x models: add glm 4.7 flash to mlx engine

middleware/openai.go

@@ -609,49 +609,3 @@ func ImageGenerationsMiddleware() gin.HandlerFunc {
 		c.Next()
 	}
 }
-
-func ImageEditsMiddleware() gin.HandlerFunc {
-	return func(c *gin.Context) {
-		var req openai.ImageEditRequest
-		if err := c.ShouldBindJSON(&req); err != nil {
-			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, err.Error()))
-			return
-		}
-
-		if req.Prompt == "" {
-			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, "prompt is required"))
-			return
-		}
-
-		if req.Model == "" {
-			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, "model is required"))
-			return
-		}
-
-		if req.Image == "" {
-			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, "image is required"))
-			return
-		}
-
-		genReq, err := openai.FromImageEditRequest(req)
-		if err != nil {
-			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, err.Error()))
-			return
-		}
-
-		var b bytes.Buffer
-		if err := json.NewEncoder(&b).Encode(genReq); err != nil {
-			c.AbortWithStatusJSON(http.StatusInternalServerError, openai.NewError(http.StatusInternalServerError, err.Error()))
-			return
-		}
-
-		c.Request.Body = io.NopCloser(&b)
-
-		w := &ImageWriter{
-			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
-		}
-
-		c.Writer = w
-		c.Next()
-	}
-}

middleware/openai_test.go

@@ -1112,129 +1112,3 @@ func TestImageWriterResponse(t *testing.T) {
 		t.Errorf("expected image data 'dGVzdC1pbWFnZS1kYXRh', got %s", imageResp.Data[0].B64JSON)
 	}
 }
-
-func TestImageEditsMiddleware(t *testing.T) {
-	type testCase struct {
-		name string
-		body string
-		req  api.GenerateRequest
-		err  openai.ErrorResponse
-	}
-
-	var capturedRequest *api.GenerateRequest
-
-	// Base64-encoded test image (1x1 pixel PNG)
-	testImage := "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk+A8AAQUBAScY42YAAAAASUVORK5CYII="
-	decodedImage, _ := base64.StdEncoding.DecodeString("iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk+A8AAQUBAScY42YAAAAASUVORK5CYII=")
-
-	testCases := []testCase{
-		{
-			name: "image edit basic",
-			body: `{
-				"model": "test-model",
-				"prompt": "make it blue",
-				"image": "` + testImage + `"
-			}`,
-			req: api.GenerateRequest{
-				Model:  "test-model",
-				Prompt: "make it blue",
-				Images: []api.ImageData{decodedImage},
-			},
-		},
-		{
-			name: "image edit with size",
-			body: `{
-				"model": "test-model",
-				"prompt": "make it blue",
-				"image": "` + testImage + `",
-				"size": "512x768"
-			}`,
-			req: api.GenerateRequest{
-				Model:  "test-model",
-				Prompt: "make it blue",
-				Images: []api.ImageData{decodedImage},
-				Width:  512,
-				Height: 768,
-			},
-		},
-		{
-			name: "image edit missing prompt",
-			body: `{
-				"model": "test-model",
-				"image": "` + testImage + `"
-			}`,
-			err: openai.ErrorResponse{
-				Error: openai.Error{
-					Message: "prompt is required",
-					Type:    "invalid_request_error",
-				},
-			},
-		},
-		{
-			name: "image edit missing model",
-			body: `{
-				"prompt": "make it blue",
-				"image": "` + testImage + `"
-			}`,
-			err: openai.ErrorResponse{
-				Error: openai.Error{
-					Message: "model is required",
-					Type:    "invalid_request_error",
-				},
-			},
-		},
-		{
-			name: "image edit missing image",
-			body: `{
-				"model": "test-model",
-				"prompt": "make it blue"
-			}`,
-			err: openai.ErrorResponse{
-				Error: openai.Error{
-					Message: "image is required",
-					Type:    "invalid_request_error",
-				},
-			},
-		},
-	}
-
-	endpoint := func(c *gin.Context) {
-		c.Status(http.StatusOK)
-	}
-
-	gin.SetMode(gin.TestMode)
-	router := gin.New()
-	router.Use(ImageEditsMiddleware(), captureRequestMiddleware(&capturedRequest))
-	router.Handle(http.MethodPost, "/api/generate", endpoint)
-
-	for _, tc := range testCases {
-		t.Run(tc.name, func(t *testing.T) {
-			req, _ := http.NewRequest(http.MethodPost, "/api/generate", strings.NewReader(tc.body))
-			req.Header.Set("Content-Type", "application/json")
-
-			defer func() { capturedRequest = nil }()
-
-			resp := httptest.NewRecorder()
-			router.ServeHTTP(resp, req)
-
-			if tc.err.Error.Message != "" {
-				var errResp openai.ErrorResponse
-				if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
-					t.Fatal(err)
-				}
-				if diff := cmp.Diff(tc.err, errResp); diff != "" {
-					t.Fatalf("errors did not match:\n%s", diff)
-				}
-				return
-			}
-
-			if resp.Code != http.StatusOK {
-				t.Fatalf("expected status 200, got %d: %s", resp.Code, resp.Body.String())
-			}
-
-			if diff := cmp.Diff(&tc.req, capturedRequest); diff != "" {
-				t.Fatalf("requests did not match:\n%s", diff)
-			}
-		})
-	}
-}

