prelim gpt-image (cant test, no access)
This commit is contained in:
reanon
+5
-3
@@ -30,6 +30,7 @@ const MODEL_FAMILY_FRIENDLY_NAME: { [f in ModelFamily]: string } = {
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"o3": "OpenAI o3",
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"o4-mini": "OpenAI o4 mini",
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"dall-e": "DALL-E",
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"gpt-image": "GPT Image",
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claude: "Claude (Sonnet)",
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"claude-opus": "Claude (Opus)",
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"gemini-flash": "Gemini Flash",
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@@ -63,6 +64,7 @@ const MODEL_FAMILY_FRIENDLY_NAME: { [f in ModelFamily]: string } = {
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"azure-o3": "Azure o3",
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"azure-o4-mini": "Azure o4 mini",
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"azure-dall-e": "Azure DALL-E",
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"azure-gpt-image": "Azure GPT Image",
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};
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const converter = new showdown.Converter();
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@@ -213,15 +215,15 @@ function getServerTitle() {
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}
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function buildRecentImageSection() {
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const dalleModels: ModelFamily[] = ["azure-dall-e", "dall-e"];
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const imageModels: ModelFamily[] = ["azure-dall-e", "dall-e", "gpt-image", "azure-gpt-image"];
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if (
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!config.showRecentImages ||
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dalleModels.every((f) => !config.allowedModelFamilies.includes(f))
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imageModels.every((f) => !config.allowedModelFamilies.includes(f))
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) {
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return "";
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}
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let html = `<h2>Recent DALL-E Generations</h2>`;
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let html = `<h2>Recent Image Generations</h2>`;
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const recentImages = getLastNImages(12).reverse();
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if (recentImages.length === 0) {
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html += `<p>No images yet.</p>`;
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@@ -11,7 +11,7 @@ import { ProxyResHandlerWithBody } from "./middleware/response";
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import { ProxyReqManager } from "./middleware/request/proxy-req-manager";
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import { createQueuedProxyMiddleware } from "./middleware/request/proxy-middleware-factory";
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const KNOWN_MODELS = ["dall-e-2", "dall-e-3"];
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const KNOWN_MODELS = ["dall-e-2", "dall-e-3", "gpt-image-1"];
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let modelListCache: any = null;
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let modelListValid = 0;
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@@ -58,27 +58,46 @@ function transformResponseForChat(
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req: Request
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): Record<string, any> {
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const prompt = imageBody.data[0].revised_prompt ?? req.body.prompt;
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const isGptImage = req.body.model?.includes("gpt-image") || false;
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const content = imageBody.data
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.map((item) => {
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const { url, b64_json } = item;
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// The gpt-image-1 model always returns b64_json
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// Format will depend on output_format parameter (defaults to png)
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// For simplicity, we'll assume png if not specified
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const format = req.body.output_format || "png";
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if (b64_json) {
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return ``;
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return ``;
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} else {
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return ``;
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}
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})
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.join("\n\n");
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// Prepare the usage information - gpt-image-1 includes detailed token usage
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let usage = {
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prompt_tokens: 0,
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completion_tokens: req.outputTokens,
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total_tokens: req.outputTokens,
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};
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// If this is a gpt-image-1 response, it includes detailed usage info
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if (imageBody.usage) {
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usage = {
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prompt_tokens: imageBody.usage.input_tokens || 0,
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completion_tokens: imageBody.usage.output_tokens || 0,
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total_tokens: imageBody.usage.total_tokens || 0,
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};
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}
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return {
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id: "dalle-" + req.id,
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id: req.body.model?.includes("gpt-image") ? "gptimage-" + req.id : "dalle-" + req.id,
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object: "chat.completion",
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created: Date.now(),
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model: req.body.model,
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usage: {
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prompt_tokens: 0,
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completion_tokens: req.outputTokens,
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total_tokens: req.outputTokens,
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},
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usage,
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choices: [
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{
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message: { role: "assistant", content },
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@@ -89,6 +108,56 @@ function transformResponseForChat(
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};
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}
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// Filter parameters based on the model being used to avoid sending unsupported parameters
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function filterModelParameters(manager: ProxyReqManager) {
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const req = manager.request;
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const originalBody = req.body;
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const modelName = originalBody?.model || "";
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// Skip if no body or it's not an object
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if (!originalBody || typeof originalBody !== 'object') return;
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// Create a deep copy of the body to filter
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const filteredBody = { ...originalBody };
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// Define allowed parameters for each model
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if (modelName.includes('dall-e-2')) {
