formattedText, " \t")
+	return formattedText
+}
+
 // generateYAMLEntry generates a YAML entry for a model using the specified anchor
-func generateYAMLEntry(model ProcessedModel, familyAnchor string) string {
+func generateYAMLEntry(model ProcessedModel, quantization string) string {
+	modelConfig, err := importers.DiscoverModelConfig("https://huggingface.co/"+model.ModelID, json.RawMessage(`{ "quantization": "`+quantization+`"}`))
+	if err != nil {
+		panic(err)
+	}
+
 	// Extract model name from ModelID
 	parts := strings.Split(model.ModelID, "/")
 	modelName := model.ModelID
@@ -22,18 +70,6 @@ func generateYAMLEntry(model ProcessedModel, familyAnchor string) string {
 	modelName = strings.ReplaceAll(modelName, "-q3_k_m", "")
 	modelName = strings.ReplaceAll(modelName, "-q2_k", "")
 
-	fileName := ""
-	checksum := ""
-	if model.PreferredModelFile != nil {
-		fileParts := strings.Split(model.PreferredModelFile.Path, "/")
-		if len(fileParts) > 0 {
-			fileName = fileParts[len(fileParts)-1]
-		}
-		checksum = model.PreferredModelFile.SHA256
-	} else {
-		fileName = model.ModelID
-	}
-
 	description := model.ReadmeContent
 	if description == "" {
 		description = fmt.Sprintf("AI model: %s", modelName)
@@ -41,142 +77,88 @@ func generateYAMLEntry(model ProcessedModel, familyAnchor string) string {
 
 	// Clean up description to prevent YAML linting issues
 	description = cleanTextContent(description)
+	formattedDescription := formatTextContent(description)
 
-	// Format description for YAML (indent each line and ensure no trailing spaces)
-	lines := strings.Split(description, "\n")
-	var formattedLines []string
-	for _, line := range lines {
-		if strings.TrimSpace(line) == "" {
-			// Keep empty lines as empty (no indentation)
-			formattedLines = append(formattedLines, "")
-		} else {
-			// Add indentation to non-empty lines
-			formattedLines = append(formattedLines, "    "+line)
-		}
+	configFile := formatTextContent(modelConfig.ConfigFile)
+
+	filesYAML, _ := yaml.Marshal(modelConfig.Files)
+
+	// Files section: list items need 4 spaces (not 6), since files: is at 2 spaces
+	files := formatTextContentWithIndent(string(filesYAML), 4, 4)
+
+	// Build metadata sections
+	var metadataSections []string
+
+	// Add license if present
+	if model.License != "" {
+		metadataSections = append(metadataSections, fmt.Sprintf(`  license: "%s"`, model.License))
 	}
-	formattedDescription := strings.Join(formattedLines, "\n")
-	// Remove any trailing spaces from the formatted description
-	formattedDescription = strings.TrimRight(formattedDescription, " \t")
+
+	// Add tags if present
+	if len(model.Tags) > 0 {
+		tagsYAML, _ := yaml.Marshal(model.Tags)
+		tagsFormatted := formatTextContentWithIndent(string(tagsYAML), 4, 4)
+		tagsFormatted = strings.TrimRight(tagsFormatted, "\n")
+		metadataSections = append(metadataSections, fmt.Sprintf("  tags:\n%s", tagsFormatted))
+	}
+
+	// Add icon if present
+	if model.Icon != "" {
+		metadataSections = append(metadataSections, fmt.Sprintf(`  icon: %s`, model.Icon))
+	}
+
+	// Build the metadata block
+	metadataBlock := ""
+	if len(metadataSections) > 0 {
+		metadataBlock = strings.Join(metadataSections, "\n") + "\n"
+	}
+
 	yamlTemplate := ""
-	if checksum != "" {
-		yamlTemplate = `- !!merge <<: *%s
-  name: "%s"
+	yamlTemplate = `- name: "%s"
+  url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
   urls:
     - https://huggingface.co/%s
   description: |
-%s
+%s%s
   overrides:
-    parameters:
-      model: %s
+%s
   files:
-    - filename: %s
-      sha256: %s
-      uri: huggingface://%s/%s`
-		return fmt.Sprintf(yamlTemplate,
-			familyAnchor,
-			modelName,
-			model.ModelID,
-			formattedDescription,
-			fileName,
-			fileName,
-			checksum,
-			model.ModelID,
-			fileName,
-		)
-	} else {
-		yamlTemplate = `- !!merge <<: *%s
-  name: "%s"
-  urls:
-    - https://huggingface.co/%s
-  description: |
-%s
-  overrides:
-    parameters:
-      model: %s`
-		return fmt.Sprintf(yamlTemplate,
-			familyAnchor,
-			modelName,
-			model.ModelID,
-			formattedDescription,
-			fileName,
-		)
+%s`
+	// Trim trailing newlines from formatted sections to prevent extra blank lines
+	formattedDescription = strings.TrimRight(formattedDescription, "\n")
+	configFile = strings.TrimRight(configFile, "\n")
+	files = strings.TrimRight(files, "\n")
+	// Add newline before metadata block if present
+	if metadataBlock != "" {
+		metadataBlock = "\n" + strings.TrimRight(metadataBlock, "\n")
 	}
-}
-
-// extractModelFamilies extracts all YAML anchors from the gallery index.yaml file
-func extractModelFamilies() ([]ModelFamily, error) {
-	// Read the index.yaml file
-	indexPath := getGalleryIndexPath()
-	content, err := os.ReadFile(indexPath)
-	if err != nil {
-		return nil, fmt.Errorf("failed to read %s: %w", indexPath, err)
-	}
-
-	lines := strings.Split(string(content), "\n")
-	var families []ModelFamily
-
-	for _, line := range lines {
-		line = strings.TrimSpace(line)
-		// Look for YAML anchors (lines starting with "- &")
-		if strings.HasPrefix(line, "- &") {
-			// Extract the anchor name (everything after "- &")
-			anchor := strings.TrimPrefix(line, "- &")
-			// Remove any trailing colon or other characters
-			anchor = strings.Split(anchor, ":")[0]
-			anchor = strings.Split(anchor, " ")[0]
-
-			if anchor != "" {
-				families = append(families, ModelFamily{
-					Anchor: anchor,
-					Name:   anchor, // Use anchor as name for now
-				})
-			}
-		}
-	}
-
-	return families, nil
+	return fmt.Sprintf(yamlTemplate,
+		modelName,
+		model.ModelID,
+		formattedDescription,
+		metadataBlock,
+		configFile,
+		files,
+	)
 }
 
 // generateYAMLForModels generates YAML entries for selected models and appends to index.yaml
-func generateYAMLForModels(ctx context.Context, models []ProcessedModel) error {
-	// Extract available model families
-	families, err := extractModelFamilies()
-	if err != nil {
-		return fmt.Errorf("failed to extract model families: %w", err)
-	}
-
-	fmt.Printf("Found %d model families: %v\n", len(families),
-		func() []string {
-			var names []string
-			for _, f := range families {
-				names = append(names, f.Anchor)
-			}
-			return names
-		}())
+func generateYAMLForModels(ctx context.Context, models []ProcessedModel, quantization string) error {
 
 	// Generate YAML entries for each model
 	var yamlEntries []string
 	for _, model := range models {
-		fmt.Printf("Selecting family for model: %s\n", model.ModelID)
-
-		// Select appropriate family for this model
-		familyAnchor, err := selectModelFamily(ctx, model, families)
-		if err != nil {
-			fmt.Printf("Error selecting family for %s: %v, using default\n", model.ModelID, err)
-			familyAnchor = "llama3" // Default fallback
-		}
-
-		fmt.Printf("Selected family '%s' for model %s\n", familyAnchor, model.ModelID)
+		fmt.Printf("Generating YAML entry for model: %s\n", model.ModelID)
 
 		// Generate YAML entry
-		yamlEntry := generateYAMLEntry(model, familyAnchor)
+		yamlEntry := generateYAMLEntry(model, quantization)
 		yamlEntries = append(yamlEntries, yamlEntry)
 	}
 
-	// Append to index.yaml
+	// Prepend to index.yaml (write at the top)
 	if len(yamlEntries) > 0 {
 		indexPath := getGalleryIndexPath()
-		fmt.Printf("Appending YAML entries to %s...\n", indexPath)
+		fmt.Printf("Prepending YAML entries to %s...\n", indexPath)
 
 		// Read current content
 		content, err := os.ReadFile(indexPath)
@@ -184,11 +166,26 @@ func generateYAMLForModels(ctx context.Context, models []ProcessedModel) error {
 			return fmt.Errorf("failed to read %s: %w", indexPath, err)
 		}
 
-		// Append new entries
-		// Remove trailing whitespace from existing content and join entries without extra newlines
-		existingContent := strings.TrimRight(string(content), " \t\n\r")
+		existingContent := string(content)
 		yamlBlock := strings.Join(yamlEntries, "\n")
-		newContent := existingContent + "\n" + yamlBlock + "\n"
+
+		// Check if file starts with "---"
+		var newContent string
+		if strings.HasPrefix(existingContent, "---\n") {
+			// File starts with "---", prepend new entries after it
+			restOfContent := strings.TrimPrefix(existingContent, "---\n")
+			// Ensure proper spacing: "---\n" + new entries + "\n" + rest of content
+			newContent = "---\n" + yamlBlock + "\n" + restOfContent
+		} else if strings.HasPrefix(existingContent, "---") {
+			// File starts with "---" but no newline after
+			restOfContent := strings.TrimPrefix(existingContent, "---")
+			newContent = "---\n" + yamlBlock + "\n" + strings.TrimPrefix(restOfContent, "\n")
+		} else {
+			// No "---" at start, prepend new entries at the very beginning
+			// Trim leading whitespace from existing content
+			existingContent = strings.TrimLeft(existingContent, " \t\n\r")
+			newContent = yamlBlock + "\n" + existingContent
+		}
 
 		// Write back to file
 		err = os.WriteFile(indexPath, []byte(newContent), 0644)
@@ -196,7 +193,7 @@ func generateYAMLForModels(ctx context.Context, models []ProcessedModel) error {
 			return fmt.Errorf("failed to write %s: %w", indexPath, err)
 		}
 
-		fmt.Printf("Successfully added %d models to %s\n", len(yamlEntries), indexPath)
+		fmt.Printf("Successfully prepended %d models to %s\n", len(yamlEntries), indexPath)
 	}
 
 	return nil

.github/gallery-agent/main.go

@@ -34,6 +34,9 @@ type ProcessedModel struct {
 	ReadmeContentPreview    string               `json:"readme_content_preview,omitempty"`
 	QuantizationPreferences []string             `json:"quantization_preferences"`
 	ProcessingError         string               `json:"processing_error,omitempty"`
+	Tags                    []string             `json:"tags,omitempty"`
+	License                 string               `json:"license,omitempty"`
+	Icon                    string               `json:"icon,omitempty"`
 }
 
 // SearchResult represents the complete result of searching and processing models
@@ -116,14 +119,24 @@ func main() {
 	}
 
 	fmt.Println(result.FormattedOutput)
+	var models []ProcessedModel
 
-	// Use AI agent to select the most interesting models
-	fmt.Println("Using AI agent to select the most interesting models...")
-	models, err := selectMostInterestingModels(context.Background(), result)
-	if err != nil {
-		fmt.Fprintf(os.Stderr, "Error in model selection: %v\n", err)
-		// Continue with original result if selection fails
+	if len(result.Models) > 1 {
+		fmt.Println("More than one model found (", len(result.Models), "), using AI agent to select the most interesting models")
+		for _, model := range result.Models {
+			fmt.Println("Model: ", model.ModelID)
+		}
+		// Use AI agent to select the most interesting models
+		fmt.Println("Using AI agent to select the most interesting models...")
+		models, err = selectMostInterestingModels(context.Background(), result)
+		if err != nil {
+			fmt.Fprintf(os.Stderr, "Error in model selection: %v\n", err)
+			// Continue with original result if selection fails
+			models = result.Models
+		}
+	} else if len(result.Models) == 1 {
 		models = result.Models
+		fmt.Println("Only one model found, using it directly")
 	}
 
 	fmt.Print(models)
@@ -154,7 +167,7 @@ func main() {
 			addedModelURLs = append(addedModelURLs, modelURL)
 		}
 		fmt.Println("Generating YAML entries for selected models...")
-		err = generateYAMLForModels(context.Background(), models)
+		err = generateYAMLForModels(context.Background(), models, quantization)
 		if err != nil {
 			fmt.Fprintf(os.Stderr, "Error generating YAML entries: %v\n", err)
 			os.Exit(1)
@@ -312,9 +325,28 @@ func searchAndProcessModels(searchTerm string, limit int, quantization string) (
 				outputBuilder.WriteString(fmt.Sprintf("   README Content Preview: %s\n",
 					processedModel.ReadmeContentPreview))
 			} else {
-				continue
+				fmt.Printf("   Warning: Failed to get real readme: %v\n", err)
 			}
 			fmt.Println("Real readme got", readmeContent)
+
+			// Extract metadata (tags, license) from README using LLM
+			fmt.Println("Extracting metadata for", model.ModelID, "waiting...")
+			tags, license, err := extractModelMetadata(context.Background(), processedModel)
+			if err == nil {
+				processedModel.Tags = tags
+				processedModel.License = license
+				outputBuilder.WriteString(fmt.Sprintf("   Tags: %v\n", tags))
+				outputBuilder.WriteString(fmt.Sprintf("   License: %s\n", license))
+			} else {
+				fmt.Printf("   Warning: Failed to extract metadata: %v\n", err)
+			}
+
+			// Extract icon from README or use HuggingFace avatar
+			icon := extractModelIcon(processedModel)
+			if icon != "" {
+				processedModel.Icon = icon
+				outputBuilder.WriteString(fmt.Sprintf("   Icon: %s\n", icon))
+			}
 			// Get README content
 			// readmeContent, err := client.GetReadmeContent(model.ModelID, details.ReadmeFile.Path)
 			// if err == nil {

.github/gallery-agent/testing.go

@@ -25,7 +25,7 @@ func runSyntheticMode() error {
 
 	// Generate YAML entries and append to gallery/index.yaml
 	fmt.Println("Generating YAML entries for synthetic models...")
-	err := generateYAMLForModels(context.Background(), models)
+	err := generateYAMLForModels(context.Background(), models, "Q4_K_M")
 	if err != nil {
 		return fmt.Errorf("error generating YAML entries: %w", err)
 	}
@@ -138,6 +138,25 @@ func (g *SyntheticDataGenerator) GenerateProcessedModel() ProcessedModel {
 
 	readmeContent := g.generateReadmeContent(modelName, author)
 
+	// Generate sample metadata
+	licenses := []string{"apache-2.0", "mit", "llama2", "gpl-3.0", "bsd", ""}
+	license := licenses[g.rand.Intn(len(licenses))]
+
+	sampleTags := []string{"llm", "gguf", "gpu", "cpu", "text-to-text", "chat", "instruction-tuned"}
+	numTags := g.rand.Intn(4) + 3 // 3-6 tags
+	tags := make([]string, numTags)
+	for i := 0; i < numTags; i++ {
+		tags[i] = sampleTags[g.rand.Intn(len(sampleTags))]
+	}
+	// Remove duplicates
+	tags = g.removeDuplicates(tags)
+
+	// Optionally include icon (50% chance)
+	icon := ""
+	if g.rand.Intn(2) == 0 {
+		icon = fmt.Sprintf("https://cdn-avatars.huggingface.co/v1/production/uploads/%s.png", g.randomString(24))
+	}
+
 	return ProcessedModel{
 		ModelID:                 modelID,
 		Author:                  author,
@@ -150,6 +169,9 @@ func (g *SyntheticDataGenerator) GenerateProcessedModel() ProcessedModel {
 		ReadmeContentPreview:    truncateString(readmeContent, 200),
 		QuantizationPreferences: []string{"Q4_K_M", "Q4_K_S", "Q3_K_M", "Q2_K"},
 		ProcessingError:         "",
+		Tags:                    tags,
+		License:                 license,
+		Icon:                    icon,
 	}
 }
 
@@ -179,6 +201,18 @@ func (g *SyntheticDataGenerator) randomDate() string {
 	return pastDate.Format("2006-01-02T15:04:05.000Z")
 }
 
+func (g *SyntheticDataGenerator) removeDuplicates(slice []string) []string {
+	keys := make(map[string]bool)
+	result := []string{}
+	for _, item := range slice {
+		if !keys[item] {
+			keys[item] = true
+			result = append(result, item)
+		}
+	}
+	return result
+}
+
 func (g *SyntheticDataGenerator) generateReadmeContent(modelName, author string) string {
 	templates := []string{
 		fmt.Sprintf("# %s Model\n\nThis is a %s model developed by %s. It's designed for various natural language processing tasks including text generation, question answering, and conversation.\n\n## Features\n\n- High-quality text generation\n- Efficient inference\n- Multiple quantization options\n- Easy to use with LocalAI\n\n## Usage\n\nUse this model with LocalAI for various AI tasks.", strings.Title(modelName), modelName, author),

.github/workflows/backend_build.yml

@@ -1,5 +1,5 @@
 ---
-name: 'build python backend container images (reusable)'
+name: 'build backend container images (reusable)'
 
 on:
   workflow_call:
@@ -53,6 +53,11 @@ on:
         description: 'Skip drivers'
         default: 'false'
         type: string
+      ubuntu-version:
+        description: 'Ubuntu version'
+        required: false
+        default: '2204'
+        type: string
     secrets:
       dockerUsername:
         required: false
@@ -208,6 +213,7 @@ jobs:
             CUDA_MINOR_VERSION=${{ inputs.cuda-minor-version }}
             BASE_IMAGE=${{ inputs.base-image }}
             BACKEND=${{ inputs.backend }}
+            UBUNTU_VERSION=${{ inputs.ubuntu-version }}
           context: ${{ inputs.context }}
           file: ${{ inputs.dockerfile }}
           cache-from: type=gha
@@ -228,6 +234,7 @@ jobs:
             CUDA_MINOR_VERSION=${{ inputs.cuda-minor-version }}
             BASE_IMAGE=${{ inputs.base-image }}
             BACKEND=${{ inputs.backend }}
+            UBUNTU_VERSION=${{ inputs.ubuntu-version }}
           context: ${{ inputs.context }}
           file: ${{ inputs.dockerfile }}
           cache-from: type=gha

.github/workflows/backend_build_darwin.yml

@@ -74,7 +74,7 @@ jobs:
           BACKEND=${{ inputs.backend }} BUILD_TYPE=${{ inputs.build-type }} USE_PIP=${{ inputs.use-pip }} make build-darwin-${{ inputs.lang }}-backend
 
       - name: Upload ${{ inputs.backend }}.tar
-        uses: actions/upload-artifact@v5
+        uses: actions/upload-artifact@v6
         with:
           name: ${{ inputs.backend }}-tar
           path: backend-images/${{ inputs.backend }}.tar
@@ -85,7 +85,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Download ${{ inputs.backend }}.tar
-        uses: actions/download-artifact@v6
+        uses: actions/download-artifact@v7
         with:
           name: ${{ inputs.backend }}-tar
           path: .

.github/workflows/backend_pr.yml

@@ -52,6 +52,7 @@ jobs:
       dockerfile: ${{ matrix.dockerfile }}
       skip-drivers: ${{ matrix.skip-drivers }}
       context: ${{ matrix.context }}
+      ubuntu-version: ${{ matrix.ubuntu-version }}
     secrets:
       quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
       quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
@@ -69,7 +70,7 @@ jobs:
       tag-suffix: ${{ matrix.tag-suffix }}
       lang: ${{ matrix.lang || 'python' }}
       use-pip: ${{ matrix.backend == 'diffusers' }}
-      runs-on: "macOS-14"
+      runs-on: "macos-latest"
     secrets:
       quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
       quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}

.github/workflows/build-test.yaml

@@ -37,7 +37,7 @@ jobs:
           make build-launcher-darwin
           ls -liah dist
       - name: Upload macOS launcher artifacts
-        uses: actions/upload-artifact@v5
+        uses: actions/upload-artifact@v6
         with:
           name: launcher-macos
           path: dist/
@@ -60,7 +60,7 @@ jobs:
           sudo apt-get install golang gcc libgl1-mesa-dev xorg-dev libxkbcommon-dev
           make build-launcher-linux
       - name: Upload Linux launcher artifacts
-        uses: actions/upload-artifact@v5
+        uses: actions/upload-artifact@v6
         with:
           name: launcher-linux
           path: local-ai-launcher-linux.tar.xz

.github/workflows/bump_deps.yaml

@@ -49,7 +49,7 @@ jobs:
           rm -rfv ${{ matrix.variable }}_message.txt
           rm -rfv ${{ matrix.variable }}_commit.txt
       - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v7
+        uses: peter-evans/create-pull-request@v8
         with:
           token: ${{ secrets.UPDATE_BOT_TOKEN }}
           push-to-fork: ci-forks/LocalAI

.github/workflows/bump_docs.yaml

@@ -17,7 +17,7 @@ jobs:
         run: |
           bash .github/bump_docs.sh ${{ matrix.repository }}
       - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v7
+        uses: peter-evans/create-pull-request@v8
         with:
           token: ${{ secrets.UPDATE_BOT_TOKEN }}
           push-to-fork: ci-forks/LocalAI

.github/workflows/checksum_checker.yaml

@@ -35,7 +35,7 @@ jobs:
           sudo chmod 777 /hf_cache
           bash .github/checksum_checker.sh gallery/index.yaml
       - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v7
+        uses: peter-evans/create-pull-request@v8
         with:
           token: ${{ secrets.UPDATE_BOT_TOKEN }}
           push-to-fork: ci-forks/LocalAI

.github/workflows/dependabot_auto.yml

@@ -14,7 +14,7 @@ jobs:
     steps:
       - name: Dependabot metadata
         id: metadata
-        uses: dependabot/fetch-metadata@v2.4.0
+        uses: dependabot/fetch-metadata@v2.5.0
         with:
           github-token: "${{ secrets.GITHUB_TOKEN }}"
           skip-commit-verification: true

.github/workflows/deploy-explorer.yaml

@@ -33,7 +33,7 @@ jobs:
         run: |
           CGO_ENABLED=0 make build
       - name: rm
-        uses: appleboy/ssh-action@v1.2.3
+        uses: appleboy/ssh-action@v1.2.5
         with:
             host: ${{ secrets.EXPLORER_SSH_HOST }}
             username: ${{ secrets.EXPLORER_SSH_USERNAME }}
@@ -53,7 +53,7 @@ jobs:
             rm: true
             target: ./local-ai
       - name: restarting
-        uses: appleboy/ssh-action@v1.2.3
+        uses: appleboy/ssh-action@v1.2.5
         with:
             host: ${{ secrets.EXPLORER_SSH_HOST }}
             username: ${{ secrets.EXPLORER_SSH_USERNAME }}

.github/workflows/gallery-agent.yaml

@@ -38,20 +38,33 @@ jobs:
         uses: actions/setup-go@v5
         with:
           go-version: '1.21'
-
+      - name: Proto Dependencies
+        run: |
+          # Install protoc
+          curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
+          unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
+          rm protoc.zip
+          go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
+          go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
+          PATH="$PATH:$HOME/go/bin" make protogen-go
+      - uses: mudler/localai-github-action@v1.1
+        with:
+          model: 'https://huggingface.co/bartowski/Qwen_Qwen3-1.7B-GGUF'
 
       - name: Run gallery agent
         env:
-          OPENAI_MODEL: ${{ secrets.OPENAI_MODEL }}
+          #OPENAI_MODEL: ${{ secrets.OPENAI_MODEL }}
+          OPENAI_MODE: Qwen_Qwen3-1.7B-GGUF
+          OPENAI_BASE_URL: "http://localhost:8080"
           OPENAI_KEY: ${{ secrets.OPENAI_KEY }}
-          OPENAI_BASE_URL: ${{ secrets.OPENAI_BASE_URL }}
+          #OPENAI_BASE_URL: ${{ secrets.OPENAI_BASE_URL }}
           SEARCH_TERM: ${{ github.event.inputs.search_term || 'GGUF' }}
           LIMIT: ${{ github.event.inputs.limit || '15' }}
           QUANTIZATION: ${{ github.event.inputs.quantization || 'Q4_K_M' }}
           MAX_MODELS: ${{ github.event.inputs.max_models || '1' }}
         run: |
           export GALLERY_INDEX_PATH=$PWD/gallery/index.yaml
-          go run .github/gallery-agent
+          go run ./.github/gallery-agent
 
       - name: Check for changes
         id: check_changes
@@ -69,28 +82,28 @@ jobs:
         id: read_summary
         if: steps.check_changes.outputs.changes == 'true'
         run: |
-          if [ -f ".github/gallery-agent/gallery-agent-summary.json" ]; then
+          if [ -f "./gallery-agent-summary.json" ]; then
             echo "summary_exists=true" >> $GITHUB_OUTPUT
             # Extract summary data using jq
-            echo "search_term=$(jq -r '.search_term' .github/gallery-agent/gallery-agent-summary.json)" >> $GITHUB_OUTPUT
-            echo "total_found=$(jq -r '.total_found' .github/gallery-agent/gallery-agent-summary.json)" >> $GITHUB_OUTPUT
-            echo "models_added=$(jq -r '.models_added' .github/gallery-agent/gallery-agent-summary.json)" >> $GITHUB_OUTPUT
-            echo "quantization=$(jq -r '.quantization' .github/gallery-agent/gallery-agent-summary.json)" >> $GITHUB_OUTPUT
-            echo "processing_time=$(jq -r '.processing_time' .github/gallery-agent/gallery-agent-summary.json)" >> $GITHUB_OUTPUT
+            echo "search_term=$(jq -r '.search_term' ./gallery-agent-summary.json)" >> $GITHUB_OUTPUT
+            echo "total_found=$(jq -r '.total_found' ./gallery-agent-summary.json)" >> $GITHUB_OUTPUT
+            echo "models_added=$(jq -r '.models_added' ./gallery-agent-summary.json)" >> $GITHUB_OUTPUT
+            echo "quantization=$(jq -r '.quantization' ./gallery-agent-summary.json)" >> $GITHUB_OUTPUT
+            echo "processing_time=$(jq -r '.processing_time' ./gallery-agent-summary.json)" >> $GITHUB_OUTPUT
             
             # Create a formatted list of added models with URLs
-            added_models=$(jq -r 'range(0; .added_model_ids | length) as $i | "- [\(.added_model_ids[$i])](\(.added_model_urls[$i]))"' .github/gallery-agent/gallery-agent-summary.json | tr '\n' '\n')
+            added_models=$(jq -r 'range(0; .added_model_ids | length) as $i | "- [\(.added_model_ids[$i])](\(.added_model_urls[$i]))"' ./gallery-agent-summary.json | tr '\n' '\n')
             echo "added_models<<EOF" >> $GITHUB_OUTPUT
             echo "$added_models" >> $GITHUB_OUTPUT
             echo "EOF" >> $GITHUB_OUTPUT
-            rm -f .github/gallery-agent/gallery-agent-summary.json
+            rm -f ./gallery-agent-summary.json
           else
             echo "summary_exists=false" >> $GITHUB_OUTPUT
           fi
 
       - name: Create Pull Request
         if: steps.check_changes.outputs.changes == 'true'
-        uses: peter-evans/create-pull-request@v7
+        uses: peter-evans/create-pull-request@v8
         with:
           token: ${{ secrets.UPDATE_BOT_TOKEN }}
           push-to-fork: ci-forks/LocalAI

.github/workflows/generate_grpc_cache.yaml

@@ -16,7 +16,7 @@ jobs:
     strategy:
       matrix:
         include:
-          - grpc-base-image: ubuntu:22.04
+          - grpc-base-image: ubuntu:24.04
             runs-on: 'ubuntu-latest'
             platforms: 'linux/amd64,linux/arm64'
     runs-on: ${{matrix.runs-on}}

.github/workflows/generate_intel_image.yaml

@@ -15,7 +15,7 @@ jobs:
     strategy:
       matrix:
         include:
-          - base-image: intel/oneapi-basekit:2025.2.0-0-devel-ubuntu22.04
+          - base-image: intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04
             runs-on: 'arc-runner-set'
             platforms: 'linux/amd64'
     runs-on: ${{matrix.runs-on}}
@@ -53,7 +53,7 @@ jobs:
             BASE_IMAGE=${{ matrix.base-image }}
           context: .
           file: ./Dockerfile
-          tags: quay.io/go-skynet/intel-oneapi-base:latest
+          tags: quay.io/go-skynet/intel-oneapi-base:24.04
           push: true
           target: intel
           platforms: ${{ matrix.platforms }}

.github/workflows/image-pr.yml

@@ -1,68 +1,95 @@
 ---
-name: 'build container images tests'
-
-on:
-  pull_request:
-
-concurrency:
-  group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
-  cancel-in-progress: true
-
-jobs:
-  image-build:
-    uses: ./.github/workflows/image_build.yml
-    with:
-      tag-latest: ${{ matrix.tag-latest }}
-      tag-suffix: ${{ matrix.tag-suffix }}
-      build-type: ${{ matrix.build-type }}
-      cuda-major-version: ${{ matrix.cuda-major-version }}
-      cuda-minor-version: ${{ matrix.cuda-minor-version }}
-      platforms: ${{ matrix.platforms }}
-      runs-on: ${{ matrix.runs-on }}
-      base-image: ${{ matrix.base-image }}
-      grpc-base-image: ${{ matrix.grpc-base-image }}
-      makeflags: ${{ matrix.makeflags }}
-    secrets:
-      dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
-      dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
-      quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
-      quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
-    strategy:
-      # Pushing with all jobs in parallel
-      # eats the bandwidth of all the nodes
-      max-parallel: ${{ github.event_name != 'pull_request' && 4 || 8 }}
-      fail-fast: false
-      matrix:
-        include:
-          - build-type: 'cublas'
-            cuda-major-version: "12"
-            cuda-minor-version: "0"
-            platforms: 'linux/amd64'
-            tag-latest: 'false'
-            tag-suffix: '-gpu-nvidia-cuda-12'
-            runs-on: 'ubuntu-latest'
-            base-image: "ubuntu:22.04"
-            makeflags: "--jobs=3 --output-sync=target"
-          - build-type: 'hipblas'
-            platforms: 'linux/amd64'
-            tag-latest: 'false'
-            tag-suffix: '-hipblas'
-            base-image: "rocm/dev-ubuntu-22.04:6.4.3"
-            grpc-base-image: "ubuntu:22.04"
-            runs-on: 'ubuntu-latest'
-            makeflags: "--jobs=3 --output-sync=target"
-          - build-type: 'sycl'
-            platforms: 'linux/amd64'
-            tag-latest: 'false'
-            base-image: "quay.io/go-skynet/intel-oneapi-base:latest"
-            grpc-base-image: "ubuntu:22.04"
-            tag-suffix: 'sycl'
-            runs-on: 'ubuntu-latest'
-            makeflags: "--jobs=3 --output-sync=target"
-          - build-type: 'vulkan'
-            platforms: 'linux/amd64'
-            tag-latest: 'false'
-            tag-suffix: '-vulkan-core'
-            runs-on: 'ubuntu-latest'
-            base-image: "ubuntu:22.04"
-            makeflags: "--jobs=4 --output-sync=target"
+  name: 'build container images tests'
+  
+  on:
+    pull_request:
+  
+  concurrency:
+    group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
+    cancel-in-progress: true
+  
+  jobs:
+    image-build:
+      uses: ./.github/workflows/image_build.yml
+      with:
+        tag-latest: ${{ matrix.tag-latest }}
+        tag-suffix: ${{ matrix.tag-suffix }}
+        build-type: ${{ matrix.build-type }}
+        cuda-major-version: ${{ matrix.cuda-major-version }}
+        cuda-minor-version: ${{ matrix.cuda-minor-version }}
+        platforms: ${{ matrix.platforms }}
+        runs-on: ${{ matrix.runs-on }}
+        base-image: ${{ matrix.base-image }}
+        grpc-base-image: ${{ matrix.grpc-base-image }}
+        makeflags: ${{ matrix.makeflags }}
+        ubuntu-version: ${{ matrix.ubuntu-version }}
+      secrets:
+        dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
+        dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
+        quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
+        quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
+      strategy:
+        # Pushing with all jobs in parallel
+        # eats the bandwidth of all the nodes
+        max-parallel: ${{ github.event_name != 'pull_request' && 4 || 8 }}
+        fail-fast: false
+        matrix:
+          include:
+            - build-type: 'cublas'
+              cuda-major-version: "12"
+              cuda-minor-version: "8"
+              platforms: 'linux/amd64'
+              tag-latest: 'false'
+              tag-suffix: '-gpu-nvidia-cuda-12'
+              runs-on: 'ubuntu-latest'
+              base-image: "ubuntu:24.04"
+              makeflags: "--jobs=3 --output-sync=target"
+              ubuntu-version: '2404'
+            - build-type: 'cublas'
+              cuda-major-version: "13"
+              cuda-minor-version: "0"
+              platforms: 'linux/amd64'
+              tag-latest: 'false'
+              tag-suffix: '-gpu-nvidia-cuda-13'
+              runs-on: 'ubuntu-latest'
+              base-image: "ubuntu:22.04"
+              makeflags: "--jobs=3 --output-sync=target"
+              ubuntu-version: '2404'
+            - build-type: 'hipblas'
+              platforms: 'linux/amd64'
+              tag-latest: 'false'
+              tag-suffix: '-hipblas'
+              base-image: "rocm/dev-ubuntu-24.04:6.4.4"
+              grpc-base-image: "ubuntu:24.04"
+              runs-on: 'ubuntu-latest'
+              makeflags: "--jobs=3 --output-sync=target"
+              ubuntu-version: '2404'
+            - build-type: 'sycl'
+              platforms: 'linux/amd64'
+              tag-latest: 'false'
+              base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
+              grpc-base-image: "ubuntu:24.04"
+              tag-suffix: 'sycl'
+              runs-on: 'ubuntu-latest'
+              makeflags: "--jobs=3 --output-sync=target"
+              ubuntu-version: '2404'
+            - build-type: 'vulkan'
+              platforms: 'linux/amd64,linux/arm64'
+              tag-latest: 'false'
+              tag-suffix: '-vulkan-core'
+              runs-on: 'ubuntu-latest'
+              base-image: "ubuntu:24.04"
+              makeflags: "--jobs=4 --output-sync=target"
+              ubuntu-version: '2404'
+            - build-type: 'cublas'
+              cuda-major-version: "13"
+              cuda-minor-version: "0"
+              platforms: 'linux/arm64'
+              tag-latest: 'false'
+              tag-suffix: '-nvidia-l4t-arm64-cuda-13'
+              base-image: "ubuntu:24.04"
+              runs-on: 'ubuntu-24.04-arm'
+              makeflags: "--jobs=4 --output-sync=target"
+              skip-drivers: 'false'
+              ubuntu-version: '2404'
+  