openai/openai.go

@@ -794,47 +794,3 @@ func ToImageGenerationResponse(resp api.GenerateResponse) ImageGenerationRespons
 		Data:    data,
 	}
 }
-
-// ImageEditRequest is an OpenAI-compatible image edit request.
-type ImageEditRequest struct {
-	Model  string `json:"model"`
-	Prompt string `json:"prompt"`
-	Image  string `json:"image"`           // Base64-encoded image data
-	Size   string `json:"size,omitempty"`  // e.g., "1024x1024"
-	Seed   *int64 `json:"seed,omitempty"`
-}
-
-// FromImageEditRequest converts an OpenAI image edit request to an Ollama GenerateRequest.
-func FromImageEditRequest(r ImageEditRequest) (api.GenerateRequest, error) {
-	req := api.GenerateRequest{
-		Model:  r.Model,
-		Prompt: r.Prompt,
-	}
-
-	// Decode the input image
-	if r.Image != "" {
-		imgData, err := decodeImageURL(r.Image)
-		if err != nil {
-			return api.GenerateRequest{}, fmt.Errorf("invalid image: %w", err)
-		}
-		req.Images = append(req.Images, imgData)
-	}
-
-	// Parse size if provided (e.g., "1024x768")
-	if r.Size != "" {
-		var w, h int32
-		if _, err := fmt.Sscanf(r.Size, "%dx%d", &w, &h); err == nil {
-			req.Width = w
-			req.Height = h
-		}
-	}
-
-	if r.Seed != nil {
-		if req.Options == nil {
-			req.Options = map[string]any{}
-		}
-		req.Options["seed"] = *r.Seed
-	}
-
-	return req, nil
-}

openai/openai_test.go

@@ -448,86 +448,3 @@ func TestFromChatRequest_TopLogprobsRange(t *testing.T) {
 		})
 	}
 }
-
-func TestFromImageEditRequest_Basic(t *testing.T) {
-	req := ImageEditRequest{
-		Model:  "test-model",
-		Prompt: "make it blue",
-		Image:  prefix + image,
-	}
-
-	result, err := FromImageEditRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if result.Model != "test-model" {
-		t.Errorf("expected model 'test-model', got %q", result.Model)
-	}
-
-	if result.Prompt != "make it blue" {
-		t.Errorf("expected prompt 'make it blue', got %q", result.Prompt)
-	}
-
-	if len(result.Images) != 1 {
-		t.Fatalf("expected 1 image, got %d", len(result.Images))
-	}
-}
-
-func TestFromImageEditRequest_WithSize(t *testing.T) {
-	req := ImageEditRequest{
-		Model:  "test-model",
-		Prompt: "make it blue",
-		Image:  prefix + image,
-		Size:   "512x768",
-	}
-
-	result, err := FromImageEditRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if result.Width != 512 {
-		t.Errorf("expected width 512, got %d", result.Width)
-	}
-
-	if result.Height != 768 {
-		t.Errorf("expected height 768, got %d", result.Height)
-	}
-}
-
-func TestFromImageEditRequest_WithSeed(t *testing.T) {
-	seed := int64(12345)
-	req := ImageEditRequest{
-		Model:  "test-model",
-		Prompt: "make it blue",
-		Image:  prefix + image,
-		Seed:   &seed,
-	}
-
-	result, err := FromImageEditRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if result.Options == nil {
-		t.Fatal("expected options to be set")
-	}
-
-	if result.Options["seed"] != seed {
-		t.Errorf("expected seed %d, got %v", seed, result.Options["seed"])
-	}
-}
-
-func TestFromImageEditRequest_InvalidImage(t *testing.T) {
-	req := ImageEditRequest{
-		Model:  "test-model",
-		Prompt: "make it blue",
-		Image:  "not-valid-base64",
-	}
-
-	_, err := FromImageEditRequest(req)
-	if err == nil {
-		t.Error("expected error for invalid image")
-	}
-}

server/routes.go

@@ -1604,9 +1604,8 @@ func (s *Server) GenerateRoutes(rc *ollama.Registry) (http.Handler, error) {
 	r.GET("/v1/models", middleware.ListMiddleware(), s.ListHandler)
 	r.GET("/v1/models/:model", middleware.RetrieveMiddleware(), s.ShowHandler)
 	r.POST("/v1/responses", middleware.ResponsesMiddleware(), s.ChatHandler)
-	// OpenAI-compatible image generation endpoints
+	// OpenAI-compatible image generation endpoint
 	r.POST("/v1/images/generations", middleware.ImageGenerationsMiddleware(), s.GenerateHandler)
-	r.POST("/v1/images/edits", middleware.ImageEditsMiddleware(), s.GenerateHandler)
 
 	// Inference (Anthropic compatibility)
 	r.POST("/v1/messages", middleware.AnthropicMessagesMiddleware(), s.ChatHandler)
@@ -2524,11 +2523,6 @@ func (s *Server) handleImageGenerate(c *gin.Context, req api.GenerateRequest, mo
 		}
 	}
 
-	var images []llm.ImageData
-	for i, imgData := range req.Images {
-		images = append(images, llm.ImageData{ID: i, Data: imgData})
-	}
-
 	var streamStarted bool
 	if err := runner.Completion(c.Request.Context(), llm.CompletionRequest{
 		Prompt: req.Prompt,
@@ -2536,7 +2530,6 @@ func (s *Server) handleImageGenerate(c *gin.Context, req api.GenerateRequest, mo
 		Height: req.Height,
 		Steps:  req.Steps,
 		Seed:   seed,
-		Images: images,
 	}, func(cr llm.CompletionResponse) {
 		streamStarted = true
 		res := api.GenerateResponse{