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// DALL-E 2 parameters
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const allowedParams = [
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'model', 'prompt', 'n', 'size', 'response_format', 'user'
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];
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// Remove any parameter not in the allowed list
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Object.keys(filteredBody).forEach(key => {
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if (!allowedParams.includes(key)) {
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delete filteredBody[key];
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}
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});
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req.log.info({ model: 'dall-e-2', params: Object.keys(filteredBody) }, "Filtered parameters for DALL-E 2");
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} else if (modelName.includes('dall-e-3')) {
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// DALL-E 3 parameters
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const allowedParams = [
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'model', 'prompt', 'n', 'quality', 'size', 'style', 'response_format', 'user'
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];
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// Remove any parameter not in the allowed list
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Object.keys(filteredBody).forEach(key => {
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if (!allowedParams.includes(key)) {
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delete filteredBody[key];
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}
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});
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req.log.info({ model: 'dall-e-3', params: Object.keys(filteredBody) }, "Filtered parameters for DALL-E 3");
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} else if (modelName.includes('gpt-image')) {
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// For gpt-image-1, we can use all parameters
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req.log.info({ model: 'gpt-image-1', params: Object.keys(filteredBody) }, "Using all parameters for GPT Image");
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}
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// Use the proper method to update the body
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manager.setBody(filteredBody);
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}
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function replacePath(manager: ProxyReqManager) {
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const req = manager.request;
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const pathname = req.url.split("?")[0];
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@@ -100,7 +169,7 @@ function replacePath(manager: ProxyReqManager) {
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const openaiImagesProxy = createQueuedProxyMiddleware({
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target: "https://api.openai.com",
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mutations: [replacePath, addKey, finalizeBody],
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mutations: [replacePath, filterModelParameters, addKey, finalizeBody],
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blockingResponseHandler: openaiImagesResponseHandler,
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});
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@@ -116,6 +185,17 @@ openaiImagesRouter.post(
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}),
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openaiImagesProxy
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);
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// Add support for the /v1/images/edits endpoint (used by gpt-image-1 for image editing)
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openaiImagesRouter.post(
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"/v1/images/edits",
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ipLimiter,
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createPreprocessorMiddleware({
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inApi: "openai-image",
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outApi: "openai-image",
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service: "openai",
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}),
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openaiImagesProxy
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);
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openaiImagesRouter.post(
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"/v1/chat/completions",
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ipLimiter,
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@@ -1,20 +1,58 @@
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import { z } from "zod";
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import { Request } from "express";
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import { OpenAIV1ChatCompletionSchema } from "./openai";
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import { APIFormatTransformer } from "./index";
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// Extend the Express Request type to include multimodal content
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declare global {
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namespace Express {
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interface Request {
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multimodalContent?: {
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prompt?: string;
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images?: string[];
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};
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}
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}
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}
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// https://platform.openai.com/docs/api-reference/images/create
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export const OpenAIV1ImagesGenerationSchema = z
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.object({
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prompt: z.string().max(4000),
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prompt: z.string().max(32000), // gpt-image-1 supports up to 32000 chars
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model: z.string().max(100).optional(),
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quality: z.enum(["standard", "hd"]).optional().default("standard"),
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n: z.number().int().min(1).max(4).optional().default(1),
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response_format: z.enum(["url", "b64_json"]).optional(),
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// Support for image inputs (multimodal capability of gpt-image-1)
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image: z.union([
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z.string(), // single image (base64 or URL)
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z.array(z.string()) // array of images
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]).optional(),
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mask: z.string().optional(), // mask image for editing
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// Different quality options based on model
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quality: z
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.union([
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z.enum(["standard", "hd"]), // dall-e-3 options
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z.enum(["high", "medium", "low"]), // gpt-image-1 options
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z.literal("auto") // default for gpt-image-1
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])
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.optional()
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.default("standard"),
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n: z.number().int().min(1).max(10).optional().default(1), // gpt-image-1 supports up to 10
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response_format: z.enum(["url", "b64_json"]).optional(), // Note: gpt-image-1 always returns b64_json
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// Enhanced size options for gpt-image-1
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size: z