.github/workflows/image.yml

@@ -1,154 +1,187 @@
 ---
-name: 'build container images'
-
-on:
-  push:
-    branches:
-      - master
-    tags:
-      - '*'
-
-concurrency:
-  group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
-  cancel-in-progress: true
-
-jobs:
-  hipblas-jobs:
-    uses: ./.github/workflows/image_build.yml
-    with:
-      tag-latest: ${{ matrix.tag-latest }}
-      tag-suffix: ${{ matrix.tag-suffix }}
-      build-type: ${{ matrix.build-type }}
-      cuda-major-version: ${{ matrix.cuda-major-version }}
-      cuda-minor-version: ${{ matrix.cuda-minor-version }}
-      platforms: ${{ matrix.platforms }}
-      runs-on: ${{ matrix.runs-on }}
-      base-image: ${{ matrix.base-image }}
-      grpc-base-image: ${{ matrix.grpc-base-image }}
-      aio: ${{ matrix.aio }}
-      makeflags: ${{ matrix.makeflags }}
-    secrets:
-      dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
-      dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
-      quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
-      quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
-    strategy:
-      matrix:
-        include:
-          - build-type: 'hipblas'
-            platforms: 'linux/amd64'
-            tag-latest: 'auto'
-            tag-suffix: '-gpu-hipblas'
-            base-image: "rocm/dev-ubuntu-22.04:6.4.3"
-            grpc-base-image: "ubuntu:22.04"
-            runs-on: 'ubuntu-latest'
-            makeflags: "--jobs=3 --output-sync=target"
-            aio: "-aio-gpu-hipblas"
-
-  core-image-build:
-    uses: ./.github/workflows/image_build.yml
-    with:
-      tag-latest: ${{ matrix.tag-latest }}
-      tag-suffix: ${{ matrix.tag-suffix }}
-      build-type: ${{ matrix.build-type }}
-      cuda-major-version: ${{ matrix.cuda-major-version }}
-      cuda-minor-version: ${{ matrix.cuda-minor-version }}
-      platforms: ${{ matrix.platforms }}
-      runs-on: ${{ matrix.runs-on }}
-      aio: ${{ matrix.aio }}
-      base-image: ${{ matrix.base-image }}
-      grpc-base-image: ${{ matrix.grpc-base-image }}
-      makeflags: ${{ matrix.makeflags }}
-      skip-drivers: ${{ matrix.skip-drivers }}
-    secrets:
-      dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
-      dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
-      quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
-      quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
-    strategy:
-      #max-parallel: ${{ github.event_name != 'pull_request' && 2 || 4 }}
-      matrix:
-        include:
-          - build-type: ''
-            platforms: 'linux/amd64,linux/arm64'
-            tag-latest: 'auto'
-            tag-suffix: ''
-            base-image: "ubuntu:22.04"
-            runs-on: 'ubuntu-latest'
-            aio: "-aio-cpu"
-            makeflags: "--jobs=4 --output-sync=target"
-            skip-drivers: 'false'
-          - build-type: 'cublas'
-            cuda-major-version: "11"
-            cuda-minor-version: "7"
-            platforms: 'linux/amd64'
-            tag-latest: 'auto'
-            tag-suffix: '-gpu-nvidia-cuda-11'
-            runs-on: 'ubuntu-latest'
-            base-image: "ubuntu:22.04"
-            makeflags: "--jobs=4 --output-sync=target"
-            skip-drivers: 'false'
-            aio: "-aio-gpu-nvidia-cuda-11"
-          - build-type: 'cublas'
-            cuda-major-version: "12"
-            cuda-minor-version: "0"
-            platforms: 'linux/amd64'
-            tag-latest: 'auto'
-            tag-suffix: '-gpu-nvidia-cuda-12'
-            runs-on: 'ubuntu-latest'
-            base-image: "ubuntu:22.04"
-            skip-drivers: 'false'
-            makeflags: "--jobs=4 --output-sync=target"
-            aio: "-aio-gpu-nvidia-cuda-12"
-          - build-type: 'vulkan'
-            platforms: 'linux/amd64'
-            tag-latest: 'auto'
-            tag-suffix: '-gpu-vulkan'
-            runs-on: 'ubuntu-latest'
-            base-image: "ubuntu:22.04"
-            skip-drivers: 'false'
-            makeflags: "--jobs=4 --output-sync=target"
-            aio: "-aio-gpu-vulkan"
-          - build-type: 'intel'
-            platforms: 'linux/amd64'
-            tag-latest: 'auto'
-            base-image: "quay.io/go-skynet/intel-oneapi-base:latest"
-            grpc-base-image: "ubuntu:22.04"
-            tag-suffix: '-gpu-intel'
-            runs-on: 'ubuntu-latest'
-            makeflags: "--jobs=3 --output-sync=target"
-            aio: "-aio-gpu-intel"
-
-  gh-runner:
-    uses: ./.github/workflows/image_build.yml
-    with:
-      tag-latest: ${{ matrix.tag-latest }}
-      tag-suffix: ${{ matrix.tag-suffix }}
-      build-type: ${{ matrix.build-type }}
-      cuda-major-version: ${{ matrix.cuda-major-version }}
-      cuda-minor-version: ${{ matrix.cuda-minor-version }}
-      platforms: ${{ matrix.platforms }}
-      runs-on: ${{ matrix.runs-on }}
-      aio: ${{ matrix.aio }}
-      base-image: ${{ matrix.base-image }}
-      grpc-base-image: ${{ matrix.grpc-base-image }}
-      makeflags: ${{ matrix.makeflags }}
-      skip-drivers: ${{ matrix.skip-drivers }}
-    secrets:
-      dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
-      dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
-      quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
-      quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
-    strategy:
-      matrix:
-        include:
-          - build-type: 'cublas'
-            cuda-major-version: "12"
-            cuda-minor-version: "0"
-            platforms: 'linux/arm64'
-            tag-latest: 'auto'
-            tag-suffix: '-nvidia-l4t-arm64'
-            base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
-            runs-on: 'ubuntu-24.04-arm'
-            makeflags: "--jobs=4 --output-sync=target"
-            skip-drivers: 'true'
+  name: 'build container images'
+  
+  on:
+    push:
+      branches:
+        - master
+      tags:
+        - '*'
+  
+  concurrency:
+    group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
+    cancel-in-progress: true
+  
+  jobs:
+    hipblas-jobs:
+      uses: ./.github/workflows/image_build.yml
+      with:
+        tag-latest: ${{ matrix.tag-latest }}
+        tag-suffix: ${{ matrix.tag-suffix }}
+        build-type: ${{ matrix.build-type }}
+        cuda-major-version: ${{ matrix.cuda-major-version }}
+        cuda-minor-version: ${{ matrix.cuda-minor-version }}
+        platforms: ${{ matrix.platforms }}
+        runs-on: ${{ matrix.runs-on }}
+        base-image: ${{ matrix.base-image }}
+        grpc-base-image: ${{ matrix.grpc-base-image }}
+        aio: ${{ matrix.aio }}
+        makeflags: ${{ matrix.makeflags }}
+        ubuntu-version: ${{ matrix.ubuntu-version }}
+        ubuntu-codename: ${{ matrix.ubuntu-codename }}
+      secrets:
+        dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
+        dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
+        quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
+        quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
+      strategy:
+        matrix:
+          include:
+            - build-type: 'hipblas'
+              platforms: 'linux/amd64'
+              tag-latest: 'auto'
+              tag-suffix: '-gpu-hipblas'
+              base-image: "rocm/dev-ubuntu-24.04:6.4.4"
+              grpc-base-image: "ubuntu:24.04"
+              runs-on: 'ubuntu-latest'
+              makeflags: "--jobs=3 --output-sync=target"
+              aio: "-aio-gpu-hipblas"
+              ubuntu-version: '2404'
+              ubuntu-codename: 'noble'
+  
+    core-image-build:
+      uses: ./.github/workflows/image_build.yml
+      with:
+        tag-latest: ${{ matrix.tag-latest }}
+        tag-suffix: ${{ matrix.tag-suffix }}
+        build-type: ${{ matrix.build-type }}
+        cuda-major-version: ${{ matrix.cuda-major-version }}
+        cuda-minor-version: ${{ matrix.cuda-minor-version }}
+        platforms: ${{ matrix.platforms }}
+        runs-on: ${{ matrix.runs-on }}
+        aio: ${{ matrix.aio }}
+        base-image: ${{ matrix.base-image }}
+        grpc-base-image: ${{ matrix.grpc-base-image }}
+        makeflags: ${{ matrix.makeflags }}
+        skip-drivers: ${{ matrix.skip-drivers }}
+        ubuntu-version: ${{ matrix.ubuntu-version }}
+        ubuntu-codename: ${{ matrix.ubuntu-codename }}
+      secrets:
+        dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
+        dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
+        quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
+        quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
+      strategy:
+        #max-parallel: ${{ github.event_name != 'pull_request' && 2 || 4 }}
+        matrix:
+          include:
+            - build-type: ''
+              platforms: 'linux/amd64,linux/arm64'
+              tag-latest: 'auto'
+              tag-suffix: ''
+              base-image: "ubuntu:24.04"
+              runs-on: 'ubuntu-latest'
+              aio: "-aio-cpu"
+              makeflags: "--jobs=4 --output-sync=target"
+              skip-drivers: 'false'
+              ubuntu-version: '2404'
+              ubuntu-codename: 'noble'
+            - build-type: 'cublas'
+              cuda-major-version: "12"
+              cuda-minor-version: "8"
+              platforms: 'linux/amd64'
+              tag-latest: 'auto'
+              tag-suffix: '-gpu-nvidia-cuda-12'
+              runs-on: 'ubuntu-latest'
+              base-image: "ubuntu:24.04"
+              skip-drivers: 'false'
+              makeflags: "--jobs=4 --output-sync=target"
+              aio: "-aio-gpu-nvidia-cuda-12"
+              ubuntu-version: '2404'
+              ubuntu-codename: 'noble'
+            - build-type: 'cublas'
+              cuda-major-version: "13"
+              cuda-minor-version: "0"
+              platforms: 'linux/amd64'
+              tag-latest: 'auto'
+              tag-suffix: '-gpu-nvidia-cuda-13'
+              runs-on: 'ubuntu-latest'
+              base-image: "ubuntu:22.04"
+              skip-drivers: 'false'
+              makeflags: "--jobs=4 --output-sync=target"
+              aio: "-aio-gpu-nvidia-cuda-13"
+              ubuntu-version: '2404'
+              ubuntu-codename: 'noble'
+            - build-type: 'vulkan'
+              platforms: 'linux/amd64,linux/arm64'
+              tag-latest: 'auto'
+              tag-suffix: '-gpu-vulkan'
+              runs-on: 'ubuntu-latest'
+              base-image: "ubuntu:24.04"
+              skip-drivers: 'false'
+              makeflags: "--jobs=4 --output-sync=target"
+              aio: "-aio-gpu-vulkan"
+              ubuntu-version: '2404'
+              ubuntu-codename: 'noble'
+            - build-type: 'intel'
+              platforms: 'linux/amd64'
+              tag-latest: 'auto'
+              base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
+              grpc-base-image: "ubuntu:24.04"
+              tag-suffix: '-gpu-intel'
+              runs-on: 'ubuntu-latest'
+              makeflags: "--jobs=3 --output-sync=target"
+              aio: "-aio-gpu-intel"
+              ubuntu-version: '2404'
+              ubuntu-codename: 'noble'
+  
+    gh-runner:
+      uses: ./.github/workflows/image_build.yml
+      with:
+        tag-latest: ${{ matrix.tag-latest }}
+        tag-suffix: ${{ matrix.tag-suffix }}
+        build-type: ${{ matrix.build-type }}
+        cuda-major-version: ${{ matrix.cuda-major-version }}
+        cuda-minor-version: ${{ matrix.cuda-minor-version }}
+        platforms: ${{ matrix.platforms }}
+        runs-on: ${{ matrix.runs-on }}
+        aio: ${{ matrix.aio }}
+        base-image: ${{ matrix.base-image }}
+        grpc-base-image: ${{ matrix.grpc-base-image }}
+        makeflags: ${{ matrix.makeflags }}
+        skip-drivers: ${{ matrix.skip-drivers }}
+        ubuntu-version: ${{ matrix.ubuntu-version }}
+        ubuntu-codename: ${{ matrix.ubuntu-codename }}
+      secrets:
+        dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
+        dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
+        quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
+        quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
+      strategy:
+        matrix:
+          include:
+            - build-type: 'cublas'
+              cuda-major-version: "12"
+              cuda-minor-version: "0"
+              platforms: 'linux/arm64'
+              tag-latest: 'auto'
+              tag-suffix: '-nvidia-l4t-arm64'
+              base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
+              runs-on: 'ubuntu-24.04-arm'
+              makeflags: "--jobs=4 --output-sync=target"
+              skip-drivers: 'true'
+              ubuntu-version: "2204"
+              ubuntu-codename: 'jammy'
+            - build-type: 'cublas'
+              cuda-major-version: "13"
+              cuda-minor-version: "0"
+              platforms: 'linux/arm64'
+              tag-latest: 'auto'
+              tag-suffix: '-nvidia-l4t-arm64-cuda-13'
+              base-image: "ubuntu:24.04"
+              runs-on: 'ubuntu-24.04-arm'
+              makeflags: "--jobs=4 --output-sync=target"
+              skip-drivers: 'false'
+              ubuntu-version: '2404'
+              ubuntu-codename: 'noble'
+  

.github/workflows/image_build.yml

@@ -23,7 +23,7 @@ on:
         type: string
       cuda-minor-version:
         description: 'CUDA minor version'
-        default: "4"
+        default: "9"
         type: string
       platforms:
         description: 'Platforms'
@@ -56,6 +56,16 @@ on:
         required: false
         default: ''
         type: string
+      ubuntu-version:
+        description: 'Ubuntu version'
+        required: false
+        default: '2204'
+        type: string
+      ubuntu-codename:
+        description: 'Ubuntu codename'
+        required: false
+        default: 'noble'
+        type: string
     secrets:
       dockerUsername:
         required: true
@@ -238,6 +248,8 @@ jobs:
             GRPC_VERSION=v1.65.0
             MAKEFLAGS=${{ inputs.makeflags }}
             SKIP_DRIVERS=${{ inputs.skip-drivers }}
+            UBUNTU_VERSION=${{ inputs.ubuntu-version }}
+            UBUNTU_CODENAME=${{ inputs.ubuntu-codename }}
           context: .
           file: ./Dockerfile
           cache-from: type=gha
@@ -265,6 +277,8 @@ jobs:
             GRPC_VERSION=v1.65.0
             MAKEFLAGS=${{ inputs.makeflags }}
             SKIP_DRIVERS=${{ inputs.skip-drivers }}
+            UBUNTU_VERSION=${{ inputs.ubuntu-version }}
+            UBUNTU_CODENAME=${{ inputs.ubuntu-codename }}
           context: .
           file: ./Dockerfile
           cache-from: type=gha

.github/workflows/stalebot.yml

@@ -10,7 +10,7 @@ jobs:
   stale:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # v9
+      - uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # v9
         with:
           stale-issue-message: 'This issue is stale because it has been open 90 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
           stale-pr-message: 'This PR is stale because it has been open 90 days with no activity. Remove stale label or comment or this will be closed in 10 days.'

.github/workflows/test-extra.yml

@@ -238,7 +238,7 @@ jobs:
       - name: Dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install build-essential ffmpeg
+          sudo apt-get install -y build-essential ffmpeg
           sudo apt-get install -y ca-certificates cmake curl patch espeak espeak-ng python3-pip
           # Install UV
           curl -LsSf https://astral.sh/uv/install.sh | sh
@@ -247,3 +247,98 @@ jobs:
         run: |
           make --jobs=5 --output-sync=target -C backend/python/coqui
           make --jobs=5 --output-sync=target -C backend/python/coqui test
+  tests-moonshine:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Clone
+        uses: actions/checkout@v6
+        with:
+          submodules: true
+      - name: Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential ffmpeg
+          sudo apt-get install -y ca-certificates cmake curl patch python3-pip
+          # Install UV
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          pip install --user --no-cache-dir grpcio-tools==1.64.1
+      - name: Test moonshine
+        run: |
+          make --jobs=5 --output-sync=target -C backend/python/moonshine
+          make --jobs=5 --output-sync=target -C backend/python/moonshine test
+  tests-pocket-tts:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Clone
+        uses: actions/checkout@v6
+        with:
+          submodules: true
+      - name: Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential ffmpeg
+          sudo apt-get install -y ca-certificates cmake curl patch python3-pip
+          # Install UV
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          pip install --user --no-cache-dir grpcio-tools==1.64.1
+      - name: Test pocket-tts
+        run: |
+          make --jobs=5 --output-sync=target -C backend/python/pocket-tts
+          make --jobs=5 --output-sync=target -C backend/python/pocket-tts test
+  tests-qwen-tts:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Clone
+        uses: actions/checkout@v6
+        with:
+          submodules: true
+      - name: Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential ffmpeg
+          sudo apt-get install -y ca-certificates cmake curl patch python3-pip
+          # Install UV
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          pip install --user --no-cache-dir grpcio-tools==1.64.1
+      - name: Test qwen-tts
+        run: |
+          make --jobs=5 --output-sync=target -C backend/python/qwen-tts
+          make --jobs=5 --output-sync=target -C backend/python/qwen-tts test
+  tests-qwen-asr:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Clone
+        uses: actions/checkout@v6
+        with:
+          submodules: true
+      - name: Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential ffmpeg sox
+          sudo apt-get install -y ca-certificates cmake curl patch python3-pip
+          # Install UV
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          pip install --user --no-cache-dir grpcio-tools==1.64.1
+      - name: Test qwen-asr
+        run: |
+          make --jobs=5 --output-sync=target -C backend/python/qwen-asr
+          make --jobs=5 --output-sync=target -C backend/python/qwen-asr test
+  tests-voxcpm:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Clone
+        uses: actions/checkout@v6
+        with:
+          submodules: true
+      - name: Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential ffmpeg
+          sudo apt-get install -y ca-certificates cmake curl patch python3-pip
+          # Install UV
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          pip install --user --no-cache-dir grpcio-tools==1.64.1
+      - name: Test voxcpm
+        run: |
+          make --jobs=5 --output-sync=target -C backend/python/voxcpm
+          make --jobs=5 --output-sync=target -C backend/python/voxcpm test

.github/workflows/test.yml

@@ -109,11 +109,6 @@ jobs:
           sudo apt-get update
           sudo apt-get install -y cuda-nvcc-${CUDA_VERSION} libcublas-dev-${CUDA_VERSION}
           export CUDACXX=/usr/local/cuda/bin/nvcc
-
-
-          # The python3-grpc-tools package in 22.04 is too old
-          pip install --user grpcio-tools==1.71.0 grpcio==1.71.0
-
           make -C backend/python/transformers
 
           make backends/huggingface backends/llama-cpp backends/local-store backends/silero-vad backends/piper backends/whisper backends/stablediffusion-ggml
@@ -190,7 +185,7 @@ jobs:
           limit-access-to-actor: true
 
   tests-apple:
-    runs-on: macOS-14
+    runs-on: macos-latest
     strategy:
       matrix:
         go-version: ['1.25.x']
@@ -210,7 +205,7 @@ jobs:
       - name: Dependencies
         run: |
           brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm
-          pip install --user --no-cache-dir grpcio-tools==1.71.0 grpcio==1.71.0
+          pip install --user --no-cache-dir grpcio-tools grpcio
       - name: Build llama-cpp-darwin
         run: |
           make protogen-go

.github/workflows/tests-e2e.yml

@@ -0,0 +1,56 @@
+---
+name: 'E2E Backend Tests'
+
+on:
+  pull_request:
+  push:
+    branches:
+      - master
+    tags:
+      - '*'
+
+concurrency:
+  group: ci-tests-e2e-backend-${{ github.head_ref || github.ref }}-${{ github.repository }}
+  cancel-in-progress: true
+
+jobs:
+  tests-e2e-backend:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        go-version: ['1.25.x']
+    steps:
+      - name: Clone
+        uses: actions/checkout@v6
+        with:
+          submodules: true
+      - name: Setup Go ${{ matrix.go-version }}
+        uses: actions/setup-go@v5
+        with:
+          go-version: ${{ matrix.go-version }}
+          cache: false
+      - name: Display Go version
+        run: go version
+      - name: Proto Dependencies
+        run: |
+          # Install protoc
+          curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
+          unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
+          rm protoc.zip
+          go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
+          go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
+          PATH="$PATH:$HOME/go/bin" make protogen-go
+      - name: Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential
+      - name: Test Backend E2E
+        run: |
+          PATH="$PATH:$HOME/go/bin" make build-mock-backend test-e2e
+      - name: Setup tmate session if tests fail
+        if: ${{ failure() }}
+        uses: mxschmitt/action-tmate@v3.23
+        with:
+          detached: true
+          connect-timeout-seconds: 180
+          limit-access-to-actor: true

.github/workflows/update_swagger.yaml

@@ -25,7 +25,7 @@ jobs:
         run: |
           make protogen-go swagger
       - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v7
+        uses: peter-evans/create-pull-request@v8
         with:
           token: ${{ secrets.UPDATE_BOT_TOKEN }}
           push-to-fork: ci-forks/LocalAI

.gitignore

@@ -25,6 +25,7 @@ go-bert
 # LocalAI build binary
 LocalAI
 /local-ai
+/local-ai-launcher
 # prevent above rules from omitting the helm chart
 !charts/*
 # prevent above rules from omitting the api/localai folder
@@ -35,6 +36,8 @@ LocalAI
 models/*
 test-models/
 test-dir/
+tests/e2e-aio/backends
+tests/e2e-aio/models
 
 release/
 

.goreleaser.yaml

@@ -22,6 +22,9 @@ builds:
     goarch:
       - amd64
       - arm64
+    ignore:
+      - goos: darwin
+        goarch: amd64
 archives:
   - formats: [ 'binary' ] # this removes the tar of the archives, leaving the binaries alone
     name_template: local-ai-{{ .Tag }}-{{ .Os }}-{{ .Arch }}{{ if .Arm }}v{{ .Arm }}{{ end }}

AGENTS.md

@@ -0,0 +1,290 @@
+# Build and testing
+
+Building and testing the project depends on the components involved and the platform where development is taking place. Due to the amount of context required it's usually best not to try building or testing the project unless the user requests it. If you must build the project then inspect the Makefile in the project root and the Makefiles of any backends that are effected by changes you are making. In addition the workflows in .github/workflows can be used as a reference when it is unclear how to build or test a component. The primary Makefile contains targets for building inside or outside Docker, if the user has not previously specified a preference then ask which they would like to use.
+
+## Building a specified backend
+
+Let's say the user wants to build a particular backend for a given platform. For example let's say they want to build coqui for ROCM/hipblas
+
+- The Makefile has targets like `docker-build-coqui` created with `generate-docker-build-target` at the time of writing. Recently added backends may require a new target.
+- At a minimum we need to set the BUILD_TYPE, BASE_IMAGE build-args
+  - Use .github/workflows/backend.yml as a reference it lists the needed args in the `include` job strategy matrix
+  - l4t and cublas also requires the CUDA major and minor version
+- You can pretty print a command like `DOCKER_MAKEFLAGS=-j$(nproc --ignore=1) BUILD_TYPE=hipblas BASE_IMAGE=rocm/dev-ubuntu-24.04:6.4.4 make docker-build-coqui`
+- Unless the user specifies that they want you to run the command, then just print it because not all agent frontends handle long running jobs well and the output may overflow your context
+- The user may say they want to build AMD or ROCM instead of hipblas, or Intel instead of SYCL or NVIDIA insted of l4t or cublas. Ask for confirmation if there is ambiguity.
+- Sometimes the user may need extra parameters to be added to `docker build` (e.g. `--platform` for cross-platform builds or `--progress` to view the full logs), in which case you can generate the `docker build` command directly.
+
+## Adding a New Backend
+
+When adding a new backend to LocalAI, you need to update several files to ensure the backend is properly built, tested, and registered. Here's a step-by-step guide based on the pattern used for adding backends like `moonshine`:
+
+### 1. Create Backend Directory Structure
+
+Create the backend directory under the appropriate location:
+- **Python backends**: `backend/python/<backend-name>/`
+- **Go backends**: `backend/go/<backend-name>/`
+- **C++ backends**: `backend/cpp/<backend-name>/`
+
+For Python backends, you'll typically need:
+- `backend.py` - Main gRPC server implementation
+- `Makefile` - Build configuration
+- `install.sh` - Installation script for dependencies
+- `protogen.sh` - Protocol buffer generation script
+- `requirements.txt` - Python dependencies
+- `run.sh` - Runtime script
+- `test.py` / `test.sh` - Test files
+
+### 2. Add Build Configurations to `.github/workflows/backend.yml`
+
+Add build matrix entries for each platform/GPU type you want to support. Look at similar backends (e.g., `chatterbox`, `faster-whisper`) for reference.
+
+**Placement in file:**
+- CPU builds: Add after other CPU builds (e.g., after `cpu-chatterbox`)
+- CUDA 12 builds: Add after other CUDA 12 builds (e.g., after `gpu-nvidia-cuda-12-chatterbox`)
+- CUDA 13 builds: Add after other CUDA 13 builds (e.g., after `gpu-nvidia-cuda-13-chatterbox`)
+
+**Additional build types you may need:**
+- ROCm/HIP: Use `build-type: 'hipblas'` with `base-image: "rocm/dev-ubuntu-24.04:6.4.4"`
+- Intel/SYCL: Use `build-type: 'intel'` or `build-type: 'sycl_f16'`/`sycl_f32` with `base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"`
+- L4T (ARM): Use `build-type: 'l4t'` with `platforms: 'linux/arm64'` and `runs-on: 'ubuntu-24.04-arm'`
+
+### 3. Add Backend Metadata to `backend/index.yaml`
+
+**Step 3a: Add Meta Definition**
+
+Add a YAML anchor definition in the `## metas` section (around line 2-300). Look for similar backends to use as a template such as `diffusers` or `chatterbox`
+
+**Step 3b: Add Image Entries**
+
+Add image entries at the end of the file, following the pattern of similar backends such as `diffusers` or `chatterbox`. Include both `latest` (production) and `master` (development) tags.
+
+### 4. Update the Makefile
+
+The Makefile needs to be updated in several places to support building and testing the new backend:
+
+**Step 4a: Add to `.NOTPARALLEL`**
+
+Add `backends/<backend-name>` to the `.NOTPARALLEL` line (around line 2) to prevent parallel execution conflicts:
+
+```makefile
+.NOTPARALLEL: ... backends/<backend-name>
+```
+
+**Step 4b: Add to `prepare-test-extra`**
+
+Add the backend to the `prepare-test-extra` target (around line 312) to prepare it for testing:
+
+```makefile
+prepare-test-extra: protogen-python
+	...
+	$(MAKE) -C backend/python/<backend-name>
+```
+
+**Step 4c: Add to `test-extra`**
+
+Add the backend to the `test-extra` target (around line 319) to run its tests:
+
+```makefile
+test-extra: prepare-test-extra
+	...
+	$(MAKE) -C backend/python/<backend-name> test
+```
+
+**Step 4d: Add Backend Definition**
+
+Add a backend definition variable in the backend definitions section (around line 428-457). The format depends on the backend type:
+
+**For Python backends with root context** (like `faster-whisper`, `coqui`):
+```makefile
+BACKEND_<BACKEND_NAME> = <backend-name>|python|.|false|true
+```
+
+**For Python backends with `./backend` context** (like `chatterbox`, `moonshine`):
+```makefile
+BACKEND_<BACKEND_NAME> = <backend-name>|python|./backend|false|true
+```
+
+**For Go backends**:
+```makefile
+BACKEND_<BACKEND_NAME> = <backend-name>|golang|.|false|true
+```
+
+**Step 4e: Generate Docker Build Target**
+
+Add an eval call to generate the docker-build target (around line 480-501):
+
+```makefile
+$(eval $(call generate-docker-build-target,$(BACKEND_<BACKEND_NAME>)))
+```
+
+**Step 4f: Add to `docker-build-backends`**
+
+Add `docker-build-<backend-name>` to the `docker-build-backends` target (around line 507):
+
+```makefile
+docker-build-backends: ... docker-build-<backend-name>
+```
+
+**Determining the Context:**
+
+- If the backend is in `backend/python/<backend-name>/` and uses `./backend` as context in the workflow file, use `./backend` context
+- If the backend is in `backend/python/<backend-name>/` but uses `.` as context in the workflow file, use `.` context
+- Check similar backends to determine the correct context
+
+### 5. Verification Checklist
+
+After adding a new backend, verify:
+
+- [ ] Backend directory structure is complete with all necessary files
+- [ ] Build configurations added to `.github/workflows/backend.yml` for all desired platforms
+- [ ] Meta definition added to `backend/index.yaml` in the `## metas` section
+- [ ] Image entries added to `backend/index.yaml` for all build variants (latest + development)
+- [ ] Tag suffixes match between workflow file and index.yaml
+- [ ] Makefile updated with all 6 required changes (`.NOTPARALLEL`, `prepare-test-extra`, `test-extra`, backend definition, docker-build target eval, `docker-build-backends`)
+- [ ] No YAML syntax errors (check with linter)
+- [ ] No Makefile syntax errors (check with linter)
+- [ ] Follows the same pattern as similar backends (e.g., if it's a transcription backend, follow `faster-whisper` pattern)
+
+### 6. Example: Adding a Python Backend
+
+For reference, when `moonshine` was added:
+- **Files created**: `backend/python/moonshine/{backend.py, Makefile, install.sh, protogen.sh, requirements.txt, run.sh, test.py, test.sh}`
+- **Workflow entries**: 3 build configurations (CPU, CUDA 12, CUDA 13)
+- **Index entries**: 1 meta definition + 6 image entries (cpu, cuda12, cuda13 × latest/development)
+- **Makefile updates**: 
+  - Added to `.NOTPARALLEL` line
+  - Added to `prepare-test-extra` and `test-extra` targets
+  - Added `BACKEND_MOONSHINE = moonshine|python|./backend|false|true`
+  - Added eval for docker-build target generation
+  - Added `docker-build-moonshine` to `docker-build-backends`
+
+# Coding style
+
+- The project has the following .editorconfig
+
+```
+root = true
+
+[*]
+indent_style = space
+indent_size = 2
+end_of_line = lf
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+
+[*.go]
+indent_style = tab
+
+[Makefile]
+indent_style = tab
+
+[*.proto]
+indent_size = 2
+
+[*.py]
+indent_size = 4
+
+[*.js]
+indent_size = 2
+
+[*.yaml]
+indent_size = 2
+
+[*.md]
+trim_trailing_whitespace = false
+```
+
+- Use comments sparingly to explain why code does something, not what it does. Comments are there to add context that would be difficult to deduce from reading the code.
+- Prefer modern Go e.g. use `any` not `interface{}`
+
+# Logging
+
+Use `github.com/mudler/xlog` for logging which has the same API as slog.
+
+# llama.cpp Backend
+
+The llama.cpp backend (`backend/cpp/llama-cpp/grpc-server.cpp`) is a gRPC adaptation of the upstream HTTP server (`llama.cpp/tools/server/server.cpp`). It uses the same underlying server infrastructure from `llama.cpp/tools/server/server-context.cpp`.
+
+## Building and Testing
+
+- Test llama.cpp backend compilation: `make backends/llama-cpp`
+- The backend is built as part of the main build process
+- Check `backend/cpp/llama-cpp/Makefile` for build configuration
+
+## Architecture
+
+- **grpc-server.cpp**: gRPC server implementation, adapts HTTP server patterns to gRPC
+- Uses shared server infrastructure: `server-context.cpp`, `server-task.cpp`, `server-queue.cpp`, `server-common.cpp`
+- The gRPC server mirrors the HTTP server's functionality but uses gRPC instead of HTTP
+
+## Common Issues When Updating llama.cpp
+
+When fixing compilation errors after upstream changes:
+1. Check how `server.cpp` (HTTP server) handles the same change
+2. Look for new public APIs or getter methods
+3. Store copies of needed data instead of accessing private members
+4. Update function calls to match new signatures
+5. Test with `make backends/llama-cpp`
+
+## Key Differences from HTTP Server
+
+- gRPC uses `BackendServiceImpl` class with gRPC service methods
+- HTTP server uses `server_routes` with HTTP handlers
+- Both use the same `server_context` and task queue infrastructure
+- gRPC methods: `LoadModel`, `Predict`, `PredictStream`, `Embedding`, `Rerank`, `TokenizeString`, `GetMetrics`, `Health`
+
+## Tool Call Parsing Maintenance
+
+When working on JSON/XML tool call parsing functionality, always check llama.cpp for reference implementation and updates:
+
+### Checking for XML Parsing Changes
+
+1. **Review XML Format Definitions**: Check `llama.cpp/common/chat-parser-xml-toolcall.h` for `xml_tool_call_format` struct changes
+2. **Review Parsing Logic**: Check `llama.cpp/common/chat-parser-xml-toolcall.cpp` for parsing algorithm updates
+3. **Review Format Presets**: Check `llama.cpp/common/chat-parser.cpp` for new XML format presets (search for `xml_tool_call_format form`)
+4. **Review Model Lists**: Check `llama.cpp/common/chat.h` for `COMMON_CHAT_FORMAT_*` enum values that use XML parsing:
+   - `COMMON_CHAT_FORMAT_GLM_4_5`
+   - `COMMON_CHAT_FORMAT_MINIMAX_M2`
+   - `COMMON_CHAT_FORMAT_KIMI_K2`
+   - `COMMON_CHAT_FORMAT_QWEN3_CODER_XML`
+   - `COMMON_CHAT_FORMAT_APRIEL_1_5`
+   - `COMMON_CHAT_FORMAT_XIAOMI_MIMO`
+   - Any new formats added
+
+### Model Configuration Options
+
+Always check `llama.cpp` for new model configuration options that should be supported in LocalAI:
+
+1. **Check Server Context**: Review `llama.cpp/tools/server/server-context.cpp` for new parameters
+2. **Check Chat Params**: Review `llama.cpp/common/chat.h` for `common_chat_params` struct changes
+3. **Check Server Options**: Review `llama.cpp/tools/server/server.cpp` for command-line argument changes
+4. **Examples of options to check**:
+   - `ctx_shift` - Context shifting support
+   - `parallel_tool_calls` - Parallel tool calling
+   - `reasoning_format` - Reasoning format options
+   - Any new flags or parameters
+
+### Implementation Guidelines
+
+1. **Feature Parity**: Always aim for feature parity with llama.cpp's implementation
+2. **Test Coverage**: Add tests for new features matching llama.cpp's behavior
+3. **Documentation**: Update relevant documentation when adding new formats or options
+4. **Backward Compatibility**: Ensure changes don't break existing functionality
+
+### Files to Monitor
+
+- `llama.cpp/common/chat-parser-xml-toolcall.h` - Format definitions
+- `llama.cpp/common/chat-parser-xml-toolcall.cpp` - Parsing logic
+- `llama.cpp/common/chat-parser.cpp` - Format presets and model-specific handlers
+- `llama.cpp/common/chat.h` - Format enums and parameter structures
+- `llama.cpp/tools/server/server-context.cpp` - Server configuration options
+
+# Documentation
+
+The project documentation is located in `docs/content`. When adding new features or changing existing functionality, it is crucial to update the documentation to reflect these changes. This helps users understand how to use the new capabilities and ensures the documentation stays relevant.
+
+- **Feature Documentation**: If you add a new feature (like a new backend or API endpoint), create a new markdown file in `docs/content/features/` explaining what it is, how to configure it, and how to use it.
+- **Configuration**: If you modify configuration options, update the relevant sections in `docs/content/`.
+- **Examples**: providing concrete examples (like YAML configuration blocks) is highly encouraged to help users get started quickly.