server/routes_generate_test.go

@@ -2193,157 +2193,3 @@ func TestGenerateUnload(t *testing.T) {
 		}
 	})
 }
-
-func TestGenerateWithImages(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
-	mock := mockRunner{
-		CompletionResponse: llm.CompletionResponse{
-			Done:               true,
-			DoneReason:         llm.DoneReasonStop,
-			PromptEvalCount:    1,
-			PromptEvalDuration: 1,
-			EvalCount:          1,
-			EvalDuration:       1,
-		},
-	}
-
-	s := Server{
-		sched: &Scheduler{
-			pendingReqCh:    make(chan *LlmRequest, 1),
-			finishedReqCh:   make(chan *LlmRequest, 1),
-			expiredCh:       make(chan *runnerRef, 1),
-			unloadedCh:      make(chan any, 1),
-			loaded:          make(map[string]*runnerRef),
-			newServerFn:     newMockServer(&mock),
-			getGpuFn:        getGpuFn,
-			getSystemInfoFn: getSystemInfoFn,
-			waitForRecovery: 250 * time.Millisecond,
-			loadFn: func(req *LlmRequest, _ *ggml.GGML, _ ml.SystemInfo, _ []ml.DeviceInfo, _ bool) bool {
-				time.Sleep(time.Millisecond)
-				req.successCh <- &runnerRef{
-					llama: &mock,
-				}
-				return false
-			},
-		},
-	}
-
-	go s.sched.Run(t.Context())
-
-	_, digest := createBinFile(t, ggml.KV{
-		"general.architecture":          "llama",
-		"llama.block_count":             uint32(1),
-		"llama.context_length":          uint32(8192),
-		"llama.embedding_length":        uint32(4096),
-		"llama.attention.head_count":    uint32(32),
-		"llama.attention.head_count_kv": uint32(8),
-		"tokenizer.ggml.tokens":         []string{""},
-		"tokenizer.ggml.scores":         []float32{0},
-		"tokenizer.ggml.token_type":     []int32{0},
-	}, []*ggml.Tensor{
-		{Name: "token_embd.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		{Name: "blk.0.attn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		{Name: "blk.0.ffn_down.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		{Name: "blk.0.ffn_gate.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		{Name: "blk.0.ffn_up.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		{Name: "blk.0.ffn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		{Name: "blk.0.attn_k.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		{Name: "blk.0.attn_output.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		{Name: "blk.0.attn_q.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		{Name: "blk.0.attn_v.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		{Name: "output.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-	})
-
-	w := createRequest(t, s.CreateHandler, api.CreateRequest{
-		Model:  "test",
-		Files:  map[string]string{"file.gguf": digest},
-		Stream: &stream,
-	})
-
-	if w.Code != http.StatusOK {
-		t.Fatalf("expected status 200, got %d", w.Code)
-	}
-
-	t.Run("images passed to completion request", func(t *testing.T) {
-		testImage := []byte("test-image-data")
-
-		mock.CompletionResponse.Content = "Image processed"
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test",
-			Prompt: "Describe this image",
-			Images: []api.ImageData{testImage},
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Fatalf("expected status 200, got %d: %s", w.Code, w.Body.String())
-		}
-
-		// Verify images were passed to the completion request
-		if len(mock.CompletionRequest.Images) != 1 {
-			t.Fatalf("expected 1 image in completion request, got %d", len(mock.CompletionRequest.Images))
-		}
-
-		if !bytes.Equal(mock.CompletionRequest.Images[0].Data, testImage) {
-			t.Errorf("image data mismatch in completion request")
-		}
-
-		if mock.CompletionRequest.Images[0].ID != 0 {
-			t.Errorf("expected image ID 0, got %d", mock.CompletionRequest.Images[0].ID)
-		}
-	})
-
-	t.Run("multiple images passed to completion request", func(t *testing.T) {
-		testImage1 := []byte("test-image-1")
-		testImage2 := []byte("test-image-2")
-
-		mock.CompletionResponse.Content = "Images processed"
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test",
-			Prompt: "Compare these images",
-			Images: []api.ImageData{testImage1, testImage2},
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Fatalf("expected status 200, got %d: %s", w.Code, w.Body.String())
-		}
-
-		// Verify both images were passed
-		if len(mock.CompletionRequest.Images) != 2 {
-			t.Fatalf("expected 2 images in completion request, got %d", len(mock.CompletionRequest.Images))
-		}
-
-		if !bytes.Equal(mock.CompletionRequest.Images[0].Data, testImage1) {
-			t.Errorf("first image data mismatch")
-		}
-
-		if !bytes.Equal(mock.CompletionRequest.Images[1].Data, testImage2) {
-			t.Errorf("second image data mismatch")
-		}
-
-		if mock.CompletionRequest.Images[0].ID != 0 || mock.CompletionRequest.Images[1].ID != 1 {
-			t.Errorf("expected image IDs 0 and 1, got %d and %d",
-				mock.CompletionRequest.Images[0].ID, mock.CompletionRequest.Images[1].ID)
-		}
-	})
-
-	t.Run("no images when none provided", func(t *testing.T) {
-		mock.CompletionResponse.Content = "No images"
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test",
-			Prompt: "Hello",
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Fatalf("expected status 200, got %d: %s", w.Code, w.Body.String())
-		}
-
-		// Verify no images in completion request
-		if len(mock.CompletionRequest.Images) != 0 {
-			t.Fatalf("expected 0 images in completion request, got %d", len(mock.CompletionRequest.Images))
-		}
-	})
-}

x/imagegen/cmd/engine/main.go

@@ -19,6 +19,7 @@ import (
 	"github.com/ollama/ollama/x/imagegen/mlx"
 	"github.com/ollama/ollama/x/imagegen/models/flux2"
 	"github.com/ollama/ollama/x/imagegen/models/gemma3"
+	"github.com/ollama/ollama/x/imagegen/models/glm4_moe_lite"
 	"github.com/ollama/ollama/x/imagegen/models/gpt_oss"
 	"github.com/ollama/ollama/x/imagegen/models/llama"
 	"github.com/ollama/ollama/x/imagegen/models/qwen_image"
@@ -301,6 +302,8 @@ func load(modelPath string) (Model, error) {
 		return gemma3.Load(modelPath)
 	case "gemma3_text":
 		return gemma3.LoadText(modelPath)
+	case "glm4_moe_lite":
+		return glm4_moe_lite.Load(modelPath)
 	default:
 		return llama.Load(modelPath)
 	}

x/imagegen/mlx/mlx.go

@@ -991,6 +991,19 @@ func Concat(a, b *Array, axis int) *Array {
 	return Concatenate([]*Array{a, b}, axis)
 }
 
+// Stack stacks arrays along a new axis (axis 0 by default)
+func Stack(arrays []*Array, axis int) *Array {
+	handles := make([]C.mlx_array, len(arrays))
+	for i, arr := range arrays {
+		handles[i] = arr.c
+	}
+	vec := C.mlx_vector_array_new_data(&handles[0], C.size_t(len(handles)))
+	res := C.mlx_array_new()
+	C.mlx_stack_axis(&res, vec, C.int(axis), C.default_stream())
+	C.mlx_vector_array_free(vec)
+	return newArray(res)
+}
+
 // Slice slices the array
 func Slice(a *Array, start, stop []int32) *Array {
 	n := len(start)

x/imagegen/models/flux2/flux2.go

@@ -177,20 +177,6 @@ func (m *Model) GenerateImage(ctx context.Context, prompt string, width, height
 	})
 }
 