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.enum(["256x256", "512x512", "1024x1024", "1792x1024", "1024x1792"])
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.union([
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// dalle models
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z.enum(["256x256", "512x512", "1024x1024", "1792x1024", "1024x1792"]),
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// gpt-image-1 models (adds landscape, portrait, auto)
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z.enum(["1024x1024", "1536x1024", "1024x1536", "auto"])
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])
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.optional()
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.default("1024x1024"),
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style: z.enum(["vivid", "natural"]).optional().default("vivid"),
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style: z.enum(["vivid", "natural"]).optional().default("vivid"), // dall-e-3 only
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// New gpt-image-1 specific parameters
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background: z.enum(["transparent", "opaque", "auto"]).optional(), // gpt-image-1 only
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moderation: z.enum(["low", "auto"]).optional(), // gpt-image-1 only
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output_compression: z.number().int().min(0).max(100).optional(), // gpt-image-1 only
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output_format: z.enum(["png", "jpeg", "webp"]).optional(), // gpt-image-1 only
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user: z.string().max(500).optional(),
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})
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.strip();
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@@ -34,9 +72,41 @@ export const transformOpenAIToOpenAIImage: APIFormatTransformer<
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}
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const { messages } = result.data;
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const prompt = messages.filter((m) => m.role === "user").pop()?.content;
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if (Array.isArray(prompt)) {
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throw new Error("Image generation prompt must be a text message.");
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const userMessage = messages.filter((m) => m.role === "user").pop();
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if (!userMessage) {
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throw new Error("No user message found in the request.");
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}
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const content = userMessage.content;
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// Handle array content (multimodal content with text and images)
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if (Array.isArray(content)) {
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const textParts: string[] = [];
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const imageParts: string[] = [];
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// Process content parts, extracting text and images
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content.forEach(part => {
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if (typeof part === 'string') {
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textParts.push(part);
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} else if (part.type === 'image_url') {
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// Extract image URL or base64 data from the content
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const imageUrl = typeof part.image_url === 'string'
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? part.image_url
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: part.image_url.url;
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imageParts.push(imageUrl);
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}
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});
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// Join all text parts to form the prompt
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const prompt = textParts.join('\n');
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// For gpt-image-1, we'll pass both the text prompt and image(s)
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req.multimodalContent = {
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prompt,
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images: imageParts
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};
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} else if (typeof content !== 'string') {
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throw new Error("Image generation prompt must be a text message or multimodal content.");
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}
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if (body.stream) {
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@@ -49,20 +119,172 @@ export const transformOpenAIToOpenAIImage: APIFormatTransformer<
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// character name or wrapping the entire thing in quotes. We will look for
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// the index of "Image:" and use everything after that as the prompt.
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const index = prompt?.toLowerCase().indexOf("image:");
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if (index === -1 || !prompt) {
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throw new Error(
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`Start your prompt with 'Image:' followed by a description of the image you want to generate (received: ${prompt}).`
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);
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// For multimodal requests (image editing with gpt-image-1), we don't require the "Image:" prefix
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const isMultimodalRequest = Array.isArray(content) && req.multimodalContent?.images && req.multimodalContent.images.length > 0;
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// Only enforce the "Image:" prefix for non-multimodal requests
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if (!isMultimodalRequest && typeof content === 'string') {
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const textIndex = content.toLowerCase().indexOf("image:");
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if (textIndex === -1) {
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throw new Error(
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`Start your prompt with 'Image:' followed by a description of the image you want to generate (received: ${content}).`
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);
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}
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}
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// TODO: Add some way to specify parameters via chat message
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const transformed = {
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model: body.model.includes("dall-e") ? body.model : "dall-e-3",
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quality: "standard",
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size: "1024x1024",
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response_format: "url",
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prompt: prompt.slice(index! + 6).trim(),
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// Determine which model to use (gpt-image-1 or dall-e-3)
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const isGptImage = body.model?.includes("gpt-image") || false;
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// Get the correct text prompt either from multimodal content or plain string content
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let textPrompt: string | undefined;
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let index = -1;
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if (Array.isArray(content)) {
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textPrompt = req.multimodalContent?.prompt;
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} else if (typeof content === 'string') {
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index = content.toLowerCase().indexOf("image:");
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textPrompt = index !== -1 ? content.slice(index + 6).trim() : content;
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}
