luboslenco
164 lines
3.4 KiB
TypeScript
164 lines
3.4 KiB
TypeScript
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function text_to_image_node_init() {
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array_push(nodes_material_neural, text_to_image_node_def);
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map_set(parser_material_node_vectors, "NEURAL_TEXT_TO_IMAGE", neural_node_vector);
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map_set(ui_nodes_custom_buttons, "text_to_image_node_button", text_to_image_node_button);
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}
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function text_to_image_node_sd_args(dir: string, prompt: string): string[] {
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let argv: string[] = [
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dir + "/sd_vulkan",
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"-m",
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dir + "/v1-5-pruned-emaonly.safetensors",
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"--offload-to-cpu",
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"-W",
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"512",
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"-H",
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"512",
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"--steps",
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"40",
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"-s",
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"-1",
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"-o",
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dir + "/output.png",
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"-p",
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"'" + prompt + "'",
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null
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];
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return argv;
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}
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function text_to_image_node_qwen_args(dir: string, prompt: string): string[] {
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let argv: string[] = [
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dir + "/sd_vulkan",
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"--diffusion-model",
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dir + "/Qwen_Image-Q4_K_S.gguf",
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"--vae",
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dir + "/Qwen_Image-VAE.safetensors",
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"--qwen2vl",
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dir + "/Qwen2.5-VL-7B-Instruct-Q4_K_S.gguf",
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"--sampling-method",
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"euler",
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"--offload-to-cpu",
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"-W",
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"512",
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"-H",
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"512",
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"--steps",
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"20",
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"-s",
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"-1",
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"-o",
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dir + "/output.png",
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"-p",
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"'" + prompt + "'",
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null
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];
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return argv;
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}
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function text_to_image_node_wan_args(dir: string, prompt: string): string[] {
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let argv: string[] = [
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dir + "/sd_vulkan",
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"-M",
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"vid_gen",
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"--diffusion-model",
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dir + "/Wan2.2-T2V-A14B-LowNoise-Q4_K_S.gguf",
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"--high-noise-diffusion-model",
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dir + "/Wan2.2-T2V-A14B-HighNoise-Q4_K_S.gguf",
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"--vae",
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dir + "/Wan2.1_VAE.safetensors",
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"--t5xxl",
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dir + "/umt5-xxl-encoder-Q4_K_S.gguf",
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"--sampling-method",
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"euler",
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"--steps",
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"20",
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"--high-noise-sampling-method",
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"euler",
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"--high-noise-steps",
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"10",
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"-W",
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"512",
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"-H",
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"512",
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"--offload-to-cpu",
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"-s",
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"-1",
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"-o",
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dir + "/output.png",
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"-p",
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prompt,
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null
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];
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return argv;
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}
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function text_to_image_node_button(node_id: i32) {
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let node: ui_node_t = ui_get_node(ui_nodes_get_canvas(true).nodes, node_id);
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let node_name: string = parser_material_node_name(node);
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let h: ui_handle_t = ui_handle(node_name);
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let models: string[] = [ "Stable Diffusion", "Qwen Image", "Wan" ];
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let model: i32 = ui_combo(ui_nest(h, 0), models, tr("Model"));
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let prompt: string = ui_text_area(ui_nest(h, 1), ui_align_t.LEFT, true, tr("prompt"), true);
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node.buttons[0].height = string_split(prompt, "\n").length + 2;
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if (neural_node_button(node, models[model])) {
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let dir: string = neural_node_dir();
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if (prompt == "") {
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prompt = ".";
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}
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let argv: string[];
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if (model == 0) {
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argv = text_to_image_node_sd_args(dir, prompt);
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}
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else if (model == 1) {
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argv = text_to_image_node_qwen_args(dir, prompt);
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}
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else {
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argv = text_to_image_node_wan_args(dir, prompt);
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}
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iron_exec_async(argv[0], argv.buffer);
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sys_notify_on_update(neural_node_check_result, node);
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}
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}
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let text_to_image_node_def: ui_node_t = {
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id : 0,
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name : _tr("Text to Image"),
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type : "NEURAL_TEXT_TO_IMAGE",
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x : 0,
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y : 0,
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color : 0xff4982a0,
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inputs : [],
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outputs : [ {
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id : 0,
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node_id : 0,
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name : _tr("Color"),
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type : "RGBA",
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color : 0xffc7c729,
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default_value : f32_array_create_xyzw(0.0, 0.0, 0.0, 1.0),
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min : 0.0,
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max : 1.0,
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precision : 100,
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display : 0
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} ],
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buttons : [ {
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name : "text_to_image_node_button",
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type : "CUSTOM",
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output : -1,
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default_value : f32_array_create_x(0),
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data : null,
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min : 0.0,
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max : 1.0,
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precision : 100,
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height : 1
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} ],
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width : 0,
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flags : 0
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};
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