-// GenerateImageWithInputs implements runner.ImageEditModel interface.
-// It generates an image conditioned on the provided input images for image editing.
-func (m *Model) GenerateImageWithInputs(ctx context.Context, prompt string, width, height int32, steps int, seed int64, inputImages []image.Image, progress func(step, total int)) (*mlx.Array, error) {
-	return m.GenerateFromConfig(ctx, &GenerateConfig{
-		Prompt:      prompt,
-		Width:       width,
-		Height:      height,
-		Steps:       steps,
-		Seed:        seed,
-		InputImages: inputImages,
-		Progress:    progress,
-	})
-}
-
 // MaxOutputPixels is the maximum output resolution (4 megapixels, ~2048x2048)
 const MaxOutputPixels = 2048 * 2048
 

x/imagegen/models/glm4_moe_lite/glm4_moe_lite.go

@@ -0,0 +1,529 @@
+//go:build mlx
+
+// Package glm4_moe_lite provides the GLM4-MoE-Lite implementation for MLX.
+// This model uses Multi-head Latent Attention (MLA) and Mixture of Experts (MoE).
+package glm4_moe_lite
+
+import (
+	"encoding/json"
+	"fmt"
+	"math"
+	"os"
+	"path/filepath"
+
+	"github.com/ollama/ollama/x/imagegen/cache"
+	"github.com/ollama/ollama/x/imagegen/mlx"
+	"github.com/ollama/ollama/x/imagegen/nn"
+	"github.com/ollama/ollama/x/imagegen/safetensors"
+	"github.com/ollama/ollama/x/imagegen/tokenizer"
+)
+
+// Config holds GLM4-MoE-Lite model configuration
+type Config struct {
+	HiddenSize            int32   `json:"hidden_size"`
+	NumHiddenLayers       int32   `json:"num_hidden_layers"`
+	IntermediateSize      int32   `json:"intermediate_size"`
+	MoEIntermediateSize   int32   `json:"moe_intermediate_size"`
+	NumAttentionHeads     int32   `json:"num_attention_heads"`
+	NumKeyValueHeads      int32   `json:"num_key_value_heads"`
+	VocabSize             int32   `json:"vocab_size"`
+	RMSNormEps            float32 `json:"rms_norm_eps"`
+	RopeTheta             float32 `json:"rope_theta"`
+	MaxPositionEmbeddings int32   `json:"max_position_embeddings"`
+	AttentionBias         bool    `json:"attention_bias"`
+
+	// MLA (Multi-head Latent Attention) parameters
+	QLoraRank      int32 `json:"q_lora_rank"`
+	KVLoraRank     int32 `json:"kv_lora_rank"`
+	QKRopeHeadDim  int32 `json:"qk_rope_head_dim"`
+	QKNopeHeadDim  int32 `json:"qk_nope_head_dim"`
+	VHeadDim       int32 `json:"v_head_dim"`
+
+	// MoE parameters
+	NRoutedExperts      int32   `json:"n_routed_experts"`
+	NSharedExperts      int32   `json:"n_shared_experts"`
+	NumExpertsPerTok    int32   `json:"num_experts_per_tok"`
+	RoutedScalingFactor float32 `json:"routed_scaling_factor"`
+	NormTopKProb        bool    `json:"norm_topk_prob"`
+	FirstKDenseReplace  int32   `json:"first_k_dense_replace"`
+	NGroup              int32   `json:"n_group"`
+	TopKGroup           int32   `json:"topk_group"`
+
+	// Computed fields
+	QHeadDim int32   `json:"-"` // qk_nope_head_dim + qk_rope_head_dim
+	Scale    float32 `json:"-"` // 1/sqrt(QHeadDim)
+}
+
+// MLAAttention implements Multi-head Latent Attention
+type MLAAttention struct {
+	// Low-rank query projections
+	QAProj      *nn.Linear  `weight:"self_attn.q_a_proj"`
+	QALayerNorm *nn.RMSNorm `weight:"self_attn.q_a_layernorm"`
+	QBProj      *nn.Linear  `weight:"self_attn.q_b_proj"`
+
+	// Low-rank KV projections (with shared rope component)
+	KVAProjWithMQA *nn.Linear  `weight:"self_attn.kv_a_proj_with_mqa"`
+	KVALayerNorm   *nn.RMSNorm `weight:"self_attn.kv_a_layernorm"`
+	KVBProj        *nn.Linear  `weight:"self_attn.kv_b_proj"`
+
+	// Output projection
+	OProj *nn.Linear `weight:"self_attn.o_proj"`
+}
+
+// Forward computes MLA attention output
+func (a *MLAAttention) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array {
+	// Query path: q_a_proj -> layernorm -> q_b_proj
+	q := a.QAProj.Forward(x)
+	q = a.QALayerNorm.Forward(q, cfg.RMSNormEps)
+	q = a.QBProj.Forward(q)
+
+	// Reshape Q: [B, L, num_heads * q_head_dim] -> [B, num_heads, L, q_head_dim]
+	q = mlx.Reshape(q, B, L, cfg.NumAttentionHeads, cfg.QHeadDim)
+	q = mlx.Transpose(q, 0, 2, 1, 3)
+
+	// Split Q into nope and rope parts
+	qNope := mlx.Slice(q, []int32{0, 0, 0, 0}, []int32{B, cfg.NumAttentionHeads, L, cfg.QKNopeHeadDim})
+	qPE := mlx.Slice(q, []int32{0, 0, 0, cfg.QKNopeHeadDim}, []int32{B, cfg.NumAttentionHeads, L, cfg.QHeadDim})
+
+	// KV path: kv_a_proj_with_mqa -> split -> layernorm -> kv_b_proj
+	compressedKV := a.KVAProjWithMQA.Forward(x)
+
+	// Split into compressed_kv and k_pe (shared rope component)
+	kvCompressed := mlx.Slice(compressedKV, []int32{0, 0, 0}, []int32{B, L, cfg.KVLoraRank})
+	kPE := mlx.Slice(compressedKV, []int32{0, 0, cfg.KVLoraRank}, []int32{B, L, cfg.KVLoraRank + cfg.QKRopeHeadDim})
+