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// Validate that we have a text prompt
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if (!textPrompt) {
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throw new Error("No text prompt found in the request.");
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}
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// Determine the exact model being used
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let modelName = "dall-e-2"; // Default
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if (isGptImage) {
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modelName = "gpt-image-1";
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} else if (body.model?.includes("dall-e-3")) {
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modelName = "dall-e-3";
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} else if (body.model?.includes("dall-e-2")) {
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modelName = "dall-e-2";
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} else {
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// If no specific model requested, default to dall-e-3
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modelName = "dall-e-3";
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}
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// Start with basic parameters common to all models
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const transformed: any = {
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model: modelName,
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prompt: textPrompt,
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};
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// Add model-specific parameters
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if (modelName === "gpt-image-1") {
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// GPT Image specific parameters
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transformed.quality = "auto"; // Default quality for gpt-image-1
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transformed.size = "1024x1024"; // Default size (square)
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transformed.moderation = "low"; // Always set moderation to low for gpt-image-1
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// Optional GPT Image parameters
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if (body.background) transformed.background = body.background;
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if (body.output_format) transformed.output_format = body.output_format;
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if (body.output_compression) transformed.output_compression = body.output_compression;
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// Handle specific quality settings for gpt-image-1
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if (body.quality && ["high", "medium", "low", "auto"].includes(body.quality)) {
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transformed.quality = body.quality;
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}
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// Handle specific size settings for gpt-image-1
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if (body.size && ["1024x1024", "1536x1024", "1024x1536", "auto"].includes(body.size)) {
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transformed.size = body.size;
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}
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// No response_format for gpt-image-1 as it always returns b64_json
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} else if (modelName === "dall-e-3") {
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// DALL-E 3 specific parameters
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transformed.size = "1024x1024"; // Default size
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transformed.response_format = "url"; // Default format
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transformed.quality = "standard"; // Default quality
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||||
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||||
// Handle DALL-E 3 style parameter
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if (body.style && ["vivid", "natural"].includes(body.style)) {
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transformed.style = body.style;
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||||
} else {
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transformed.style = "vivid"; // Default style
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||||
}
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// Handle specific quality settings for dall-e-3
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if (body.quality && ["standard", "hd"].includes(body.quality)) {
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transformed.quality = body.quality;
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}
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// Handle specific size settings for dall-e-3
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if (body.size && ["1024x1024", "1792x1024", "1024x1792"].includes(body.size)) {
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||||
transformed.size = body.size;
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||||
}
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||||
} else {
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// DALL-E 2 specific parameters
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transformed.size = "1024x1024"; // Default size
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||||
transformed.response_format = "url"; // Default format
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||||
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// NO quality parameter for dall-e-2
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// Explicitly remove the quality parameter before sending
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delete transformed.quality;
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||||
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// Handle specific size settings for dall-e-2
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if (body.size && ["256x256", "512x512", "1024x1024"].includes(body.size)) {
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transformed.size = body.size;
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||||
}
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||||
}
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||||
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||||
// Handle common parameters
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||||
if (body.n && !isNaN(parseInt(body.n))) {
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||||
// For dall-e-3, only n=1 is supported
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||||
if (modelName === "dall-e-3" && parseInt(body.n) > 1) {
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||||
transformed.n = 1;
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} else {
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||||
transformed.n = parseInt(body.n);
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||||
}
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||||
}
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||||
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||||
// Handle response_format for non-gpt-image models
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||||
if (!isGptImage && body.response_format && ["url", "b64_json"].includes(body.response_format)) {
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||||
transformed.response_format = body.response_format;
|
||||
}
|
||||
|
||||
// If this is gpt-image-1 and we have image content, add it to the transformed request
|
||||
if (isGptImage && req.multimodalContent?.images && req.multimodalContent.images.length > 0) {
|
||||
// For the edit endpoint, we need to format the images properly
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||||
transformed.image = req.multimodalContent.images.length === 1
|
||||
? req.multimodalContent.images[0]
|
||||
: req.multimodalContent.images;
|
||||
|
||||
// Any request with images for gpt-image-1 should use the edits endpoint
|
||||
req.log.info(`${req.multimodalContent.images.length} image(s) detected for gpt-image-1, using images/edits endpoint`);
|
||||
if (req.path.startsWith("/v1/chat/completions")) {
|
||||
req.url = req.url.replace("/v1/chat/completions", "/v1/images/edits");
|
||||
}
|
||||
}
|
||||
// For dall-e-2, we need to make sure we don't introduce unsupported parameters
|
||||
// due to default values in the schema. Let's bypass Zod schema validation here
|
||||
// for dall-e-2 and only include the supported parameters.