CONTRIBUTING.md

@@ -78,6 +78,20 @@ LOCALAI_IMAGE_TAG=test LOCALAI_IMAGE=local-ai-aio make run-e2e-aio
 
 We are welcome the contribution of the documents, please open new PR or create a new issue. The documentation is available under `docs/` https://github.com/mudler/LocalAI/tree/master/docs
 
+### Gallery YAML Schema
+
+LocalAI provides a JSON Schema for gallery model YAML files at:
+
+`core/schema/gallery-model.schema.json`
+
+This schema mirrors the internal gallery model configuration and can be used by editors (such as VS Code) to enable autocomplete, validation, and inline documentation when creating or modifying gallery files.
+
+To use it with the YAML language server, add the following comment at the top of a gallery YAML file:
+
+```yaml
+# yaml-language-server: $schema=../core/schema/gallery-model.schema.json
+```
+
 ## Community and Communication
 
 - You can reach out via the Github issue tracker.

Dockerfile

@@ -1,6 +1,7 @@
-ARG BASE_IMAGE=ubuntu:22.04
+ARG BASE_IMAGE=ubuntu:24.04
 ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
 ARG INTEL_BASE_IMAGE=${BASE_IMAGE}
+ARG UBUNTU_CODENAME=noble
 
 FROM ${BASE_IMAGE} AS requirements
 
@@ -9,7 +10,7 @@ ENV DEBIAN_FRONTEND=noninteractive
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
         ca-certificates curl wget espeak-ng libgomp1 \
-        ffmpeg libopenblas-base libopenblas-dev && \
+        ffmpeg libopenblas0 libopenblas-dev sox && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
@@ -23,6 +24,7 @@ ARG SKIP_DRIVERS=false
 ARG TARGETARCH
 ARG TARGETVARIANT
 ENV BUILD_TYPE=${BUILD_TYPE}
+ARG UBUNTU_VERSION=2404
 
 RUN mkdir -p /run/localai
 RUN echo "default" > /run/localai/capability
@@ -33,11 +35,45 @@ RUN <<EOT bash
         apt-get update && \
         apt-get install -y  --no-install-recommends \
             software-properties-common pciutils wget gpg-agent && \
-        wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-        wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
-        apt-get update && \
-        apt-get install -y \
-            vulkan-sdk && \
+        apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
+            libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
+            libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
+            git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
+            ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
+            clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils mesa-vulkan-drivers
+        if [ "amd64" = "$TARGETARCH" ]; then
+            wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
+            tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
+            rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
+            mkdir -p /opt/vulkan-sdk && \
+            mv 1.4.335.0 /opt/vulkan-sdk/ && \
+            cd /opt/vulkan-sdk/1.4.335.0 && \
+            ./vulkansdk --no-deps --maxjobs \
+                vulkan-loader \
+                vulkan-validationlayers \
+                vulkan-extensionlayer \
+                vulkan-tools \
+                shaderc && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
+            rm -rf /opt/vulkan-sdk
+        fi
+        if [ "arm64" = "$TARGETARCH" ]; then
+            mkdir vulkan && cd vulkan && \
+            curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
+            tar -xvf vulkan-sdk.tar.xz && \
+            rm vulkan-sdk.tar.xz && \
+            cd 1.4.335.0 && \
+            cp -rfv aarch64/bin/* /usr/bin/ && \
+            cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
+            cp -rfv aarch64/include/* /usr/include/ && \
+            cp -rfv aarch64/share/* /usr/share/ && \
+            cd ../.. && \
+            rm -rf vulkan
+        fi
+        ldconfig && \
         apt-get clean && \
         rm -rf /var/lib/apt/lists/* && \
         echo "vulkan" > /run/localai/capability
@@ -46,15 +82,19 @@ EOT
 
 # CuBLAS requirements
 RUN <<EOT bash
-    if [ "${BUILD_TYPE}" = "cublas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
+    if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
         apt-get update && \
         apt-get install -y  --no-install-recommends \
             software-properties-common pciutils
         if [ "amd64" = "$TARGETARCH" ]; then
-            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
+            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
         fi
         if [ "arm64" = "$TARGETARCH" ]; then
-            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb
+            if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
+                curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
+            else
+                curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
+            fi
         fi
         dpkg -i cuda-keyring_1.1-1_all.deb && \
         rm -f cuda-keyring_1.1-1_all.deb && \
@@ -65,26 +105,34 @@ RUN <<EOT bash
             libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
             libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
             libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
-            libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} && \
+            libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
+        if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
+            apt-get install -y --no-install-recommends \
+            libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
+        fi
         apt-get clean && \
         rm -rf /var/lib/apt/lists/* && \
-        echo "nvidia" > /run/localai/capability
+        echo "nvidia-cuda-${CUDA_MAJOR_VERSION}" > /run/localai/capability
     fi
 EOT
 
 RUN <<EOT bash
     if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
-        echo "nvidia-l4t" > /run/localai/capability
+        echo "nvidia-l4t-cuda-${CUDA_MAJOR_VERSION}" > /run/localai/capability
     fi
 EOT
 
 # https://github.com/NVIDIA/Isaac-GR00T/issues/343
 RUN <<EOT bash
     if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
-        wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu2204-0.6.0_0.6.0-1_arm64.deb && \
-        dpkg -i cudss-local-tegra-repo-ubuntu2204-0.6.0_0.6.0-1_arm64.deb && \
-        cp /var/cudss-local-tegra-repo-ubuntu2204-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
-        apt-get update && apt-get -y install cudss
+        wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
+        dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
+        cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
+        apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
+        wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
+        dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
+        cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
+        apt-get update && apt-get install -y nvpl
     fi
 EOT
 
@@ -128,13 +176,12 @@ ENV PATH=/opt/rocm/bin:${PATH}
 # The requirements-core target is common to all images.  It should not be placed in requirements-core unless every single build will use it.
 FROM requirements-drivers AS build-requirements
 
-ARG GO_VERSION=1.22.6
-ARG CMAKE_VERSION=3.26.4
+ARG GO_VERSION=1.25.4
+ARG CMAKE_VERSION=3.31.10
 ARG CMAKE_FROM_SOURCE=false
 ARG TARGETARCH
 ARG TARGETVARIANT
 
-
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
         build-essential \
@@ -171,14 +218,6 @@ RUN go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2 && \
 COPY --chmod=644 custom-ca-certs/* /usr/local/share/ca-certificates/
 RUN update-ca-certificates
 
-
-# OpenBLAS requirements and stable diffusion
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        libopenblas-dev && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
-
 RUN test -n "$TARGETARCH" \
     || (echo 'warn: missing $TARGETARCH, either set this `ARG` manually, or run using `docker buildkit`')
 
@@ -199,9 +238,10 @@ WORKDIR /build
 # https://community.intel.com/t5/Intel-oneAPI-Math-Kernel-Library/APT-Repository-not-working-signatures-invalid/m-p/1599436/highlight/true#M36143
 # This is a temporary workaround until Intel fixes their repository
 FROM ${INTEL_BASE_IMAGE} AS intel
+ARG UBUNTU_CODENAME=noble
 RUN wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
 gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
-RUN echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy/lts/2350 unified" > /etc/apt/sources.list.d/intel-graphics.list
+RUN echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu ${UBUNTU_CODENAME}/lts/2350 unified" > /etc/apt/sources.list.d/intel-graphics.list
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
         intel-oneapi-runtime-libs && \

Dockerfile.aio

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=ubuntu:22.04
+ARG BASE_IMAGE=ubuntu:24.04
 
 FROM ${BASE_IMAGE} 
 

Makefile

@@ -1,12 +1,20 @@
+# Disable parallel execution for backend builds
+.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/piper backends/stablediffusion-ggml backends/whisper backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/moonshine backends/pocket-tts backends/qwen-tts backends/qwen-asr backends/voxcpm backends/whisperx
+
 GOCMD=go
 GOTEST=$(GOCMD) test
 GOVET=$(GOCMD) vet
 BINARY_NAME=local-ai
 LAUNCHER_BINARY_NAME=local-ai-launcher
 
+UBUNTU_VERSION?=2404
+UBUNTU_CODENAME?=noble
+
 GORELEASER?=
 
 export BUILD_TYPE?=
+export CUDA_MAJOR_VERSION?=13
+export CUDA_MINOR_VERSION?=0
 
 GO_TAGS?=
 BUILD_ID?=
@@ -152,7 +160,17 @@ test: test-models/testmodel.ggml protogen-go
 ########################################################
 
 docker-build-aio:
-	docker build --build-arg MAKEFLAGS="--jobs=5 --output-sync=target" -t local-ai:tests -f Dockerfile .
+	docker build \
+		--build-arg MAKEFLAGS="--jobs=5 --output-sync=target" \
+		--build-arg BASE_IMAGE=$(BASE_IMAGE) \
+		--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
+		--build-arg BUILD_TYPE=$(BUILD_TYPE) \
+		--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
+		--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
+		--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
+		--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
+		--build-arg GO_TAGS="$(GO_TAGS)" \
+		-t local-ai:tests -f Dockerfile .
 	BASE_IMAGE=local-ai:tests DOCKER_AIO_IMAGE=local-ai-aio:test $(MAKE) docker-aio
 
 e2e-aio:
@@ -171,20 +189,29 @@ run-e2e-aio: protogen-go
 ########################################################
 
 prepare-e2e:
-	mkdir -p $(TEST_DIR)
-	cp -rfv $(abspath ./tests/e2e-fixtures)/gpu.yaml $(TEST_DIR)/gpu.yaml
-	test -e $(TEST_DIR)/ggllm-test-model.bin || wget -q https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GGUF/resolve/main/codellama-7b-instruct.Q2_K.gguf -O $(TEST_DIR)/ggllm-test-model.bin
-	docker build --build-arg IMAGE_TYPE=core --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg CUDA_MAJOR_VERSION=12 --build-arg CUDA_MINOR_VERSION=0 -t localai-tests .
+	docker build \
+		--build-arg IMAGE_TYPE=core \
+		--build-arg BUILD_TYPE=$(BUILD_TYPE) \
+		--build-arg BASE_IMAGE=$(BASE_IMAGE) \
+		--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
+		--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
+		--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
+		--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
+		--build-arg GO_TAGS="$(GO_TAGS)" \
+		--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
+		-t localai-tests .
 
 run-e2e-image:
-	ls -liah $(abspath ./tests/e2e-fixtures)
-	docker run -p 5390:8080 -e MODELS_PATH=/models -e THREADS=1 -e DEBUG=true -d --rm -v $(TEST_DIR):/models --gpus all --name e2e-tests-$(RANDOM) localai-tests
+	docker run -p 5390:8080 -e MODELS_PATH=/models -e THREADS=1 -e DEBUG=true -d --rm -v $(TEST_DIR):/models --name e2e-tests-$(RANDOM) localai-tests
 
-test-e2e:
+test-e2e: build-mock-backend prepare-e2e run-e2e-image
 	@echo 'Running e2e tests'
 	BUILD_TYPE=$(BUILD_TYPE) \
-	LOCALAI_API=http://$(E2E_BRIDGE_IP):5390/v1 \
+	LOCALAI_API=http://$(E2E_BRIDGE_IP):5390 \
 	$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
+	$(MAKE) clean-mock-backend
+	$(MAKE) teardown-e2e
+	docker rmi localai-tests
 
 teardown-e2e:
 	rm -rf $(TEST_DIR) || true
@@ -265,7 +292,7 @@ protoc:
 	  echo "Unsupported OS: $$OS_NAME"; exit 1; \
 	fi; \
 	URL=https://github.com/protocolbuffers/protobuf/releases/download/v31.1/$$FILE; \
-	curl -L -s $$URL -o protoc.zip && \
+	curl -L $$URL -o protoc.zip && \
 	unzip -j -d $(CURDIR) protoc.zip bin/protoc && rm protoc.zip
 
 .PHONY: protogen-go
@@ -284,17 +311,33 @@ prepare-test-extra: protogen-python
 	$(MAKE) -C backend/python/diffusers
 	$(MAKE) -C backend/python/chatterbox
 	$(MAKE) -C backend/python/vllm
+	$(MAKE) -C backend/python/vllm-omni
+	$(MAKE) -C backend/python/vibevoice
+	$(MAKE) -C backend/python/moonshine
+	$(MAKE) -C backend/python/pocket-tts
+	$(MAKE) -C backend/python/qwen-tts
+	$(MAKE) -C backend/python/qwen-asr
+	$(MAKE) -C backend/python/voxcpm
+	$(MAKE) -C backend/python/whisperx
 
 test-extra: prepare-test-extra
 	$(MAKE) -C backend/python/transformers test
 	$(MAKE) -C backend/python/diffusers test
 	$(MAKE) -C backend/python/chatterbox test
 	$(MAKE) -C backend/python/vllm test
+	$(MAKE) -C backend/python/vllm-omni test
+	$(MAKE) -C backend/python/vibevoice test
+	$(MAKE) -C backend/python/moonshine test
+	$(MAKE) -C backend/python/pocket-tts test
+	$(MAKE) -C backend/python/qwen-tts test
+	$(MAKE) -C backend/python/qwen-asr test
+	$(MAKE) -C backend/python/voxcpm test
+	$(MAKE) -C backend/python/whisperx test
 
 DOCKER_IMAGE?=local-ai
 DOCKER_AIO_IMAGE?=local-ai-aio
 IMAGE_TYPE?=core
-BASE_IMAGE?=ubuntu:22.04
+BASE_IMAGE?=ubuntu:24.04
 
 docker:
 	docker build \
@@ -303,24 +346,34 @@ docker:
 		--build-arg GO_TAGS="$(GO_TAGS)" \
 		--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
 		--build-arg BUILD_TYPE=$(BUILD_TYPE) \
+		--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
+		--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
+		--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
+		--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
 		-t $(DOCKER_IMAGE) .
 
-docker-cuda11:
+docker-cuda12:
 	docker build \
-		--build-arg CUDA_MAJOR_VERSION=11 \
-		--build-arg CUDA_MINOR_VERSION=8 \
+		--build-arg CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
+		--build-arg CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION} \
 		--build-arg BASE_IMAGE=$(BASE_IMAGE) \
 		--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
 		--build-arg GO_TAGS="$(GO_TAGS)" \
 		--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
 		--build-arg BUILD_TYPE=$(BUILD_TYPE) \
-		-t $(DOCKER_IMAGE)-cuda-11 .
+		--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
+		--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
+		-t $(DOCKER_IMAGE)-cuda-12 .
 
 docker-aio:
 	@echo "Building AIO image with base $(BASE_IMAGE) as $(DOCKER_AIO_IMAGE)"
 	docker build \
 		--build-arg BASE_IMAGE=$(BASE_IMAGE) \
 		--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
+		--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
+		--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
+		--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
+		--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
 		-t $(DOCKER_AIO_IMAGE) -f Dockerfile.aio .
 
 docker-aio-all:
@@ -329,60 +382,31 @@ docker-aio-all:
 
 docker-image-intel:
 	docker build \
-		--build-arg BASE_IMAGE=quay.io/go-skynet/intel-oneapi-base:latest \
+		--build-arg BASE_IMAGE=intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04 \
 		--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
 		--build-arg GO_TAGS="$(GO_TAGS)" \
 		--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
-		--build-arg BUILD_TYPE=intel -t $(DOCKER_IMAGE) .
+		--build-arg BUILD_TYPE=intel \
+		--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
+		--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
+		--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
+		--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
+		-t $(DOCKER_IMAGE) .
 
 ########################################################
 ## Backends
 ########################################################
 
+# Pattern rule for standard backends (docker-based)
+# This matches all backends that use docker-build-* and docker-save-*
+backends/%: docker-build-% docker-save-% build
+	./local-ai backends install "ocifile://$(abspath ./backend-images/$*.tar)"
 
-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)"
-
-backends/piper: docker-build-piper docker-save-piper build
-	./local-ai backends install "ocifile://$(abspath ./backend-images/piper.tar)"
-
-backends/stablediffusion-ggml: docker-build-stablediffusion-ggml docker-save-stablediffusion-ggml build
-	./local-ai backends install "ocifile://$(abspath ./backend-images/stablediffusion-ggml.tar)"
-
-backends/whisper: docker-build-whisper docker-save-whisper build
-	./local-ai backends install "ocifile://$(abspath ./backend-images/whisper.tar)"
-
-backends/silero-vad: docker-build-silero-vad docker-save-silero-vad build
-	./local-ai backends install "ocifile://$(abspath ./backend-images/silero-vad.tar)"
-
-backends/local-store: docker-build-local-store docker-save-local-store build
-	./local-ai backends install "ocifile://$(abspath ./backend-images/local-store.tar)"
-
-backends/huggingface: docker-build-huggingface docker-save-huggingface build
-	./local-ai backends install "ocifile://$(abspath ./backend-images/huggingface.tar)"
-
-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://$(abspath ./backend-images/kokoro.tar)"
-
-backends/chatterbox: docker-build-chatterbox docker-save-chatterbox build
-	./local-ai backends install "ocifile://$(abspath ./backend-images/chatterbox.tar)"
-
+# Darwin-specific backends (keep as explicit targets since they have special build logic)
 backends/llama-cpp-darwin: build
 	bash ./scripts/build/llama-cpp-darwin.sh
 	./local-ai backends install "ocifile://$(abspath ./backend-images/llama-cpp.tar)"
 
-backends/neutts: docker-build-neutts docker-save-neutts build
-	./local-ai backends install "ocifile://$(abspath ./backend-images/neutts.tar)"
-
 build-darwin-python-backend: build
 	bash ./scripts/build/python-darwin.sh
 
@@ -412,112 +436,102 @@ backends/stablediffusion-ggml-darwin:
 backend-images:
 	mkdir -p backend-images
 
-docker-build-llama-cpp:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:llama-cpp -f backend/Dockerfile.llama-cpp .
+# Backend metadata: BACKEND_NAME | DOCKERFILE_TYPE | BUILD_CONTEXT | PROGRESS_FLAG | NEEDS_BACKEND_ARG
+# llama-cpp is special - uses llama-cpp Dockerfile and doesn't need BACKEND arg
+BACKEND_LLAMA_CPP = llama-cpp|llama-cpp|.|false|false
 
-docker-build-bark-cpp:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:bark-cpp -f backend/Dockerfile.golang --build-arg BACKEND=bark-cpp .
+# Golang backends
+BACKEND_PIPER = piper|golang|.|false|true
+BACKEND_LOCAL_STORE = local-store|golang|.|false|true
+BACKEND_HUGGINGFACE = huggingface|golang|.|false|true
+BACKEND_SILERO_VAD = silero-vad|golang|.|false|true
+BACKEND_STABLEDIFFUSION_GGML = stablediffusion-ggml|golang|.|--progress=plain|true
+BACKEND_WHISPER = whisper|golang|.|false|true
 
-docker-build-piper:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:piper -f backend/Dockerfile.golang --build-arg BACKEND=piper .
+# Python backends with root context
+BACKEND_RERANKERS = rerankers|python|.|false|true
+BACKEND_TRANSFORMERS = transformers|python|.|false|true
+BACKEND_FASTER_WHISPER = faster-whisper|python|.|false|true
+BACKEND_COQUI = coqui|python|.|false|true
+BACKEND_RFDETR = rfdetr|python|.|false|true
+BACKEND_KITTEN_TTS = kitten-tts|python|.|false|true
+BACKEND_NEUTTS = neutts|python|.|false|true
+BACKEND_KOKORO = kokoro|python|.|false|true
+BACKEND_VLLM = vllm|python|.|false|true
+BACKEND_VLLM_OMNI = vllm-omni|python|.|false|true
+BACKEND_DIFFUSERS = diffusers|python|.|--progress=plain|true
+BACKEND_CHATTERBOX = chatterbox|python|.|false|true
+BACKEND_VIBEVOICE = vibevoice|python|.|--progress=plain|true
+BACKEND_MOONSHINE = moonshine|python|.|false|true
+BACKEND_POCKET_TTS = pocket-tts|python|.|false|true
+BACKEND_QWEN_TTS = qwen-tts|python|.|false|true
+BACKEND_QWEN_ASR = qwen-asr|python|.|false|true
+BACKEND_VOXCPM = voxcpm|python|.|false|true
+BACKEND_WHISPERX = whisperx|python|.|false|true
 
-docker-build-local-store:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:local-store -f backend/Dockerfile.golang --build-arg BACKEND=local-store .
+# Helper function to build docker image for a backend
+# Usage: $(call docker-build-backend,BACKEND_NAME,DOCKERFILE_TYPE,BUILD_CONTEXT,PROGRESS_FLAG,NEEDS_BACKEND_ARG)
+define docker-build-backend
+	docker build $(if $(filter-out false,$(4)),$(4)) \
+		--build-arg BUILD_TYPE=$(BUILD_TYPE) \
+		--build-arg BASE_IMAGE=$(BASE_IMAGE) \
+		--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
+		--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
+		--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
+		--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
+		$(if $(filter true,$(5)),--build-arg BACKEND=$(1)) \
+		-t local-ai-backend:$(1) -f backend/Dockerfile.$(2) $(3)
+endef
 
-docker-build-huggingface:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:huggingface -f backend/Dockerfile.golang --build-arg BACKEND=huggingface .
+# Generate docker-build targets from backend definitions
+define generate-docker-build-target
+docker-build-$(word 1,$(subst |, ,$(1))):
+	$$(call docker-build-backend,$(word 1,$(subst |, ,$(1))),$(word 2,$(subst |, ,$(1))),$(word 3,$(subst |, ,$(1))),$(word 4,$(subst |, ,$(1))),$(word 5,$(subst |, ,$(1))))
+endef
 
-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
+# Generate all docker-build targets
+$(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP)))
+$(eval $(call generate-docker-build-target,$(BACKEND_PIPER)))
+$(eval $(call generate-docker-build-target,$(BACKEND_LOCAL_STORE)))
+$(eval $(call generate-docker-build-target,$(BACKEND_HUGGINGFACE)))
+$(eval $(call generate-docker-build-target,$(BACKEND_SILERO_VAD)))
+$(eval $(call generate-docker-build-target,$(BACKEND_STABLEDIFFUSION_GGML)))
+$(eval $(call generate-docker-build-target,$(BACKEND_WHISPER)))
+$(eval $(call generate-docker-build-target,$(BACKEND_RERANKERS)))
+$(eval $(call generate-docker-build-target,$(BACKEND_TRANSFORMERS)))
+$(eval $(call generate-docker-build-target,$(BACKEND_FASTER_WHISPER)))
+$(eval $(call generate-docker-build-target,$(BACKEND_COQUI)))
+$(eval $(call generate-docker-build-target,$(BACKEND_RFDETR)))
+$(eval $(call generate-docker-build-target,$(BACKEND_KITTEN_TTS)))
+$(eval $(call generate-docker-build-target,$(BACKEND_NEUTTS)))
+$(eval $(call generate-docker-build-target,$(BACKEND_KOKORO)))
+$(eval $(call generate-docker-build-target,$(BACKEND_VLLM)))
+$(eval $(call generate-docker-build-target,$(BACKEND_VLLM_OMNI)))
+$(eval $(call generate-docker-build-target,$(BACKEND_DIFFUSERS)))
+$(eval $(call generate-docker-build-target,$(BACKEND_CHATTERBOX)))
+$(eval $(call generate-docker-build-target,$(BACKEND_VIBEVOICE)))
+$(eval $(call generate-docker-build-target,$(BACKEND_MOONSHINE)))
+$(eval $(call generate-docker-build-target,$(BACKEND_POCKET_TTS)))
+$(eval $(call generate-docker-build-target,$(BACKEND_QWEN_TTS)))
+$(eval $(call generate-docker-build-target,$(BACKEND_QWEN_ASR)))
+$(eval $(call generate-docker-build-target,$(BACKEND_VOXCPM)))
+$(eval $(call generate-docker-build-target,$(BACKEND_WHISPERX)))
 
-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
+# Pattern rule for docker-save targets
+docker-save-%: backend-images
+	docker save local-ai-backend:$* -o backend-images/$*.tar
 
-docker-save-kitten-tts: backend-images
-	docker save local-ai-backend:kitten-tts -o backend-images/kitten-tts.tar
+docker-build-backends: docker-build-llama-cpp docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-transformers docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-qwen-asr docker-build-voxcpm docker-build-whisperx
 
-docker-save-chatterbox: backend-images
-	docker save local-ai-backend:chatterbox -o backend-images/chatterbox.tar
+########################################################
+### Mock Backend for E2E Tests
+########################################################
 
-docker-build-neutts:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:neutts -f backend/Dockerfile.python --build-arg BACKEND=neutts ./backend
+build-mock-backend: protogen-go
+	$(GOCMD) build -o tests/e2e/mock-backend/mock-backend ./tests/e2e/mock-backend
 
-docker-save-neutts: backend-images
-	docker save local-ai-backend:neutts -o backend-images/neutts.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
-
-docker-save-huggingface: backend-images
-	docker save local-ai-backend:huggingface -o backend-images/huggingface.tar
-
-docker-save-local-store: backend-images
-	docker save local-ai-backend:local-store -o backend-images/local-store.tar
-
-docker-build-silero-vad:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:silero-vad -f backend/Dockerfile.golang --build-arg BACKEND=silero-vad .
-
-docker-save-silero-vad: backend-images
-	docker save local-ai-backend:silero-vad -o backend-images/silero-vad.tar
-
-docker-save-piper: backend-images
-	docker save local-ai-backend:piper -o backend-images/piper.tar
-
-docker-save-llama-cpp: backend-images
-	docker save local-ai-backend:llama-cpp -o backend-images/llama-cpp.tar
-
-docker-save-bark-cpp: backend-images
-	docker save local-ai-backend:bark-cpp -o backend-images/bark-cpp.tar
-
-docker-build-stablediffusion-ggml:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:stablediffusion-ggml -f backend/Dockerfile.golang --build-arg BACKEND=stablediffusion-ggml .
-
-docker-save-stablediffusion-ggml: backend-images
-	docker save local-ai-backend:stablediffusion-ggml -o backend-images/stablediffusion-ggml.tar
-
-docker-build-rerankers:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:rerankers -f backend/Dockerfile.python --build-arg BACKEND=rerankers .
-
-docker-build-vllm:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:vllm -f backend/Dockerfile.python --build-arg BACKEND=vllm .
-
-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 --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-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  .
-
-docker-save-whisper: backend-images
-	docker save local-ai-backend:whisper -o backend-images/whisper.tar
-
-docker-build-faster-whisper:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:faster-whisper -f backend/Dockerfile.python --build-arg BACKEND=faster-whisper .
-
-docker-build-coqui:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:coqui -f backend/Dockerfile.python --build-arg BACKEND=coqui .
-
-docker-build-bark:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:bark -f backend/Dockerfile.python --build-arg BACKEND=bark .
-
-docker-build-chatterbox:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:chatterbox -f backend/Dockerfile.python --build-arg BACKEND=chatterbox ./backend
-
-docker-build-exllama2:
-	docker build --build-arg BUILD_TYPE=$(BUILD_TYPE) --build-arg BASE_IMAGE=$(BASE_IMAGE) -t local-ai-backend:exllama2 -f backend/Dockerfile.python --build-arg BACKEND=exllama2 .
-
-docker-build-backends: docker-build-llama-cpp docker-build-rerankers docker-build-vllm docker-build-transformers docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-bark docker-build-chatterbox docker-build-exllama2
+clean-mock-backend:
+	rm -f tests/e2e/mock-backend/mock-backend
 
 ########################################################
 ### END Backends

README.md

@@ -33,7 +33,7 @@
 <img src="https://img.shields.io/badge/X-%23000000.svg?style=for-the-badge&logo=X&logoColor=white&label=LocalAI_API" alt="Follow LocalAI_API"/>
 </a>
 <a href="https://discord.gg/uJAeKSAGDy" target="blank">
-<img src="https://dcbadge.vercel.app/api/server/uJAeKSAGDy?style=flat-square&theme=default-inverted" alt="Join LocalAI Discord Community"/>
+<img src="https://img.shields.io/badge/dynamic/json?color=blue&label=Discord&style=for-the-badge&query=approximate_member_count&url=https%3A%2F%2Fdiscordapp.com%2Fapi%2Finvites%2FuJAeKSAGDy%3Fwith_counts%3Dtrue&logo=discord" alt="Join LocalAI Discord Community"/>
 </a>
 </p>
 
@@ -51,37 +51,29 @@
 **LocalAI** is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that's compatible with OpenAI (Elevenlabs, Anthropic... ) API specifications for local AI inferencing. It allows you to run LLMs, generate images, audio (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families. Does not require GPU. It is created and maintained by [Ettore Di Giacinto](https://github.com/mudler).
 