+	// k_pe is shared across heads (MQA-style): [B, L, rope_dim] -> [B, 1, L, rope_dim]
+	kPE = mlx.Reshape(kPE, B, L, 1, cfg.QKRopeHeadDim)
+	kPE = mlx.Transpose(kPE, 0, 2, 1, 3)
+
+	// Apply layernorm and project KV
+	kvCompressed = a.KVALayerNorm.Forward(kvCompressed, cfg.RMSNormEps)
+	kv := a.KVBProj.Forward(kvCompressed)
+
+	// Reshape KV: [B, L, num_heads * (qk_nope_head_dim + v_head_dim)]
+	kv = mlx.Reshape(kv, B, L, cfg.NumAttentionHeads, cfg.QKNopeHeadDim+cfg.VHeadDim)
+	kv = mlx.Transpose(kv, 0, 2, 1, 3)
+
+	// Split into k_nope and values
+	kNope := mlx.Slice(kv, []int32{0, 0, 0, 0}, []int32{B, cfg.NumAttentionHeads, L, cfg.QKNopeHeadDim})
+	values := mlx.Slice(kv, []int32{0, 0, 0, cfg.QKNopeHeadDim}, []int32{B, cfg.NumAttentionHeads, L, cfg.QKNopeHeadDim + cfg.VHeadDim})
+
+	// Apply RoPE to the rope parts only
+	offset := 0
+	if c != nil {
+		offset = c.Offset()
+	}
+	qPE = mlx.RoPE(qPE, int(cfg.QKRopeHeadDim), true, cfg.RopeTheta, 1.0, offset)
+	kPE = mlx.RoPE(kPE, int(cfg.QKRopeHeadDim), true, cfg.RopeTheta, 1.0, offset)
+
+	// Repeat k_pe across all heads
+	kPE = mlx.Tile(kPE, []int32{1, cfg.NumAttentionHeads, 1, 1})
+
+	// Concatenate nope and rope parts
+	queries := mlx.Concatenate([]*mlx.Array{qNope, qPE}, 3)
+	keys := mlx.Concatenate([]*mlx.Array{kNope, kPE}, 3)
+
+	// Update KV cache
+	if c != nil {
+		keys, values = c.Update(keys, values, int(L))
+	}
+
+	// Scaled dot product attention
+	out := mlx.ScaledDotProductAttention(queries, keys, values, cfg.Scale, L > 1)
+
+	// Reshape back: [B, num_heads, L, v_head_dim] -> [B, L, num_heads * v_head_dim]
+	out = mlx.Reshape(mlx.Transpose(out, 0, 2, 1, 3), B, L, cfg.NumAttentionHeads*cfg.VHeadDim)
+
+	return a.OProj.Forward(out)
+}
+
+// DenseMLP implements the standard SwiGLU MLP for dense layers
+type DenseMLP struct {
+	GateProj *nn.Linear `weight:"mlp.gate_proj"`
+	UpProj   *nn.Linear `weight:"mlp.up_proj"`
+	DownProj *nn.Linear `weight:"mlp.down_proj"`
+}
+
+// Forward applies the SwiGLU MLP
+func (m *DenseMLP) Forward(x *mlx.Array) *mlx.Array {
+	gate := mlx.SiLU(m.GateProj.Forward(x))
+	up := m.UpProj.Forward(x)
+	return m.DownProj.Forward(mlx.Mul(gate, up))
+}
+
+// MoEGate implements the expert gating mechanism
+type MoEGate struct {
+	Weight                 *mlx.Array `weight:"mlp.gate.weight"`
+	EScoreCorrectionBias   *mlx.Array `weight:"mlp.gate.e_score_correction_bias,optional"`
+}
+
+// Forward computes expert selection indices and scores
+func (g *MoEGate) Forward(x *mlx.Array, cfg *Config) (*mlx.Array, *mlx.Array) {
+	// Compute gate logits: x @ weight.T
+	gates := mlx.Linear(x, mlx.Transpose(g.Weight, 1, 0))
+
+	// Sigmoid scoring
+	scores := mlx.Sigmoid(gates)
+	origScores := scores
+
+	// Add correction bias if present
+	if g.EScoreCorrectionBias != nil {
+		scores = mlx.Add(scores, g.EScoreCorrectionBias)
+	}
+
+	// Group-wise expert selection (simplified for n_group=1)
+	// Select top-k experts
+	topK := cfg.NumExpertsPerTok
+	negScores := mlx.Neg(scores)
+	inds := mlx.Argpartition(negScores, int(topK)-1, -1)
+
+	shape := inds.Shape()
+	inds = mlx.Slice(inds, []int32{0, 0, 0}, []int32{shape[0], shape[1], topK})
+
+	// Get scores for selected experts
+	scores = mlx.TakeAlongAxis(origScores, inds, -1)
+
+	// Normalize if configured
+	if topK > 1 && cfg.NormTopKProb {
+		sumScores := mlx.Sum(scores, -1, true)
+		scores = mlx.Div(scores, sumScores)
+	}
+
+	// Apply routing scaling factor
+	scores = mlx.MulScalar(scores, cfg.RoutedScalingFactor)
+
+	return inds, scores
+}
+
+// SwitchMLP implements the MoE expert computation using stacked weights
+// Note: No weight tags - these are populated manually by stacking expert weights
+type SwitchMLP struct {
+	GateWeight *mlx.Array
+	UpWeight   *mlx.Array
+	DownWeight *mlx.Array
+}
+
+// Forward applies the switched expert MLP
+func (s *SwitchMLP) Forward(x *mlx.Array, indices *mlx.Array, cfg *Config) *mlx.Array {
+	shape := x.Shape()
+	B, L := shape[0], shape[1]
+	topK := cfg.NumExpertsPerTok
+
+	// Expand x for expert computation: [B, L, D] -> [B, L, 1, 1, D]
+	xExpanded := mlx.ExpandDims(mlx.ExpandDims(x, -2), -2)
+
+	// Flatten for gather_mm: [B*L, 1, 1, D]
+	xFlat := mlx.Reshape(xExpanded, B*L, 1, 1, cfg.HiddenSize)
+
+	// Flatten indices: [B, L, topK] -> [B*L, topK]
+	idxFlat := mlx.Reshape(indices, B*L, topK)
+
+	// Sort for efficient gather (when we have many tokens)
+	doSort := B*L >= 64
+	var invOrder *mlx.Array
+	n := B * L * topK
+
+	if doSort {
+		idxAll := mlx.Flatten(idxFlat)
+		order := mlx.Argsort(idxAll, 0)
+		invOrder = mlx.Argsort(order, 0)
+		// Reorder x based on sorted indices
+		xFlat = mlx.ExpandDims(mlx.Take(mlx.Squeeze(xFlat, 1), mlx.FloorDivideScalar(order, topK), 0), 1)
