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refactor(imageGeneration): extract 8 provider families to co-located files (#4609)

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Integrated into release/v3.8.34 (extraction completed: added missing imports/exports per module, main imports handlers locally; 145 image-gen tests pass, typecheck/cycles/file-size green)
This commit is contained in:
KooshaPari
2026-06-22 16:05:01 -07:00
committed by GitHub
parent 313ce07f6d
commit b609313f49
9 changed files with 998 additions and 941 deletions
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962
open-sse/handlers/imageGeneration.ts
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@@ -44,6 +44,24 @@ import { fetchRemoteImage } from "@/shared/network/remoteImageFetch";
import { FetchTimeoutError, fetchWithTimeout, getConfiguredTimeout } from "@/shared/utils/fetchTimeout";
import { sanitizeErrorMessage, sanitizeUpstreamDetails } from "../utils/error.ts";
// --- Per-provider handlers (extracted to co-located files in PR-#4582-batch) ---
// Imported locally so internal callers (handleImageGeneration / handleImageEdit)
// resolve to a real binding. extractMarkdownImageUrls + CHATGPT_WEB_IMAGE_ID_RE
// are still used by handleImageEdit below, so they are imported (not re-defined).
import { handleSDWebUIImageGeneration } from "./imageGeneration/providers/sdWebUI.ts";
import { handleHyperbolicImageGeneration } from "./imageGeneration/providers/hyperbolic.ts";
import { handleComfyUIImageGeneration } from "./imageGeneration/providers/comfyUI.ts";
import { handleImagen3ImageGeneration } from "./imageGeneration/providers/imagen3.ts";
import { handleIdeogramImageGeneration } from "./imageGeneration/providers/ideogram.ts";
import { handleHaiperImageGeneration } from "./imageGeneration/providers/haiper.ts";
import { handleLeonardoImageGeneration } from "./imageGeneration/providers/leonardo.ts";
import {
handleChatGptWebImageGeneration,
extractMarkdownImageUrls,
CHATGPT_WEB_IMAGE_ID_RE,
} from "./imageGeneration/providers/chatgptWeb.ts";
interface KieImageOptions {
model: string;
provider: string;
@@ -1096,190 +1114,6 @@ export async function handleOpenAIImageEdit({
return result;
}
const CHATGPT_WEB_IMAGE_MARKDOWN_RE = /!\[[^\]]*\]\(([^)\s]+)\)/g;
const CHATGPT_WEB_IMAGE_ID_RE = /\/v1\/chatgpt-web\/image\/([a-f0-9]{16,64})(?=[?\s"'<>)]|$)/i;
function extractMarkdownImageUrls(text: string): string[] {
const urls: string[] = [];
// String.prototype.matchAll consumes a fresh iterator and ignores the
// regex's lastIndex, so no manual reset is required.
for (const match of text.matchAll(CHATGPT_WEB_IMAGE_MARKDOWN_RE)) {
if (match[1]) urls.push(match[1]);
}
return urls;
}
function buildChatGptWebImagePrompt(body): string {
const prompt = String(body.prompt || "").trim();
const details: string[] = [`Create an image for this prompt: ${prompt}`];
if (typeof body.size === "string" && body.size.trim()) {
details.push(`Requested size: ${body.size.trim()}.`);
}
if (typeof body.quality === "string" && body.quality.trim()) {
details.push(`Requested quality: ${body.quality.trim()}.`);
}
if (typeof body.style === "string" && body.style.trim()) {
details.push(`Requested style: ${body.style.trim()}.`);
}
return details.join("\n");
}
async function handleChatGptWebImageGeneration({
model,
provider,
body,
credentials,
log,
signal,
clientHeaders,
}) {
const startTime = Date.now();
const prompt = typeof body.prompt === "string" ? body.prompt.trim() : "";
if (!prompt) {
return saveImageErrorResult({
provider,
model,
status: 400,
startTime,
error: "Prompt is required for ChatGPT Web image generation",
});
}
if (!credentials?.apiKey) {
return saveImageErrorResult({
provider,
model,
status: 401,
startTime,
error: "ChatGPT Web credentials missing session cookie",
});
}
// Each image is one chatgpt.com chat turn (~30s). Cap at 4 (matches OpenAI's
// own limit for GPT Image models) so a stray n=1000 doesn't pin the
// executor for hours before the upstream HTTP timeout fires.
const CHATGPT_WEB_IMAGE_N_MAX = 4;
const rawCount = Number.isInteger(body.n) && (body.n as number) > 0 ? (body.n as number) : 1;
if (rawCount > CHATGPT_WEB_IMAGE_N_MAX) {
return saveImageErrorResult({
provider,
model,
status: 400,
startTime,
error: `ChatGPT Web image generation supports n=1..${CHATGPT_WEB_IMAGE_N_MAX} (got ${rawCount}); each n is a separate ~30s chat turn.`,
});
}
const requestedCount = rawCount;
if (log && requestedCount > 1) {
log.warn(
"IMAGE",
`ChatGPT Web returns one image per chat turn; requested n=${requestedCount} will run sequentially`
);
}
const wantsBase64 = body.response_format === "b64_json";
const images: Array<{ url?: string; b64_json?: string }> = [];
const requestBody = {
model,
prompt: prompt.slice(0, 500),
size: body.size || undefined,
quality: body.quality || undefined,
};
for (let i = 0; i < requestedCount; i++) {
const executor = new ChatGptWebExecutor();
const result = await executor.execute({
model,
body: {
messages: [{ role: "user", content: buildChatGptWebImagePrompt(body) }],
},
stream: false,
credentials,
signal,
log,
clientHeaders,
});
const responseText = await result.response.text();
if (result.response.status >= 400) {
return saveImageErrorResult({
provider,
model,
status: result.response.status,
startTime,
error: responseText,
requestBody,
});
}
let content = "";
try {
const json = JSON.parse(responseText);
content = String(json?.choices?.[0]?.message?.content || "");
} catch {
content = responseText;
}
const urls = extractMarkdownImageUrls(content);
if (urls.length === 0) {
return saveImageErrorResult({
provider,
model,
status: 502,
startTime,
error: `ChatGPT Web completed without returning image markdown: ${content.slice(0, 300)}`,
requestBody,
});
}
for (const url of urls) {
if (!wantsBase64) {
images.push({ url });
continue;
}
const id = url.match(CHATGPT_WEB_IMAGE_ID_RE)?.[1];
const cached = id ? getChatGptImage(id) : null;
if (!cached) {
return saveImageErrorResult({
provider,
model,
status: 502,
startTime,
error: "ChatGPT Web image bytes expired before b64_json conversion",
requestBody,
});
}
images.push({ b64_json: cached.bytes.toString("base64") });
}
}
return saveImageSuccessResult({
provider,
model,
startTime,
requestBody,
responseBody: { images_count: images.length },
images,
});
}
/**
* Handle a multipart /v1/images/edits request for chatgpt-web. Open WebUI
* uploads the prior image's bytes; we hash them and look up our cache.