 
-## 📚🆕 Local Stack Family
+## Local Stack Family
 
-🆕 LocalAI is now part of a comprehensive suite of AI tools designed to work together:
+Liking LocalAI? LocalAI is part of an integrated suite of AI infrastructure tools, you might also like:
 
-<table>
-  <tr>
-    <td width="50%" valign="top">
-      <a href="https://github.com/mudler/LocalAGI">
-        <img src="https://raw.githubusercontent.com/mudler/LocalAGI/refs/heads/main/webui/react-ui/public/logo_2.png" width="300" alt="LocalAGI Logo">
-      </a>
-    </td>
-    <td width="50%" valign="top">
-      <h3><a href="https://github.com/mudler/LocalAGI">LocalAGI</a></h3>
-      <p>A powerful Local AI agent management platform that serves as a drop-in replacement for OpenAI's Responses API, enhanced with advanced agentic capabilities.</p>
-    </td>
-  </tr>
-  <tr>
-    <td width="50%" valign="top">
-      <a href="https://github.com/mudler/LocalRecall">
-        <img src="https://raw.githubusercontent.com/mudler/LocalRecall/refs/heads/main/static/localrecall_horizontal.png" width="300" alt="LocalRecall Logo">
-      </a>
-    </td>
-    <td width="50%" valign="top">
-      <h3><a href="https://github.com/mudler/LocalRecall">LocalRecall</a></h3>
-      <p>A REST-ful API and knowledge base management system that provides persistent memory and storage capabilities for AI agents.</p>
-    </td>
-  </tr>
-</table>
+- **[LocalAGI](https://github.com/mudler/LocalAGI)** - AI agent orchestration platform with OpenAI Responses API compatibility and advanced agentic capabilities
+- **[LocalRecall](https://github.com/mudler/LocalRecall)** - MCP/REST API knowledge base system providing persistent memory and storage for AI agents
+- 🆕 **[Cogito](https://github.com/mudler/cogito)** - Go library for building intelligent, co-operative agentic software and LLM-powered workflows, focusing on improving results for small, open source language models that scales to any LLM. Powers LocalAGI and LocalAI MCP/Agentic capabilities
+- 🆕 **[Wiz](https://github.com/mudler/wiz)** - Terminal-based AI agent accessible via Ctrl+Space keybinding. Portable, local-LLM friendly shell assistant with TUI/CLI modes, tool execution with approval, MCP protocol support, and multi-shell compatibility (zsh, bash, fish)
+- 🆕 **[SkillServer](https://github.com/mudler/skillserver)** - Simple, centralized skills database for AI agents via MCP. Manages skills as Markdown files with MCP server integration, web UI for editing, Git synchronization, and full-text search capabilities
 
-## Screenshots
 
+## Screenshots / Video
+
+### Youtube video
+
+<h1 align="center">
+  <br>
+  <a href="https://www.youtube.com/watch?v=PDqYhB9nNHA" target="_blank"> <img width="300" src="https://img.youtube.com/vi/PDqYhB9nNHA/0.jpg"> </a><br>
+<br>
+</h1>
+
+
+### Screenshots
 
 | Talk Interface | Generate Audio |
 | --- | --- |
@@ -101,6 +93,8 @@
 
 ## 💻 Quickstart
 
+> ⚠️ **Note:** The `install.sh` script is currently experiencing issues due to the heavy changes currently undergoing in LocalAI and may produce broken or misconfigured installations. Please use Docker installation (see below) or manual binary installation until [issue #8032](https://github.com/mudler/LocalAI/issues/8032) is resolved.
+
 Run the installer script:
 
 ```bash
@@ -118,7 +112,7 @@ For more installation options, see [Installer Options](https://localai.io/instal
 
 > Note: the DMGs are not signed by Apple as quarantined. See https://github.com/mudler/LocalAI/issues/6268 for a workaround, fix is tracked here: https://github.com/mudler/LocalAI/issues/6244
 
-Or run with docker:
+### Containers (Docker, podman, ...)
 
 > **💡 Docker Run vs Docker Start**
 > 
@@ -127,55 +121,59 @@ Or run with docker:
 > 
 > If you've already run LocalAI before and want to start it again, use: `docker start -i local-ai`
 
-### CPU only image:
+#### CPU only image:
 
 ```bash
 docker run -ti --name local-ai -p 8080:8080 localai/localai:latest
 ```
 
-### NVIDIA GPU Images:
+#### NVIDIA GPU Images:
 
 ```bash
+# CUDA 13.0
+docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-13
+
 # CUDA 12.0
 docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12
 
-# CUDA 11.7
-docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-11
-
 # NVIDIA Jetson (L4T) ARM64
+# CUDA 12 (for Nvidia AGX Orin and similar platforms)
 docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64
+
+# CUDA 13 (for Nvidia DGX Spark)
+docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64-cuda-13
 ```
 
-### AMD GPU Images (ROCm):
+#### AMD GPU Images (ROCm):
 
 ```bash
 docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-gpu-hipblas
 ```
 
-### Intel GPU Images (oneAPI):
+#### Intel GPU Images (oneAPI):
 
 ```bash
 docker run -ti --name local-ai -p 8080:8080 --device=/dev/dri/card1 --device=/dev/dri/renderD128 localai/localai:latest-gpu-intel
 ```
 
-### Vulkan GPU Images:
+#### Vulkan GPU Images:
 
 ```bash
 docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan
 ```
 
-### AIO Images (pre-downloaded models):
+#### AIO Images (pre-downloaded models):
 
 ```bash
 # CPU version
 docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-cpu
 
+# NVIDIA CUDA 13 version
+docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-13
+
 # NVIDIA CUDA 12 version
 docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-12
 
-# NVIDIA CUDA 11 version
-docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-11
-
 # Intel GPU version
 docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-gpu-intel
 
@@ -206,6 +204,7 @@ For more information, see [💻 Getting started](https://localai.io/basics/getti
 
 ## 📰 Latest project news
 
+- December 2025: [Dynamic Memory Resource reclaimer](https://github.com/mudler/LocalAI/pull/7583), [Automatic fitting of models to multiple GPUS(llama.cpp)](https://github.com/mudler/LocalAI/pull/7584), [Added Vibevoice backend](https://github.com/mudler/LocalAI/pull/7494)
 - November 2025: Major improvements to the UX. Among these: [Import models via URL](https://github.com/mudler/LocalAI/pull/7245) and [Multiple chats and history](https://github.com/mudler/LocalAI/pull/7325)
 - October 2025: 🔌 [Model Context Protocol (MCP)](https://localai.io/docs/features/mcp/) support added for agentic capabilities with external tools
 - September 2025: New Launcher application for MacOS and Linux, extended support to many backends for Mac and Nvidia L4T devices. Models: Added MLX-Audio, WAN 2.2. WebUI improvements and Python-based backends now ships portable python environments.
@@ -240,6 +239,7 @@ Roadmap items: [List of issues](https://github.com/mudler/LocalAI/issues?q=is%3A
 - 🔈 [Audio to Text](https://localai.io/features/audio-to-text/) (Audio transcription with `whisper.cpp`)
 - 🎨 [Image generation](https://localai.io/features/image-generation)
 - 🔥 [OpenAI-alike tools API](https://localai.io/features/openai-functions/) 
+- ⚡ [Realtime API](https://localai.io/features/openai-realtime/) (Speech-to-speech) 
 - 🧠 [Embeddings generation for vector databases](https://localai.io/features/embeddings/)
 - ✍️ [Constrained grammars](https://localai.io/features/constrained_grammars/)
 - 🖼️ [Download Models directly from Huggingface ](https://localai.io/models/)
@@ -258,39 +258,39 @@ LocalAI supports a comprehensive range of AI backends with multiple acceleration
 ### Text Generation & Language Models
 | Backend | Description | Acceleration Support |
 |---------|-------------|---------------------|
-| **llama.cpp** | LLM inference in C/C++ | CUDA 11/12, ROCm, Intel SYCL, Vulkan, Metal, CPU |
-| **vLLM** | Fast LLM inference with PagedAttention | CUDA 12, ROCm, Intel |
-| **transformers** | HuggingFace transformers framework | CUDA 11/12, ROCm, Intel, CPU |
-| **exllama2** | GPTQ inference library | CUDA 12 |
+| **llama.cpp** | LLM inference in C/C++ | CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, CPU |
+| **vLLM** | Fast LLM inference with PagedAttention | CUDA 12/13, ROCm, Intel |
+| **transformers** | HuggingFace transformers framework | CUDA 12/13, ROCm, Intel, CPU |
 | **MLX** | Apple Silicon LLM inference | Metal (M1/M2/M3+) |
 | **MLX-VLM** | Apple Silicon Vision-Language Models | Metal (M1/M2/M3+) |
 
 ### Audio & Speech Processing
 | Backend | Description | Acceleration Support |
 |---------|-------------|---------------------|
-| **whisper.cpp** | OpenAI Whisper in C/C++ | CUDA 12, ROCm, Intel SYCL, Vulkan, CPU |
-| **faster-whisper** | Fast Whisper with CTranslate2 | CUDA 12, ROCm, Intel, CPU |
-| **bark** | Text-to-audio generation | CUDA 12, ROCm, Intel |
-| **bark-cpp** | C++ implementation of Bark | CUDA, Metal, CPU |
-| **coqui** | Advanced TTS with 1100+ languages | CUDA 12, ROCm, Intel, CPU |
-| **kokoro** | Lightweight TTS model | CUDA 12, ROCm, Intel, CPU |
-| **chatterbox** | Production-grade TTS | CUDA 11/12, CPU |
+| **whisper.cpp** | OpenAI Whisper in C/C++ | CUDA 12/13, ROCm, Intel SYCL, Vulkan, CPU |
+| **faster-whisper** | Fast Whisper with CTranslate2 | CUDA 12/13, ROCm, Intel, CPU |
+| **coqui** | Advanced TTS with 1100+ languages | CUDA 12/13, ROCm, Intel, CPU |
+| **kokoro** | Lightweight TTS model | CUDA 12/13, ROCm, Intel, CPU |
+| **chatterbox** | Production-grade TTS | CUDA 12/13, CPU |
 | **piper** | Fast neural TTS system | CPU |
 | **kitten-tts** | Kitten TTS models | CPU |
 | **silero-vad** | Voice Activity Detection | CPU |
-| **neutts** | Text-to-speech with voice cloning | CUDA 12, ROCm, CPU |
+| **neutts** | Text-to-speech with voice cloning | CUDA 12/13, ROCm, CPU |
+| **vibevoice** | Real-time TTS with voice cloning | CUDA 12/13, ROCm, Intel, CPU |
+| **pocket-tts** | Lightweight CPU-based TTS | CUDA 12/13, ROCm, Intel, CPU |
+| **qwen-tts** | High-quality TTS with custom voice, voice design, and voice cloning | CUDA 12/13, ROCm, Intel, CPU |
 
 ### Image & Video Generation
 | Backend | Description | Acceleration Support |
 |---------|-------------|---------------------|
-| **stablediffusion.cpp** | Stable Diffusion in C/C++ | CUDA 12, Intel SYCL, Vulkan, CPU |
-| **diffusers** | HuggingFace diffusion models | CUDA 11/12, ROCm, Intel, Metal, CPU |
+| **stablediffusion.cpp** | Stable Diffusion in C/C++ | CUDA 12/13, Intel SYCL, Vulkan, CPU |
+| **diffusers** | HuggingFace diffusion models | CUDA 12/13, ROCm, Intel, Metal, CPU |
 
 ### Specialized AI Tasks
 | Backend | Description | Acceleration Support |
 |---------|-------------|---------------------|
-| **rfdetr** | Real-time object detection | CUDA 12, Intel, CPU |
-| **rerankers** | Document reranking API | CUDA 11/12, ROCm, Intel, CPU |
+| **rfdetr** | Real-time object detection | CUDA 12/13, Intel, CPU |
+| **rerankers** | Document reranking API | CUDA 12/13, ROCm, Intel, CPU |
 | **local-store** | Vector database | CPU |
 | **huggingface** | HuggingFace API integration | API-based |
 
@@ -298,13 +298,14 @@ LocalAI supports a comprehensive range of AI backends with multiple acceleration
 
 | Acceleration Type | Supported Backends | Hardware Support |
 |-------------------|-------------------|------------------|
-| **NVIDIA CUDA 11** | llama.cpp, whisper, stablediffusion, diffusers, rerankers, bark, chatterbox | Nvidia hardware |
 | **NVIDIA CUDA 12** | All CUDA-compatible backends | Nvidia hardware |
-| **AMD ROCm** | llama.cpp, whisper, vllm, transformers, diffusers, rerankers, coqui, kokoro, bark, neutts | AMD Graphics |
-| **Intel oneAPI** | llama.cpp, whisper, stablediffusion, vllm, transformers, diffusers, rfdetr, rerankers, exllama2, coqui, kokoro, bark | Intel Arc, Intel iGPUs |
-| **Apple Metal** | llama.cpp, whisper, diffusers, MLX, MLX-VLM, bark-cpp | Apple M1/M2/M3+ |
+| **NVIDIA CUDA 13** | All CUDA-compatible backends | Nvidia hardware |
+| **AMD ROCm** | llama.cpp, whisper, vllm, transformers, diffusers, rerankers, coqui, kokoro, neutts, vibevoice, pocket-tts, qwen-tts | AMD Graphics |
+| **Intel oneAPI** | llama.cpp, whisper, stablediffusion, vllm, transformers, diffusers, rfdetr, rerankers, coqui, kokoro, vibevoice, pocket-tts, qwen-tts | Intel Arc, Intel iGPUs |
+| **Apple Metal** | llama.cpp, whisper, diffusers, MLX, MLX-VLM | Apple M1/M2/M3+ |
 | **Vulkan** | llama.cpp, whisper, stablediffusion | Cross-platform GPUs |
-| **NVIDIA Jetson** | llama.cpp, whisper, stablediffusion, diffusers, rfdetr | ARM64 embedded AI |
+| **NVIDIA Jetson (CUDA 12)** | llama.cpp, whisper, stablediffusion, diffusers, rfdetr | ARM64 embedded AI (AGX Orin, etc.) |
+| **NVIDIA Jetson (CUDA 13)** | llama.cpp, whisper, stablediffusion, diffusers, rfdetr | ARM64 embedded AI (DGX Spark) |
 | **CPU Optimized** | All backends | AVX/AVX2/AVX512, quantization support |
 
 ### 🔗 Community and integrations
@@ -323,6 +324,10 @@ Agentic Libraries:
 MCPs:
 - https://github.com/mudler/MCPs
 
+OS Assistant:
+
+- https://github.com/mudler/Keygeist - Keygeist is an AI-powered keyboard operator that listens for key combinations and responds with AI-generated text typed directly into your Linux box.
+
 Model galleries
 - https://github.com/go-skynet/model-gallery
 
@@ -397,6 +402,10 @@ A huge thank you to our generous sponsors who support this project covering CI e
   </a>
 </p>
 
+### Individual sponsors
+
+A special thanks to individual sponsors that contributed to the project, a full list is in [Github](https://github.com/sponsors/mudler) and [buymeacoffee](https://buymeacoffee.com/mudler), a special shout out goes to [drikster80](https://github.com/drikster80) for being generous. Thank you everyone!
+
 ## 🌟 Star history
 
 [![LocalAI Star history Chart](https://api.star-history.com/svg?repos=go-skynet/LocalAI&type=Date)](https://star-history.com/#go-skynet/LocalAI&Date)

backend/Dockerfile.golang

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=ubuntu:22.04
+ARG BASE_IMAGE=ubuntu:24.04
 
 FROM ${BASE_IMAGE} AS builder
 ARG BACKEND=rerankers
@@ -12,14 +12,15 @@ ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
 ENV DEBIAN_FRONTEND=noninteractive
 ARG TARGETARCH
 ARG TARGETVARIANT
-ARG GO_VERSION=1.22.6
+ARG GO_VERSION=1.25.4
+ARG UBUNTU_VERSION=2404
 
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
         build-essential \
         git ccache \
         ca-certificates \
-        make cmake \
+        make cmake wget \
         curl unzip \
         libssl-dev && \
     apt-get clean && \
@@ -32,17 +33,52 @@ ENV PATH=/usr/local/cuda/bin:${PATH}
 # HipBLAS requirements
 ENV PATH=/opt/rocm/bin:${PATH}
 
+
 # Vulkan requirements
 RUN <<EOT bash
     if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
         apt-get update && \
         apt-get install -y  --no-install-recommends \
             software-properties-common pciutils wget gpg-agent && \
-        wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-        wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
-        apt-get update && \
-        apt-get install -y \
-            vulkan-sdk && \
+        apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
+            libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
+            libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
+            git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
+            ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
+            clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
+        if [ "amd64" = "$TARGETARCH" ]; then
+            wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
+            tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
+            rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
+            mkdir -p /opt/vulkan-sdk && \
+            mv 1.4.335.0 /opt/vulkan-sdk/ && \
+            cd /opt/vulkan-sdk/1.4.335.0 && \
+            ./vulkansdk --no-deps --maxjobs \
+                vulkan-loader \
+                vulkan-validationlayers \
+                vulkan-extensionlayer \
+                vulkan-tools \
+                shaderc && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
+            rm -rf /opt/vulkan-sdk
+        fi
+        if [ "arm64" = "$TARGETARCH" ]; then
+            mkdir vulkan && cd vulkan && \
+            curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
+            tar -xvf vulkan-sdk.tar.xz && \
+            rm vulkan-sdk.tar.xz && \
+            cd 1.4.335.0 && \
+            cp -rfv aarch64/bin/* /usr/bin/ && \
+            cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
+            cp -rfv aarch64/include/* /usr/include/ && \
+            cp -rfv aarch64/share/* /usr/share/ && \
+            cd ../.. && \
+            rm -rf vulkan
+        fi
+        ldconfig && \
         apt-get clean && \
         rm -rf /var/lib/apt/lists/*
     fi
@@ -50,15 +86,19 @@ EOT
 
 # CuBLAS requirements
 RUN <<EOT bash
-    if [ "${BUILD_TYPE}" = "cublas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
+    if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
         apt-get update && \
         apt-get install -y  --no-install-recommends \
             software-properties-common pciutils
         if [ "amd64" = "$TARGETARCH" ]; then
-            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
+            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
         fi
         if [ "arm64" = "$TARGETARCH" ]; then
-            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb
+            if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
+                curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
+            else
+                curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
+            fi
         fi
         dpkg -i cuda-keyring_1.1-1_all.deb && \
         rm -f cuda-keyring_1.1-1_all.deb && \
@@ -69,12 +109,31 @@ RUN <<EOT bash
             libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
             libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
             libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
-            libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} && \
+            libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
+        if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
+            apt-get install -y --no-install-recommends \
+            libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
+        fi
         apt-get clean && \
         rm -rf /var/lib/apt/lists/*
     fi
 EOT
 
+
+# https://github.com/NVIDIA/Isaac-GR00T/issues/343
+RUN <<EOT bash
+    if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
+        wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
+        dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
+        cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
+        apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
+        wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
+        dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
+        cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
+        apt-get update && apt-get install -y nvpl
+    fi
+EOT
+
 # If we are building with clblas support, we need the libraries for the builds
 RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
         apt-get update && \
@@ -123,6 +182,8 @@ EOT
 
 COPY . /LocalAI
 
+RUN git config --global --add safe.directory /LocalAI
+
 RUN cd /LocalAI && make protogen-go && make -C /LocalAI/backend/go/${BACKEND} build
 
 FROM scratch

backend/Dockerfile.llama-cpp

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=ubuntu:22.04
+ARG BASE_IMAGE=ubuntu:24.04
 ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
 
 
@@ -10,7 +10,8 @@ FROM ${GRPC_BASE_IMAGE} AS grpc
 ARG GRPC_MAKEFLAGS="-j4 -Otarget"
 ARG GRPC_VERSION=v1.65.0
 ARG CMAKE_FROM_SOURCE=false
-ARG CMAKE_VERSION=3.26.4
+# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
+ARG CMAKE_VERSION=3.31.10
 
 ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
 
@@ -20,13 +21,13 @@ RUN apt-get update && \
     apt-get install -y --no-install-recommends \
         ca-certificates \
         build-essential curl libssl-dev \
-        git && \
+        git wget && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
 # Install CMake (the version in 22.04 is too old)
 RUN <<EOT bash
-    if [ "${CMAKE_FROM_SOURCE}}" = "true" ]; then
+    if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
         curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
     else
         apt-get update && \
@@ -50,6 +51,13 @@ RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shall
     rm -rf /build
 
 FROM ${BASE_IMAGE} AS builder
+ARG CMAKE_FROM_SOURCE=false
+ARG CMAKE_VERSION=3.31.10
+# We can target specific CUDA ARCHITECTURES like --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
+ARG CUDA_DOCKER_ARCH
+ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
+ARG CMAKE_ARGS
+ENV CMAKE_ARGS=${CMAKE_ARGS}
 ARG BACKEND=rerankers
 ARG BUILD_TYPE
 ENV BUILD_TYPE=${BUILD_TYPE}
@@ -61,7 +69,8 @@ ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
 ENV DEBIAN_FRONTEND=noninteractive
 ARG TARGETARCH
 ARG TARGETVARIANT
-ARG GO_VERSION=1.22.6
+ARG GO_VERSION=1.25.4
+ARG UBUNTU_VERSION=2404
 
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
@@ -69,8 +78,9 @@ RUN apt-get update && \
         ccache git \
         ca-certificates \
         make \
+        pkg-config libcurl4-openssl-dev \
         curl unzip \
-        libssl-dev && \
+        libssl-dev wget && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
@@ -80,17 +90,52 @@ ENV PATH=/usr/local/cuda/bin:${PATH}
 # HipBLAS requirements
 ENV PATH=/opt/rocm/bin:${PATH}
 
+
 # Vulkan requirements
 RUN <<EOT bash
     if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
         apt-get update && \
         apt-get install -y  --no-install-recommends \
             software-properties-common pciutils wget gpg-agent && \
-        wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-        wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
-        apt-get update && \
-        apt-get install -y \
-            vulkan-sdk && \
+        apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
+            libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
+            libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
+            git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
+            ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
+            clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
+        if [ "amd64" = "$TARGETARCH" ]; then
+            wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
+            tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
+            rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
+            mkdir -p /opt/vulkan-sdk && \
+            mv 1.4.335.0 /opt/vulkan-sdk/ && \
+            cd /opt/vulkan-sdk/1.4.335.0 && \
+            ./vulkansdk --no-deps --maxjobs \
+                vulkan-loader \
+                vulkan-validationlayers \
+                vulkan-extensionlayer \
+                vulkan-tools \
+                shaderc && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
+            rm -rf /opt/vulkan-sdk
+        fi
+        if [ "arm64" = "$TARGETARCH" ]; then
+            mkdir vulkan && cd vulkan && \
+            curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
+            tar -xvf vulkan-sdk.tar.xz && \
+            rm vulkan-sdk.tar.xz && \
+            cd 1.4.335.0 && \
+            cp -rfv aarch64/bin/* /usr/bin/ && \
+            cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
+            cp -rfv aarch64/include/* /usr/include/ && \
+            cp -rfv aarch64/share/* /usr/share/ && \
+            cd ../.. && \
+            rm -rf vulkan
+        fi
+        ldconfig && \
         apt-get clean && \
         rm -rf /var/lib/apt/lists/*
     fi
@@ -98,15 +143,19 @@ EOT
 
 # CuBLAS requirements
 RUN <<EOT bash
-    if [ "${BUILD_TYPE}" = "cublas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
+    if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
         apt-get update && \
         apt-get install -y  --no-install-recommends \
             software-properties-common pciutils
         if [ "amd64" = "$TARGETARCH" ]; then
-            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
+            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
         fi
         if [ "arm64" = "$TARGETARCH" ]; then
-            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb
+            if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
+                curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
+            else
+                curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
+            fi
         fi
         dpkg -i cuda-keyring_1.1-1_all.deb && \
         rm -f cuda-keyring_1.1-1_all.deb && \
@@ -117,12 +166,31 @@ RUN <<EOT bash
             libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
             libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
             libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
-            libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} && \
+            libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
+        if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
+            apt-get install -y --no-install-recommends \
+            libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
+        fi
         apt-get clean && \
         rm -rf /var/lib/apt/lists/*
     fi
 EOT
 
+
+# https://github.com/NVIDIA/Isaac-GR00T/issues/343
+RUN <<EOT bash
+    if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
+        wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
+        dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
+        cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
+        apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
+        wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
+        dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
+        cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
+        apt-get update && apt-get install -y nvpl
+    fi
+EOT
+
 # If we are building with clblas support, we need the libraries for the builds
 RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
         apt-get update && \
@@ -164,7 +232,7 @@ EOT
 
 # Install CMake (the version in 22.04 is too old)
 RUN <<EOT bash
-    if [ "${CMAKE_FROM_SOURCE}}" = "true" ]; then
+    if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
         curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
     else
         apt-get update && \
@@ -180,19 +248,30 @@ COPY --from=grpc /opt/grpc /usr/local
 
 COPY . /LocalAI
 
-## Otherwise just run the normal build
-RUN <<EOT bash
-if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then \
-        cd /LocalAI/backend/cpp/llama-cpp && make llama-cpp-fallback && \
-        make llama-cpp-grpc && make llama-cpp-rpc-server; \
-    else \
-        cd /LocalAI/backend/cpp/llama-cpp && make llama-cpp-avx && \
-        make llama-cpp-avx2 && \
-        make llama-cpp-avx512 && \
-        make llama-cpp-fallback && \
-        make llama-cpp-grpc && \
-        make llama-cpp-rpc-server; \
-    fi  
+RUN <<'EOT' bash
+set -euxo pipefail
+
+if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
+  CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
+  export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
+  echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
+  rm -rf /LocalAI/backend/cpp/llama-cpp-*-build
+fi
+
+if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
+  cd /LocalAI/backend/cpp/llama-cpp
+  make llama-cpp-fallback
+  make llama-cpp-grpc
+  make llama-cpp-rpc-server
+else
+  cd /LocalAI/backend/cpp/llama-cpp
+  make llama-cpp-avx
+  make llama-cpp-avx2
+  make llama-cpp-avx512
+  make llama-cpp-fallback
+  make llama-cpp-grpc
+  make llama-cpp-rpc-server
+fi
 EOT
 
 

backend/Dockerfile.python

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=ubuntu:22.04
+ARG BASE_IMAGE=ubuntu:24.04
 
 FROM ${BASE_IMAGE} AS builder
 ARG BACKEND=rerankers
@@ -12,6 +12,7 @@ ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
 ENV DEBIAN_FRONTEND=noninteractive
 ARG TARGETARCH
 ARG TARGETVARIANT
+ARG UBUNTU_VERSION=2404
 
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
@@ -21,7 +22,7 @@ RUN apt-get update && \
         espeak-ng \
         curl \
         libssl-dev \
-        git \
+        git wget \
         git-lfs \
         unzip clang \
         upx-ucl \
@@ -30,8 +31,15 @@ RUN apt-get update && \
         python3-dev llvm \
         python3-venv make cmake && \
     apt-get clean && \
-    rm -rf /var/lib/apt/lists/* && \
-    pip install --upgrade pip
+    rm -rf /var/lib/apt/lists/*
+
+RUN <<EOT bash
+    if [ "${UBUNTU_VERSION}" = "2404" ]; then
+        pip install --break-system-packages --user --upgrade pip
+    else
+        pip install --upgrade pip
+    fi
+EOT
 
 
 # Cuda
@@ -46,11 +54,45 @@ RUN <<EOT bash
         apt-get update && \
         apt-get install -y  --no-install-recommends \
             software-properties-common pciutils wget gpg-agent && \
-        wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-        wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
-        apt-get update && \
-        apt-get install -y \
-            vulkan-sdk && \
+        apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
+            libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
+            libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
+            git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
+            ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
+            clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
+        if [ "amd64" = "$TARGETARCH" ]; then
+            wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
+            tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
+            rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
+            mkdir -p /opt/vulkan-sdk && \
+            mv 1.4.335.0 /opt/vulkan-sdk/ && \
+            cd /opt/vulkan-sdk/1.4.335.0 && \
+            ./vulkansdk --no-deps --maxjobs \
+                vulkan-loader \
+                vulkan-validationlayers \
+                vulkan-extensionlayer \
+                vulkan-tools \
+                shaderc && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
+            rm -rf /opt/vulkan-sdk
+        fi
+        if [ "arm64" = "$TARGETARCH" ]; then
+            mkdir vulkan && cd vulkan && \
+            curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
+            tar -xvf vulkan-sdk.tar.xz && \
+            rm vulkan-sdk.tar.xz && \
+            cd 1.4.335.0 && \
+            cp -rfv aarch64/bin/* /usr/bin/ && \
+            cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
+            cp -rfv aarch64/include/* /usr/include/ && \
+            cp -rfv aarch64/share/* /usr/share/ && \
+            cd ../.. && \
+            rm -rf vulkan
+        fi
+        ldconfig && \
         apt-get clean && \
         rm -rf /var/lib/apt/lists/*
     fi
@@ -58,15 +100,19 @@ EOT
 
 # CuBLAS requirements
 RUN <<EOT bash
-    if [ "${BUILD_TYPE}" = "cublas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
+    if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
         apt-get update && \
         apt-get install -y  --no-install-recommends \
             software-properties-common pciutils
         if [ "amd64" = "$TARGETARCH" ]; then
-            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
+            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
         fi
         if [ "arm64" = "$TARGETARCH" ]; then
-            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb
+            if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
+                curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
+            else
+                curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
+            fi
         fi
         dpkg -i cuda-keyring_1.1-1_all.deb && \
         rm -f cuda-keyring_1.1-1_all.deb && \
@@ -77,12 +123,31 @@ RUN <<EOT bash
             libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
             libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
             libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
-            libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} && \
+            libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
+        if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
+            apt-get install -y --no-install-recommends \
+            libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
+        fi
         apt-get clean && \
         rm -rf /var/lib/apt/lists/*
     fi
 EOT
 
+
+# https://github.com/NVIDIA/Isaac-GR00T/issues/343
+RUN <<EOT bash
+    if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
+        wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
+        dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
+        cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
+        apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
+        wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
+        dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
+        cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
+        apt-get update && apt-get install -y nvpl
+    fi
+EOT
+
 # If we are building with clblas support, we need the libraries for the builds
 RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
         apt-get update && \
@@ -103,21 +168,40 @@ RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
         # to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
         ldconfig \
     ; fi
+
+RUN if [ "${BUILD_TYPE}" = "hipblas" ]; then \
+    ln -s /opt/rocm-**/lib/llvm/lib/libomp.so /usr/lib/libomp.so \
+    ; fi
+
 # Install uv as a system package
 RUN curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=/usr/bin sh
 ENV PATH="/root/.cargo/bin:${PATH}"
-
+# Increase timeout for uv installs behind slow networks
+ENV UV_HTTP_TIMEOUT=180
 RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
 
 # Install grpcio-tools (the version in 22.04 is too old)
-RUN pip install --user grpcio-tools==1.71.0 grpcio==1.71.0
+RUN <<EOT bash
+    if [ "${UBUNTU_VERSION}" = "2404" ]; then
+        pip install --break-system-packages --user grpcio-tools==1.71.0 grpcio==1.71.0
+    else
+        pip install grpcio-tools==1.71.0 grpcio==1.71.0
+    fi
+EOT
 
-COPY python/${BACKEND} /${BACKEND}
-COPY backend.proto /${BACKEND}/backend.proto
-COPY python/common/ /${BACKEND}/common
+
+COPY backend/python/${BACKEND} /${BACKEND}
+COPY backend/backend.proto /${BACKEND}/backend.proto
+COPY backend/python/common/ /${BACKEND}/common
+COPY scripts/build/package-gpu-libs.sh /package-gpu-libs.sh
 
 RUN cd /${BACKEND} && PORTABLE_PYTHON=true make
 
+# Package GPU libraries into the backend's lib directory
+RUN mkdir -p /${BACKEND}/lib && \
+    TARGET_LIB_DIR="/${BACKEND}/lib" BUILD_TYPE="${BUILD_TYPE}" CUDA_MAJOR_VERSION="${CUDA_MAJOR_VERSION}" \
+    bash /package-gpu-libs.sh "/${BACKEND}/lib"
+
 FROM scratch
 ARG BACKEND=rerankers
 COPY --from=builder /${BACKEND}/ /

backend/README.md

@@ -46,7 +46,7 @@ The backend system provides language-specific Dockerfiles that handle the build
 - **vllm**: High-performance LLM inference
 - **mlx**: Apple Silicon optimization
 - **diffusers**: Stable Diffusion models
-- **Audio**: bark, coqui, faster-whisper, kitten-tts
+- **Audio**: coqui, faster-whisper, kitten-tts
 - **Vision**: mlx-vlm, rfdetr
 - **Specialized**: rerankers, chatterbox, kokoro
 
@@ -55,7 +55,6 @@ The backend system provides language-specific Dockerfiles that handle the build
 - **stablediffusion-ggml**: Stable Diffusion in Go with GGML Cpp backend
 - **huggingface**: Hugging Face model integration
 - **piper**: Text-to-speech synthesis Golang with C bindings using rhaspy/piper
-- **bark-cpp**: Bark TTS models Golang with Cpp bindings
 - **local-store**: Vector storage backend
 
 #### C++ Backends (`cpp/`)
@@ -65,7 +64,7 @@ The backend system provides language-specific Dockerfiles that handle the build
 ## Hardware Acceleration Support
 
 ### CUDA (NVIDIA)
-- **Versions**: CUDA 11.x, 12.x
+- **Versions**: CUDA 12.x, 13.x
 - **Features**: cuBLAS, cuDNN, TensorRT optimization
 - **Targets**: x86_64, ARM64 (Jetson)
 
@@ -132,8 +131,7 @@ For ARM64/Mac builds, docker can't be used, and the makefile in the respective b
 ### Build Types
 
 - **`cpu`**: CPU-only optimization
-- **`cublas11`**: CUDA 11.x with cuBLAS
-- **`cublas12`**: CUDA 12.x with cuBLAS
+- **`cublas12`**, **`cublas13`**: CUDA 12.x, 13.x with cuBLAS
 - **`hipblas`**: ROCm with rocBLAS
 - **`intel`**: Intel oneAPI optimization
 - **`vulkan`**: Vulkan-based acceleration
@@ -210,4 +208,4 @@ When contributing to the backend system:
 2. **Add Tests**: Include comprehensive test coverage
 3. **Document**: Provide clear usage examples
 4. **Optimize**: Consider performance and resource usage
-5. **Validate**: Test across different hardware targets
+5. **Validate**: Test across different hardware targets

backend/backend.proto

@@ -17,6 +17,7 @@ service Backend {
   rpc GenerateVideo(GenerateVideoRequest) returns (Result) {}
   rpc AudioTranscription(TranscriptRequest) returns (TranscriptResult) {}
   rpc TTS(TTSRequest) returns (Result) {}
+  rpc TTSStream(TTSRequest) returns (stream Reply) {}
   rpc SoundGeneration(SoundGenerationRequest) returns (Result) {}
   rpc TokenizeString(PredictOptions) returns (TokenizationResponse) {}
   rpc Status(HealthMessage) returns (StatusResponse) {}
@@ -32,6 +33,8 @@ service Backend {
   rpc GetMetrics(MetricsRequest) returns (MetricsResponse);
 
   rpc VAD(VADRequest) returns (VADResponse) {}
+
+  rpc ModelMetadata(ModelOptions) returns (ModelMetadataResponse) {}
 }
 
 // Define the empty request
@@ -282,6 +285,7 @@ message TranscriptRequest {
   uint32 threads = 4;
   bool translate = 5;
   bool diarize = 6;
+  string prompt = 7;
 }
 
 message TranscriptResult {
@@ -295,12 +299,12 @@ message TranscriptSegment {
   int64 end = 3;
   string text = 4;
   repeated int32 tokens = 5;
+  string speaker = 6;
 }
 
 message GenerateImageRequest {
   int32 height = 1;
   int32 width = 2;
-  int32 mode = 3;
   int32 step = 4;
   int32 seed = 5;
   string positive_prompt = 6;
@@ -410,3 +414,8 @@ message Detection {
 message DetectResponse {
   repeated Detection Detections = 1;
 }
+
+message ModelMetadataResponse {
+  bool supports_thinking = 1;
+  string rendered_template = 2;  // The rendered chat template with enable_thinking=true (empty if not applicable)
+}

backend/cpp/llama-cpp/CMakeLists.txt

@@ -70,4 +70,4 @@ target_link_libraries(${TARGET} PRIVATE common llama mtmd ${CMAKE_THREAD_LIBS_IN
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)
   add_dependencies(${TARGET} BUILD_INFO)
-endif()
+endif()

backend/cpp/llama-cpp/Makefile

@@ -1,5 +1,5 @@
 
-LLAMA_VERSION?=583cb83416467e8abf9b37349dcf1f6a0083745a
+LLAMA_VERSION?=2634ed207a17db1a54bd8df0555bd8499a6ab691
 LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
 
 CMAKE_ARGS?=
@@ -7,7 +7,8 @@ BUILD_TYPE?=
 NATIVE?=false
 ONEAPI_VARS?=/opt/intel/oneapi/setvars.sh
 TARGET?=--target grpc-server
-JOBS?=$(shell nproc)
+JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 1)
+ARCH?=$(shell uname -m)
 
 # Disable Shared libs as we are linking on static gRPC and we can't mix shared and static
 CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF -DLLAMA_CURL=OFF
@@ -106,21 +107,21 @@ llama-cpp-avx: llama.cpp
 	cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-avx-build
 	$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-avx-build purge
 	$(info ${GREEN}I llama-cpp build info:avx${RESET})
-	CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off" $(MAKE) VARIANT="llama-cpp-avx-build" build-llama-cpp-grpc-server
+	CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) VARIANT="llama-cpp-avx-build" build-llama-cpp-grpc-server
 	cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-avx-build/grpc-server llama-cpp-avx
 
 llama-cpp-fallback: llama.cpp
 	cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-fallback-build
 	$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-fallback-build purge
 	$(info ${GREEN}I llama-cpp build info:fallback${RESET})
-	CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off" $(MAKE) VARIANT="llama-cpp-fallback-build" build-llama-cpp-grpc-server
+	CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) VARIANT="llama-cpp-fallback-build" build-llama-cpp-grpc-server
 	cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-fallback-build/grpc-server llama-cpp-fallback
 
 llama-cpp-grpc: llama.cpp
 	cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build
 	$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build purge
 	$(info ${GREEN}I llama-cpp build info:grpc${RESET})
-	CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off" TARGET="--target grpc-server --target rpc-server" $(MAKE) VARIANT="llama-cpp-grpc-build" build-llama-cpp-grpc-server
+	CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" TARGET="--target grpc-server --target rpc-server" $(MAKE) VARIANT="llama-cpp-grpc-build" build-llama-cpp-grpc-server
 	cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build/grpc-server llama-cpp-grpc
 
 llama-cpp-rpc-server: llama-cpp-grpc

backend/cpp/llama-cpp/package.sh

@@ -6,6 +6,7 @@
 set -e
 
 CURDIR=$(dirname "$(realpath $0)")
+REPO_ROOT="${CURDIR}/../../.."
 