|
||||
if (modelName === "dall-e-2") {
|
||||
// Only include parameters that dall-e-2 supports
|
||||
const filteredTransformed: any = {};
|
||||
|
||||
// List of parameters supported by dall-e-2
|
||||
const supportedParams = [
|
||||
"model", "prompt", "n", "size", "response_format", "user"
|
||||
];
|
||||
|
||||
// Copy only supported parameters
|
||||
for (const param of supportedParams) {
|
||||
if (transformed[param] !== undefined) {
|
||||
filteredTransformed[param] = transformed[param];
|
||||
}
|
||||
}
|
||||
|
||||
// Log what we're sending
|
||||
req.log.info({ params: Object.keys(filteredTransformed) }, "Filtered parameters for dall-e-2");
|
||||
|
||||
return filteredTransformed;
|
||||
}
|
||||
|
||||
// For other models, use the schema as normal
|
||||
return OpenAIV1ImagesGenerationSchema.parse(transformed);
|
||||
};
|
||||
|
||||
@@ -13,9 +13,19 @@ export type OpenAIImageGenerationResult = {
|
||||
created: number;
|
||||
data: {
|
||||
revised_prompt?: string;
|
||||
url: string;
|
||||
b64_json: string;
|
||||
url?: string; // gpt-image-1 doesn't return URLs, only b64_json
|
||||
b64_json?: string;
|
||||
}[];
|
||||
// Added for gpt-image-1 responses
|
||||
usage?: {
|
||||
total_tokens: number;
|
||||
input_tokens: number;
|
||||
output_tokens: number;
|
||||
input_tokens_details?: {
|
||||
text_tokens: number;
|
||||
image_tokens: number;
|
||||
};
|
||||
};
|
||||
};
|
||||
|
||||
async function downloadImage(url: string) {
|
||||
@@ -65,11 +75,16 @@ export async function mirrorGeneratedImage(
|
||||
let mirror: string;
|
||||
if (item.b64_json) {
|
||||
mirror = await saveB64Image(item.b64_json);
|
||||
} else {
|
||||
} else if (item.url) {
|
||||
mirror = await downloadImage(item.url);
|
||||
} else {
|
||||
req.log.warn("No image data found in response");
|
||||
continue;
|
||||
}
|
||||
// Set the URL to our mirrored version
|
||||
item.url = `${host}/user_content/${path.basename(mirror)}`;
|
||||
await createThumbnail(mirror);
|
||||
// Add to image history with the local URL
|
||||
addToImageHistory({
|
||||
url: item.url,
|
||||
prompt,
|
||||
|
||||
@@ -84,6 +84,7 @@ export class AzureOpenAIKeyProvider implements KeyProvider<AzureOpenAIKey> {
|
||||
"azure-o3Tokens": 0,
|
||||
"azure-o4-miniTokens": 0,
|
||||
"azure-dall-eTokens": 0,
|
||||
"azure-gpt-imageTokens": 0,
|
||||
modelIds: [],
|
||||
};
|
||||
this.keys.push(newKey);
|
||||
|
||||
@@ -124,6 +124,7 @@ export class OpenAIKeyProvider implements KeyProvider<OpenAIKey> {
|
||||
"o3Tokens": 0,
|
||||
"o4-miniTokens": 0,
|
||||
"dall-eTokens": 0,
|
||||
"gpt-imageTokens": 0,
|
||||
modelIds: [],
|
||||
};
|
||||
this.keys.push(newKey);
|
||||
|
||||
@@ -34,7 +34,8 @@ export type OpenAIModelFamily =