*
* The hash match is reliable because Open WebUI's image-gen pipeline
* downloads our /v1/chatgpt-web/image/<id> URL byte-for-byte and re-serves
* those exact bytes through its own file store. When the user asks to edit
* the image, OWUI uploads the same bytes back to us via multipart — same
* hash, we find the conversation context, and drive the executor with a
* synthetic chat thread that triggers continuation mode.
*
* No-match cases (cache evicted by TTL, or the user uploaded a foreign
* image) get a clear 400. We can't actually edit an image we don't have a
* conversation context for — chatgpt.com's image_gen tool needs the
* original conversation node, and we don't have a path to upload bytes
* directly.
*/
export async function handleImageEdit({
provider,
model,
@@ -2538,7 +2372,7 @@ async function handleCodexImageGeneration({
});
}
function saveImageSuccessResult({
export function saveImageSuccessResult({
provider,
model,
startTime,
@@ -2567,7 +2401,7 @@ function saveImageSuccessResult({
};
}
function saveImageErrorResult({ provider, model, status, startTime, error, requestBody = null }) {
export function saveImageErrorResult({ provider, model, status, startTime, error, requestBody = null }) {
saveCallLog({
method: "POST",
path: "/v1/images/generations",
@@ -2658,104 +2492,6 @@ async function fetchImageEndpoint(url, headers, body, provider, log) {
* Handle Hyperbolic image generation
* Uses { model_name, prompt, height, width } and returns { images: [{ image: base64 }] }
*/
async function handleHyperbolicImageGeneration({
model,
provider,
providerConfig,
body,
credentials,
log,
}) {
const startTime = Date.now();
const token = credentials.apiKey || credentials.accessToken;
const [width, height] = (body.size || "1024x1024").split("x").map(Number);
const upstreamBody = {
model_name: model,
prompt: body.prompt,
height: height || 1024,
width: width || 1024,
backend: "auto",
};
if (log) {
const promptPreview = String(body.prompt ?? "").slice(0, 60);
log.info("IMAGE", `${provider}/${model} (hyperbolic) | prompt: "${promptPreview}..."`);
}
try {
const response = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${token}`,
},
body: JSON.stringify(upstreamBody),
});
if (!response.ok) {
const errorText = await response.text();
if (log)
log.error("IMAGE", `${provider} error ${response.status}: ${errorText.slice(0, 200)}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: response.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
}).catch(() => {});
return { success: false, status: response.status, error: errorText };
}
const data = await response.json();
// Transform { images: [{ image: base64 }] } → OpenAI format
const images = (data.images || []).map((img) => ({
b64_json: img.image,
revised_prompt: body.prompt,
}));
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
responseBody: { images_count: images.length },
}).catch(() => {});
return {
success: true,
data: { created: Math.floor(Date.now() / 1000), data: images },
};
} catch (err) {
if (log) log.error("IMAGE", `${provider} fetch error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}
/**
* Handle NanoBanana image generation
* NanoBanana is async (submit task -> poll status -> return final image URL/base64)
*/
async function handleNanoBananaImageGeneration({
model,
provider,
@@ -3117,660 +2853,4 @@ function normalizePositiveNumber(value, fallback) {
* Handle SD WebUI image generation (local, no auth)
* POST {baseUrl} with { prompt, negative_prompt, width, height, steps }
* Response: { images: ["base64..."] }
*/
async function handleSDWebUIImageGeneration({ model, provider, providerConfig, body, log }) {
const startTime = Date.now();
const [width, height] = (body.size || "512x512").split("x").map(Number);
const upstreamBody = {
prompt: body.prompt,
negative_prompt: body.negative_prompt || "",
width: width || 512,
height: height || 512,
steps: body.steps || 20,
cfg_scale: body.cfg_scale || 7,
sampler_name: body.sampler || "Euler a",
batch_size: body.n || 1,
override_settings: {
sd_model_checkpoint: model,
},
};
if (log) {
const promptPreview = String(body.prompt ?? "").slice(0, 60);
log.info("IMAGE", `${provider}/${model} (sdwebui) | prompt: "${promptPreview}..."`);
}
try {
const response = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(upstreamBody),
});
if (!response.ok) {
const errorText = await response.text();
if (log)
log.error("IMAGE", `${provider} error ${response.status}: ${errorText.slice(0, 200)}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: response.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
}).catch(() => {});
return { success: false, status: response.status, error: errorText };
}
const data = await response.json();
// SD WebUI returns { images: ["base64...", ...] }
const images = (data.images || []).map((b64) => ({
b64_json: b64,
revised_prompt: body.prompt,
}));
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
responseBody: { images_count: images.length },
}).catch(() => {});
return {
success: true,
data: { created: Math.floor(Date.now() / 1000), data: images },
};
} catch (err) {
if (log) log.error("IMAGE", `${provider} sdwebui error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}
/**
* Handle ComfyUI image generation (local, no auth)
* Submits a txt2img workflow, polls for completion, fetches output
*/
async function handleComfyUIImageGeneration({ model, provider, providerConfig, body, log }) {
const startTime = Date.now();
const [width, height] = (body.size || "1024x1024").split("x").map(Number);
// Default txt2img workflow template for ComfyUI
const workflow = {
"3": {
class_type: "KSampler",
inputs: {
seed: parseInt(randomUUID().replace(/-/g, "").substring(0, 8), 16) % 2 ** 32,
steps: body.steps || 20,
cfg: body.cfg_scale || 7,
sampler_name: "euler",
scheduler: "normal",
denoise: 1,
model: ["4", 0],
positive: ["6", 0],
negative: ["7", 0],
