#!/usr/bin/env python3 """ ModelScope image generation backend. Configuration keys: MODELSCOPE_API_KEY (required) MODELSCOPE_MODEL (optional) MODELSCOPE_BASE_URL (optional) """ import os import time import requests from image_backends.backend_common import ( MAX_RETRIES, http_error, is_rate_limit_error, normalize_image_size, require_api_key, resolve_output_path, retry_delay, poll_json, download_image ) DEFAULT_ENDPOINT = "https://api-inference.modelscope.cn" DEFAULT_MODEL = "Tongyi-MAI/Z-Image-Turbo" # Resolution must be 64-aligned. ASPECT_RATIO_SIZE_MAP = { "512px": { "1:1": "1024*1024", "3:4": "768*1024", "4:3": "1024*768", "9:16": "576*1024", "16:9": "1024*576" }, "1K": { "1:1": "1280*1280", "3:4": "960*1280", "4:3": "1280*960", "9:16": "576*1024", "16:9": "1024*576" }, "2K": { "1:1": "2048*2048", "3:4": "1536*2048", "4:3": "2048*1536", "9:16": "1152*2048", "16:9": "2048*1152" }, "4K": { "1:1": "2048*2048", "3:4": "1920*2560", "4:3": "2560*1920", "9:16": "1728*3072", "16:9": "3072*1728" } } def _resolve_url(base_url: str) -> str: """Resolve the ModelScope generation endpoint.""" base = base_url.rstrip("/") if base.endswith("/v1"): base = base.removesuffix("/v1") return base def _resolve_size(aspect_ratio: str, image_size: str) -> str: """Resolve the target resolution for a ratio and logical size preset. Args: aspect_ratio (str): The aspect ratio string. Supported values: '1:1', '3:4', '4:3', '9:16', '16:9'. image_size (str): The logical size preset. Supported values: '512px', '1K', '2K', '4K'. """ normalized = normalize_image_size(image_size) size = (ASPECT_RATIO_SIZE_MAP.get(normalized) or {}).get(aspect_ratio) if not size: supported = sorted(ASPECT_RATIO_SIZE_MAP["1K"]) raise ValueError( f"Unsupported aspect ratio '{aspect_ratio}' for ModelScope backend. " f"Supported: {supported}" ) return size def _generate_image(api_key: str, prompt: str, aspect_ratio: str = "1:1", image_size: str = "1K", output_dir: str = None, filename: str = None, model: str = DEFAULT_MODEL, base_url: str = DEFAULT_ENDPOINT) -> str: """Generate one image with the ModelScope backend.""" size = _resolve_size(aspect_ratio, image_size) url = _resolve_url(base_url)+'/v1/images/generations' common_headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json", } payload = { "model": model, "prompt": prompt, "size": size.replace("*", "x"), } print("[ModelScope Models]") print(f" Model: {model}") print(f" Prompt: {prompt[:120]}{'...' if len(prompt) > 120 else ''}") print(f" Aspect Ratio: {aspect_ratio}") print(f" Resolution: {size}") print() print(" [..] Generating...", end="", flush=True) start = time.time() response = requests.post(url, headers={**common_headers,"X-ModelScope-Async-Mode": "true"}, json=payload, timeout=300) if (response.status_code != 200): raise http_error(response, "ModelScope image generation") task_id = response.json()["task_id"] data = poll_json( url=f"{_resolve_url(base_url)}/v1/tasks/{task_id}", headers={**common_headers, "X-ModelScope-Task-Type": "image_generation"}, status_label="task_status", ready_values=["SUCCEED"], failed_values=["FAILED"], ) elapsed = time.time() - start print(f"\n [DONE] Response received ({elapsed:.1f}s)") path = resolve_output_path(prompt, output_dir, filename, ".png") return download_image(data["output_images"][0], path) def generate(prompt: str, aspect_ratio: str = "1:1", image_size: str = "1K", output_dir: str = None, filename: str = None, model: str = None, max_retries: int = MAX_RETRIES) -> str: """Generate an image with retries using the ModelScope backend.""" api_key = require_api_key( "MODELSCOPE_API_KEY", message="No API key found. Set MODELSCOPE_API_KEY in the current environment or the project-root .env.", ) base_url = os.environ.get("MODELSCOPE_BASE_URL") or DEFAULT_ENDPOINT resolved_model = model or os.environ.get("MODELSCOPE_MODEL") or DEFAULT_MODEL last_error = None for attempt in range(max_retries + 1): try: return _generate_image( api_key=api_key, prompt=prompt, aspect_ratio=aspect_ratio, image_size=image_size, output_dir=output_dir, filename=filename, model=resolved_model, base_url=base_url, ) except Exception as exc: last_error = exc if attempt >= max_retries: break limited = is_rate_limit_error(exc) delay = retry_delay(attempt, rate_limited=limited) label = "Rate limit hit" if limited else f"Error: {exc}" print(f"\n [WARN] {label}. Retrying in {delay}s...") time.sleep(delay) raise RuntimeError(f"Failed after {max_retries + 1} attempts. Last error: {last_error}")