 # Create lib directory
 mkdir -p $CURDIR/package/lib
@@ -37,6 +38,15 @@ else
     exit 1
 fi
 
+# Package GPU libraries based on BUILD_TYPE
+# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
+GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
+if [ -f "$GPU_LIB_SCRIPT" ]; then
+    echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
+    source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
+    package_gpu_libs
+fi
+
 echo "Packaging completed successfully" 
 ls -liah $CURDIR/package/
 ls -liah $CURDIR/package/lib/

backend/cpp/llama-cpp/patches/01-llava.patch

@@ -1,13 +0,0 @@
-diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
-index 3cd0d2fa..6c5e811a 100644
---- a/tools/mtmd/clip.cpp
-+++ b/tools/mtmd/clip.cpp
-@@ -2608,7 +2608,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
-                 struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
-                 int* patches_data = (int*)malloc(ggml_nbytes(patches));
-                 for (int i = 0; i < num_patches; i++) {
--                    patches_data[i] = i + 1;
-+                    patches_data[i] = i;
-                 }
-                 ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
-                 free(patches_data);

backend/cpp/llama-cpp/prepare.sh

@@ -1,11 +1,14 @@
 #!/bin/bash
 
 ## Patches
+
 ## Apply patches from the `patches` directory
-for patch in $(ls patches); do
-    echo "Applying patch $patch"
-    patch -d llama.cpp/ -p1 < patches/$patch
-done 
+if [ -d "patches" ]; then
+    for patch in $(ls patches); do
+        echo "Applying patch $patch"
+        patch -d llama.cpp/ -p1 < patches/$patch
+    done 
+fi
 
 set -e
 
@@ -26,30 +29,3 @@ else
 fi
 set -e
 
-# Now to keep maximum compatibility with the original server.cpp, we need to remove the index.html.gz.hpp and loading.html.hpp includes
-# and remove the main function
-# TODO: upstream this to the original server.cpp by extracting the upstream main function to a separate file
-awk '
-/int[ \t]+main[ \t]*\(/ {          # If the line starts the main function
-    in_main=1;                     # Set a flag
-    open_braces=0;                 # Track number of open braces
-}
-in_main {
-    open_braces += gsub(/\{/, "{"); # Count opening braces
-    open_braces -= gsub(/\}/, "}"); # Count closing braces
-    if (open_braces == 0) {         # If all braces are closed
-        in_main=0;                  # End skipping
-    }
-    next;                           # Skip lines inside main
-}
-!in_main                           # Print lines not inside main
-' "llama.cpp/tools/server/server.cpp" > llama.cpp/tools/grpc-server/server.cpp
-
-# remove index.html.gz.hpp and loading.html.hpp includes
-if [[ "$OSTYPE" == "darwin"* ]]; then
-    # macOS
-    sed -i '' '/#include "index\.html\.gz\.hpp"/d; /#include "loading\.html\.hpp"/d' llama.cpp/tools/grpc-server/server.cpp
-else
-    # Linux and others
-    sed -i '/#include "index\.html\.gz\.hpp"/d; /#include "loading\.html\.hpp"/d' llama.cpp/tools/grpc-server/server.cpp
-fi

backend/go/bark-cpp/Makefile

@@ -1,51 +0,0 @@
-INCLUDE_PATH := $(abspath ./)
-LIBRARY_PATH := $(abspath ./)
-
-AR?=ar
-
-CMAKE_ARGS?=-DGGML_NATIVE=OFF
-BUILD_TYPE?=
-GOCMD=go
-# keep standard at C11 and C++11
-CXXFLAGS = -I. -I$(INCLUDE_PATH)/sources/bark.cpp/examples -I$(INCLUDE_PATH)/sources/bark.cpp/encodec.cpp/ggml/include -I$(INCLUDE_PATH)/sources/bark.cpp/spm-headers -I$(INCLUDE_PATH)/sources/bark.cpp -O3 -DNDEBUG -std=c++17 -fPIC
-LDFLAGS  = -L$(LIBRARY_PATH) -L$(LIBRARY_PATH)/sources/bark.cpp/build/examples -lbark -lstdc++ -lm
-
-# bark.cpp
-BARKCPP_REPO?=https://github.com/PABannier/bark.cpp.git
-BARKCPP_VERSION?=5d5be84f089ab9ea53b7a793f088d3fbf7247495
-
-# warnings
-CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function
-
-## bark.cpp
-sources/bark.cpp:
-	git clone --recursive $(BARKCPP_REPO) sources/bark.cpp && \
-	cd sources/bark.cpp && \
-	git checkout $(BARKCPP_VERSION) && \
-	git submodule update --init --recursive --depth 1 --single-branch
-
-sources/bark.cpp/build/libbark.a: sources/bark.cpp
-	cd sources/bark.cpp && \
-	mkdir -p build && \
-	cd build && \
-	cmake $(CMAKE_ARGS) .. && \
-	cmake --build . --config Release
-
-gobark.o:
-	$(CXX) $(CXXFLAGS) gobark.cpp -o gobark.o -c $(LDFLAGS)
-
-libbark.a: sources/bark.cpp/build/libbark.a gobark.o
-	cp $(INCLUDE_PATH)/sources/bark.cpp/build/libbark.a ./
-	$(AR) rcs libbark.a gobark.o
-
-bark-cpp: libbark.a
-	CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH="$(CURDIR)" LIBRARY_PATH=$(CURDIR) \
-	$(GOCMD) build -v -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o bark-cpp ./
-
-package:
-	bash package.sh
-
-build: bark-cpp package
-
-clean:
-	rm -f gobark.o libbark.a

backend/go/bark-cpp/gobark.cpp

@@ -1,85 +0,0 @@
-#include <iostream>
-#include <tuple>
-
-#include "bark.h"
-#include "gobark.h"
-#include "common.h"
-#include "ggml.h"
-
-struct bark_context *c;
-
-void bark_print_progress_callback(struct bark_context *bctx, enum bark_encoding_step step, int progress, void *user_data) {
-    if (step == bark_encoding_step::SEMANTIC) {
-        printf("\rGenerating semantic tokens... %d%%", progress);
-    } else if (step == bark_encoding_step::COARSE) {
-        printf("\rGenerating coarse tokens... %d%%", progress);
-    } else if (step == bark_encoding_step::FINE) {
-        printf("\rGenerating fine tokens... %d%%", progress);
-    }
-    fflush(stdout);
-}
-
-int load_model(char *model) {
-    // initialize bark context
-    struct bark_context_params ctx_params = bark_context_default_params();
-    bark_params params;
-
-    params.model_path = model;
-
-   // ctx_params.verbosity = verbosity;
-    ctx_params.progress_callback = bark_print_progress_callback;
-    ctx_params.progress_callback_user_data = nullptr;
-
-    struct bark_context *bctx = bark_load_model(params.model_path.c_str(), ctx_params, params.seed);
-    if (!bctx) {
-        fprintf(stderr, "%s: Could not load model\n", __func__);
-        return 1;
-    }
-
-    c = bctx;
-
-    return 0;
-}
-
-int tts(char *text,int  threads, char *dst ) {
-
-    ggml_time_init();
-    const int64_t t_main_start_us = ggml_time_us();
-
-    // generate audio
-    if (!bark_generate_audio(c, text, threads)) {
-        fprintf(stderr, "%s: An error occurred. If the problem persists, feel free to open an issue to report it.\n", __func__);
-        return 1;
-    }
-
-    const float *audio_data = bark_get_audio_data(c);
-    if (audio_data == NULL) {
-        fprintf(stderr, "%s: Could not get audio data\n", __func__);
-        return 1;
-    }
-
-    const int audio_arr_size = bark_get_audio_data_size(c);
-
-    std::vector<float> audio_arr(audio_data, audio_data + audio_arr_size);
-
-    write_wav_on_disk(audio_arr, dst);
-
-    // report timing
-    {
-        const int64_t t_main_end_us = ggml_time_us();
-        const int64_t t_load_us = bark_get_load_time(c);
-        const int64_t t_eval_us = bark_get_eval_time(c);
-
-        printf("\n\n");
-        printf("%s:     load time = %8.2f ms\n", __func__, t_load_us / 1000.0f);
-        printf("%s:     eval time = %8.2f ms\n", __func__, t_eval_us / 1000.0f);
-        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
-    }
-    
-    return 0;
-}
-
-int unload() {
-    bark_free(c);
-}
-

backend/go/bark-cpp/gobark.go

@@ -1,52 +0,0 @@
-package main
-
-// #cgo CXXFLAGS: -I${SRCDIR}/sources/bark.cpp/ -I${SRCDIR}/sources/bark.cpp/encodec.cpp -I${SRCDIR}/sources/bark.cpp/encodec.cpp/ggml/include -I${SRCDIR}/sources/bark.cpp/examples -I${SRCDIR}/sources/bark.cpp/spm-headers
-// #cgo LDFLAGS: -L${SRCDIR}/ -L${SRCDIR}/sources/bark.cpp/build/examples -L${SRCDIR}/sources/bark.cpp/build/encodec.cpp/ggml/src/ -L${SRCDIR}/sources/bark.cpp/build/encodec.cpp/ -lbark -lencodec -lcommon -lggml -lgomp
-// #include <gobark.h>
-// #include <stdlib.h>
-import "C"
-
-import (
-	"fmt"
-	"unsafe"
-
-	"github.com/mudler/LocalAI/pkg/grpc/base"
-	pb "github.com/mudler/LocalAI/pkg/grpc/proto"
-)
-
-type Bark struct {
-	base.SingleThread
-	threads int
-}
-
-func (sd *Bark) Load(opts *pb.ModelOptions) error {
-
-	sd.threads = int(opts.Threads)
-
-	modelFile := C.CString(opts.ModelFile)
-	defer C.free(unsafe.Pointer(modelFile))
-
-	ret := C.load_model(modelFile)
-	if ret != 0 {
-		return fmt.Errorf("inference failed")
-	}
-
-	return nil
-}
-
-func (sd *Bark) TTS(opts *pb.TTSRequest) error {
-	t := C.CString(opts.Text)
-	defer C.free(unsafe.Pointer(t))
-
-	dst := C.CString(opts.Dst)
-	defer C.free(unsafe.Pointer(dst))
-
-	threads := C.int(sd.threads)
-
-	ret := C.tts(t, threads, dst)
-	if ret != 0 {
-		return fmt.Errorf("inference failed")
-	}
-
-	return nil
-}

backend/go/bark-cpp/gobark.h

@@ -1,8 +0,0 @@
-#ifdef __cplusplus
-extern "C" {
-#endif
-int load_model(char *model);
-int tts(char *text,int  threads, char *dst );
-#ifdef __cplusplus
-}
-#endif

backend/go/bark-cpp/main.go

@@ -1,20 +0,0 @@
-package main
-
-// Note: this is started internally by LocalAI and a server is allocated for each model
-import (
-	"flag"
-
-	grpc "github.com/mudler/LocalAI/pkg/grpc"
-)
-
-var (
-	addr = flag.String("addr", "localhost:50051", "the address to connect to")
-)
-
-func main() {
-	flag.Parse()
-
-	if err := grpc.StartServer(*addr, &Bark{}); err != nil {
-		panic(err)
-	}
-}

backend/go/bark-cpp/package.sh

@@ -1,41 +0,0 @@
-#!/bin/bash
-
-# Script to copy the appropriate libraries based on architecture
-# This script is used in the final stage of the Dockerfile
-
-set -e
-
-CURDIR=$(dirname "$(realpath $0)")
-
-# Create lib directory
-mkdir -p $CURDIR/package/lib
-cp -avrf $CURDIR/bark-cpp $CURDIR/package/
-cp -rfv $CURDIR/run.sh $CURDIR/package/
-
-# Detect architecture and copy appropriate libraries
-if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
-    # x86_64 architecture
-    echo "Detected x86_64 architecture, copying x86_64 libraries..."
-    cp -arfLv /lib64/ld-linux-x86-64.so.2 $CURDIR/package/lib/ld.so
-    cp -arfLv /lib/x86_64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
-    cp -arfLv /lib/x86_64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
-    cp -arfLv /lib/x86_64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
-    cp -arfLv /lib/x86_64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
-    cp -arfLv /lib/x86_64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
-elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
-    # ARM64 architecture
-    echo "Detected ARM64 architecture, copying ARM64 libraries..."
-    cp -arfLv /lib/ld-linux-aarch64.so.1 $CURDIR/package/lib/ld.so
-    cp -arfLv /lib/aarch64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
-    cp -arfLv /lib/aarch64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
-    cp -arfLv /lib/aarch64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
-    cp -arfLv /lib/aarch64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
-    cp -arfLv /lib/aarch64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
-else
-    echo "Error: Could not detect architecture"
-    exit 1
-fi
-
-echo "Packaging completed successfully" 
-ls -liah $CURDIR/package/
-ls -liah $CURDIR/package/lib/

backend/go/bark-cpp/run.sh

@@ -1,13 +0,0 @@
-#!/bin/bash
-set -ex
-
-CURDIR=$(dirname "$(realpath $0)")
-export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
-
-# If there is a lib/ld.so, use it
-if [ -f $CURDIR/lib/ld.so ]; then
-	echo "Using lib/ld.so"
-	exec $CURDIR/lib/ld.so $CURDIR/bark-cpp "$@"
-fi
-
-exec $CURDIR/bark-cpp "$@"

backend/go/local-store/debug.go

@@ -4,11 +4,11 @@
 package main
 
 import (
-	"github.com/rs/zerolog/log"
+	"github.com/mudler/xlog"
 )
 
 func assert(cond bool, msg string) {
 	if !cond {
-		log.Fatal().Stack().Msg(msg)
+		xlog.Fatal().Stack().Msg(msg)
 	}
 }

backend/go/local-store/main.go

@@ -7,8 +7,7 @@ import (
 	"os"
 
 	grpc "github.com/mudler/LocalAI/pkg/grpc"
-	"github.com/rs/zerolog"
-	"github.com/rs/zerolog/log"
+	"github.com/mudler/xlog"
 )
 
 var (
@@ -16,7 +15,7 @@ var (
 )
 
 func main() {
-	log.Logger = log.Output(zerolog.ConsoleWriter{Out: os.Stderr})
+	xlog.SetLogger(xlog.NewLogger(xlog.LogLevel(os.Getenv("LOCALAI_LOG_LEVEL")), os.Getenv("LOCALAI_LOG_FORMAT")))
 
 	flag.Parse()
 

backend/go/local-store/store.go

@@ -12,7 +12,7 @@ import (
 	"github.com/mudler/LocalAI/pkg/grpc/base"
 	pb "github.com/mudler/LocalAI/pkg/grpc/proto"
 
-	"github.com/rs/zerolog/log"
+	"github.com/mudler/xlog"
 )
 
 type Store struct {
@@ -135,7 +135,7 @@ func (s *Store) StoresSet(opts *pb.StoresSetOptions) error {
 			} else {
 				sample = k.Floats
 			}
-			log.Debug().Msgf("Key is not normalized: %v", sample)
+			xlog.Debug("Key is not normalized", "sample", sample)
 		}
 
 		kvs[i] = Pair{
@@ -238,7 +238,7 @@ func (s *Store) StoresDelete(opts *pb.StoresDeleteOptions) error {
 			assert(!hasKey(s.keys, k), fmt.Sprintf("Key exists, but was not found: t=%d, %v", len(tail_ks), k))
 		}
 
-		log.Debug().Msgf("Delete: found = %v, t = %d, j = %d, len(merge_ks) = %d, len(merge_vs) = %d", found, len(tail_ks), j, len(merge_ks), len(merge_vs))
+		xlog.Debug("Delete", "found", found, "tailLen", len(tail_ks), "j", j, "mergeKeysLen", len(merge_ks), "mergeValuesLen", len(merge_vs))
 	}
 
 	merge_ks = append(merge_ks, tail_ks...)
@@ -261,7 +261,7 @@ func (s *Store) StoresDelete(opts *pb.StoresDeleteOptions) error {
 	}(), "Keys to delete still present")
 
 	if len(s.keys) != l {
-		log.Debug().Msgf("Delete: Some keys not found: len(s.keys) = %d, l = %d", len(s.keys), l)
+		xlog.Debug("Delete: Some keys not found", "keysLen", len(s.keys), "expectedLen", l)
 	}
 
 	return nil
@@ -273,7 +273,7 @@ func (s *Store) StoresGet(opts *pb.StoresGetOptions) (pb.StoresGetResult, error)
 	ks := sortIntoKeySlicese(opts.Keys)
 
 	if len(s.keys) == 0 {
-		log.Debug().Msgf("Get: No keys in store")
+		xlog.Debug("Get: No keys in store")
 	}
 
 	if s.keyLen == -1 {
@@ -305,7 +305,7 @@ func (s *Store) StoresGet(opts *pb.StoresGetOptions) (pb.StoresGetResult, error)
 	}
 
 	if len(pbKeys) != len(opts.Keys) {
-		log.Debug().Msgf("Get: Some keys not found: len(pbKeys) = %d, len(opts.Keys) = %d, len(s.Keys) = %d", len(pbKeys), len(opts.Keys), len(s.keys))
+		xlog.Debug("Get: Some keys not found", "pbKeysLen", len(pbKeys), "optsKeysLen", len(opts.Keys), "storeKeysLen", len(s.keys))
 	}
 
 	return pb.StoresGetResult{
@@ -507,7 +507,7 @@ func (s *Store) StoresFind(opts *pb.StoresFindOptions) (pb.StoresFindResult, err
 			} else {
 				sample = tk
 			}
-			log.Debug().Msgf("Trying to compare non-normalized key with normalized keys: %v", sample)
+			xlog.Debug("Trying to compare non-normalized key with normalized keys", "sample", sample)
 		}
 
 		return s.StoresFindFallback(opts)

backend/go/stablediffusion-ggml/Makefile

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
 
 # stablediffusion.cpp (ggml)
 STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
-STABLEDIFFUSION_GGML_VERSION?=0ebe6fe118f125665939b27c89f34ed38716bff8
+STABLEDIFFUSION_GGML_VERSION?=e411520407663e1ddf8ff2e5ed4ff3a116fbbc97
 
 CMAKE_ARGS+=-DGGML_MAX_NAME=128
 
@@ -28,7 +28,12 @@ else ifeq ($(BUILD_TYPE),clblas)
 	CMAKE_ARGS+=-DGGML_CLBLAST=ON -DCLBlast_DIR=/some/path
 # If it's hipblas we do have also to set CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++
 else ifeq ($(BUILD_TYPE),hipblas)
-	CMAKE_ARGS+=-DSD_HIPBLAS=ON -DGGML_HIPBLAS=ON
+	ROCM_HOME ?= /opt/rocm
+	ROCM_PATH ?= /opt/rocm
+	export CXX=$(ROCM_HOME)/llvm/bin/clang++
+	export CC=$(ROCM_HOME)/llvm/bin/clang
+	AMDGPU_TARGETS?=gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102,gfx1200,gfx1201
+	CMAKE_ARGS+=-DSD_HIPBLAS=ON -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
 else ifeq ($(BUILD_TYPE),vulkan)
 	CMAKE_ARGS+=-DSD_VULKAN=ON -DGGML_VULKAN=ON
 else ifeq ($(OS),Darwin)

backend/go/stablediffusion-ggml/gosd.cpp

@@ -1,3 +1,5 @@
+#include "stable-diffusion.h"
+#include <cmath>
 #include <cstdint>
 #define GGML_MAX_NAME 128
 
@@ -6,7 +8,9 @@
 #include <time.h>
 #include <string>
 #include <vector>
+#include <map>
 #include <filesystem>
+#include <algorithm>
 #include "gosd.h"
 
 #define STB_IMAGE_IMPLEMENTATION
@@ -20,11 +24,13 @@
 #define STB_IMAGE_RESIZE_IMPLEMENTATION
 #define STB_IMAGE_RESIZE_STATIC
 #include "stb_image_resize.h"
+#include <stdlib.h>
+#include <regex>
 
 // Names of the sampler method, same order as enum sample_method in stable-diffusion.h
 const char* sample_method_str[] = {
-    "default",
     "euler",
+    "euler_a",
     "heun",
     "dpm2",
     "dpm++2s_a",
@@ -35,29 +41,384 @@ const char* sample_method_str[] = {
     "lcm",
     "ddim_trailing",
     "tcd",
-    "euler_a",
 };
 
 static_assert(std::size(sample_method_str) == SAMPLE_METHOD_COUNT, "sample method mismatch");
 
 // Names of the sigma schedule overrides, same order as sample_schedule in stable-diffusion.h
 const char* schedulers[] = {
-    "default",
     "discrete",
     "karras",
     "exponential",
     "ays",
     "gits",
+    "sgm_uniform",
+    "simple",
     "smoothstep",
+    "kl_optimal",
+    "lcm",
 };
 
-static_assert(std::size(schedulers) == SCHEDULE_COUNT, "schedulers mismatch");
+static_assert(std::size(schedulers) == SCHEDULER_COUNT, "schedulers mismatch");
 
+// New enum string arrays
+const char* rng_type_str[] = {
+    "std_default",
+    "cuda",
+    "cpu",
+};
+static_assert(std::size(rng_type_str) == RNG_TYPE_COUNT, "rng type mismatch");
+
+const char* prediction_str[] = {
+    "epsilon",
+    "v",
+    "edm_v",
+    "flow",
+    "flux_flow",
+    "flux2_flow",
+};
+static_assert(std::size(prediction_str) == PREDICTION_COUNT, "prediction mismatch");
+
+const char* lora_apply_mode_str[] = {
+    "auto",
+    "immediately",
+    "at_runtime",
+};
+static_assert(std::size(lora_apply_mode_str) == LORA_APPLY_MODE_COUNT, "lora apply mode mismatch");
+
+constexpr const char* sd_type_str[] = {
+    "f32",      // 0
+    "f16",      // 1
+    "q4_0",     // 2
+    "q4_1",     // 3
+    nullptr,    // 4
+    nullptr,    // 5
+    "q5_0",     // 6
+    "q5_1",     // 7
+    "q8_0",     // 8
+    "q8_1",     // 9
+    "q2_k",     // 10
+    "q3_k",     // 11
+    "q4_k",     // 12
+    "q5_k",     // 13
+    "q6_k",     // 14
+    "q8_k",     // 15
+    "iq2_xxs",  // 16
+    "iq2_xs",   // 17
+    "iq3_xxs",  // 18
+    "iq1_s",    // 19
+    "iq4_nl",   // 20
+    "iq3_s",    // 21
+    "iq2_s",    // 22
+    "iq4_xs",   // 23
+    "i8",       // 24
+    "i16",      // 25
+    "i32",      // 26
+    "i64",      // 27
+    "f64",      // 28
+    "iq1_m",    // 29
+    "bf16",     // 30
+    nullptr, nullptr, nullptr, nullptr,  // 31-34
+    "tq1_0",    // 35
+    "tq2_0",    // 36
+    nullptr, nullptr,           // 37-38
+    "mxfp4"     // 39
+};
+static_assert(std::size(sd_type_str) == SD_TYPE_COUNT, "sd type mismatch");
+
+sd_ctx_params_t ctx_params;
 sd_ctx_t* sd_c;
 // Moved from the context (load time) to generation time params
-scheduler_t scheduler = scheduler_t::DEFAULT;
+scheduler_t scheduler = SCHEDULER_COUNT;
+sample_method_t sample_method = SAMPLE_METHOD_COUNT;
 
-sample_method_t sample_method;
+// Storage for embeddings (needs to persist for the lifetime of ctx_params)
+static std::vector<sd_embedding_t> embedding_vec;
+// Storage for embedding strings (needs to persist as long as embedding_vec references them)
+static std::vector<std::string> embedding_strings;
+
+// Storage for LoRAs (needs to persist for the lifetime of generation params)
+static std::vector<sd_lora_t> lora_vec;
+// Storage for LoRA strings (needs to persist as long as lora_vec references them)
+static std::vector<std::string> lora_strings;
+// Storage for lora_dir path
+static std::string lora_dir_path;
+
+// Build embeddings vector from directory, similar to upstream CLI
+static void build_embedding_vec(const char* embedding_dir) {
+    embedding_vec.clear();
+    embedding_strings.clear();
+
+    if (!embedding_dir || strlen(embedding_dir) == 0) {
+        return;
+    }
+
+    if (!std::filesystem::exists(embedding_dir) || !std::filesystem::is_directory(embedding_dir)) {
+        fprintf(stderr, "Embedding directory does not exist or is not a directory: %s\n", embedding_dir);
+        return;
+    }
+
+    static const std::vector<std::string> valid_ext = {".pt", ".safetensors", ".gguf"};
+
+    for (const auto& entry : std::filesystem::directory_iterator(embedding_dir)) {
+        if (!entry.is_regular_file()) {
+            continue;
+        }
+
+        auto path = entry.path();
+        std::string ext = path.extension().string();
+
+        bool valid = false;
+        for (const auto& e : valid_ext) {
+            if (ext == e) {
+                valid = true;
+                break;
+            }
+        }
+        if (!valid) {
+            continue;
+        }
+
+        std::string name = path.stem().string();
+        std::string full_path = path.string();
+
+        // Store strings in persistent storage
+        embedding_strings.push_back(name);
+        embedding_strings.push_back(full_path);
+
+        sd_embedding_t item;
+        item.name = embedding_strings[embedding_strings.size() - 2].c_str();
+        item.path = embedding_strings[embedding_strings.size() - 1].c_str();
+
+        embedding_vec.push_back(item);
+        fprintf(stderr, "Found embedding: %s -> %s\n", item.name, item.path);
+    }
+
+    fprintf(stderr, "Loaded %zu embeddings from %s\n", embedding_vec.size(), embedding_dir);
+}
+
+// Discover LoRA files in directory and build a map of name -> path
+static std::map<std::string, std::string> discover_lora_files(const char* lora_dir) {
+    std::map<std::string, std::string> lora_map;
+
+    if (!lora_dir || strlen(lora_dir) == 0) {
+        fprintf(stderr, "LoRA directory not specified\n");
+        return lora_map;
+    }
+
+    if (!std::filesystem::exists(lora_dir) || !std::filesystem::is_directory(lora_dir)) {
+        fprintf(stderr, "LoRA directory does not exist or is not a directory: %s\n", lora_dir);
+        return lora_map;
+    }
+
+    static const std::vector<std::string> valid_ext = {".safetensors", ".ckpt", ".pt", ".gguf"};
+
+    fprintf(stderr, "Discovering LoRA files in: %s\n", lora_dir);
+
+    for (const auto& entry : std::filesystem::directory_iterator(lora_dir)) {
+        if (!entry.is_regular_file()) {
+            continue;
+        }
+
+        auto path = entry.path();
+        std::string ext = path.extension().string();
+
+        bool valid = false;
+        for (const auto& e : valid_ext) {
+            if (ext == e) {
+                valid = true;
+                break;
+            }
+        }
+        if (!valid) {
+            continue;
+        }
+
+        std::string name = path.stem().string();  // stem() already removes extension
+        std::string full_path = path.string();
+
+        // Store the name (without extension) -> full path mapping
+        // This allows users to specify just the name in <lora:name:strength>
+        lora_map[name] = full_path;
+
+        fprintf(stderr, "Found LoRA file: %s -> %s\n", name.c_str(), full_path.c_str());
+    }
+
+    fprintf(stderr, "Discovered %zu LoRA files in %s\n", lora_map.size(), lora_dir);
+    return lora_map;
+}
+
+// Helper function to check if a path is absolute (matches upstream)
+static bool is_absolute_path(const std::string& p) {
+#ifdef _WIN32
+    // Windows: C:/path or C:\path
+    return p.size() > 1 && std::isalpha(static_cast<unsigned char>(p[0])) && p[1] == ':';
+#else
+    // Unix: /path
+    return !p.empty() && p[0] == '/';
+#endif
+}
+
+// Parse LoRAs from prompt string (e.g., "<lora:name:1.0>" or "<lora:name>")
+// Returns a vector of LoRA info and the cleaned prompt with LoRA tags removed
+// Matches upstream implementation more closely
+static std::pair<std::vector<sd_lora_t>, std::string> parse_loras_from_prompt(const std::string& prompt, const char* lora_dir) {
+    std::vector<sd_lora_t> loras;
+    std::string cleaned_prompt = prompt;
+
+    if (!lora_dir || strlen(lora_dir) == 0) {
+        fprintf(stderr, "LoRA directory not set, cannot parse LoRAs from prompt\n");
+        return {loras, cleaned_prompt};
+    }
+
+    // Discover LoRA files for name-based lookup
+    std::map<std::string, std::string> discovered_lora_map = discover_lora_files(lora_dir);
+
+    // Map to accumulate multipliers for the same LoRA (matches upstream)
+    std::map<std::string, float> lora_map;
+    std::map<std::string, float> high_noise_lora_map;
+
+    static const std::regex re(R"(<lora:([^:>]+):([^>]+)>)");
+    static const std::vector<std::string> valid_ext = {".pt", ".safetensors", ".gguf"};
+    std::smatch m;
+
+    std::string tmp = prompt;
+
+    fprintf(stderr, "Parsing LoRAs from prompt: %s\n", prompt.c_str());
+
+    while (std::regex_search(tmp, m, re)) {
+        std::string raw_path = m[1].str();
+        const std::string raw_mul = m[2].str();
+
+        float mul = 0.f;
+        try {
+            mul = std::stof(raw_mul);
+        } catch (...) {
+            tmp = m.suffix().str();
+            cleaned_prompt = std::regex_replace(cleaned_prompt, re, "", std::regex_constants::format_first_only);
+            fprintf(stderr, "Invalid LoRA multiplier '%s', skipping\n", raw_mul.c_str());
+            continue;
+        }
+
+        bool is_high_noise = false;
+        static const std::string prefix = "|high_noise|";
+        if (raw_path.rfind(prefix, 0) == 0) {
+            raw_path.erase(0, prefix.size());
+            is_high_noise = true;
+        }
+
+        std::filesystem::path final_path;
+        if (is_absolute_path(raw_path)) {
+            final_path = raw_path;
+        } else {
+            // Try name-based lookup first
+            auto it = discovered_lora_map.find(raw_path);
+            if (it != discovered_lora_map.end()) {
+                final_path = it->second;
+            } else {
+                // Try case-insensitive lookup
+                bool found = false;
+                for (const auto& pair : discovered_lora_map) {
+                    std::string lower_name = raw_path;
+                    std::string lower_key = pair.first;
+                    std::transform(lower_name.begin(), lower_name.end(), lower_name.begin(), ::tolower);
+                    std::transform(lower_key.begin(), lower_key.end(), lower_key.begin(), ::tolower);
+                    if (lower_name == lower_key) {
+                        final_path = pair.second;
+                        found = true;
+                        break;
+                    }
+                }
+                if (!found) {
+                    // Try as relative path in lora_dir
+                    final_path = std::filesystem::path(lora_dir) / raw_path;
+                }
+            }
+        }
+
+        // Try adding extensions if file doesn't exist
+        if (!std::filesystem::exists(final_path)) {
+            bool found = false;
+            for (const auto& ext : valid_ext) {
+                std::filesystem::path try_path = final_path;
+                try_path += ext;
+                if (std::filesystem::exists(try_path)) {
+                    final_path = try_path;
+                    found = true;
+                    break;
+                }
+            }
+            if (!found) {
+                fprintf(stderr, "WARNING: LoRA file not found: %s\n", final_path.lexically_normal().string().c_str());
+                tmp = m.suffix().str();
+                cleaned_prompt = std::regex_replace(cleaned_prompt, re, "", std::regex_constants::format_first_only);
+                continue;
+            }
+        }
+
+        // Normalize path (matches upstream)
+        const std::string key = final_path.lexically_normal().string();
+
+        // Accumulate multiplier if same LoRA appears multiple times (matches upstream)
+        if (is_high_noise) {
+            high_noise_lora_map[key] += mul;
+        } else {
+            lora_map[key] += mul;
+        }
+
+        fprintf(stderr, "Parsed LoRA: path='%s', multiplier=%.2f, is_high_noise=%s\n",
+                key.c_str(), mul, is_high_noise ? "true" : "false");
+
+        cleaned_prompt = std::regex_replace(cleaned_prompt, re, "", std::regex_constants::format_first_only);
+        tmp = m.suffix().str();
+    }
+
+    // Build final LoRA vector from accumulated maps (matches upstream)
+    // Store all path strings first to ensure they persist
+    for (const auto& kv : lora_map) {
+        lora_strings.push_back(kv.first);
+    }
+    for (const auto& kv : high_noise_lora_map) {
+        lora_strings.push_back(kv.first);
+    }
+
+    // Now build the LoRA vector with pointers to the stored strings
+    size_t string_idx = 0;
+    for (const auto& kv : lora_map) {
+        sd_lora_t item;
+        item.is_high_noise = false;
+        item.path = lora_strings[string_idx].c_str();
+        item.multiplier = kv.second;
+        loras.push_back(item);
+        string_idx++;
+    }
+
+    for (const auto& kv : high_noise_lora_map) {
+        sd_lora_t item;
+        item.is_high_noise = true;
+        item.path = lora_strings[string_idx].c_str();
+        item.multiplier = kv.second;
+        loras.push_back(item);
+        string_idx++;
+    }
+
+    // Clean up extra spaces
+    std::regex space_regex(R"(\s+)");
+    cleaned_prompt = std::regex_replace(cleaned_prompt, space_regex, " ");
+    // Trim leading/trailing spaces
+    size_t first = cleaned_prompt.find_first_not_of(" \t");
+    if (first != std::string::npos) {
+        cleaned_prompt.erase(0, first);
+    }
+    size_t last = cleaned_prompt.find_last_not_of(" \t");
+    if (last != std::string::npos) {
+        cleaned_prompt.erase(last + 1);
+    }
+
+    fprintf(stderr, "Parsed %zu LoRA(s) from prompt. Cleaned prompt: %s\n", loras.size(), cleaned_prompt.c_str());
+
+    return {loras, cleaned_prompt};
+}
 