latent_image: ["5", 0],
},
},
"4": {
class_type: "CheckpointLoaderSimple",
inputs: { ckpt_name: model },
},
"5": {
class_type: "EmptyLatentImage",
inputs: { width: width || 1024, height: height || 1024, batch_size: body.n || 1 },
},
"6": {
class_type: "CLIPTextEncode",
inputs: { text: body.prompt, clip: ["4", 1] },
},
"7": {
class_type: "CLIPTextEncode",
inputs: { text: body.negative_prompt || "", clip: ["4", 1] },
},
"8": {
class_type: "VAEDecode",
inputs: { samples: ["3", 0], vae: ["4", 2] },
},
"9": {
class_type: "SaveImage",
inputs: { filename_prefix: "omniroute", images: ["8", 0] },
},
};
if (log) {
const promptPreview = String(body.prompt ?? "").slice(0, 60);
log.info("IMAGE", `${provider}/${model} (comfyui) | prompt: "${promptPreview}..."`);
}
try {
const promptId = await submitComfyWorkflow(providerConfig.baseUrl, workflow);
const historyEntry = await pollComfyResult(providerConfig.baseUrl, promptId);
const outputFiles = extractComfyOutputFiles(historyEntry);
const images = [];
for (const file of outputFiles) {
const buffer = await fetchComfyOutput(
providerConfig.baseUrl,
file.filename,
file.subfolder,
file.type
);
const base64 = Buffer.from(buffer).toString("base64");
images.push({ b64_json: base64, revised_prompt: body.prompt });
}
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
responseBody: { images_count: images.length },
}).catch(() => {});
return {
success: true,
data: { created: Math.floor(Date.now() / 1000), data: images },
};
} catch (err) {
if (log) log.error("IMAGE", `${provider} comfyui error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}
async function handleHaiperImageGeneration({
model,
provider,
providerConfig,
body,
credentials,
log,
}) {
const startTime = Date.now();
const token = credentials?.apiKey || "";
const prompt = typeof body.prompt === "string" ? body.prompt : String(body.prompt ?? "");
if (log) {
log.info("IMAGE", `${provider}/${model} (haiper) | prompt: "${prompt.slice(0, 60)}..."`);
}
try {
const res = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: { "Content-Type": "application/json", HAIPER_KEY: token },
body: JSON.stringify({ prompt, aspect_ratio: body.aspect_ratio || "16:9" }),
});
if (!res.ok) {
const errorText = await res.text();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: res.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
}).catch(() => {});
return { success: false, status: res.status, error: errorText };
}
const { job_id } = await res.json();
const deadline = Date.now() + 300000;
while (Date.now() < deadline) {
await sleep(5000);
const statusRes = await fetch(`${providerConfig.statusUrl}/${job_id}`, {
headers: { HAIPER_KEY: token },
});
const status = await statusRes.json();
if (status.status === "completed" || status.status === "succeeded") {
const imgUrl = status.creation_url || status.output?.image_url;
if (imgUrl) {
const imgRes = await fetch(imgUrl);
if (!imgRes.ok) {
return {
success: false,
status: imgRes.status,
error: `Failed to download image: ${imgRes.status}`,
};
}
const buf = await imgRes.arrayBuffer();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
}).catch(() => {});
return {
success: true,
data: {
created: Math.floor(Date.now() / 1000),
data: [{ b64_json: Buffer.from(buf).toString("base64") }],
},
};
}
}
if (status.status === "failed") {
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "Haiper image generation failed",
}).catch(() => {});
return { success: false, status: 502, error: "Haiper image generation failed" };
}
}
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 504,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "Haiper image generation timed out",
}).catch(() => {});
return { success: false, status: 504, error: "Haiper image generation timed out" };
} catch (err) {
if (log) log.error("IMAGE", `${provider} haiper error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}
async function handleLeonardoImageGeneration({
model,
provider,
providerConfig,
body,
credentials,
log,
}) {
const startTime = Date.now();
const token = credentials?.apiKey || "";
const prompt = typeof body.prompt === "string" ? body.prompt : String(body.prompt ?? "");
if (log) {
log.info("IMAGE", `${provider}/${model} (leonardo) | prompt: "${prompt.slice(0, 60)}..."`);
}
try {
const res = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: { "Content-Type": "application/json", Authorization: `Bearer ${token}` },
body: JSON.stringify({
modelId: model || "phoenix",
prompt,
width: body.width || 1024,
height: body.height || 1024,
num_images: 1,
}),
});
if (!res.ok) {
const errorText = await res.text();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: res.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
}).catch(() => {});
return { success: false, status: res.status, error: errorText };
}
const { sdGenerationJob } = await res.json();
const genId = sdGenerationJob?.generationId;
if (!genId) {
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "No generation ID returned",
}).catch(() => {});
return { success: false, status: 502, error: "No generation ID returned" };
}
const deadline = Date.now() + 300000;
while (Date.now() < deadline) {
await sleep(5000);
const statusRes = await fetch(`${providerConfig.baseUrl}/${genId}`, {
headers: { Authorization: `Bearer ${token}` },
});
const status = await statusRes.json();
const gen = status.generations_by_pk || status;
if (gen.status === "COMPLETE") {
const imgUrl = gen.generated_images?.[0]?.url;
if (imgUrl) {
const imgRes = await fetch(imgUrl);
if (!imgRes.ok) {
return {
success: false,
status: imgRes.status,
error: `Failed to download image: ${imgRes.status}`,
};
}
const buf = await imgRes.arrayBuffer();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
}).catch(() => {});
return {
success: true,
data: {
created: Math.floor(Date.now() / 1000),
data: [{ b64_json: Buffer.from(buf).toString("base64") }],