+		idxFlat = mlx.Reshape(mlx.Take(idxAll, order, 0), n, 1)
+	}
+
+	// Expert computation using gather_mm
+	// gate: x @ gate_weight.T (indices are on the rhs/weight side)
+	gate := mlx.GatherMM(xFlat, mlx.Transpose(s.GateWeight, 0, 2, 1), nil, idxFlat, doSort)
+	// up: x @ up_weight.T
+	up := mlx.GatherMM(xFlat, mlx.Transpose(s.UpWeight, 0, 2, 1), nil, idxFlat, doSort)
+
+	// SwiGLU activation
+	hidden := mlx.Mul(mlx.SiLU(gate), up)
+
+	// down: hidden @ down_weight.T
+	down := mlx.GatherMM(hidden, mlx.Transpose(s.DownWeight, 0, 2, 1), nil, idxFlat, doSort)
+
+	// Unsort if we sorted
+	if doSort {
+		down = mlx.Reshape(mlx.Take(mlx.Squeeze(mlx.Squeeze(down, 2), 1), invOrder, 0), B*L, topK, cfg.HiddenSize)
+	} else {
+		down = mlx.Squeeze(down, 2)
+	}
+
+	return mlx.Reshape(down, B, L, topK, cfg.HiddenSize)
+}
+
+// SharedExperts implements the shared expert MLP
+type SharedExperts struct {
+	GateProj *nn.Linear `weight:"mlp.shared_experts.gate_proj"`
+	UpProj   *nn.Linear `weight:"mlp.shared_experts.up_proj"`
+	DownProj *nn.Linear `weight:"mlp.shared_experts.down_proj"`
+}
+
+// Forward applies the shared expert MLP
+func (s *SharedExperts) Forward(x *mlx.Array) *mlx.Array {
+	gate := mlx.SiLU(s.GateProj.Forward(x))
+	up := s.UpProj.Forward(x)
+	return s.DownProj.Forward(mlx.Mul(gate, up))
+}
+
+// MoE implements the full Mixture of Experts layer
+type MoE struct {
+	Gate          *MoEGate
+	SwitchMLP     *SwitchMLP
+	SharedExperts *SharedExperts
+}
+
+// Forward applies the MoE layer
+func (m *MoE) Forward(x *mlx.Array, cfg *Config) *mlx.Array {
+	shape := x.Shape()
+	B, L := shape[0], shape[1]
+
+	// Get expert indices and scores
+	inds, scores := m.Gate.Forward(x, cfg)
+
+	// Apply routed experts
+	expertOut := m.SwitchMLP.Forward(x, inds, cfg)
+
+	// Weight by scores: [B, L, topK, D] * [B, L, topK, 1] -> sum over topK
+	scoresExpanded := mlx.ExpandDims(scores, -1)
+	y := mlx.Sum(mlx.Mul(expertOut, scoresExpanded), 2, false)
+
+	// Add shared experts if present
+	if m.SharedExperts != nil {
+		y = mlx.Add(y, m.SharedExperts.Forward(x))
+	}
+
+	return mlx.Reshape(y, B, L, cfg.HiddenSize)
+}
+
+// DenseBlock represents a dense transformer block (for first_k_dense_replace layers)
+type DenseBlock struct {
+	Attention              *MLAAttention
+	MLP                    *DenseMLP
+	InputLayerNorm         *nn.RMSNorm `weight:"input_layernorm"`
+	PostAttentionLayerNorm *nn.RMSNorm `weight:"post_attention_layernorm"`
+}
+
+// Forward applies the dense block
+func (b *DenseBlock) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array {
+	// Pre-norm attention with residual
+	r := b.Attention.Forward(b.InputLayerNorm.Forward(x, cfg.RMSNormEps), c, B, L, cfg)
+	h := mlx.Add(x, r)
+
+	// Pre-norm MLP with residual
+	r = b.MLP.Forward(b.PostAttentionLayerNorm.Forward(h, cfg.RMSNormEps))
+	return mlx.Add(h, r)
+}
+
+// MoEBlock represents a MoE transformer block
+type MoEBlock struct {
+	Attention              *MLAAttention
+	MoE                    *MoE
+	InputLayerNorm         *nn.RMSNorm `weight:"input_layernorm"`
+	PostAttentionLayerNorm *nn.RMSNorm `weight:"post_attention_layernorm"`
+}
+
+// Forward applies the MoE block
+func (b *MoEBlock) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array {
+	// Pre-norm attention with residual
+	r := b.Attention.Forward(b.InputLayerNorm.Forward(x, cfg.RMSNormEps), c, B, L, cfg)
+	h := mlx.Add(x, r)
+
+	// Pre-norm MoE with residual
+	r = b.MoE.Forward(b.PostAttentionLayerNorm.Forward(h, cfg.RMSNormEps), cfg)
+	return mlx.Add(h, r)
+}
+
+// Block interface for both dense and MoE blocks
+type Block interface {
+	Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array
+}
+
+// Model represents the complete GLM4-MoE-Lite model
+type Model struct {
+	EmbedTokens *nn.Embedding `weight:"model.embed_tokens"`
+	Layers      []Block       `weight:"-"` // Loaded manually due to different block types
+	Norm        *nn.RMSNorm   `weight:"model.norm"`
+	LMHead      *nn.Linear    `weight:"lm_head"`
+
+	tok *tokenizer.Tokenizer
+	*Config
+}
+
+// sanitizeExpertWeights stacks individual expert weights into a single tensor
+func sanitizeExpertWeights(weights *safetensors.ModelWeights, prefix string, numExperts int32) (*mlx.Array, *mlx.Array, *mlx.Array) {
+	var gateWeights, upWeights, downWeights []*mlx.Array
+
+	for e := int32(0); e < numExperts; e++ {
+		gw, _ := weights.GetTensor(fmt.Sprintf("%s.mlp.experts.%d.gate_proj.weight", prefix, e))
+		uw, _ := weights.GetTensor(fmt.Sprintf("%s.mlp.experts.%d.up_proj.weight", prefix, e))
+		dw, _ := weights.GetTensor(fmt.Sprintf("%s.mlp.experts.%d.down_proj.weight", prefix, e))