 // Copied from the upstream CLI
 static void sd_log_cb(enum sd_log_level_t level, const char* log, void* data) {
@@ -98,7 +459,7 @@ int load_model(const char *model, char *model_path, char* options[], int threads
 
     const char *stableDiffusionModel = "";
     if (diff == 1 ) {
-        stableDiffusionModel = model;
+        stableDiffusionModel = strdup(model);
         model = "";
     }
 
@@ -109,8 +470,38 @@ int load_model(const char *model, char *model_path, char* options[], int threads
     const char *vae_path  = "";
     const char *scheduler_str = "";
     const char *sampler = "";
+    const char *clip_vision_path = "";
+    const char *llm_path = "";
+    const char *llm_vision_path = "";
+    const char *diffusion_model_path = stableDiffusionModel;
+    const char *high_noise_diffusion_model_path = "";
+    const char *taesd_path  = "";
+    const char *control_net_path = "";
+    const char *embedding_dir = "";
+    const char *photo_maker_path = "";
+    const char *tensor_type_rules = "";
     char *lora_dir = model_path;
-    bool lora_dir_allocated = false;
+
+    bool vae_decode_only = true;
+    int n_threads = threads;
+    enum sd_type_t wtype = SD_TYPE_COUNT;
+    enum rng_type_t rng_type = CUDA_RNG;
+    enum rng_type_t sampler_rng_type = RNG_TYPE_COUNT;
+    enum prediction_t prediction = PREDICTION_COUNT;
+    enum lora_apply_mode_t lora_apply_mode = LORA_APPLY_AUTO;
+    bool offload_params_to_cpu = false;
+    bool keep_clip_on_cpu = false;
+    bool keep_control_net_on_cpu = false;
+    bool keep_vae_on_cpu = false;
+    bool diffusion_flash_attn = false;
+    bool tae_preview_only = false;
+    bool diffusion_conv_direct = false;
+    bool vae_conv_direct = false;
+    bool force_sdxl_vae_conv_scale = false;
+    bool chroma_use_dit_mask = true;
+    bool chroma_use_t5_mask = false;
+    int chroma_t5_mask_pad = 1;
+    float flow_shift = INFINITY;
 
     fprintf(stderr, "parsing options: %p\n", options);
 
@@ -123,16 +514,16 @@ int load_model(const char *model, char *model_path, char* options[], int threads
         }
 
         if (!strcmp(optname, "clip_l_path")) {
-            clip_l_path = optval;
+            clip_l_path = strdup(optval);
         }
         if (!strcmp(optname, "clip_g_path")) {
-            clip_g_path = optval;
+            clip_g_path = strdup(optval);
         }
         if (!strcmp(optname, "t5xxl_path")) {
-            t5xxl_path = optval;
+            t5xxl_path = strdup(optval);
         }
         if (!strcmp(optname, "vae_path")) {
-            vae_path = optval;
+            vae_path = strdup(optval);
         }
         if (!strcmp(optname, "scheduler")) {
             scheduler_str = optval;
@@ -147,18 +538,201 @@ int load_model(const char *model, char *model_path, char* options[], int threads
                 std::filesystem::path lora_path(optval);
                 std::filesystem::path full_lora_path = model_path_str / lora_path;
                 lora_dir = strdup(full_lora_path.string().c_str());
-                lora_dir_allocated = true;
-                fprintf(stderr, "Lora dir resolved to: %s\n", lora_dir);
+                lora_dir_path = full_lora_path.string();
+                fprintf(stderr, "LoRA dir resolved to: %s\n", lora_dir);
             } else {
                 lora_dir = strdup(optval);
-                lora_dir_allocated = true;
+                lora_dir_path = std::string(optval);
                 fprintf(stderr, "No model path provided, using lora dir as-is: %s\n", lora_dir);
             }
+            // Discover LoRAs immediately when directory is set
+            if (lora_dir && strlen(lora_dir) > 0) {
+                discover_lora_files(lora_dir);
+            }
+        }
+
+        // New parsing
+        if (!strcmp(optname, "clip_vision_path")) clip_vision_path = strdup(optval);
+        if (!strcmp(optname, "llm_path")) llm_path = strdup(optval);
+        if (!strcmp(optname, "llm_vision_path")) llm_vision_path = strdup(optval);
+        if (!strcmp(optname, "diffusion_model_path")) diffusion_model_path = strdup(optval);
+        if (!strcmp(optname, "high_noise_diffusion_model_path")) high_noise_diffusion_model_path = strdup(optval);
+        if (!strcmp(optname, "taesd_path")) taesd_path = strdup(optval);
+        if (!strcmp(optname, "control_net_path")) control_net_path = strdup(optval);
+        if (!strcmp(optname, "embedding_dir")) {
+            // Path join with model dir
+            if (model_path && strlen(model_path) > 0) {
+                std::filesystem::path model_path_str(model_path);
+                std::filesystem::path embedding_path(optval);
+                std::filesystem::path full_embedding_path = model_path_str / embedding_path;
+                embedding_dir = strdup(full_embedding_path.string().c_str());
+                fprintf(stderr, "Embedding dir resolved to: %s\n", embedding_dir);
+            } else {
+                embedding_dir = strdup(optval);
+                fprintf(stderr, "No model path provided, using embedding dir as-is: %s\n", embedding_dir);
+            }
+        }
+        if (!strcmp(optname, "photo_maker_path")) photo_maker_path = strdup(optval);
+        if (!strcmp(optname, "tensor_type_rules")) tensor_type_rules = strdup(optval);
+
+        if (!strcmp(optname, "vae_decode_only")) vae_decode_only = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "offload_params_to_cpu")) offload_params_to_cpu = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "keep_clip_on_cpu")) keep_clip_on_cpu = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "keep_control_net_on_cpu")) keep_control_net_on_cpu = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "keep_vae_on_cpu")) keep_vae_on_cpu = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "diffusion_flash_attn")) diffusion_flash_attn = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "tae_preview_only")) tae_preview_only = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "diffusion_conv_direct")) diffusion_conv_direct = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "vae_conv_direct")) vae_conv_direct = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "force_sdxl_vae_conv_scale")) force_sdxl_vae_conv_scale = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "chroma_use_dit_mask")) chroma_use_dit_mask = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+        if (!strcmp(optname, "chroma_use_t5_mask")) chroma_use_t5_mask = (strcmp(optval, "true") == 0 || strcmp(optval, "1") == 0);
+
+        if (!strcmp(optname, "n_threads")) n_threads = atoi(optval);
+        if (!strcmp(optname, "chroma_t5_mask_pad")) chroma_t5_mask_pad = atoi(optval);
+
+        if (!strcmp(optname, "flow_shift")) flow_shift = atof(optval);
+
+        if (!strcmp(optname, "rng_type")) {
+            int found = -1;
+            for (int m = 0; m < RNG_TYPE_COUNT; m++) {
+                if (!strcmp(optval, rng_type_str[m])) {
+                    found = m;
+                    break;
+                }
+            }
+            if (found != -1) {
+                rng_type = (rng_type_t)found;
+                fprintf(stderr, "Found rng_type: %s\n", optval);
+            } else {
+                fprintf(stderr, "Invalid rng_type: %s, using default\n", optval);
+            }
+        }
+        if (!strcmp(optname, "sampler_rng_type")) {
+            int found = -1;
+            for (int m = 0; m < RNG_TYPE_COUNT; m++) {
+                if (!strcmp(optval, rng_type_str[m])) {
+                    found = m;
+                    break;
+                }
+            }
+            if (found != -1) {
+                sampler_rng_type = (rng_type_t)found;
+                fprintf(stderr, "Found sampler_rng_type: %s\n", optval);
+            } else {
+                fprintf(stderr, "Invalid sampler_rng_type: %s, using default\n", optval);
+            }
+        }
+        if (!strcmp(optname, "prediction")) {
+            int found = -1;
+            for (int m = 0; m < PREDICTION_COUNT; m++) {
+                if (!strcmp(optval, prediction_str[m])) {
+                    found = m;
+                    break;
+                }
+            }
+            if (found != -1) {
+                prediction = (prediction_t)found;
+                fprintf(stderr, "Found prediction: %s\n", optval);
+            } else {
+                fprintf(stderr, "Invalid prediction: %s, using default\n", optval);
+            }
+        }
+        if (!strcmp(optname, "lora_apply_mode")) {
+            int found = -1;
+            for (int m = 0; m < LORA_APPLY_MODE_COUNT; m++) {
+                if (!strcmp(optval, lora_apply_mode_str[m])) {
+                    found = m;
+                    break;
+                }
+            }
+            if (found != -1) {
+                lora_apply_mode = (lora_apply_mode_t)found;
+                fprintf(stderr, "Found lora_apply_mode: %s\n", optval);
+            } else {
+                fprintf(stderr, "Invalid lora_apply_mode: %s, using default\n", optval);
+            }
+        }
+        if (!strcmp(optname, "wtype")) {
+            int found = -1;
+            for (int m = 0; m < SD_TYPE_COUNT; m++) {
+                if (sd_type_str[m] && !strcmp(optval, sd_type_str[m])) {
+                    found = m;
+                    break;
+                }
+            }
+            if (found != -1) {
+                wtype = (sd_type_t)found;
+                fprintf(stderr, "Found wtype: %s\n", optval);
+            } else {
+                fprintf(stderr, "Invalid wtype: %s, using default\n", optval);
+            }
         }
     }
 
     fprintf(stderr, "parsed options\n");
 
+    // Build embeddings vector from directory if provided
+    build_embedding_vec(embedding_dir);
+
+    fprintf (stderr, "Creating context\n");
+    sd_ctx_params_init(&ctx_params);
+    ctx_params.model_path = model;
+    ctx_params.clip_l_path = clip_l_path;
+    ctx_params.clip_g_path = clip_g_path;
+    ctx_params.clip_vision_path = clip_vision_path;
+    ctx_params.t5xxl_path = t5xxl_path;
+    ctx_params.llm_path = llm_path;
+    ctx_params.llm_vision_path = llm_vision_path;
+    ctx_params.diffusion_model_path = diffusion_model_path;
+    ctx_params.high_noise_diffusion_model_path = high_noise_diffusion_model_path;
+    ctx_params.vae_path = vae_path;
+    ctx_params.taesd_path = taesd_path;
+    ctx_params.control_net_path = control_net_path;
+    if (lora_dir && strlen(lora_dir) > 0) {
+        lora_dir_path = std::string(lora_dir);
+        fprintf(stderr, "LoRA model directory set to: %s\n", lora_dir);
+        // Discover LoRAs at load time for logging
+        discover_lora_files(lora_dir);
+    } else {
+        fprintf(stderr, "WARNING: LoRA model directory not set. LoRAs in prompts will not be loaded.\n");
+    }
+    // Set embeddings array and count
+    ctx_params.embeddings = embedding_vec.empty() ? NULL : embedding_vec.data();
+    ctx_params.embedding_count = static_cast<uint32_t>(embedding_vec.size());
+    ctx_params.photo_maker_path = photo_maker_path;
+    ctx_params.tensor_type_rules = tensor_type_rules;
+    ctx_params.vae_decode_only = vae_decode_only;
+    // XXX: Setting to true causes a segfault on the second run
+    ctx_params.free_params_immediately = false;
+    ctx_params.n_threads = n_threads;
+    ctx_params.rng_type = rng_type;
+    ctx_params.keep_clip_on_cpu = keep_clip_on_cpu;
+    if (wtype != SD_TYPE_COUNT) ctx_params.wtype = wtype;
+    if (sampler_rng_type != RNG_TYPE_COUNT) ctx_params.sampler_rng_type = sampler_rng_type;
+    if (prediction != PREDICTION_COUNT) ctx_params.prediction = prediction;
+    if (lora_apply_mode != LORA_APPLY_MODE_COUNT) ctx_params.lora_apply_mode = lora_apply_mode;
+    ctx_params.offload_params_to_cpu = offload_params_to_cpu;
+    ctx_params.keep_control_net_on_cpu = keep_control_net_on_cpu;
+    ctx_params.keep_vae_on_cpu = keep_vae_on_cpu;
+    ctx_params.diffusion_flash_attn = diffusion_flash_attn;
+    ctx_params.tae_preview_only = tae_preview_only;
+    ctx_params.diffusion_conv_direct = diffusion_conv_direct;
+    ctx_params.vae_conv_direct = vae_conv_direct;
+    ctx_params.force_sdxl_vae_conv_scale = force_sdxl_vae_conv_scale;
+    ctx_params.chroma_use_dit_mask = chroma_use_dit_mask;
+    ctx_params.chroma_use_t5_mask = chroma_use_t5_mask;
+    ctx_params.chroma_t5_mask_pad = chroma_t5_mask_pad;
+    ctx_params.flow_shift = flow_shift;
+    sd_ctx_t* sd_ctx = new_sd_ctx(&ctx_params);
+
+    if (sd_ctx == NULL) {
+        fprintf (stderr, "failed loading model (generic error)\n");
+        // TODO: Clean up allocated memory
+        return 1;
+    }
+    fprintf (stderr, "Created context: OK\n");
+
     int sample_method_found = -1;
     for (int m = 0; m < SAMPLE_METHOD_COUNT; m++) {
         if (!strcmp(sampler, sample_method_str[m])) {
@@ -167,54 +741,24 @@ int load_model(const char *model, char *model_path, char* options[], int threads
         }
     }
     if (sample_method_found == -1) {
-        fprintf(stderr, "Invalid sample method, default to EULER_A!\n");
-        sample_method_found = sample_method_t::SAMPLE_METHOD_DEFAULT;
+        sample_method_found = sd_get_default_sample_method(sd_ctx);
+        fprintf(stderr, "Invalid sample method, using default: %s\n", sample_method_str[sample_method_found]);
     }
     sample_method = (sample_method_t)sample_method_found;
 
-    for (int d = 0; d < SCHEDULE_COUNT; d++) {
+    for (int d = 0; d < SCHEDULER_COUNT; d++) {
         if (!strcmp(scheduler_str, schedulers[d])) {
             scheduler = (scheduler_t)d;
             fprintf (stderr, "Found scheduler: %s\n", scheduler_str);
         }
     }
-
-    fprintf (stderr, "Creating context\n");
-    sd_ctx_params_t ctx_params;
-    sd_ctx_params_init(&ctx_params);
-    ctx_params.model_path = model;
-    ctx_params.clip_l_path = clip_l_path;
-    ctx_params.clip_g_path = clip_g_path;
-    ctx_params.t5xxl_path = t5xxl_path;
-    ctx_params.diffusion_model_path = stableDiffusionModel;
-    ctx_params.vae_path = vae_path;
-    ctx_params.taesd_path = "";
-    ctx_params.control_net_path = "";
-    ctx_params.lora_model_dir = lora_dir;
-    ctx_params.embedding_dir = "";
-    ctx_params.vae_decode_only = false;
-    ctx_params.free_params_immediately = false;
-    ctx_params.n_threads = threads;
-    ctx_params.rng_type = STD_DEFAULT_RNG;
-    sd_ctx_t* sd_ctx = new_sd_ctx(&ctx_params);
-
-    if (sd_ctx == NULL) {
-        fprintf (stderr, "failed loading model (generic error)\n");
-        // Clean up allocated memory
-        if (lora_dir_allocated && lora_dir) {
-            free(lora_dir);
-        }
-        return 1;
+    if (scheduler == SCHEDULER_COUNT) {
+      scheduler = sd_get_default_scheduler(sd_ctx, sample_method);
+      fprintf(stderr, "Invalid scheduler, using default: %s\n", schedulers[scheduler]);
     }
-    fprintf (stderr, "Created context: OK\n");
 
     sd_c = sd_ctx;
 
-    // Clean up allocated memory
-    if (lora_dir_allocated && lora_dir) {
-        free(lora_dir);
-    }
-
     return 0;
 }
 
@@ -243,12 +787,66 @@ sd_tiling_params_t* sd_img_gen_params_get_vae_tiling_params(sd_img_gen_params_t
 sd_img_gen_params_t* sd_img_gen_params_new(void) {
     sd_img_gen_params_t *params = (sd_img_gen_params_t *)std::malloc(sizeof(sd_img_gen_params_t));
     sd_img_gen_params_init(params);
+    sd_sample_params_init(&params->sample_params);
+    sd_cache_params_init(&params->cache);
+    params->control_strength = 0.9f;
     return params;
 }
 
+// Storage for cleaned prompt strings (needs to persist)
+static std::string cleaned_prompt_storage;
+static std::string cleaned_negative_prompt_storage;
+
 void sd_img_gen_params_set_prompts(sd_img_gen_params_t *params, const char *prompt, const char *negative_prompt) {
-    params->prompt = prompt;
-    params->negative_prompt = negative_prompt;
+    // Clear previous LoRA data
+    lora_vec.clear();
+    lora_strings.clear();
+
+    // Parse LoRAs from prompt
+    std::string prompt_str = prompt ? prompt : "";
+    std::string negative_prompt_str = negative_prompt ? negative_prompt : "";
+
+    // Get lora_dir from ctx_params if available, otherwise use stored path
+    const char* lora_dir_to_use = lora_dir_path.empty() ? nullptr : lora_dir_path.c_str();
+
+    auto [loras, cleaned_prompt] = parse_loras_from_prompt(prompt_str, lora_dir_to_use);
+    lora_vec = loras;
+    cleaned_prompt_storage = cleaned_prompt;
+
+    // Also check negative prompt for LoRAs (though this is less common)
+    auto [neg_loras, cleaned_negative] = parse_loras_from_prompt(negative_prompt_str, lora_dir_to_use);
+    // Merge negative prompt LoRAs (though typically not used)
+    if (!neg_loras.empty()) {
+        fprintf(stderr, "Note: Found %zu LoRAs in negative prompt (may not be supported)\n", neg_loras.size());
+    }
+    cleaned_negative_prompt_storage = cleaned_negative;
+
+    // Set the cleaned prompts
+    params->prompt = cleaned_prompt_storage.c_str();
+    params->negative_prompt = cleaned_negative_prompt_storage.c_str();
+
+    // Set LoRAs in params
+    params->loras = lora_vec.empty() ? nullptr : lora_vec.data();
+    params->lora_count = static_cast<uint32_t>(lora_vec.size());
+
+    fprintf(stderr, "Set prompts with %zu LoRAs. Original prompt: %s\n", lora_vec.size(), prompt ? prompt : "(null)");
+    fprintf(stderr, "Cleaned prompt: %s\n", cleaned_prompt_storage.c_str());
+
+    // Debug: Verify LoRAs are set correctly
+    if (params->loras && params->lora_count > 0) {
+        fprintf(stderr, "DEBUG: LoRAs set in params structure:\n");
+        for (uint32_t i = 0; i < params->lora_count; i++) {
+            fprintf(stderr, "  params->loras[%u]: path='%s' (ptr=%p), multiplier=%.2f, is_high_noise=%s\n",
+                    i,
+                    params->loras[i].path ? params->loras[i].path : "(null)",
+                    (void*)params->loras[i].path,
+                    params->loras[i].multiplier,
+                    params->loras[i].is_high_noise ? "true" : "false");
+        }
+    } else {
+        fprintf(stderr, "DEBUG: No LoRAs set in params structure (loras=%p, lora_count=%u)\n",
+                (void*)params->loras, params->lora_count);
+    }
 }
 
 void sd_img_gen_params_set_dimensions(sd_img_gen_params_t *params, int width, int height) {
@@ -260,7 +858,7 @@ void sd_img_gen_params_set_seed(sd_img_gen_params_t *params, int64_t seed) {
     params->seed = seed;
 }
 
-int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, char *src_image, float strength, char *mask_image, char **ref_images, int ref_images_count) {
+int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, char *src_image, float strength, char *mask_image, char* ref_images[], int ref_images_count) {
 
     sd_image_t* results;
 
@@ -440,6 +1038,24 @@ int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, cha
         }
     }
 
+    // Log LoRA information
+    if (p->loras && p->lora_count > 0) {
+        fprintf(stderr, "Using %u LoRA(s) in generation:\n", p->lora_count);
+        for (uint32_t i = 0; i < p->lora_count; i++) {
+            fprintf(stderr, "  LoRA[%u]: path='%s', multiplier=%.2f, is_high_noise=%s\n",
+                    i,
+                    p->loras[i].path ? p->loras[i].path : "(null)",
+                    p->loras[i].multiplier,
+                    p->loras[i].is_high_noise ? "true" : "false");
+        }
+    } else {
+        fprintf(stderr, "No LoRAs specified for this generation\n");
+    }
+
+    fprintf(stderr, "Generating image with params: \nctx\n---\n%s\ngen\n---\n%s\n",
+            sd_ctx_params_to_str(&ctx_params),
+            sd_img_gen_params_to_str(p));
+
     results = generate_image(sd_c, p);
 
     std::free(p);
@@ -472,9 +1088,12 @@ int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, cha
     fprintf (stderr, "Channel: %d\n", results[0].channel);
     fprintf (stderr, "Data: %p\n", results[0].data);
 
-    stbi_write_png(dst, results[0].width, results[0].height, results[0].channel,
-                       results[0].data, 0, NULL);
-    fprintf (stderr, "Saved resulting image to '%s'\n", dst);
+    int ret = stbi_write_png(dst, results[0].width, results[0].height, results[0].channel,
+                             results[0].data, 0, NULL);
+    if (ret)
+      fprintf (stderr, "Saved resulting image to '%s'\n", dst);
+    else
+      fprintf(stderr, "Failed to write image to '%s'\n", dst);
 
     // Clean up
     free(results[0].data);
@@ -485,12 +1104,14 @@ int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, cha
     for (auto buffer : ref_image_buffers) {
         if (buffer) free(buffer);
     }
-    fprintf (stderr, "gen_image is done: %s", dst);
+    fprintf (stderr, "gen_image is done: %s\n", dst);
+    fflush(stderr);
 
-    return 0;
+    return !ret;
 }
 
 int unload() {
     free_sd_ctx(sd_c);
     return 0;
 }
+

backend/go/stablediffusion-ggml/gosd.go

@@ -22,7 +22,7 @@ type SDGGML struct {
 
 var (
 	LoadModel func(model, model_apth string, options []uintptr, threads int32, diff int) int
-	GenImage  func(params uintptr, steps int, dst string, cfgScale float32, srcImage string, strength float32, maskImage string, refImages []string, refImagesCount int) int
+	GenImage  func(params uintptr, steps int, dst string, cfgScale float32, srcImage string, strength float32, maskImage string, refImages []uintptr, refImagesCount int) int
 
 	TilingParamsSetEnabled       func(params uintptr, enabled bool)
 	TilingParamsSetTileSizes     func(params uintptr, tileSizeX int, tileSizeY int)
@@ -95,12 +95,12 @@ func (sd *SDGGML) Load(opts *pb.ModelOptions) error {
 	sd.cfgScale = opts.CFGScale
 
 	ret := LoadModel(modelFile, modelPathC, options, opts.Threads, diffusionModel)
+	runtime.KeepAlive(keepAlive)
+	fmt.Fprintf(os.Stderr, "LoadModel: %d\n", ret)
 	if ret != 0 {
 		return fmt.Errorf("could not load model")
 	}
 
-	runtime.KeepAlive(keepAlive)
-
 	return nil
 }
 
@@ -123,10 +123,15 @@ func (sd *SDGGML) GenerateImage(opts *pb.GenerateImageRequest) error {
 		}
 	}
 
+	// At the time of writing Purego doesn't recurse into slices and convert Go strings to pointers so we need to do that
+	var keepAlive []any
 	refImagesCount := len(opts.RefImages)
-	refImages := make([]string, refImagesCount, refImagesCount+1)
-	copy(refImages, opts.RefImages)
-	*(*uintptr)(unsafe.Add(unsafe.Pointer(&refImages), refImagesCount)) = 0
+	refImages := make([]uintptr, refImagesCount, refImagesCount+1)
+	for i, ri := range opts.RefImages {
+		bytep := CString(ri)
+		refImages[i] = uintptr(unsafe.Pointer(bytep))
+		keepAlive = append(keepAlive, bytep)
+	}
 
 	// Default strength for img2img (0.75 is a good default)
 	strength := float32(0.75)
@@ -140,6 +145,8 @@ func (sd *SDGGML) GenerateImage(opts *pb.GenerateImageRequest) error {
 	TilingParamsSetEnabled(vaep, false)
 
 	ret := GenImage(p, int(opts.Step), dst, sd.cfgScale, srcImage, strength, maskImage, refImages, refImagesCount)
+	runtime.KeepAlive(keepAlive)
+	fmt.Fprintf(os.Stderr, "GenImage: %d\n", ret)
 	if ret != 0 {
 		return fmt.Errorf("inference failed")
 	}

backend/go/stablediffusion-ggml/gosd.h

@@ -17,7 +17,7 @@ void sd_img_gen_params_set_dimensions(sd_img_gen_params_t *params, int width, in
 void sd_img_gen_params_set_seed(sd_img_gen_params_t *params, int64_t seed);
 
 int load_model(const char *model, char *model_path, char* options[], int threads, int diffusionModel);
-int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, char *src_image, float strength, char *mask_image, char **ref_images, int ref_images_count);
+int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, char *src_image, float strength, char *mask_image, char* ref_images[], int ref_images_count);
 #ifdef __cplusplus
 }
 #endif

backend/go/stablediffusion-ggml/package.sh

@@ -6,6 +6,7 @@
 set -e
 
 CURDIR=$(dirname "$(realpath $0)")
+REPO_ROOT="${CURDIR}/../../.."
 
 # Create lib directory
 mkdir -p $CURDIR/package/lib
@@ -50,6 +51,15 @@ else
     exit 1
 fi
 
+# Package GPU libraries based on BUILD_TYPE
+# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
+GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
+if [ -f "$GPU_LIB_SCRIPT" ]; then
+    echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
+    source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
+    package_gpu_libs
+fi
+
 echo "Packaging completed successfully"
 ls -liah $CURDIR/package/
 ls -liah $CURDIR/package/lib/

backend/go/whisper/.gitignore

@@ -3,5 +3,5 @@ sources/
 build/
 package/
 whisper
-libgowhisper.so
-
+*.so
+compile_commands.json

backend/go/whisper/Makefile

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
 
 # whisper.cpp version
 WHISPER_REPO?=https://github.com/ggml-org/whisper.cpp
-WHISPER_CPP_VERSION?=19ceec8eac980403b714d603e5ca31653cd42a3f
+WHISPER_CPP_VERSION?=aa1bc0d1a6dfd70dbb9f60c11df12441e03a9075
 SO_TARGET?=libgowhisper.so
 
 CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF

backend/go/whisper/gowhisper.cpp

@@ -107,7 +107,7 @@ int vad(float pcmf32[], size_t pcmf32_len, float **segs_out,
 }
 
 int transcribe(uint32_t threads, char *lang, bool translate, bool tdrz,
-               float pcmf32[], size_t pcmf32_len, size_t *segs_out_len) {
+               float pcmf32[], size_t pcmf32_len, size_t *segs_out_len, char *prompt) {
   whisper_full_params wparams =
       whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
 
@@ -122,8 +122,10 @@ int transcribe(uint32_t threads, char *lang, bool translate, bool tdrz,
   wparams.debug_mode = true;
   wparams.print_progress = true;
   wparams.tdrz_enable = tdrz;
+  wparams.initial_prompt = prompt;
 
   fprintf(stderr, "info: Enable tdrz: %d\n", tdrz);
+  fprintf(stderr, "info: Initial prompt: \"%s\"\n", prompt);
 
   if (whisper_full(ctx, wparams, pcmf32, pcmf32_len)) {
     fprintf(stderr, "error: transcription failed\n");

backend/go/whisper/gowhisper.go

@@ -17,7 +17,7 @@ var (
 	CppLoadModel                 func(modelPath string) int
 	CppLoadModelVAD              func(modelPath string) int
 	CppVAD                       func(pcmf32 []float32, pcmf32Size uintptr, segsOut unsafe.Pointer, segsOutLen unsafe.Pointer) int
-	CppTranscribe                func(threads uint32, lang string, translate bool, diarize bool, pcmf32 []float32, pcmf32Len uintptr, segsOutLen unsafe.Pointer) int
+	CppTranscribe                func(threads uint32, lang string, translate bool, diarize bool, pcmf32 []float32, pcmf32Len uintptr, segsOutLen unsafe.Pointer, prompt string) int
 	CppGetSegmentText            func(i int) string
 	CppGetSegmentStart           func(i int) int64
 	CppGetSegmentEnd             func(i int) int64
@@ -123,15 +123,16 @@ func (w *Whisper) AudioTranscription(opts *pb.TranscriptRequest) (pb.TranscriptR
 	segsLen := uintptr(0xdeadbeef)
 	segsLenPtr := unsafe.Pointer(&segsLen)
 
-	if ret := CppTranscribe(opts.Threads, opts.Language, opts.Translate, opts.Diarize, data, uintptr(len(data)), segsLenPtr); ret != 0 {
+	if ret := CppTranscribe(opts.Threads, opts.Language, opts.Translate, opts.Diarize, data, uintptr(len(data)), segsLenPtr, opts.Prompt); ret != 0 {
 		return pb.TranscriptResult{}, fmt.Errorf("Failed Transcribe")
 	}
 
 	segments := []*pb.TranscriptSegment{}
 	text := ""
 	for i := range int(segsLen) {
-		s := CppGetSegmentStart(i)
-		t := CppGetSegmentEnd(i)
+		// segment start/end conversion factor taken from https://github.com/ggml-org/whisper.cpp/blob/master/examples/cli/cli.cpp#L895
+		s := CppGetSegmentStart(i) * (10000000)
+		t := CppGetSegmentEnd(i) * (10000000)
 		txt := strings.Clone(CppGetSegmentText(i))
 		tokens := make([]int32, CppNTokens(i))
 

backend/go/whisper/gowhisper.h

@@ -7,7 +7,8 @@ int load_model_vad(const char *const model_path);
 int vad(float pcmf32[], size_t pcmf32_size, float **segs_out,
         size_t *segs_out_len);
 int transcribe(uint32_t threads, char *lang, bool translate, bool tdrz,
-               float pcmf32[], size_t pcmf32_len, size_t *segs_out_len);
+               float pcmf32[], size_t pcmf32_len, size_t *segs_out_len,
+               char *prompt);
 const char *get_segment_text(int i);
 int64_t get_segment_t0(int i);
 int64_t get_segment_t1(int i);

backend/go/whisper/package.sh

@@ -6,6 +6,7 @@
 set -e
 
 CURDIR=$(dirname "$(realpath $0)")
+REPO_ROOT="${CURDIR}/../../.."
 