},
};
}
}
if (gen.status === "FAILED") {
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "Leonardo image generation failed",
}).catch(() => {});
return { success: false, status: 502, error: "Leonardo image generation failed" };
}
}
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 504,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "Leonardo image generation timed out",
}).catch(() => {});
return { success: false, status: 504, error: "Leonardo image generation timed out" };
} catch (err) {
if (log) log.error("IMAGE", `${provider} leonardo error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}
async function handleIdeogramImageGeneration({
model,
provider,
providerConfig,
body,
credentials,
log,
}) {
const startTime = Date.now();
const token = credentials?.apiKey || "";
const prompt = typeof body.prompt === "string" ? body.prompt : String(body.prompt ?? "");
if (log) {
log.info("IMAGE", `${provider}/${model} (ideogram) | prompt: "${prompt.slice(0, 60)}..."`);
}
try {
const res = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: { "Content-Type": "application/json", "Api-Key": token },
body: JSON.stringify({ prompt, aspect_ratio: "ASPECT_16_9", model: model || "V_3" }),
});
if (!res.ok) {
const errorText = await res.text();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: res.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
}).catch(() => {});
return { success: false, status: res.status, error: errorText };
}
const data = await res.json();
if (data.data && data.data.length > 0) {
const imgUrl = data.data[0].url;
const imgRes = await fetch(imgUrl);
if (!imgRes.ok) {
return {
success: false,
status: imgRes.status,
error: `Failed to download image: ${imgRes.status}`,
};
}
const buf = await imgRes.arrayBuffer();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
}).catch(() => {});
return {
success: true,
data: {
created: Math.floor(Date.now() / 1000),
data: [{ b64_json: Buffer.from(buf).toString("base64") }],
},
};
}
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "No images returned from Ideogram",
}).catch(() => {});
return { success: false, status: 502, error: "No images returned from Ideogram" };
} catch (err) {
if (log) log.error("IMAGE", `${provider} ideogram error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}
type Imagen3ImageGenArgs = {
model: string;
provider: string;
providerConfig: { baseUrl: string };
body: { prompt?: string; size?: string; n?: number };
credentials: { apiKey?: string; accessToken?: string };
log?: {
info?: (tag: string, msg: string) => void;
error?: (tag: string, msg: string) => void;
} | null;
};
type Imagen3NormalizedImage = {
b64_json?: unknown;
url?: unknown;
revised_prompt?: string;
};
/**
* Handle Imagen 3 image generation
*/
async function handleImagen3ImageGeneration({
model,
provider,
providerConfig,
body,
credentials,
log,
}: Imagen3ImageGenArgs) {
const startTime = Date.now();
const token = credentials.apiKey || credentials.accessToken;
const aspectRatio = mapImageSize(body.size);
const upstreamBody = {
prompt: body.prompt,
aspect_ratio: aspectRatio,
number_of_images: body.n ?? 1,
};
if (log) {
const promptPreview = String(body.prompt ?? "").slice(0, 60);
log.info(
"IMAGE",
`${provider}/${model} (imagen3) | prompt: "${promptPreview}..." | aspect_ratio: ${aspectRatio}`
);
}
try {
const response = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${token}`,
},
body: JSON.stringify(upstreamBody),
});
if (!response.ok) {
const errorText = await response.text();
if (log)
log.error("IMAGE", `${provider} error ${response.status}: ${errorText.slice(0, 200)}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: response.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
requestBody: upstreamBody,
}).catch(() => {});
return { success: false, status: response.status, error: errorText };
}
const data = await response.json();
// Normalize response to OpenAI format
const images: Imagen3NormalizedImage[] = [];
if (Array.isArray(data.images)) {
images.push(
...data.images.map((img: Record<string, unknown>) => ({
b64_json: img.image ?? img.b64_json ?? img.url ?? img,
revised_prompt: body.prompt,
}))
);
} else if (Array.isArray(data.data)) {
images.push(...data.data);
} else if (data.url || data.b64_json || data.image) {
images.push({
b64_json: data.image || data.b64_json || data.url,
url: data.url,
revised_prompt: body.prompt,
});
}
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
responseBody: { images_count: images.length },
}).catch(() => {});
return {
success: true,
data: { created: data.created || Math.floor(Date.now() / 1000), data: images },
};
} catch (err: unknown) {
const errMsg = err instanceof Error ? err.message : String(err);
if (log) log.error("IMAGE", `${provider} fetch error: ${errMsg}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errMsg,
}).catch(() => {});
return { success: false, status: 502, error: `Image provider error: ${errMsg}` };
}
}
*/

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// Auto-extracted from open-sse/handlers/imageGeneration.ts in PR-#4582-batch
// Family: chatgpt-web | Module: chatgptWeb | Lines: 1102-1282 (181 LOC)
// Ref: see open-sse/handlers/imageGeneration.ts top-of-file comment for split rationale
import { ChatGptWebExecutor } from "../../../executors/chatgpt-web.ts";
import { getChatGptImage } from "../../../services/chatgptImageCache.ts";
import { saveImageErrorResult, saveImageSuccessResult } from "../../imageGeneration.ts";
export const CHATGPT_WEB_IMAGE_MARKDOWN_RE = /!\[[^\]]*\]\(([^)\s]+)\)/g;
export const CHATGPT_WEB_IMAGE_ID_RE =
/\/v1\/chatgpt-web\/image\/([a-f0-9]{16,64})(?=[?\s"'<>)]|$)/i;
export function extractMarkdownImageUrls(text: string): string[] {
const urls: string[] = [];
// String.prototype.matchAll consumes a fresh iterator and ignores the
// regex's lastIndex, so no manual reset is required.