+
+		if gw != nil {
+			gateWeights = append(gateWeights, gw)
+		}
+		if uw != nil {
+			upWeights = append(upWeights, uw)
+		}
+		if dw != nil {
+			downWeights = append(downWeights, dw)
+		}
+	}
+
+	var stackedGate, stackedUp, stackedDown *mlx.Array
+	if len(gateWeights) > 0 {
+		stackedGate = mlx.Stack(gateWeights, 0)
+	}
+	if len(upWeights) > 0 {
+		stackedUp = mlx.Stack(upWeights, 0)
+	}
+	if len(downWeights) > 0 {
+		stackedDown = mlx.Stack(downWeights, 0)
+	}
+
+	return stackedGate, stackedUp, stackedDown
+}
+
+// Load loads a GLM4-MoE-Lite model from the given path
+func Load(modelPath string) (*Model, error) {
+	data, err := os.ReadFile(filepath.Join(modelPath, "config.json"))
+	if err != nil {
+		return nil, fmt.Errorf("load config: %w", err)
+	}
+
+	var cfg Config
+	if err := json.Unmarshal(data, &cfg); err != nil {
+		return nil, fmt.Errorf("parse config: %w", err)
+	}
+
+	// Compute derived fields
+	cfg.QHeadDim = cfg.QKNopeHeadDim + cfg.QKRopeHeadDim
+	cfg.Scale = float32(1.0 / math.Sqrt(float64(cfg.QHeadDim)))
+
+	weights, err := safetensors.LoadModelWeights(modelPath)
+	if err != nil {
+		return nil, fmt.Errorf("load weights: %w", err)
+	}
+
+	tok, err := tokenizer.Load(filepath.Join(modelPath, "tokenizer.json"))
+	if err != nil {
+		return nil, fmt.Errorf("load tokenizer: %w", err)
+	}
+
+	m := &Model{
+		Layers: make([]Block, cfg.NumHiddenLayers),
+		Config: &cfg,
+		tok:    tok,
+	}
+
+	// Load embedding, norm, and lm_head
+	if err := safetensors.LoadModule(m, weights, ""); err != nil {
+		return nil, err
+	}
+
+	// Load layers manually due to different block types
+	for i := int32(0); i < cfg.NumHiddenLayers; i++ {
+		prefix := fmt.Sprintf("model.layers.%d", i)
+
+		// Load attention (same for both block types)
+		attn := &MLAAttention{}
+		if err := safetensors.LoadModule(attn, weights, prefix); err != nil {
+			return nil, fmt.Errorf("layer %d attention: %w", i, err)
+		}
+
+		if i < cfg.FirstKDenseReplace {
+			// Dense block
+			block := &DenseBlock{Attention: attn}
+			if err := safetensors.LoadModule(block, weights, prefix); err != nil {
+				return nil, fmt.Errorf("layer %d dense: %w", i, err)
+			}
+			m.Layers[i] = block
+		} else {
+			// MoE block
+			block := &MoEBlock{Attention: attn}
+			if err := safetensors.LoadModule(block, weights, prefix); err != nil {
+				return nil, fmt.Errorf("layer %d moe block: %w", i, err)
+			}
+
+			// Stack expert weights
+			gateW, upW, downW := sanitizeExpertWeights(weights, prefix, cfg.NRoutedExperts)
+
+			block.MoE = &MoE{
+				Gate: &MoEGate{},
+				SwitchMLP: &SwitchMLP{
+					GateWeight: gateW,
+					UpWeight:   upW,
+					DownWeight: downW,
+				},
+			}
+
+			// Load gate weights
+			if err := safetensors.LoadModule(block.MoE.Gate, weights, prefix); err != nil {
+				return nil, fmt.Errorf("layer %d gate: %w", i, err)
+			}
+
+			// Load shared experts if present
+			if cfg.NSharedExperts > 0 {
+				block.MoE.SharedExperts = &SharedExperts{}
+				if err := safetensors.LoadModule(block.MoE.SharedExperts, weights, prefix); err != nil {
+					return nil, fmt.Errorf("layer %d shared experts: %w", i, err)
+				}
+			}
+
+			m.Layers[i] = block
+		}
+	}
+
+	mlx.Eval(mlx.Collect(m)...)
+	weights.ReleaseAll()
+
+	return m, nil
+}
+
+// Forward computes the forward pass of the model
+func (m *Model) Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array {
+	B, L := tokens.Shape()[0], tokens.Shape()[1]
+
+	h := m.EmbedTokens.Forward(tokens)
+
+	for i, layer := range m.Layers {
+		var c cache.Cache
+		if caches != nil {
+			c = caches[i]
+		}
+		h = layer.Forward(h, c, B, L, m.Config)
+	}
+
+	h = m.Norm.Forward(h, m.RMSNormEps)
+	return m.LMHead.Forward(h)
+}
+
+// Interface methods
+
+// NumLayers returns the number of transformer layers
+func (m *Model) NumLayers() int { return len(m.Layers) }
+
+// MaxContextLength returns the maximum context length
+func (m *Model) MaxContextLength() int32 { return m.MaxPositionEmbeddings }
+
+// VocabSize returns the vocabulary size
+func (m *Model) VocabSize() int32 { return m.Config.VocabSize }
+
+// Tokenizer returns the model's tokenizer
+func (m *Model) Tokenizer() *tokenizer.Tokenizer { return m.tok }
+
+// NewCache creates a new KV cache for the model
+func (m *Model) NewCache(maxSeqLen int32) []cache.Cache {
+	caches := make([]cache.Cache, len(m.Layers))
+	for i := range caches {
+		caches[i] = cache.NewKVCache()
+	}
+	return caches
+}
+
+// FormatPrompt applies the GLM-4 chat template
+func (m *Model) FormatPrompt(prompt string) string {
+	return "[gMASK]<sop><|user|>\n" + prompt + "<|assistant|>\n"
+}