 # Create lib directory
 mkdir -p $CURDIR/package/lib
@@ -50,6 +51,15 @@ else
     exit 1
 fi
 
+# Package GPU libraries based on BUILD_TYPE
+# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
+GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
+if [ -f "$GPU_LIB_SCRIPT" ]; then
+    echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
+    source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
+    package_gpu_libs
+fi
+
 echo "Packaging completed successfully"
 ls -liah $CURDIR/package/
 ls -liah $CURDIR/package/lib/

backend/python/README.md

@@ -16,10 +16,8 @@ The Python backends use a unified build system based on `libbackend.sh` that pro
 - **transformers** - Hugging Face Transformers framework (PyTorch-based)
 - **vllm** - High-performance LLM inference engine
 - **mlx** - Apple Silicon optimized ML framework
-- **exllama2** - ExLlama2 quantized models
 
 ### Audio & Speech
-- **bark** - Text-to-speech synthesis
 - **coqui** - Coqui TTS models
 - **faster-whisper** - Fast Whisper speech recognition
 - **kitten-tts** - Lightweight TTS
@@ -85,7 +83,7 @@ runUnittests
 The build system automatically detects and configures for different hardware:
 
 - **CPU** - Standard CPU-only builds
-- **CUDA** - NVIDIA GPU acceleration (supports CUDA 11/12)
+- **CUDA** - NVIDIA GPU acceleration (supports CUDA 12/13)
 - **Intel** - Intel XPU/GPU optimization
 - **MLX** - Apple Silicon (M1/M2/M3) optimization
 - **HIP** - AMD GPU acceleration
@@ -95,8 +93,8 @@ The build system automatically detects and configures for different hardware:
 Backends can specify hardware-specific dependencies:
 - `requirements.txt` - Base requirements
 - `requirements-cpu.txt` - CPU-specific packages
-- `requirements-cublas11.txt` - CUDA 11 packages
 - `requirements-cublas12.txt` - CUDA 12 packages
+- `requirements-cublas13.txt` - CUDA 13 packages
 - `requirements-intel.txt` - Intel-optimized packages
 - `requirements-mps.txt` - Apple Silicon packages
 

backend/python/bark/README.md

@@ -1,16 +0,0 @@
-# Creating a separate environment for ttsbark project
-
-```
-make ttsbark
-```
-
-# Testing the gRPC server
-
-```
-<The path of your python interpreter> -m unittest test_ttsbark.py
-```
-
-For example
-```
-/opt/conda/envs/bark/bin/python -m unittest extra/grpc/bark/test_ttsbark.py
-``````

backend/python/bark/requirements-cpu.txt

@@ -1,4 +0,0 @@
-transformers
-accelerate
-torch==2.4.1
-torchaudio==2.4.1

backend/python/bark/requirements-cublas11.txt

@@ -1,5 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/cu118
-torch==2.4.1+cu118
-torchaudio==2.4.1+cu118
-transformers
-accelerate

backend/python/bark/requirements-cublas12.txt

@@ -1,4 +0,0 @@
-torch==2.4.1
-torchaudio==2.4.1
-transformers
-accelerate

backend/python/bark/requirements-hipblas.txt

@@ -1,5 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/rocm6.0
-torch==2.4.1+rocm6.0
-torchaudio==2.4.1+rocm6.0
-transformers
-accelerate

backend/python/bark/requirements-intel.txt

@@ -1,9 +0,0 @@
---extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-intel-extension-for-pytorch==2.8.10+xpu
-torch==2.3.1+cxx11.abi
-torchaudio==2.3.1+cxx11.abi
-oneccl_bind_pt==2.3.100+xpu
-optimum[openvino]
-setuptools
-transformers
-accelerate

backend/python/bark/requirements.txt

@@ -1,4 +0,0 @@
-bark==0.1.5
-grpcio==1.76.0
-protobuf
-certifi

backend/python/chatterbox/install.sh

@@ -17,4 +17,9 @@ if [ "x${BUILD_PROFILE}" == "xintel" ]; then
 fi
 EXTRA_PIP_INSTALL_FLAGS+=" --no-build-isolation"
 
+if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
+    USE_PIP=true
+fi
+
+
 installRequirements

backend/python/chatterbox/requirements-cublas13.txt

@@ -1,8 +1,8 @@
---extra-index-url https://download.pytorch.org/whl/cu118
-torch==2.6.0+cu118
-torchaudio==2.6.0+cu118
-transformers==4.46.3
+--extra-index-url https://download.pytorch.org/whl/cu130
+torch
+torchaudio
+transformers
 numpy>=1.24.0,<1.26.0
 # https://github.com/mudler/LocalAI/pull/6240#issuecomment-3329518289
 chatterbox-tts@git+https://git@github.com/mudler/chatterbox.git@faster
-accelerate
+accelerate

backend/python/chatterbox/requirements-hipblas.txt

@@ -1,6 +1,6 @@
---extra-index-url https://download.pytorch.org/whl/rocm6.0
-torch==2.6.0+rocm6.1
-torchaudio==2.6.0+rocm6.1
+--extra-index-url https://download.pytorch.org/whl/rocm6.4
+torch==2.9.1+rocm6.4
+torchaudio==2.9.1+rocm6.4
 transformers
 numpy>=1.24.0,<1.26.0
 # https://github.com/mudler/LocalAI/pull/6240#issuecomment-3329518289

backend/python/chatterbox/requirements-install.txt

@@ -0,0 +1,5 @@
+# Build dependencies needed for packages installed from source (e.g., git dependencies)
+# When using --no-build-isolation, these must be installed in the venv first
+wheel
+setuptools
+packaging

backend/python/chatterbox/requirements-intel.txt

@@ -1,7 +1,6 @@
---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
-torchaudio==2.3.1+cxx11.abi
+--extra-index-url https://download.pytorch.org/whl/xpu
+torch
+torchaudio
 transformers
 numpy>=1.24.0,<1.26.0
 # https://github.com/mudler/LocalAI/pull/6240#issuecomment-3329518289

backend/python/chatterbox/requirements-l4t13.txt

@@ -0,0 +1,7 @@
+--extra-index-url https://download.pytorch.org/whl/cu130
+torch
+torchaudio
+transformers
+numpy>=1.24.0,<1.26.0
+chatterbox-tts@git+https://git@github.com/mudler/chatterbox.git@faster
+accelerate

backend/python/common/libbackend.sh

@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 set -euo pipefail
 
-# 
+#
 # use the library by adding the following line to a script:
 # source $(dirname $0)/../common/libbackend.sh
 #
@@ -206,12 +206,12 @@ function init() {
 
 # getBuildProfile will inspect the system to determine which build profile is appropriate:
 # returns one of the following:
-# - cublas11
 # - cublas12
+# - cublas13
 # - hipblas
 # - intel
 function getBuildProfile() {
-    if [ x"${BUILD_TYPE:-}" == "xcublas" ]; then
+    if [ x"${BUILD_TYPE:-}" == "xcublas" ] || [ x"${BUILD_TYPE:-}" == "xl4t" ]; then
         if [ ! -z "${CUDA_MAJOR_VERSION:-}" ]; then
             echo ${BUILD_TYPE}${CUDA_MAJOR_VERSION}
         else
@@ -237,7 +237,14 @@ function getBuildProfile() {
 # Make the venv relocatable:
 # - rewrite venv/bin/python{,3} to relative symlinks into $(_portable_dir)
 # - normalize entrypoint shebangs to /usr/bin/env python3
+# - optionally update pyvenv.cfg to point to the portable Python directory (only at runtime)
+# Usage: _makeVenvPortable [--update-pyvenv-cfg]
 _makeVenvPortable() {
+    local update_pyvenv_cfg=false
+    if [ "${1:-}" = "--update-pyvenv-cfg" ]; then
+        update_pyvenv_cfg=true
+    fi
+
     local venv_dir="${EDIR}/venv"
     local vbin="${venv_dir}/bin"
 
@@ -255,7 +262,39 @@ _makeVenvPortable() {
     ln -s "${rel_py}" "${vbin}/python3"
     ln -s "python3" "${vbin}/python"
 
-    # 2) Rewrite shebangs of entry points to use env, so the venv is relocatable
+    # 2) Update pyvenv.cfg to point to the portable Python directory (only at runtime)
+    #    Use absolute path resolved at runtime so it works when the venv is copied
+    if [ "$update_pyvenv_cfg" = "true" ]; then
+        local pyvenv_cfg="${venv_dir}/pyvenv.cfg"
+        if [ -f "${pyvenv_cfg}" ]; then
+            local portable_dir="$(_portable_dir)"
+            # Resolve to absolute path - this ensures it works when the backend is copied
+            # Only resolve if the directory exists (it should if ensurePortablePython was called)
+            if [ -d "${portable_dir}" ]; then
+                portable_dir="$(cd "${portable_dir}" && pwd)"
+            else
+                # Fallback to relative path if directory doesn't exist yet
+                portable_dir="../python"
+            fi
+            local sed_i=(sed -i)
+            # macOS/BSD sed needs a backup suffix; GNU sed doesn't. Make it portable:
+            if sed --version >/dev/null 2>&1; then
+                sed_i=(sed -i)
+            else
+                sed_i=(sed -i '')
+            fi
+            # Update the home field in pyvenv.cfg
+            # Handle both absolute paths (starting with /) and relative paths
+            if grep -q "^home = " "${pyvenv_cfg}"; then
+                "${sed_i[@]}" "s|^home = .*|home = ${portable_dir}|" "${pyvenv_cfg}"
+            else
+                # If home field doesn't exist, add it
+                echo "home = ${portable_dir}" >> "${pyvenv_cfg}"
+            fi
+        fi
+    fi
+
+    # 3) Rewrite shebangs of entry points to use env, so the venv is relocatable
     #    Only touch text files that start with #! and reference the current venv.
     local ve_abs="${vbin}/python"
     local sed_i=(sed -i)
@@ -316,6 +355,7 @@ function ensureVenv() {
             fi
         fi
         if [ "x${PORTABLE_PYTHON}" == "xtrue" ]; then
+            # During install, only update symlinks and shebangs, not pyvenv.cfg
             _makeVenvPortable
         fi
     fi
@@ -352,13 +392,13 @@ function runProtogen() {
 #  - requirements-${BUILD_TYPE}.txt
 #  - requirements-${BUILD_PROFILE}.txt
 #
-# BUILD_PROFILE is a more specific version of BUILD_TYPE, ex: cuda-11 or cuda-12
+# BUILD_PROFILE is a more specific version of BUILD_TYPE, ex: cuda-12 or cuda-13
 # it can also include some options that we do not have BUILD_TYPES for, ex: intel
 #
 # NOTE: for BUILD_PROFILE==intel, this function does NOT automatically use the Intel python package index.
 # you may want to add the following line to a requirements-intel.txt if you use one:
 #
-# --index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+# --index-url https://download.pytorch.org/whl/xpu
 #
 # If you need to add extra flags into the pip install command you can do so by setting the variable EXTRA_PIP_INSTALL_FLAGS
 # before calling installRequirements.  For example:
@@ -420,6 +460,19 @@ function installRequirements() {
 #  - ${BACKEND_NAME}.py
 function startBackend() {
     ensureVenv
+    # Update pyvenv.cfg before running to ensure paths are correct for current location
+    # This is critical when the backend position is dynamic (e.g., copied from container)
+    if [ "x${PORTABLE_PYTHON}" == "xtrue" ] || [ -x "$(_portable_python)" ]; then
+        _makeVenvPortable --update-pyvenv-cfg
+    fi
+
+    # Set up GPU library paths if a lib directory exists
+    # This allows backends to include their own GPU libraries (CUDA, ROCm, etc.)
+    if [ -d "${EDIR}/lib" ]; then
+        export LD_LIBRARY_PATH="${EDIR}/lib:${LD_LIBRARY_PATH:-}"
+        echo "Added ${EDIR}/lib to LD_LIBRARY_PATH for GPU libraries"
+    fi
+
     if [ ! -z "${BACKEND_FILE:-}" ]; then
         exec "${EDIR}/venv/bin/python" "${BACKEND_FILE}" "$@"
     elif [ -e "${MY_DIR}/server.py" ]; then

backend/python/common/template/requirements-hipblas.txt

@@ -1,2 +1,2 @@
---extra-index-url https://download.pytorch.org/whl/rocm6.0
+--extra-index-url https://download.pytorch.org/whl/rocm6.4
 torch

backend/python/common/template/requirements-intel.txt

@@ -1,5 +1,4 @@
---extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-intel-extension-for-pytorch==2.8.10+xpu
+--extra-index-url https://download.pytorch.org/whl/xpu
 torch==2.8.0
 oneccl_bind_pt==2.8.0+xpu
 optimum[openvino]

backend/python/coqui/README.md

@@ -1,4 +1,4 @@
-# Creating a separate environment for ttsbark project
+# Creating a separate environment for coqui project
 
 ```
 make coqui

backend/python/coqui/backend.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-This is an extra gRPC server of LocalAI for Bark TTS
+This is an extra gRPC server of LocalAI for Coqui TTS
 """
 from concurrent import futures
 import time

backend/python/coqui/requirements-cublas11.txt

@@ -1,6 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/cu118
-torch==2.4.1+cu118
-torchaudio==2.4.1+cu118
-transformers==4.48.3
-accelerate
-coqui-tts

backend/python/coqui/requirements-hipblas.txt

@@ -1,6 +1,6 @@
---extra-index-url https://download.pytorch.org/whl/rocm6.0
-torch==2.4.1+rocm6.0
-torchaudio==2.4.1+rocm6.0
+--extra-index-url https://download.pytorch.org/whl/rocm6.4
+torch==2.8.0+rocm6.4
+torchaudio==2.8.0+rocm6.4
 transformers==4.48.3
 accelerate
 coqui-tts

backend/python/coqui/requirements-intel.txt

@@ -1,8 +1,6 @@
---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
-torchaudio==2.3.1+cxx11.abi
-oneccl_bind_pt==2.3.100+xpu
+--extra-index-url https://download.pytorch.org/whl/xpu
+torch==2.8.0+xpu
+torchaudio==2.8.0+xpu
 optimum[openvino]
 setuptools
 transformers==4.48.3

backend/python/diffusers/README.md

@@ -1,5 +1,136 @@
-# Creating a separate environment for the diffusers project
+# LocalAI Diffusers Backend
+
+This backend provides gRPC access to Hugging Face diffusers pipelines with dynamic pipeline loading.
+
+## Creating a separate environment for the diffusers project
 
 ```
 make diffusers
-```
+```
+
+## Dynamic Pipeline Loader
+
+The diffusers backend includes a dynamic pipeline loader (`diffusers_dynamic_loader.py`) that automatically discovers and loads diffusers pipelines at runtime. This eliminates the need for per-pipeline conditional statements - new pipelines added to diffusers become available automatically without code changes.
+
+### How It Works
+
+1. **Pipeline Discovery**: On first use, the loader scans the `diffusers` package to find all classes that inherit from `DiffusionPipeline`.
+
+2. **Registry Caching**: Discovery results are cached for the lifetime of the process to avoid repeated scanning.
+
+3. **Task Aliases**: The loader automatically derives task aliases from class names (e.g., "text-to-image", "image-to-image", "inpainting") without hardcoding.
+
+4. **Multiple Resolution Methods**: Pipelines can be resolved by:
+   - Exact class name (e.g., `StableDiffusionPipeline`)
+   - Task alias (e.g., `text-to-image`, `img2img`)
+   - Model ID (uses HuggingFace Hub to infer pipeline type)
+
+### Usage Examples
+
+```python
+from diffusers_dynamic_loader import (
+    load_diffusers_pipeline,
+    get_available_pipelines,
+    get_available_tasks,
+    resolve_pipeline_class,
+    discover_diffusers_classes,
+    get_available_classes,
+)
+
+# List all available pipelines
+pipelines = get_available_pipelines()
+print(f"Available pipelines: {pipelines[:10]}...")
+
+# List all task aliases
+tasks = get_available_tasks()
+print(f"Available tasks: {tasks}")
+
+# Resolve a pipeline class by name
+cls = resolve_pipeline_class(class_name="StableDiffusionPipeline")
+
+# Resolve by task alias
+cls = resolve_pipeline_class(task="stable-diffusion")
+
+# Load and instantiate a pipeline
+pipe = load_diffusers_pipeline(
+    class_name="StableDiffusionPipeline",
+    model_id="runwayml/stable-diffusion-v1-5",
+    torch_dtype=torch.float16
+)
+
+# Load from single file
+pipe = load_diffusers_pipeline(
+    class_name="StableDiffusionPipeline",
+    model_id="/path/to/model.safetensors",
+    from_single_file=True,
+    torch_dtype=torch.float16
+)
+
+# Discover other diffusers classes (schedulers, models, etc.)
+schedulers = discover_diffusers_classes("SchedulerMixin")
+print(f"Available schedulers: {list(schedulers.keys())[:5]}...")
+
+# Get list of available scheduler classes
+scheduler_list = get_available_classes("SchedulerMixin")
+```
+
+### Generic Class Discovery
+
+The dynamic loader can discover not just pipelines but any class type from diffusers:
+
+```python
+# Discover all scheduler classes
+schedulers = discover_diffusers_classes("SchedulerMixin")
+
+# Discover all model classes
+models = discover_diffusers_classes("ModelMixin")
+
+# Get a sorted list of available classes
+scheduler_names = get_available_classes("SchedulerMixin")
+```
+
+### Special Pipeline Handling
+
+Most pipelines are loaded dynamically through `load_diffusers_pipeline()`. Only pipelines requiring truly custom initialization logic are handled explicitly:
+
+- `FluxTransformer2DModel`: Requires quantization and custom transformer loading (cannot use dynamic loader)
+- `WanPipeline` / `WanImageToVideoPipeline`: Uses dynamic loader with special VAE (float32 dtype)
+- `SanaPipeline`: Uses dynamic loader with post-load dtype conversion for VAE/text encoder
+- `StableVideoDiffusionPipeline`: Uses dynamic loader with CPU offload handling
+- `VideoDiffusionPipeline`: Alias for DiffusionPipeline with video flags
+
+All other pipelines (StableDiffusionPipeline, StableDiffusionXLPipeline, FluxPipeline, etc.) are loaded purely through the dynamic loader.
+
+### Error Handling
+
+When a pipeline cannot be resolved, the loader provides helpful error messages listing available pipelines and tasks:
+
+```
+ValueError: Unknown pipeline class 'NonExistentPipeline'. 
+Available pipelines: AnimateDiffPipeline, AnimateDiffVideoToVideoPipeline, ...
+```
+
+## Environment Variables
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `COMPEL` | `0` | Enable Compel for prompt weighting |
+| `XPU` | `0` | Enable Intel XPU support |
+| `CLIPSKIP` | `1` | Enable CLIP skip support |
+| `SAFETENSORS` | `1` | Use safetensors format |
+| `CHUNK_SIZE` | `8` | Decode chunk size for video |
+| `FPS` | `7` | Video frames per second |
+| `DISABLE_CPU_OFFLOAD` | `0` | Disable CPU offload |
+| `FRAMES` | `64` | Number of video frames |
+| `BFL_REPO` | `ChuckMcSneed/FLUX.1-dev` | Flux base repo |
+| `PYTHON_GRPC_MAX_WORKERS` | `1` | Max gRPC workers |
+
+## Running Tests
+
+```bash
+./test.sh
+```
+
+The test suite includes:
+- Unit tests for the dynamic loader (`test_dynamic_loader.py`)
+- Integration tests for the gRPC backend (`test.py`)

backend/python/diffusers/backend.py

@@ -1,4 +1,10 @@
 #!/usr/bin/env python3
+"""
+LocalAI Diffusers Backend
+
+This backend provides gRPC access to diffusers pipelines with dynamic pipeline loading.
+New pipelines added to diffusers become available automatically without code changes.
+"""
 from concurrent import futures
 import traceback
 import argparse
@@ -17,16 +23,28 @@ import backend_pb2_grpc
 
 import grpc
 
-from diffusers import SanaPipeline, StableDiffusion3Pipeline, StableDiffusionXLPipeline, StableDiffusionDepth2ImgPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, DiffusionPipeline, \
-    EulerAncestralDiscreteScheduler, FluxPipeline, FluxTransformer2DModel, QwenImageEditPipeline, AutoencoderKLWan, WanPipeline, WanImageToVideoPipeline
-from diffusers import StableDiffusionImg2ImgPipeline, AutoPipelineForText2Image, ControlNetModel, StableVideoDiffusionPipeline, Lumina2Text2ImgPipeline
+# Import dynamic loader for pipeline discovery
+from diffusers_dynamic_loader import (
+    get_pipeline_registry,
+    resolve_pipeline_class,
+    get_available_pipelines,
+    load_diffusers_pipeline,
+)
+
+# Import specific items still needed for special cases and safety checker
+from diffusers import DiffusionPipeline, ControlNetModel
+from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKLWan
 from diffusers.pipelines.stable_diffusion import safety_checker
 from diffusers.utils import load_image, export_to_video
 from compel import Compel, ReturnedEmbeddingsType
 from optimum.quanto import freeze, qfloat8, quantize
-from transformers import CLIPTextModel, T5EncoderModel
+from transformers import T5EncoderModel
 from safetensors.torch import load_file
 
+# Import LTX-2 specific utilities
+from diffusers.pipelines.ltx2.export_utils import encode_video as ltx2_encode_video
+from diffusers import LTX2VideoTransformer3DModel, GGUFQuantizationConfig
+
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
 COMPEL = os.environ.get("COMPEL", "0") == "1"
 XPU = os.environ.get("XPU", "0") == "1"
@@ -158,6 +176,263 @@ def get_scheduler(name: str, config: dict = {}):
 
 # Implement the BackendServicer class with the service methods
 class BackendServicer(backend_pb2_grpc.BackendServicer):
+
+    def _load_pipeline(self, request, modelFile, fromSingleFile, torchType, variant):
+        """
+        Load a diffusers pipeline dynamically using the dynamic loader.
+
+        This method uses load_diffusers_pipeline() for most pipelines, falling back
+        to explicit handling only for pipelines requiring custom initialization
+        (e.g., quantization, special VAE handling).
+
+        Args:
+            request: The gRPC request containing pipeline configuration
+            modelFile: Path to the model file (for single file loading)
+            fromSingleFile: Whether to use from_single_file() vs from_pretrained()
+            torchType: The torch dtype to use
+            variant: Model variant (e.g., "fp16")
+
+        Returns:
+            The loaded pipeline instance
+        """
+        pipeline_type = request.PipelineType
+
+        # Handle IMG2IMG request flag with default pipeline
+        if request.IMG2IMG and pipeline_type == "":
+            pipeline_type = "StableDiffusionImg2ImgPipeline"
+
+        # ================================================================
+        # Special cases requiring custom initialization logic
+        # Only handle pipelines that truly need custom code (quantization,
+        # special VAE handling, etc.). All other pipelines use dynamic loading.
+        # ================================================================
+
+        # FluxTransformer2DModel - requires quantization and custom transformer loading
+        if pipeline_type == "FluxTransformer2DModel":
+            dtype = torch.bfloat16
+            bfl_repo = os.environ.get("BFL_REPO", "ChuckMcSneed/FLUX.1-dev")
+
+            transformer = FluxTransformer2DModel.from_single_file(modelFile, torch_dtype=dtype)
+            quantize(transformer, weights=qfloat8)
+            freeze(transformer)
+            text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
+            quantize(text_encoder_2, weights=qfloat8)
+            freeze(text_encoder_2)
+
+            pipe = FluxPipeline.from_pretrained(bfl_repo, transformer=None, text_encoder_2=None, torch_dtype=dtype)
+            pipe.transformer = transformer
+            pipe.text_encoder_2 = text_encoder_2
+
+            if request.LowVRAM:
+                pipe.enable_model_cpu_offload()
+            return pipe
+
+        # WanPipeline - requires special VAE with float32 dtype
+        if pipeline_type == "WanPipeline":
+            vae = AutoencoderKLWan.from_pretrained(
+                request.Model,
+                subfolder="vae",
+                torch_dtype=torch.float32
+            )
+            pipe = load_diffusers_pipeline(
+                class_name="WanPipeline",
+                model_id=request.Model,
+                vae=vae,
+                torch_dtype=torchType
+            )
+            self.txt2vid = True
+            return pipe
+
+        # WanImageToVideoPipeline - requires special VAE with float32 dtype
+        if pipeline_type == "WanImageToVideoPipeline":
+            vae = AutoencoderKLWan.from_pretrained(
+                request.Model,
+                subfolder="vae",
+                torch_dtype=torch.float32
+            )
+            pipe = load_diffusers_pipeline(
+                class_name="WanImageToVideoPipeline",
+                model_id=request.Model,
+                vae=vae,
+                torch_dtype=torchType
+            )
+            self.img2vid = True
+            return pipe
+
+        # SanaPipeline - requires special VAE and text encoder dtype conversion
+        if pipeline_type == "SanaPipeline":
+            pipe = load_diffusers_pipeline(
+                class_name="SanaPipeline",
+                model_id=request.Model,
+                variant="bf16",
+                torch_dtype=torch.bfloat16
+            )
+            pipe.vae.to(torch.bfloat16)
+            pipe.text_encoder.to(torch.bfloat16)
+            return pipe
+
+        # VideoDiffusionPipeline - alias for DiffusionPipeline with txt2vid flag
+        if pipeline_type == "VideoDiffusionPipeline":
+            self.txt2vid = True
+            pipe = load_diffusers_pipeline(
+                class_name="DiffusionPipeline",
+                model_id=request.Model,
+                torch_dtype=torchType
+            )
+            return pipe
+
+        # StableVideoDiffusionPipeline - needs img2vid flag and CPU offload
+        if pipeline_type == "StableVideoDiffusionPipeline":
+            self.img2vid = True
+            pipe = load_diffusers_pipeline(
+                class_name="StableVideoDiffusionPipeline",
+                model_id=request.Model,
+                torch_dtype=torchType,
+                variant=variant
+            )
+            if not DISABLE_CPU_OFFLOAD:
+                pipe.enable_model_cpu_offload()
+            return pipe
+
+        # LTX2ImageToVideoPipeline - needs img2vid flag, CPU offload, and special handling
+        if pipeline_type == "LTX2ImageToVideoPipeline":
+            self.img2vid = True
+            self.ltx2_pipeline = True
+            
+            # Check if loading from single file (GGUF)
+            if fromSingleFile and LTX2VideoTransformer3DModel is not None:
+                _, single_file_ext = os.path.splitext(modelFile)
+                if single_file_ext == ".gguf":
+                    # Load transformer from single GGUF file with quantization
+                    transformer_kwargs = {}
+                    quantization_config = GGUFQuantizationConfig(compute_dtype=torchType)
+                    transformer_kwargs["quantization_config"] = quantization_config
+                    
+                    transformer = LTX2VideoTransformer3DModel.from_single_file(
+                        modelFile,
+                        config=request.Model,  # Use request.Model as the config/model_id
+                        subfolder="transformer",
+                        **transformer_kwargs,
+                    )
+                    
+                    # Load pipeline with custom transformer
+                    pipe = load_diffusers_pipeline(
+                        class_name="LTX2ImageToVideoPipeline",
+                        model_id=request.Model,
+                        transformer=transformer,
+                        torch_dtype=torchType,
+                    )
+                else:
+                    # Single file but not GGUF - use standard single file loading
+                    pipe = load_diffusers_pipeline(
+                        class_name="LTX2ImageToVideoPipeline",
+                        model_id=modelFile,
+                        from_single_file=True,
+                        torch_dtype=torchType,
+                    )
+            else:
+                # Standard loading from pretrained
+                pipe = load_diffusers_pipeline(
+                    class_name="LTX2ImageToVideoPipeline",
+                    model_id=request.Model,
+                    torch_dtype=torchType,
+                    variant=variant
+                )
+            
+            if not DISABLE_CPU_OFFLOAD:
+                pipe.enable_model_cpu_offload()
+            return pipe
+
+        # LTX2Pipeline - text-to-video pipeline, needs txt2vid flag, CPU offload, and special handling
+        if pipeline_type == "LTX2Pipeline":
+            self.txt2vid = True
+            self.ltx2_pipeline = True
+            
+            # Check if loading from single file (GGUF)
+            if fromSingleFile and LTX2VideoTransformer3DModel is not None:
+                _, single_file_ext = os.path.splitext(modelFile)
+                if single_file_ext == ".gguf":
+                    # Load transformer from single GGUF file with quantization
+                    transformer_kwargs = {}
+                    quantization_config = GGUFQuantizationConfig(compute_dtype=torchType)
+                    transformer_kwargs["quantization_config"] = quantization_config
+                    
+                    transformer = LTX2VideoTransformer3DModel.from_single_file(
+                        modelFile,
+                        config=request.Model,  # Use request.Model as the config/model_id
+                        subfolder="transformer",
+                        **transformer_kwargs,
+                    )
+                    
+                    # Load pipeline with custom transformer
+                    pipe = load_diffusers_pipeline(
+                        class_name="LTX2Pipeline",
+                        model_id=request.Model,
+                        transformer=transformer,
+                        torch_dtype=torchType,
+                    )
+                else:
+                    # Single file but not GGUF - use standard single file loading
+                    pipe = load_diffusers_pipeline(
+                        class_name="LTX2Pipeline",
+                        model_id=modelFile,
+                        from_single_file=True,
+                        torch_dtype=torchType,
+                    )
+            else:
+                # Standard loading from pretrained
+                pipe = load_diffusers_pipeline(
+                    class_name="LTX2Pipeline",
+                    model_id=request.Model,
+                    torch_dtype=torchType,
+                    variant=variant
+                )
+            
+            if not DISABLE_CPU_OFFLOAD:
+                pipe.enable_model_cpu_offload()
+            return pipe
+
+        # ================================================================
+        # Dynamic pipeline loading - the default path for most pipelines
+        # Uses the dynamic loader to instantiate any pipeline by class name
+        # ================================================================
+
+        # Build kwargs for dynamic loading
+        load_kwargs = {"torch_dtype": torchType}
+
+        # Add variant if not loading from single file
+        if not fromSingleFile and variant:
+            load_kwargs["variant"] = variant
+
+        # Add use_safetensors for from_pretrained
+        if not fromSingleFile:
+            load_kwargs["use_safetensors"] = SAFETENSORS
+
+        # Determine pipeline class name - default to AutoPipelineForText2Image
+        effective_pipeline_type = pipeline_type if pipeline_type else "AutoPipelineForText2Image"
+
+        # Use dynamic loader for all pipelines
+        try:
+            pipe = load_diffusers_pipeline(
+                class_name=effective_pipeline_type,
+                model_id=modelFile if fromSingleFile else request.Model,
+                from_single_file=fromSingleFile,
+                **load_kwargs
+            )
+        except Exception as e:
+            # Provide helpful error with available pipelines
+            available = get_available_pipelines()
+            raise ValueError(
+                f"Failed to load pipeline '{effective_pipeline_type}': {e}\n"
+                f"Available pipelines: {', '.join(available[:30])}..."
+            ) from e
+
+        # Apply LowVRAM optimization if supported and requested
+        if request.LowVRAM and hasattr(pipe, 'enable_model_cpu_offload'):
+            pipe.enable_model_cpu_offload()
+
+        return pipe
+
     def Health(self, request, context):
         return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
 
@@ -231,139 +506,21 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             fromSingleFile = request.Model.startswith("http") or request.Model.startswith("/") or local
             self.img2vid = False
             self.txt2vid = False
-            ## img2img
-            if (request.PipelineType == "StableDiffusionImg2ImgPipeline") or (request.IMG2IMG and request.PipelineType == ""):
-                if fromSingleFile:
-                    self.pipe = StableDiffusionImg2ImgPipeline.from_single_file(modelFile,
-                                                                                torch_dtype=torchType)
-                else:
-                    self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(request.Model,
-                                                                               torch_dtype=torchType)
+            self.ltx2_pipeline = False
 
-            elif request.PipelineType == "StableDiffusionDepth2ImgPipeline":
-                self.pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(request.Model,
-                                                                             torch_dtype=torchType)
-            ## img2vid
-            elif request.PipelineType == "StableVideoDiffusionPipeline":
-                self.img2vid = True
-                self.pipe = StableVideoDiffusionPipeline.from_pretrained(
-                    request.Model, torch_dtype=torchType, variant=variant
-                )
-                if not DISABLE_CPU_OFFLOAD:
-                    self.pipe.enable_model_cpu_offload()
-            ## text2img
-            elif request.PipelineType == "AutoPipelineForText2Image" or request.PipelineType == "":
-                self.pipe = AutoPipelineForText2Image.from_pretrained(request.Model,
-                                                                      torch_dtype=torchType,
-                                                                      use_safetensors=SAFETENSORS,
-                                                                      variant=variant)
-            elif request.PipelineType == "StableDiffusionPipeline":
-                if fromSingleFile:
-                    self.pipe = StableDiffusionPipeline.from_single_file(modelFile,
-                                                                         torch_dtype=torchType)
-                else:
-                    self.pipe = StableDiffusionPipeline.from_pretrained(request.Model,
-                                                                        torch_dtype=torchType)
-            elif request.PipelineType == "DiffusionPipeline":
-                self.pipe = DiffusionPipeline.from_pretrained(request.Model,
-                                                              torch_dtype=torchType)
-            elif request.PipelineType == "QwenImageEditPipeline":
-                self.pipe = QwenImageEditPipeline.from_pretrained(request.Model,
-                                                                 torch_dtype=torchType)
-            elif request.PipelineType == "VideoDiffusionPipeline":
-                self.txt2vid = True
-                self.pipe = DiffusionPipeline.from_pretrained(request.Model,
-                                                              torch_dtype=torchType)
-            elif request.PipelineType == "StableDiffusionXLPipeline":
-                if fromSingleFile:
-                    self.pipe = StableDiffusionXLPipeline.from_single_file(modelFile,
-                                                                           torch_dtype=torchType,
-                                                                           use_safetensors=True)
-                else:
-                    self.pipe = StableDiffusionXLPipeline.from_pretrained(
-                        request.Model,
-                        torch_dtype=torchType,
-                        use_safetensors=True,
-                        variant=variant)
-            elif request.PipelineType == "StableDiffusion3Pipeline":
-                if fromSingleFile:
-                    self.pipe = StableDiffusion3Pipeline.from_single_file(modelFile,
-                                                                          torch_dtype=torchType,
-                                                                          use_safetensors=True)
-                else:
-                    self.pipe = StableDiffusion3Pipeline.from_pretrained(
-                        request.Model,
-                        torch_dtype=torchType,
-                        use_safetensors=True,
-                        variant=variant)
-            elif request.PipelineType == "FluxPipeline":
-                if fromSingleFile:
-                    self.pipe = FluxPipeline.from_single_file(modelFile,
-                                                              torch_dtype=torchType,
-                                                              use_safetensors=True)
-                else:
-                    self.pipe = FluxPipeline.from_pretrained(
-                        request.Model,
-                        torch_dtype=torch.bfloat16)
-                if request.LowVRAM:
-                    self.pipe.enable_model_cpu_offload()
-            elif request.PipelineType == "FluxTransformer2DModel":
-                    dtype = torch.bfloat16
-                    # specify from environment or default to "ChuckMcSneed/FLUX.1-dev"
-                    bfl_repo = os.environ.get("BFL_REPO", "ChuckMcSneed/FLUX.1-dev")
+            print(f"LoadModel: PipelineType from request: {request.PipelineType}", file=sys.stderr)
 