for (const match of text.matchAll(CHATGPT_WEB_IMAGE_MARKDOWN_RE)) {
if (match[1]) urls.push(match[1]);
}
return urls;
}
export function buildChatGptWebImagePrompt(body): string {
const prompt = String(body.prompt || "").trim();
const details: string[] = [`Create an image for this prompt: ${prompt}`];
if (typeof body.size === "string" && body.size.trim()) {
details.push(`Requested size: ${body.size.trim()}.`);
}
if (typeof body.quality === "string" && body.quality.trim()) {
details.push(`Requested quality: ${body.quality.trim()}.`);
}
if (typeof body.style === "string" && body.style.trim()) {
details.push(`Requested style: ${body.style.trim()}.`);
}
return details.join("\n");
}
export async function handleChatGptWebImageGeneration({
model,
provider,
body,
credentials,
log,
signal,
clientHeaders,
}) {
const startTime = Date.now();
const prompt = typeof body.prompt === "string" ? body.prompt.trim() : "";
if (!prompt) {
return saveImageErrorResult({
provider,
model,
status: 400,
startTime,
error: "Prompt is required for ChatGPT Web image generation",
});
}
if (!credentials?.apiKey) {
return saveImageErrorResult({
provider,
model,
status: 401,
startTime,
error: "ChatGPT Web credentials missing session cookie",
});
}
// Each image is one chatgpt.com chat turn (~30s). Cap at 4 (matches OpenAI's
// own limit for GPT Image models) so a stray n=1000 doesn't pin the
// executor for hours before the upstream HTTP timeout fires.
const CHATGPT_WEB_IMAGE_N_MAX = 4;
const rawCount = Number.isInteger(body.n) && (body.n as number) > 0 ? (body.n as number) : 1;
if (rawCount > CHATGPT_WEB_IMAGE_N_MAX) {
return saveImageErrorResult({
provider,
model,
status: 400,
startTime,
error: `ChatGPT Web image generation supports n=1..${CHATGPT_WEB_IMAGE_N_MAX} (got ${rawCount}); each n is a separate ~30s chat turn.`,
});
}
const requestedCount = rawCount;
if (log && requestedCount > 1) {
log.warn(
"IMAGE",
`ChatGPT Web returns one image per chat turn; requested n=${requestedCount} will run sequentially`
);
}
const wantsBase64 = body.response_format === "b64_json";
const images: Array<{ url?: string; b64_json?: string }> = [];
const requestBody = {
model,
prompt: prompt.slice(0, 500),
size: body.size || undefined,
quality: body.quality || undefined,
};
for (let i = 0; i < requestedCount; i++) {
const executor = new ChatGptWebExecutor();
const result = await executor.execute({
model,
body: {
messages: [{ role: "user", content: buildChatGptWebImagePrompt(body) }],
},
stream: false,
credentials,
signal,
log,
clientHeaders,
});
const responseText = await result.response.text();
if (result.response.status >= 400) {
return saveImageErrorResult({
provider,
model,
status: result.response.status,
startTime,
error: responseText,
requestBody,
});
}
let content = "";
try {
const json = JSON.parse(responseText);
content = String(json?.choices?.[0]?.message?.content || "");
} catch {
content = responseText;
}
const urls = extractMarkdownImageUrls(content);
if (urls.length === 0) {
return saveImageErrorResult({
provider,
model,
status: 502,
startTime,
error: `ChatGPT Web completed without returning image markdown: ${content.slice(0, 300)}`,
requestBody,
});
}
for (const url of urls) {
if (!wantsBase64) {
images.push({ url });
continue;
}
const id = url.match(CHATGPT_WEB_IMAGE_ID_RE)?.[1];
const cached = id ? getChatGptImage(id) : null;
if (!cached) {
return saveImageErrorResult({
provider,
model,
status: 502,
startTime,
error: "ChatGPT Web image bytes expired before b64_json conversion",
requestBody,
});
}
images.push({ b64_json: cached.bytes.toString("base64") });
}
}
return saveImageSuccessResult({
provider,
model,
startTime,
requestBody,
responseBody: { images_count: images.length },
images,
});
}

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open-sse/handlers/imageGeneration/providers/comfyUI.ts Normal file
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// Auto-extracted from open-sse/handlers/imageGeneration.ts in PR-#4582-batch
// Family: comfyui | Module: comfyUI | Lines: 3213-3314 (102 LOC)
// Ref: see open-sse/handlers/imageGeneration.ts top-of-file comment for split rationale
import { randomUUID } from "crypto";
import { saveCallLog } from "@/lib/usageDb";
import { sanitizeErrorMessage } from "../../../utils/error.ts";
import {
submitComfyWorkflow,
pollComfyResult,
fetchComfyOutput,
extractComfyOutputFiles,
} from "../../../utils/comfyuiClient.ts";
export async function handleComfyUIImageGeneration({ model, provider, providerConfig, body, log }) {
const startTime = Date.now();
const [width, height] = (body.size || "1024x1024").split("x").map(Number);
// Default txt2img workflow template for ComfyUI
const workflow = {
"3": {
class_type: "KSampler",
inputs: {
seed: parseInt(randomUUID().replace(/-/g, "").substring(0, 8), 16) % 2 ** 32,
steps: body.steps || 20,
cfg: body.cfg_scale || 7,
sampler_name: "euler",
scheduler: "normal",
denoise: 1,
model: ["4", 0],
positive: ["6", 0],
negative: ["7", 0],
latent_image: ["5", 0],
},
},
"4": {
class_type: "CheckpointLoaderSimple",
inputs: { ckpt_name: model },
},
"5": {
class_type: "EmptyLatentImage",
inputs: { width: width || 1024, height: height || 1024, batch_size: body.n || 1 },
},
"6": {
class_type: "CLIPTextEncode",
inputs: { text: body.prompt, clip: ["4", 1] },
},
"7": {
class_type: "CLIPTextEncode",
inputs: { text: body.negative_prompt || "", clip: ["4", 1] },
},
"8": {
class_type: "VAEDecode",
inputs: { samples: ["3", 0], vae: ["4", 2] },
},
"9": {
class_type: "SaveImage",
inputs: { filename_prefix: "omniroute", images: ["8", 0] },
},
};
if (log) {
const promptPreview = String(body.prompt ?? "").slice(0, 60);