|
||||
| "o3-mini"
|
||||
| "o3"
|
||||
| "o4-mini"
|
||||
| "dall-e";
|
||||
| "dall-e"
|
||||
| "gpt-image";
|
||||
export type AnthropicModelFamily = "claude" | "claude-opus";
|
||||
export type GoogleAIModelFamily =
|
||||
| "gemini-flash"
|
||||
@@ -84,6 +85,7 @@ export const MODEL_FAMILIES = (<A extends readonly ModelFamily[]>(
|
||||
"o3",
|
||||
"o4-mini",
|
||||
"dall-e",
|
||||
"gpt-image",
|
||||
"claude",
|
||||
"claude-opus",
|
||||
"gemini-flash",
|
||||
@@ -117,6 +119,7 @@ export const MODEL_FAMILIES = (<A extends readonly ModelFamily[]>(
|
||||
"azure-o3-mini",
|
||||
"azure-o3",
|
||||
"azure-o4-mini",
|
||||
"azure-gpt-image",
|
||||
] as const);
|
||||
|
||||
export const LLM_SERVICES = (<A extends readonly LLMService[]>(
|
||||
@@ -154,6 +157,7 @@ export const MODEL_FAMILY_SERVICE: {
|
||||
"o3": "openai",
|
||||
"o4-mini": "openai",
|
||||
"dall-e": "openai",
|
||||
"gpt-image": "openai",
|
||||
claude: "anthropic",
|
||||
"claude-opus": "anthropic",
|
||||
"aws-claude": "aws",
|
||||
@@ -180,6 +184,7 @@ export const MODEL_FAMILY_SERVICE: {
|
||||
"azure-o3-mini": "azure",
|
||||
"azure-o3": "azure",
|
||||
"azure-o4-mini": "azure",
|
||||
"azure-gpt-image": "azure",
|
||||
"gemini-flash": "google-ai",
|
||||
"gemini-pro": "google-ai",
|
||||
"gemini-ultra": "google-ai",
|
||||
@@ -189,9 +194,10 @@ export const MODEL_FAMILY_SERVICE: {
|
||||
"mistral-large": "mistral-ai",
|
||||
};
|
||||
|
||||
export const IMAGE_GEN_MODELS: ModelFamily[] = ["dall-e", "azure-dall-e"];
|
||||
export const IMAGE_GEN_MODELS: ModelFamily[] = ["dall-e", "azure-dall-e", "gpt-image", "azure-gpt-image"];
|
||||
|
||||
export const OPENAI_MODEL_FAMILY_MAP: { [regex: string]: OpenAIModelFamily } = {
|
||||
"^gpt-image(-\\d+)?(-preview)?(-\\d{4}-\\d{2}-\\d{2})?$": "gpt-image",
|
||||
"^gpt-4\\.5(-preview)?(-\\d{4}-\\d{2}-\\d{2})?$": "gpt45",
|
||||
"^gpt-4\\.1(-\\d{4}-\\d{2}-\\d{2})?$": "gpt41",
|
||||
"^gpt-4\\.1-mini(-\\d{4}-\\d{2}-\\d{2})?$": "gpt41-mini",
|
||||
|
||||
@@ -83,6 +83,16 @@ export function getTokenCostUsd(model: ModelFamily, tokens: number) {
|
||||
case "dall-e":
|
||||
cost = 0.00001;
|
||||
break;
|
||||
case "azure-gpt-image":
|
||||
case "gpt-image":
|
||||
// gpt-image-1 pricing:
|
||||
// Text input tokens: $5 per 1M tokens
|
||||
// Image input tokens: $10 per 1M tokens
|
||||
// Image output tokens: $40 per 1M tokens
|
||||
// Weighted average assuming a mix of text/image input and output
|
||||
// Typical cost is $0.02-$0.19 per image depending on quality
|
||||
cost = 0.000018; // Balanced estimate accounting for input/output mix
|
||||
break;
|
||||
case "aws-claude":
|
||||
case "gcp-claude":
|
||||
case "claude":
|
||||
|
||||
Reference in New Issue
Block a user