-                    transformer = FluxTransformer2DModel.from_single_file(modelFile, torch_dtype=dtype)
-                    quantize(transformer, weights=qfloat8)
-                    freeze(transformer)
-                    text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
-                    quantize(text_encoder_2, weights=qfloat8)
-                    freeze(text_encoder_2)
-
-                    self.pipe = FluxPipeline.from_pretrained(bfl_repo, transformer=None, text_encoder_2=None, torch_dtype=dtype)
-                    self.pipe.transformer = transformer
-                    self.pipe.text_encoder_2 = text_encoder_2
-
-                    if request.LowVRAM:
-                        self.pipe.enable_model_cpu_offload()
-            elif request.PipelineType == "Lumina2Text2ImgPipeline":
-                self.pipe = Lumina2Text2ImgPipeline.from_pretrained(
-                    request.Model,
-                    torch_dtype=torch.bfloat16)
-                if request.LowVRAM:
-                    self.pipe.enable_model_cpu_offload()
-            elif request.PipelineType == "SanaPipeline":
-                self.pipe = SanaPipeline.from_pretrained(
-                    request.Model,
-                    variant="bf16",
-                    torch_dtype=torch.bfloat16)
-                self.pipe.vae.to(torch.bfloat16)
-                self.pipe.text_encoder.to(torch.bfloat16)
-            elif request.PipelineType == "WanPipeline":
-                # WAN2.2 pipeline requires special VAE handling
-                vae = AutoencoderKLWan.from_pretrained(
-                    request.Model, 
-                    subfolder="vae", 
-                    torch_dtype=torch.float32
-                )
-                self.pipe = WanPipeline.from_pretrained(
-                    request.Model,
-                    vae=vae,
-                    torch_dtype=torchType
-                )
-                self.txt2vid = True  # WAN2.2 is a text-to-video pipeline
-            elif request.PipelineType == "WanImageToVideoPipeline":
-                # WAN2.2 image-to-video pipeline
-                vae = AutoencoderKLWan.from_pretrained(
-                    request.Model, 
-                    subfolder="vae", 
-                    torch_dtype=torch.float32
-                )
-                self.pipe = WanImageToVideoPipeline.from_pretrained(
-                    request.Model,
-                    vae=vae,
-                    torch_dtype=torchType
-                )
-                self.img2vid = True  # WAN2.2 image-to-video pipeline
+            # Load pipeline using dynamic loader
+            # Special cases that require custom initialization are handled first
+            self.pipe = self._load_pipeline(
+                request=request,
+                modelFile=modelFile,
+                fromSingleFile=fromSingleFile,
+                torchType=torchType,
+                variant=variant
+            )
+            
+            print(f"LoadModel: After loading - ltx2_pipeline: {self.ltx2_pipeline}, img2vid: {self.img2vid}, txt2vid: {self.txt2vid}, PipelineType: {self.PipelineType}", file=sys.stderr)
 
             if CLIPSKIP and request.CLIPSkip != 0:
                 self.clip_skip = request.CLIPSkip
@@ -501,10 +658,12 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
 
         # create a dictionary of values for the parameters
         options = {
-            "negative_prompt": request.negative_prompt,
             "num_inference_steps": steps,
         }
 
+        if hasattr(request, 'negative_prompt') and request.negative_prompt != "":
+            options["negative_prompt"] = request.negative_prompt
+
         # Handle image source: prioritize RefImages over request.src
         image_src = None
         if hasattr(request, 'ref_images') and request.ref_images and len(request.ref_images) > 0:
@@ -528,17 +687,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         if CLIPSKIP and self.clip_skip != 0:
             options["clip_skip"] = self.clip_skip
 
-        # Get the keys that we will build the args for our pipe for
-        keys = options.keys()
-
-        if request.EnableParameters != "":
-            keys = [key.strip() for key in request.EnableParameters.split(",")]
-
-        if request.EnableParameters == "none":
-            keys = []
-
-        # create a dictionary of parameters by using the keys from EnableParameters and the values from defaults
-        kwargs = {key: options.get(key) for key in keys if key in options}
+        kwargs = {}
 
         # populate kwargs from self.options.
         kwargs.update(self.options)
@@ -609,14 +758,20 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         try:
             prompt = request.prompt
             if not prompt:
+                print(f"GenerateVideo: No prompt provided for video generation.", file=sys.stderr)
                 return backend_pb2.Result(success=False, message="No prompt provided for video generation")
 
+            # Debug: Print raw request values
+            print(f"GenerateVideo: Raw request values - num_frames: {request.num_frames}, fps: {request.fps}, cfg_scale: {request.cfg_scale}, step: {request.step}", file=sys.stderr)
+
             # Set default values from request or use defaults
             num_frames = request.num_frames if request.num_frames > 0 else 81
             fps = request.fps if request.fps > 0 else 16
             cfg_scale = request.cfg_scale if request.cfg_scale > 0 else 4.0
             num_inference_steps = request.step if request.step > 0 else 40
             
+            print(f"GenerateVideo: Using values - num_frames: {num_frames}, fps: {fps}, cfg_scale: {cfg_scale}, num_inference_steps: {num_inference_steps}", file=sys.stderr)
+            
             # Prepare generation parameters
             kwargs = {
                 "prompt": prompt,
@@ -642,9 +797,86 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                 kwargs["end_image"] = load_image(request.end_image)
 
             print(f"Generating video with {kwargs=}", file=sys.stderr)
+            print(f"GenerateVideo: Pipeline type: {self.PipelineType}, ltx2_pipeline flag: {self.ltx2_pipeline}", file=sys.stderr)
 
             # Generate video frames based on pipeline type
-            if self.PipelineType == "WanPipeline":
+            if self.ltx2_pipeline or self.PipelineType in ["LTX2Pipeline", "LTX2ImageToVideoPipeline"]:
+                # LTX-2 generation with audio (supports both text-to-video and image-to-video)
+                # Determine if this is text-to-video (no image) or image-to-video (has image)
+                has_image = bool(request.start_image)
+                
+                # Remove image-related parameters that might have been added earlier
+                kwargs.pop("start_image", None)
+                kwargs.pop("end_image", None)
+                
+                # LTX2ImageToVideoPipeline uses 'image' parameter for image-to-video
+                # LTX2Pipeline (text-to-video) doesn't need an image parameter
+                if has_image:
+                    # Image-to-video: use 'image' parameter
+                    if self.PipelineType == "LTX2ImageToVideoPipeline":
+                        image = load_image(request.start_image)
+                        kwargs["image"] = image
+                        print(f"LTX-2: Using image-to-video mode with image", file=sys.stderr)
+                    else:
+                        # If pipeline type is LTX2Pipeline but we have an image, we can't do image-to-video
+                        return backend_pb2.Result(success=False, message="LTX2Pipeline does not support image-to-video. Use LTX2ImageToVideoPipeline for image-to-video generation.")
+                else:
+                    # Text-to-video: no image parameter needed
+                    # Ensure no image-related kwargs are present
+                    kwargs.pop("image", None)
+                    print(f"LTX-2: Using text-to-video mode (no image)", file=sys.stderr)
+                
+                # LTX-2 uses 'frame_rate' instead of 'fps'
+                frame_rate = float(fps)
+                kwargs["frame_rate"] = frame_rate
+                
+                # LTX-2 requires output_type="np" and return_dict=False
+                kwargs["output_type"] = "np"
+                kwargs["return_dict"] = False
+                
+                # Generate video and audio
+                print(f"LTX-2: Generating with kwargs: {kwargs}", file=sys.stderr)
+                try:
+                    video, audio = self.pipe(**kwargs)
+                    print(f"LTX-2: Generated video shape: {video.shape}, audio shape: {audio.shape}", file=sys.stderr)
+                except Exception as e:
+                    print(f"LTX-2: Error during pipe() call: {e}", file=sys.stderr)
+                    traceback.print_exc()
+                    return backend_pb2.Result(success=False, message=f"Error generating video with LTX-2 pipeline: {e}")
+                
+                # Convert video to uint8 format
+                video = (video * 255).round().astype("uint8")
+                video = torch.from_numpy(video)
+                
+                print(f"LTX-2: Converting video, shape after conversion: {video.shape}", file=sys.stderr)
+                print(f"LTX-2: Audio sample rate: {self.pipe.vocoder.config.output_sampling_rate}", file=sys.stderr)
+                print(f"LTX-2: Output path: {request.dst}", file=sys.stderr)
+                
+                # Use LTX-2's encode_video function which handles audio
+                try:
+                    ltx2_encode_video(
+                        video[0],
+                        fps=frame_rate,
+                        audio=audio[0].float().cpu(),
+                        audio_sample_rate=self.pipe.vocoder.config.output_sampling_rate,
+                        output_path=request.dst,
+                    )
+                    # Verify file was created and has content
+                    import os
+                    if os.path.exists(request.dst):
+                        file_size = os.path.getsize(request.dst)
+                        print(f"LTX-2: Video file created successfully, size: {file_size} bytes", file=sys.stderr)
+                        if file_size == 0:
+                            return backend_pb2.Result(success=False, message=f"Video file was created but is empty (0 bytes). Check LTX-2 encode_video function.")
+                    else:
+                        return backend_pb2.Result(success=False, message=f"Video file was not created at {request.dst}")
+                except Exception as e:
+                    print(f"LTX-2: Error encoding video: {e}", file=sys.stderr)
+                    traceback.print_exc()
+                    return backend_pb2.Result(success=False, message=f"Error encoding video: {e}")
+                
+                return backend_pb2.Result(message="Video generated successfully", success=True)
+            elif self.PipelineType == "WanPipeline":
                 # WAN2.2 text-to-video generation
                 output = self.pipe(**kwargs)
                 frames = output.frames[0]  # WAN2.2 returns frames in this format
@@ -683,11 +915,23 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                 output = self.pipe(**kwargs)
                 frames = output.frames[0]
             else:
+                print(f"GenerateVideo: Pipeline {self.PipelineType} does not match any known video pipeline handler", file=sys.stderr)
                 return backend_pb2.Result(success=False, message=f"Pipeline {self.PipelineType} does not support video generation")
 
-            # Export video
+            # Export video (for non-LTX-2 pipelines)
+            print(f"GenerateVideo: Exporting video to {request.dst} with fps={fps}", file=sys.stderr)
             export_to_video(frames, request.dst, fps=fps)
             
+            # Verify file was created
+            import os
+            if os.path.exists(request.dst):
+                file_size = os.path.getsize(request.dst)
+                print(f"GenerateVideo: Video file created, size: {file_size} bytes", file=sys.stderr)
+                if file_size == 0:
+                    return backend_pb2.Result(success=False, message=f"Video file was created but is empty (0 bytes)")
+            else:
+                return backend_pb2.Result(success=False, message=f"Video file was not created at {request.dst}")
+            
             return backend_pb2.Result(message="Video generated successfully", success=True)
 
         except Exception as err:

backend/python/diffusers/diffusers_dynamic_loader.py

@@ -0,0 +1,538 @@
+"""
+Dynamic Diffusers Pipeline Loader
+
+This module provides dynamic discovery and loading of diffusers pipelines at runtime,
+eliminating the need for per-pipeline conditional statements. New pipelines added to
+diffusers become available automatically without code changes.
+
+The module also supports discovering other diffusers classes like schedulers, models,
+and other components, making it a generic solution for dynamic class loading.
+
+Usage:
+    from diffusers_dynamic_loader import load_diffusers_pipeline, get_available_pipelines
+
+    # Load by class name
+    pipe = load_diffusers_pipeline(class_name="StableDiffusionPipeline", model_id="...", torch_dtype=torch.float16)
+
+    # Load by task alias
+    pipe = load_diffusers_pipeline(task="text-to-image", model_id="...", torch_dtype=torch.float16)
+
+    # Load using model_id (infers from HuggingFace Hub if possible)
+    pipe = load_diffusers_pipeline(model_id="runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+
+    # Get list of available pipelines
+    available = get_available_pipelines()
+
+    # Discover other diffusers classes (schedulers, models, etc.)
+    schedulers = discover_diffusers_classes("SchedulerMixin")
+    models = discover_diffusers_classes("ModelMixin")
+"""
+
+import importlib
+import re
+import sys
+from typing import Any, Dict, List, Optional, Tuple, Type
+
+
+# Global cache for discovered pipelines - computed once per process
+_pipeline_registry: Optional[Dict[str, Type]] = None
+_task_aliases: Optional[Dict[str, List[str]]] = None
+
+# Global cache for other discovered class types
+_class_registries: Dict[str, Dict[str, Type]] = {}
+
+
+def _camel_to_kebab(name: str) -> str:
+    """
+    Convert CamelCase to kebab-case.
+
+    Examples:
+        StableDiffusionPipeline -> stable-diffusion-pipeline
+        StableDiffusionXLImg2ImgPipeline -> stable-diffusion-xl-img-2-img-pipeline
+    """
+    # Insert hyphen before uppercase letters (but not at the start)
+    s1 = re.sub('(.)([A-Z][a-z]+)', r'\1-\2', name)
+    # Insert hyphen before uppercase letters following lowercase letters or numbers
+    s2 = re.sub('([a-z0-9])([A-Z])', r'\1-\2', s1)
+    return s2.lower()
+
+
+def _extract_task_keywords(class_name: str) -> List[str]:
+    """
+    Extract task-related keywords from a pipeline class name.
+
+    This function derives useful task aliases from the class name without
+    hardcoding per-pipeline branches.
+
+    Returns a list of potential task aliases for this pipeline.
+    """
+    aliases = []
+    name_lower = class_name.lower()
+
+    # Direct task mappings based on common patterns in class names
+    task_patterns = {
+        'text2image': ['text-to-image', 'txt2img', 'text2image'],
+        'texttoimage': ['text-to-image', 'txt2img', 'text2image'],
+        'txt2img': ['text-to-image', 'txt2img', 'text2image'],
+        'img2img': ['image-to-image', 'img2img', 'image2image'],
+        'image2image': ['image-to-image', 'img2img', 'image2image'],
+        'imagetoimage': ['image-to-image', 'img2img', 'image2image'],
+        'img2video': ['image-to-video', 'img2vid', 'img2video'],
+        'imagetovideo': ['image-to-video', 'img2vid', 'img2video'],
+        'text2video': ['text-to-video', 'txt2vid', 'text2video'],
+        'texttovideo': ['text-to-video', 'txt2vid', 'text2video'],
+        'inpaint': ['inpainting', 'inpaint'],
+        'depth2img': ['depth-to-image', 'depth2img'],
+        'depthtoimage': ['depth-to-image', 'depth2img'],
+        'controlnet': ['controlnet', 'control-net'],
+        'upscale': ['upscaling', 'upscale', 'super-resolution'],
+        'superresolution': ['upscaling', 'upscale', 'super-resolution'],
+    }
+
+    # Check for each pattern in the class name
+    for pattern, task_aliases in task_patterns.items():
+        if pattern in name_lower:
+            aliases.extend(task_aliases)
+
+    # Also detect general pipeline types from the class name structure
+    # E.g., StableDiffusionPipeline -> stable-diffusion, flux -> flux
+    # Remove "Pipeline" suffix and convert to kebab case
+    if class_name.endswith('Pipeline'):
+        base_name = class_name[:-8]  # Remove "Pipeline"
+        kebab_name = _camel_to_kebab(base_name)
+        aliases.append(kebab_name)
+
+        # Extract model family name (e.g., "stable-diffusion" from "stable-diffusion-xl-img-2-img")
+        parts = kebab_name.split('-')
+        if len(parts) >= 2:
+            # Try the first two words as a family name
+            family = '-'.join(parts[:2])
+            if family not in aliases:
+                aliases.append(family)
+
+    # If no specific task pattern matched but class contains "Pipeline", add "text-to-image" as default
+    # since most diffusion pipelines support text-to-image generation
+    if 'text-to-image' not in aliases and 'image-to-image' not in aliases:
+        # Only add for pipelines that seem to be generation pipelines (not schedulers, etc.)
+        if 'pipeline' in name_lower and not any(x in name_lower for x in ['scheduler', 'processor', 'encoder']):
+            # Don't automatically add - let it be explicit
+            pass
+
+    return list(set(aliases))  # Remove duplicates
+
+
+def discover_diffusers_classes(
+    base_class_name: str,
+    include_base: bool = True
+) -> Dict[str, Type]:
+    """
+    Discover all subclasses of a given base class from diffusers.
+
+    This function provides a generic way to discover any type of diffusers class,
+    not just pipelines. It can be used to discover schedulers, models, processors,
+    and other components.
+
+    Args:
+        base_class_name: Name of the base class to search for subclasses
+                        (e.g., "DiffusionPipeline", "SchedulerMixin", "ModelMixin")
+        include_base: Whether to include the base class itself in results
+
+    Returns:
+        Dict mapping class names to class objects
+
+    Examples:
+        # Discover all pipeline classes
+        pipelines = discover_diffusers_classes("DiffusionPipeline")
+
+        # Discover all scheduler classes
+        schedulers = discover_diffusers_classes("SchedulerMixin")
+
+        # Discover all model classes
+        models = discover_diffusers_classes("ModelMixin")
+
+        # Discover AutoPipeline classes
+        auto_pipelines = discover_diffusers_classes("AutoPipelineForText2Image")
+    """
+    global _class_registries
+
+    # Check cache first
+    if base_class_name in _class_registries:
+        return _class_registries[base_class_name]
+
+    import diffusers
+
+    # Try to get the base class from diffusers
+    base_class = None
+    try:
+        base_class = getattr(diffusers, base_class_name)
+    except AttributeError:
+        # Try to find in submodules
+        for submodule in ['schedulers', 'models', 'pipelines']:
+            try:
+                module = importlib.import_module(f'diffusers.{submodule}')
+                if hasattr(module, base_class_name):
+                    base_class = getattr(module, base_class_name)
+                    break
+            except (ImportError, ModuleNotFoundError):
+                continue
+
+    if base_class is None:
+        raise ValueError(f"Could not find base class '{base_class_name}' in diffusers")
+
+    registry: Dict[str, Type] = {}
+
+    # Include base class if requested
+    if include_base:
+        registry[base_class_name] = base_class
+
+    # Scan diffusers module for subclasses
+    for attr_name in dir(diffusers):
+        try:
+            attr = getattr(diffusers, attr_name)
+            if (isinstance(attr, type) and
+                issubclass(attr, base_class) and
+                (include_base or attr is not base_class)):
+                registry[attr_name] = attr
+        except (ImportError, AttributeError, TypeError, RuntimeError, ModuleNotFoundError):
+            continue
+
+    # Cache the results
+    _class_registries[base_class_name] = registry
+    return registry
+
+
+def get_available_classes(base_class_name: str) -> List[str]:
+    """
+    Get a sorted list of all discovered class names for a given base class.
+
+    Args:
+        base_class_name: Name of the base class (e.g., "SchedulerMixin")
+
+    Returns:
+        Sorted list of discovered class names
+    """
+    return sorted(discover_diffusers_classes(base_class_name).keys())
+
+
+def _discover_pipelines() -> Tuple[Dict[str, Type], Dict[str, List[str]]]:
+    """
+    Discover all subclasses of DiffusionPipeline from diffusers.
+
+    This function uses the generic discover_diffusers_classes() internally
+    and adds pipeline-specific task alias generation. It also includes
+    AutoPipeline classes which are special utility classes for automatic
+    pipeline selection.
+
+    Returns:
+        A tuple of (pipeline_registry, task_aliases) where:
+        - pipeline_registry: Dict mapping class names to class objects
+        - task_aliases: Dict mapping task aliases to lists of class names
+    """
+    # Use the generic discovery function
+    pipeline_registry = discover_diffusers_classes("DiffusionPipeline", include_base=True)
+
+    # Also add AutoPipeline classes - these are special utility classes that are
+    # NOT subclasses of DiffusionPipeline but are commonly used
+    import diffusers
+    auto_pipeline_classes = [
+        "AutoPipelineForText2Image",
+        "AutoPipelineForImage2Image",
+        "AutoPipelineForInpainting",
+    ]
+    for cls_name in auto_pipeline_classes:
+        try:
+            cls = getattr(diffusers, cls_name)
+            if cls is not None:
+                pipeline_registry[cls_name] = cls
+        except AttributeError:
+            # Class not available in this version of diffusers
+            pass
+
+    # Generate task aliases for pipelines
+    task_aliases: Dict[str, List[str]] = {}
+    for attr_name in pipeline_registry:
+        if attr_name == "DiffusionPipeline":
+            continue  # Skip base class for alias generation
+
+        aliases = _extract_task_keywords(attr_name)
+        for alias in aliases:
+            if alias not in task_aliases:
+                task_aliases[alias] = []
+            if attr_name not in task_aliases[alias]:
+                task_aliases[alias].append(attr_name)
+
+    return pipeline_registry, task_aliases
+
+
+def get_pipeline_registry() -> Dict[str, Type]:
+    """
+    Get the cached pipeline registry.
+
+    Returns a dictionary mapping pipeline class names to their class objects.
+    The registry is built on first access and cached for subsequent calls.
+    """
+    global _pipeline_registry, _task_aliases
+    if _pipeline_registry is None:
+        _pipeline_registry, _task_aliases = _discover_pipelines()
+    return _pipeline_registry
+
+
+def get_task_aliases() -> Dict[str, List[str]]:
+    """
+    Get the cached task aliases dictionary.
+
+    Returns a dictionary mapping task aliases (e.g., "text-to-image") to
+    lists of pipeline class names that support that task.
+    """
+    global _pipeline_registry, _task_aliases
+    if _task_aliases is None:
+        _pipeline_registry, _task_aliases = _discover_pipelines()
+    return _task_aliases
+
+
+def get_available_pipelines() -> List[str]:
+    """
+    Get a sorted list of all discovered pipeline class names.
+
+    Returns:
+        List of pipeline class names available for loading.
+    """
+    return sorted(get_pipeline_registry().keys())
+
+
+def get_available_tasks() -> List[str]:
+    """
+    Get a sorted list of all available task aliases.
+
+    Returns:
+        List of task aliases (e.g., ["text-to-image", "image-to-image", ...])
+    """
+    return sorted(get_task_aliases().keys())
+
+
+def resolve_pipeline_class(
+    class_name: Optional[str] = None,
+    task: Optional[str] = None,
+    model_id: Optional[str] = None
+) -> Type:
+    """
+    Resolve a pipeline class from class_name, task, or model_id.
+
+    Priority:
+    1. If class_name is provided, look it up directly
+    2. If task is provided, resolve through task aliases
+    3. If model_id is provided, try to infer from HuggingFace Hub
+
+    Args:
+        class_name: Exact pipeline class name (e.g., "StableDiffusionPipeline")
+        task: Task alias (e.g., "text-to-image", "img2img")
+        model_id: HuggingFace model ID (e.g., "runwayml/stable-diffusion-v1-5")
+
+    Returns:
+        The resolved pipeline class.
+
+    Raises:
+        ValueError: If no pipeline could be resolved.
+    """
+    registry = get_pipeline_registry()
+    aliases = get_task_aliases()
+
+    # 1. Direct class name lookup
+    if class_name:
+        if class_name in registry:
+            return registry[class_name]
+        # Try case-insensitive match
+        for name, cls in registry.items():
+            if name.lower() == class_name.lower():
+                return cls
+        raise ValueError(
+            f"Unknown pipeline class '{class_name}'. "
+            f"Available pipelines: {', '.join(sorted(registry.keys())[:20])}..."
+        )
+
+    # 2. Task alias lookup
+    if task:
+        task_lower = task.lower().replace('_', '-')
+        if task_lower in aliases:
+            # Return the first matching pipeline for this task
+            matching_classes = aliases[task_lower]
+            if matching_classes:
+                return registry[matching_classes[0]]
+
+        # Try partial matching
+        for alias, classes in aliases.items():
+            if task_lower in alias or alias in task_lower:
+                if classes:
+                    return registry[classes[0]]
+
+        raise ValueError(
+            f"Unknown task '{task}'. "
+            f"Available tasks: {', '.join(sorted(aliases.keys())[:20])}..."
+        )
+
+    # 3. Try to infer from HuggingFace Hub
+    if model_id:
+        try:
+            from huggingface_hub import model_info
+            info = model_info(model_id)
+
+            # Check pipeline_tag
+            if hasattr(info, 'pipeline_tag') and info.pipeline_tag:
+                tag = info.pipeline_tag.lower().replace('_', '-')
+                if tag in aliases:
+                    matching_classes = aliases[tag]
+                    if matching_classes:
+                        return registry[matching_classes[0]]
+
+            # Check model card for hints
+            if hasattr(info, 'cardData') and info.cardData:
+                card = info.cardData
+                if 'pipeline_tag' in card:
+                    tag = card['pipeline_tag'].lower().replace('_', '-')
+                    if tag in aliases:
+                        matching_classes = aliases[tag]
+                        if matching_classes:
+                            return registry[matching_classes[0]]
+
+        except ImportError:
+            # huggingface_hub not available
+            pass
+        except (KeyError, AttributeError, ValueError, OSError):
+            # Model info lookup failed - common cases:
+            # - KeyError: Missing keys in model card
+            # - AttributeError: Missing attributes on model info
+            # - ValueError: Invalid model data
+            # - OSError: Network or file access issues
+            pass
+
+        # Fallback: use DiffusionPipeline.from_pretrained which auto-detects
+        # DiffusionPipeline is always added to registry in _discover_pipelines (line 132)
+        # but use .get() with import fallback for extra safety
+        from diffusers import DiffusionPipeline
+        return registry.get('DiffusionPipeline', DiffusionPipeline)
+
+    raise ValueError(
+        "Must provide at least one of: class_name, task, or model_id. "
+        f"Available pipelines: {', '.join(sorted(registry.keys())[:20])}... "
+        f"Available tasks: {', '.join(sorted(aliases.keys())[:20])}..."
+    )
+
+
+def load_diffusers_pipeline(
+    class_name: Optional[str] = None,
+    task: Optional[str] = None,
+    model_id: Optional[str] = None,
+    from_single_file: bool = False,
+    **kwargs
+) -> Any:
+    """
+    Load a diffusers pipeline dynamically.
+
+    This function resolves the appropriate pipeline class based on the provided
+    parameters and instantiates it with the given kwargs.
+
+    Args:
+        class_name: Exact pipeline class name (e.g., "StableDiffusionPipeline")
+        task: Task alias (e.g., "text-to-image", "img2img")
+        model_id: HuggingFace model ID or local path
+        from_single_file: If True, use from_single_file() instead of from_pretrained()
+        **kwargs: Additional arguments passed to from_pretrained() or from_single_file()
+
+    Returns:
+        An instantiated pipeline object.
+
+    Raises:
+        ValueError: If no pipeline could be resolved.
+        Exception: If pipeline loading fails.
+
+    Examples:
+        # Load by class name
+        pipe = load_diffusers_pipeline(
+            class_name="StableDiffusionPipeline",
+            model_id="runwayml/stable-diffusion-v1-5",
+            torch_dtype=torch.float16
+        )
+
+        # Load by task
+        pipe = load_diffusers_pipeline(
+            task="text-to-image",
+            model_id="runwayml/stable-diffusion-v1-5",
+            torch_dtype=torch.float16
+        )
+
+        # Load from single file
+        pipe = load_diffusers_pipeline(
+            class_name="StableDiffusionPipeline",
+            model_id="/path/to/model.safetensors",
+            from_single_file=True,
+            torch_dtype=torch.float16
+        )
+    """
+    # Resolve the pipeline class
+    pipeline_class = resolve_pipeline_class(
+        class_name=class_name,
+        task=task,
+        model_id=model_id
+    )
+
+    # If no model_id provided but we have a class, we can't load
+    if model_id is None:
+        raise ValueError("model_id is required to load a pipeline")
+
+    # Load the pipeline
+    try:
+        if from_single_file:
+            # Check if the class has from_single_file method
+            if hasattr(pipeline_class, 'from_single_file'):
+                return pipeline_class.from_single_file(model_id, **kwargs)
+            else:
+                raise ValueError(
+                    f"Pipeline class {pipeline_class.__name__} does not support from_single_file(). "
+                    f"Use from_pretrained() instead."
+                )
+        else:
+            return pipeline_class.from_pretrained(model_id, **kwargs)
+
+    except Exception as e:
+        # Provide helpful error message
+        available = get_available_pipelines()
+        raise RuntimeError(
+            f"Failed to load pipeline '{pipeline_class.__name__}' from '{model_id}': {e}\n"
+            f"Available pipelines: {', '.join(available[:20])}..."
+        ) from e
+
+
+def get_pipeline_info(class_name: str) -> Dict[str, Any]:
+    """
+    Get information about a specific pipeline class.
+
+    Args:
+        class_name: The pipeline class name
+
+    Returns:
+        Dictionary with pipeline information including:
+        - name: Class name
+        - aliases: List of task aliases
+        - supports_single_file: Whether from_single_file() is available
+        - docstring: Class docstring (if available)
+    """
+    registry = get_pipeline_registry()
+    aliases = get_task_aliases()
+
+    if class_name not in registry:
+        raise ValueError(f"Unknown pipeline: {class_name}")
+
+    cls = registry[class_name]
+
+    # Find all aliases for this pipeline
+    pipeline_aliases = []
+    for alias, classes in aliases.items():
+        if class_name in classes:
+            pipeline_aliases.append(alias)
+
+    return {
+        'name': class_name,
+        'aliases': pipeline_aliases,
+        'supports_single_file': hasattr(cls, 'from_single_file'),
+        'docstring': cls.__doc__[:200] if cls.__doc__ else None
+    }

backend/python/diffusers/install.sh

@@ -16,4 +16,15 @@ if [ "x${BUILD_PROFILE}" == "xintel" ]; then
     EXTRA_PIP_INSTALL_FLAGS+=" --upgrade --index-strategy=unsafe-first-match"
 fi
 
+if [ "x${BUILD_PROFILE}" == "xl4t12" ]; then
+    USE_PIP=true
+fi
+
+# Use python 3.12 for l4t
+if [ "x${BUILD_PROFILE}" == "xl4t13" ]; then
+  PYTHON_VERSION="3.12"
+  PYTHON_PATCH="12"
+  PY_STANDALONE_TAG="20251120"
+fi
+
 installRequirements

backend/python/diffusers/requirements-cublas13.txt

@@ -1,12 +1,12 @@
---extra-index-url https://download.pytorch.org/whl/cu118
+--extra-index-url https://download.pytorch.org/whl/cu130
 git+https://github.com/huggingface/diffusers
 opencv-python
 transformers
-torchvision==0.22.1
+torchvision
 accelerate
 compel
 peft
 sentencepiece
-torch==2.7.1
+torch
+ftfy
 optimum-quanto
-ftfy

backend/python/diffusers/requirements-hipblas.txt

@@ -1,6 +1,6 @@
---extra-index-url https://download.pytorch.org/whl/rocm6.3
-torch==2.7.1+rocm6.3
-torchvision==0.22.1+rocm6.3
+--extra-index-url https://download.pytorch.org/whl/rocm6.4
+torch==2.8.0+rocm6.4
+torchvision==0.23.0+rocm6.4
 git+https://github.com/huggingface/diffusers
 opencv-python
 transformers

backend/python/diffusers/requirements-intel.txt

@@ -1,8 +1,6 @@
---extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-intel-extension-for-pytorch==2.3.110+xpu
-torch==2.5.1+cxx11.abi
-torchvision==0.20.1+cxx11.abi
-oneccl_bind_pt==2.8.0+xpu
+--extra-index-url https://download.pytorch.org/whl/xpu
+torch
+torchvision
 optimum[openvino]
 setuptools
 git+https://github.com/huggingface/diffusers

backend/python/diffusers/requirements-l4t.txt

@@ -1,12 +0,0 @@
---extra-index-url https://pypi.jetson-ai-lab.io/jp6/cu126/
-torch
-diffusers
-transformers
-accelerate
-compel
-peft
-optimum-quanto
-numpy<2
-sentencepiece
-torchvision
-ftfy

backend/python/diffusers/requirements-l4t12.txt

@@ -0,0 +1,12 @@
+--extra-index-url https://pypi.jetson-ai-lab.io/jp6/cu129/
+torch
+git+https://github.com/huggingface/diffusers
+transformers
+accelerate
+compel
+peft
+optimum-quanto
+numpy<2
+sentencepiece
+torchvision
+ftfy

backend/python/diffusers/requirements-l4t13.txt

@@ -0,0 +1,13 @@
+--extra-index-url https://download.pytorch.org/whl/cu130
+torch
+git+https://github.com/huggingface/diffusers
+transformers
+accelerate
+compel
+peft
+optimum-quanto
+numpy<2
+sentencepiece
+torchvision
+ftfy
+chardet