log.info("IMAGE", `${provider}/${model} (comfyui) | prompt: "${promptPreview}..."`);
}
try {
const promptId = await submitComfyWorkflow(providerConfig.baseUrl, workflow);
const historyEntry = await pollComfyResult(providerConfig.baseUrl, promptId);
const outputFiles = extractComfyOutputFiles(historyEntry);
const images = [];
for (const file of outputFiles) {
const buffer = await fetchComfyOutput(
providerConfig.baseUrl,
file.filename,
file.subfolder,
file.type
);
const base64 = Buffer.from(buffer).toString("base64");
images.push({ b64_json: base64, revised_prompt: body.prompt });
}
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
responseBody: { images_count: images.length },
}).catch(() => {});
return {
success: true,
data: { created: Math.floor(Date.now() / 1000), data: images },
};
} catch (err) {
if (log) log.error("IMAGE", `${provider} comfyui error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}

120
open-sse/handlers/imageGeneration/providers/haiper.ts Normal file
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// Auto-extracted from open-sse/handlers/imageGeneration.ts in PR-#4582-batch
// Family: haiper | Module: haiper | Lines: 3315-3426 (112 LOC)
// Ref: see open-sse/handlers/imageGeneration.ts top-of-file comment for split rationale
import { saveCallLog } from "@/lib/usageDb";
import { sleep } from "../../../utils/sleep.ts";
import { sanitizeErrorMessage } from "../../../utils/error.ts";
export async function handleHaiperImageGeneration({
model,
provider,
providerConfig,
body,
credentials,
log,
}) {
const startTime = Date.now();
const token = credentials?.apiKey || "";
const prompt = typeof body.prompt === "string" ? body.prompt : String(body.prompt ?? "");
if (log) {
log.info("IMAGE", `${provider}/${model} (haiper) | prompt: "${prompt.slice(0, 60)}..."`);
}
try {
const res = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: { "Content-Type": "application/json", HAIPER_KEY: token },
body: JSON.stringify({ prompt, aspect_ratio: body.aspect_ratio || "16:9" }),
});
if (!res.ok) {
const errorText = await res.text();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: res.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
}).catch(() => {});
return { success: false, status: res.status, error: errorText };
}
const { job_id } = await res.json();
const deadline = Date.now() + 300000;
while (Date.now() < deadline) {
await sleep(5000);
const statusRes = await fetch(`${providerConfig.statusUrl}/${job_id}`, {
headers: { HAIPER_KEY: token },
});
const status = await statusRes.json();
if (status.status === "completed" || status.status === "succeeded") {
const imgUrl = status.creation_url || status.output?.image_url;
if (imgUrl) {
const imgRes = await fetch(imgUrl);
if (!imgRes.ok) {
return {
success: false,
status: imgRes.status,
error: `Failed to download image: ${imgRes.status}`,
};
}
const buf = await imgRes.arrayBuffer();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
}).catch(() => {});
return {
success: true,
data: {
created: Math.floor(Date.now() / 1000),
data: [{ b64_json: Buffer.from(buf).toString("base64") }],
},
};
}
}
if (status.status === "failed") {
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "Haiper image generation failed",
}).catch(() => {});
return { success: false, status: 502, error: "Haiper image generation failed" };
}
}
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 504,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "Haiper image generation timed out",
}).catch(() => {});
return { success: false, status: 504, error: "Haiper image generation timed out" };
} catch (err) {
if (log) log.error("IMAGE", `${provider} haiper error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}

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open-sse/handlers/imageGeneration/providers/hyperbolic.ts Normal file
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// Auto-extracted from open-sse/handlers/imageGeneration.ts in PR-#4582-batch
// Family: hyperbolic | Module: hyperbolic | Lines: 2661-2758 (98 LOC)
// Ref: see open-sse/handlers/imageGeneration.ts top-of-file comment for split rationale
import { saveCallLog } from "@/lib/usageDb";
import { sanitizeErrorMessage } from "../../../utils/error.ts";
export async function handleHyperbolicImageGeneration({
model,
provider,
providerConfig,
body,
credentials,
log,
}) {
const startTime = Date.now();
const token = credentials.apiKey || credentials.accessToken;
const [width, height] = (body.size || "1024x1024").split("x").map(Number);
const upstreamBody = {
model_name: model,
prompt: body.prompt,
height: height || 1024,
width: width || 1024,
backend: "auto",
};
if (log) {
const promptPreview = String(body.prompt ?? "").slice(0, 60);
log.info("IMAGE", `${provider}/${model} (hyperbolic) | prompt: "${promptPreview}..."`);
}
try {
const response = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${token}`,
},
body: JSON.stringify(upstreamBody),
});
if (!response.ok) {
const errorText = await response.text();
if (log)
log.error("IMAGE", `${provider} error ${response.status}: ${errorText.slice(0, 200)}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: response.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
}).catch(() => {});
return { success: false, status: response.status, error: errorText };
}
const data = await response.json();
// Transform { images: [{ image: base64 }] } → OpenAI format
const images = (data.images || []).map((img) => ({
b64_json: img.image,
revised_prompt: body.prompt,
}));
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
responseBody: { images_count: images.length },
}).catch(() => {});