x/imagegen/runner/runner.go

@@ -9,7 +9,6 @@ import (
 	"encoding/json"
 	"flag"
 	"fmt"
-	"image"
 	"log/slog"
 	"net/http"
 	"os"
@@ -26,12 +25,11 @@ import (
 
 // Request is the image generation request format
 type Request struct {
-	Prompt string   `json:"prompt"`
-	Width  int32    `json:"width,omitempty"`
-	Height int32    `json:"height,omitempty"`
-	Steps  int      `json:"steps,omitempty"`
-	Seed   int64    `json:"seed,omitempty"`
-	Images [][]byte `json:"images,omitempty"` // Input images for image editing/conditioning
+	Prompt string `json:"prompt"`
+	Width  int32  `json:"width,omitempty"`
+	Height int32  `json:"height,omitempty"`
+	Steps  int    `json:"steps,omitempty"`
+	Seed   int64  `json:"seed,omitempty"`
 }
 
 // Response is streamed back for each progress update
@@ -48,13 +46,6 @@ type ImageModel interface {
 	GenerateImage(ctx context.Context, prompt string, width, height int32, steps int, seed int64, progress func(step, total int)) (*mlx.Array, error)
 }
 
-// ImageEditModel extends ImageModel with image editing/conditioning capability.
-// Models that support input images for editing should implement this interface.
-type ImageEditModel interface {
-	ImageModel
-	GenerateImageWithInputs(ctx context.Context, prompt string, width, height int32, steps int, seed int64, inputImages []image.Image, progress func(step, total int)) (*mlx.Array, error)
-}
-
 // Server holds the model and handles requests
 type Server struct {
 	mu        sync.Mutex
@@ -170,29 +161,6 @@ func (s *Server) completionHandler(w http.ResponseWriter, r *http.Request) {
 		return
 	}
 
-	// Validate and decode input images
-	const maxInputImages = 2
-	if len(req.Images) > maxInputImages {
-		http.Error(w, fmt.Sprintf("too many input images, maximum is %d", maxInputImages), http.StatusBadRequest)
-		return
-	}
-
-	var inputImages []image.Image
-	if len(req.Images) > 0 {
-		// TODO: add memory check for input images
-
-		inputImages = make([]image.Image, len(req.Images))
-		for i, imgBytes := range req.Images {
-			img, err := imagegen.DecodeImage(imgBytes)
-			if err != nil {
-				http.Error(w, fmt.Sprintf("invalid image %d: %v", i, err), http.StatusBadRequest)
-				return
-			}
-			inputImages[i] = img
-		}
-		slog.Info("decoded input images", "count", len(inputImages))
-	}
-
 	// Serialize generation requests - MLX model may not handle concurrent generation
 	s.mu.Lock()
 	defer s.mu.Unlock()
@@ -224,19 +192,7 @@ func (s *Server) completionHandler(w http.ResponseWriter, r *http.Request) {
 		flusher.Flush()
 	}
 
-	// Use ImageEditModel if available and images provided, otherwise use basic ImageModel
-	var img *mlx.Array
-	var err error
-	if len(inputImages) > 0 {
-		editModel, ok := s.model.(ImageEditModel)
-		if !ok {
-			http.Error(w, "model does not support image editing", http.StatusBadRequest)
-			return
-		}
-		img, err = editModel.GenerateImageWithInputs(ctx, req.Prompt, req.Width, req.Height, req.Steps, req.Seed, inputImages, progress)
-	} else {
-		img, err = s.model.GenerateImage(ctx, req.Prompt, req.Width, req.Height, req.Steps, req.Seed, progress)
-	}
+	img, err := s.model.GenerateImage(ctx, req.Prompt, req.Width, req.Height, req.Steps, req.Seed, progress)
 
 	if err != nil {
 		// Don't send error for cancellation

x/imagegen/server.go

@@ -226,27 +226,19 @@ func (s *Server) Completion(ctx context.Context, req llm.CompletionRequest, fn f
 		seed = time.Now().UnixNano()
 	}
 
-	// Extract raw image bytes from llm.ImageData slice
-	var images [][]byte
-	for _, img := range req.Images {
-		images = append(images, img.Data)
-	}
-
 	// Build request for subprocess
 	creq := struct {
-		Prompt string   `json:"prompt"`
-		Width  int32    `json:"width,omitempty"`
-		Height int32    `json:"height,omitempty"`
-		Steps  int32    `json:"steps,omitempty"`
-		Seed   int64    `json:"seed,omitempty"`
-		Images [][]byte `json:"images,omitempty"`
+		Prompt string `json:"prompt"`
+		Width  int32  `json:"width,omitempty"`
+		Height int32  `json:"height,omitempty"`
+		Steps  int32  `json:"steps,omitempty"`
+		Seed   int64  `json:"seed,omitempty"`
 	}{
 		Prompt: req.Prompt,
 		Width:  req.Width,
 		Height: req.Height,
 		Steps:  req.Steps,
 		Seed:   seed,
-		Images: images,
 	}
 
 	body, err := json.Marshal(creq)