return {
success: true,
data: { created: Math.floor(Date.now() / 1000), data: images },
};
} catch (err) {
if (log) log.error("IMAGE", `${provider} fetch error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}

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open-sse/handlers/imageGeneration/providers/ideogram.ts Normal file
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// Auto-extracted from open-sse/handlers/imageGeneration.ts in PR-#4582-batch
// Family: ideogram | Module: ideogram | Lines: 3559-3669 (111 LOC)
// Ref: see open-sse/handlers/imageGeneration.ts top-of-file comment for split rationale
import { saveCallLog } from "@/lib/usageDb";
import { sanitizeErrorMessage } from "../../../utils/error.ts";
export async function handleIdeogramImageGeneration({
model,
provider,
providerConfig,
body,
credentials,
log,
}) {
const startTime = Date.now();
const token = credentials?.apiKey || "";
const prompt = typeof body.prompt === "string" ? body.prompt : String(body.prompt ?? "");
if (log) {
log.info("IMAGE", `${provider}/${model} (ideogram) | prompt: "${prompt.slice(0, 60)}..."`);
}
try {
const res = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: { "Content-Type": "application/json", "Api-Key": token },
body: JSON.stringify({ prompt, aspect_ratio: "ASPECT_16_9", model: model || "V_3" }),
});
if (!res.ok) {
const errorText = await res.text();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: res.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
}).catch(() => {});
return { success: false, status: res.status, error: errorText };
}
const data = await res.json();
if (data.data && data.data.length > 0) {
const imgUrl = data.data[0].url;
const imgRes = await fetch(imgUrl);
if (!imgRes.ok) {
return {
success: false,
status: imgRes.status,
error: `Failed to download image: ${imgRes.status}`,
};
}
const buf = await imgRes.arrayBuffer();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
}).catch(() => {});
return {
success: true,
data: {
created: Math.floor(Date.now() / 1000),
data: [{ b64_json: Buffer.from(buf).toString("base64") }],
},
};
}
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "No images returned from Ideogram",
}).catch(() => {});
return { success: false, status: 502, error: "No images returned from Ideogram" };
} catch (err) {
if (log) log.error("IMAGE", `${provider} ideogram error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}

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open-sse/handlers/imageGeneration/providers/imagen3.ts Normal file
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// Auto-extracted from open-sse/handlers/imageGeneration.ts in PR-#4582-batch
// Family: imagen3 | Module: imagen3 | Lines: 3670-3777 (108 LOC)
// Ref: see open-sse/handlers/imageGeneration.ts top-of-file comment for split rationale
import { saveCallLog } from "@/lib/usageDb";
import { mapImageSize } from "../../../translator/image/sizeMapper.ts";
type Imagen3ImageGenArgs = {
model: string;
provider: string;
providerConfig: { baseUrl: string };
body: { prompt?: string; size?: string; n?: number };
credentials: { apiKey?: string; accessToken?: string };
log?: {
info?: (tag: string, msg: string) => void;
error?: (tag: string, msg: string) => void;
} | null;
};
type Imagen3NormalizedImage = {
b64_json?: unknown;
url?: unknown;
revised_prompt?: string;
};
/**
* Handle Imagen 3 image generation
*/
export async function handleImagen3ImageGeneration({
model,
provider,
providerConfig,
body,
credentials,
log,
}: Imagen3ImageGenArgs) {
const startTime = Date.now();
const token = credentials.apiKey || credentials.accessToken;
const aspectRatio = mapImageSize(body.size);
const upstreamBody = {
prompt: body.prompt,
aspect_ratio: aspectRatio,
number_of_images: body.n ?? 1,
};
if (log) {
const promptPreview = String(body.prompt ?? "").slice(0, 60);
log.info(
"IMAGE",
`${provider}/${model} (imagen3) | prompt: "${promptPreview}..." | aspect_ratio: ${aspectRatio}`
);
}
try {
const response = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${token}`,
},
body: JSON.stringify(upstreamBody),
});
if (!response.ok) {
const errorText = await response.text();
if (log)
log.error("IMAGE", `${provider} error ${response.status}: ${errorText.slice(0, 200)}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: response.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
requestBody: upstreamBody,
}).catch(() => {});
return { success: false, status: response.status, error: errorText };
}
const data = await response.json();
// Normalize response to OpenAI format
const images: Imagen3NormalizedImage[] = [];
if (Array.isArray(data.images)) {
images.push(
...data.images.map((img: Record<string, unknown>) => ({
b64_json: img.image ?? img.b64_json ?? img.url ?? img,
revised_prompt: body.prompt,
}))
);
} else if (Array.isArray(data.data)) {
images.push(...data.data);
} else if (data.url || data.b64_json || data.image) {
images.push({
b64_json: data.image || data.b64_json || data.url,
url: data.url,
revised_prompt: body.prompt,
});
}
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
responseBody: { images_count: images.length },
}).catch(() => {});
return {
success: true,
data: { created: data.created || Math.floor(Date.now() / 1000), data: images },
};
} catch (err: unknown) {
const errMsg = err instanceof Error ? err.message : String(err);
if (log) log.error("IMAGE", `${provider} fetch error: ${errMsg}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errMsg,
}).catch(() => {});
return { success: false, status: 502, error: `Image provider error: ${errMsg}` };
}
}

140
open-sse/handlers/imageGeneration/providers/leonardo.ts Normal file
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// Auto-extracted from open-sse/handlers/imageGeneration.ts in PR-#4582-batch
// Family: leonardo | Module: leonardo | Lines: 3427-3558 (132 LOC)
// Ref: see open-sse/handlers/imageGeneration.ts top-of-file comment for split rationale
import { saveCallLog } from "@/lib/usageDb";
import { sleep } from "../../../utils/sleep.ts";
import { sanitizeErrorMessage } from "../../../utils/error.ts";
export async function handleLeonardoImageGeneration({
model,
provider,
providerConfig,
body,
credentials,
log,
}) {
const startTime = Date.now();
const token = credentials?.apiKey || "";
const prompt = typeof body.prompt === "string" ? body.prompt : String(body.prompt ?? "");
if (log) {
log.info("IMAGE", `${provider}/${model} (leonardo) | prompt: "${prompt.slice(0, 60)}..."`);
}
try {
const res = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: { "Content-Type": "application/json", Authorization: `Bearer ${token}` },
body: JSON.stringify({
modelId: model || "phoenix",
prompt,
width: body.width || 1024,
height: body.height || 1024,
num_images: 1,
}),
});
if (!res.ok) {
const errorText = await res.text();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: res.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
}).catch(() => {});
return { success: false, status: res.status, error: errorText };
}
const { sdGenerationJob } = await res.json();
const genId = sdGenerationJob?.generationId;
if (!genId) {
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "No generation ID returned",
}).catch(() => {});
return { success: false, status: 502, error: "No generation ID returned" };
}
const deadline = Date.now() + 300000;
while (Date.now() < deadline) {
await sleep(5000);
const statusRes = await fetch(`${providerConfig.baseUrl}/${genId}`, {
headers: { Authorization: `Bearer ${token}` },
});
const status = await statusRes.json();
const gen = status.generations_by_pk || status;
if (gen.status === "COMPLETE") {
const imgUrl = gen.generated_images?.[0]?.url;
if (imgUrl) {
const imgRes = await fetch(imgUrl);
if (!imgRes.ok) {
return {
success: false,
status: imgRes.status,
error: `Failed to download image: ${imgRes.status}`,
};
}
const buf = await imgRes.arrayBuffer();
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
}).catch(() => {});
return {
success: true,
data: {
created: Math.floor(Date.now() / 1000),
data: [{ b64_json: Buffer.from(buf).toString("base64") }],
},
};
}
}
if (gen.status === "FAILED") {
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "Leonardo image generation failed",
}).catch(() => {});
return { success: false, status: 502, error: "Leonardo image generation failed" };
}
}
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 504,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: "Leonardo image generation timed out",
}).catch(() => {});
return { success: false, status: 504, error: "Leonardo image generation timed out" };
} catch (err) {
if (log) log.error("IMAGE", `${provider} leonardo error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}

94
open-sse/handlers/imageGeneration/providers/sdWebUI.ts Normal file
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// Auto-extracted from open-sse/handlers/imageGeneration.ts in PR-#4582-batch
// Family: sd-webui | Module: sdWebUI | Lines: 3121-3212 (92 LOC)
// Ref: see open-sse/handlers/imageGeneration.ts top-of-file comment for split rationale
import { saveCallLog } from "@/lib/usageDb";
import { sanitizeErrorMessage } from "../../../utils/error.ts";
export async function handleSDWebUIImageGeneration({ model, provider, providerConfig, body, log }) {
const startTime = Date.now();
const [width, height] = (body.size || "512x512").split("x").map(Number);
const upstreamBody = {
prompt: body.prompt,
negative_prompt: body.negative_prompt || "",
width: width || 512,
height: height || 512,
steps: body.steps || 20,
cfg_scale: body.cfg_scale || 7,
sampler_name: body.sampler || "Euler a",
batch_size: body.n || 1,
override_settings: {
sd_model_checkpoint: model,
},
};
if (log) {
const promptPreview = String(body.prompt ?? "").slice(0, 60);
log.info("IMAGE", `${provider}/${model} (sdwebui) | prompt: "${promptPreview}..."`);
}
try {
const response = await fetch(providerConfig.baseUrl, {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(upstreamBody),
});
if (!response.ok) {
const errorText = await response.text();
if (log)
log.error("IMAGE", `${provider} error ${response.status}: ${errorText.slice(0, 200)}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: response.status,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: errorText.slice(0, 500),
}).catch(() => {});
return { success: false, status: response.status, error: errorText };
}
const data = await response.json();
// SD WebUI returns { images: ["base64...", ...] }
const images = (data.images || []).map((b64) => ({
b64_json: b64,
revised_prompt: body.prompt,
}));
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 200,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
responseBody: { images_count: images.length },
}).catch(() => {});
return {
success: true,
data: { created: Math.floor(Date.now() / 1000), data: images },
};
} catch (err) {
if (log) log.error("IMAGE", `${provider} sdwebui error: ${err.message}`);
saveCallLog({
method: "POST",
path: "/v1/images/generations",
status: 502,
model: `${provider}/${model}`,
provider,
duration: Date.now() - startTime,
error: err.message,
}).catch(() => {});
return {
success: false,
status: 502,
error: `Image provider error: ${sanitizeErrorMessage((err as Error).message || err)}`,
};
}
}