zycandClaude Opus 4.8
8f32ad69d3
视频项目(ZWQ): - 演员工作台保存后「我的演员」仍 0 个:保存时加入资产库(in_library=True) - 资产详情「应用当前角色」自动送审(不再要手动点灰盾) - 「我的演员」卡 + 待生成 seed 卡补删除入口(垃圾桶,两步确认) - 新增场景未生成就刷新会丢:sceneDrafts 按项目持久化 localStorage - 场景 AI 生成强制叠加「无真人」约束,避免出真人致视频被火山拒 - 进入项目默认落到当前阶段(不再恒显第 1 阶段) - 视频已出但进度卡 3/4:submit/poll/adopt 幂等补推 current_stage→VIDEO, 否则 settle_video_completion 因阶段守卫直接返回、永不收口 completed 图片工作台(PMC): - 生图走 action 全局单飞锁致并发重跑/多平台套图被拒成「失败/少出图」: action 加 concurrent 选项,生图不占单飞锁 - 切模块再切回状态丢/数量少:每批各自落盘 pendingIds(onSubmitted 回传), 切回时全部批次各自续轮询 附:含一处早前未提交的图片创作改动(选火山+传参考图强制切 gpt-image-2 + gen 气泡溢出 CSS)。 新增/通过测试:AttachOfficialModel、商品图删除、视频卡阶段收口、参考图强切模型。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
547 lines
31 KiB
Python
547 lines
31 KiB
Python
import json
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from pathlib import Path
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from django.conf import settings
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from django.test import SimpleTestCase
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from apps.ai.script_agent import normalize_draft
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class SkillPromptBundlingTests(SimpleTestCase):
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"""守护「skills 必须随后端打进镜像」这条线 —— 历史上 skills 放仓库根、不在 Docker 构建上下文
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(`./core/backend`)→ 镜像里没有 → 提示词加载为空 → 提取模型收不到「只输出 JSON」铁律 → 吐散文
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→ 「提取结果解析失败」。这两条断言锁死:① skills 落在 BASE_DIR 内(随镜像走);② 提示词非空且带铁律。"""
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def test_skills_dir_bundled_under_base_dir(self):
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"""skills 必须在 BASE_DIR(=core/backend)内,才会被 Dockerfile 的 `COPY . .` 打进镜像。"""
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for name in ("ecommerce-entity-extract", "ecommerce-video-script"):
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self.assertTrue(
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(Path(settings.BASE_DIR) / "skills" / name / "SKILL.md").exists(),
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f"skills/{name}/SKILL.md 不在 BASE_DIR 内 —— 镜像将丢失该提示词(详见 services._skills_root)",
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)
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def test_entity_extract_prompt_loads_nonempty_with_json_rule(self):
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"""提取系统提示词必须加载到非空内容,且含「只输出 JSON」铁律(空 → 模型不吐 JSON → 解析失败)。"""
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from apps.ai.services import _load_skill_system_prompt
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prompt = _load_skill_system_prompt("ecommerce-entity-extract")
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self.assertGreater(len(prompt), 1000, "提取提示词为空/过短 —— skills 没被正确加载")
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self.assertIn("JSON", prompt)
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self.assertIn("entities", prompt)
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def test_extract_output_contract_is_hardcoded_safety_net(self):
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"""写死的提取输出契约(随代码进镜像)必须存在且含 JSON 铁律 —— 这是 skill 丢失时的兜底,
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保证提取永远收到「只输出 JSON」指令(对齐脚本 agent 的 _OUTPUT_PROTOCOL,根治"系统提示词为空"事故)。"""
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from apps.ai.services import _EXTRACT_OUTPUT_CONTRACT
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self.assertGreater(len(_EXTRACT_OUTPUT_CONTRACT), 200)
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for marker in ("entities", "segments", "character", "scene", "JSON"):
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self.assertIn(marker, _EXTRACT_OUTPUT_CONTRACT)
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class NormalizeDraftTests(SimpleTestCase):
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"""normalize_draft 对模型不按契约输出的容错(防「旁白/画面全空」回归)。"""
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def test_scriptdraft_shots_variant_fills_narration_and_visual(self):
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"""模型常见变体:{"ScriptDraft":{"basicInfo":..,"shots":[{scene,dialogue,subtitle}]}}。
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必须解开外壳 + 把 shots→segments、scene→画面、dialogue/subtitle→旁白,而非全填空占位镜。"""
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raw = json.dumps({
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"ScriptDraft": {
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"basicInfo": {"totalDuration": 60, "aspectRatio": "9:16", "totalShots": 4},
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"shots": [
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{"shotNo": 1, "duration": 15, "scene": "更衣室扯卡裆旧裤", "dialogue": "卡裆太社死！", "subtitle": "还在卡裆？"},
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{"shotNo": 2, "duration": 15, "scene": "特写拉扯面料回弹", "dialogue": "高弹不变形！", "subtitle": "裸感面料"},
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{"shotNo": 3, "duration": 15, "scene": "健身切通勤", "dialogue": "都能穿！", "subtitle": "一裤多穿"},
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{"shotNo": 4, "duration": 15, "scene": "对镜弹小黄车", "dialogue": "点小黄车抢！", "subtitle": "点击入手"},
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],
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}
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}, ensure_ascii=False)
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draft = normalize_draft(raw, aspect_ratio="9:16", total_duration=60)
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self.assertEqual(len(draft["segments"]), 4)
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self.assertEqual([s["role"] for s in draft["segments"]], ["钩子", "痛点", "卖点", "CTA"])
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for seg in draft["segments"]:
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self.assertTrue(seg["narration"], "旁白不应为空")
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self.assertTrue(seg["visual"], "画面不应为空")
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self.assertEqual(draft["segments"][0]["narration"], "卡裆太社死！")
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self.assertEqual(draft["segments"][0]["visual"], "更衣室扯卡裆旧裤")
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def test_lines_and_caption_variant_fills_narration(self):
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"""另一种变体(Doubao-Seed-2.0-P):shots 用 lines:[{role,content}] 放口播、caption 放字幕。
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必须把 lines 的 content 当对白/旁白,caption 兜底,而不是只填画面留旁白空。"""
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raw = json.dumps({
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"ScriptDraft": {
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"basicInfo": {"totalDuration": 30, "aspectRatio": "9:16"},
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"shots": [
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{"shotNo": 1, "scene": "化妆台两闺蜜", "lines": [{"role": "女主", "content": "防晒泛白太尴尬！"}, {"role": "闺蜜", "content": "试试这个！"}], "caption": "防晒踩雷？"},
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{"shotNo": 2, "scene": "手背挤膏体", "lines": [{"role": "闺蜜", "content": "质地清透不泛白"}], "caption": "清透质地"},
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],
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}
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}, ensure_ascii=False)
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draft = normalize_draft(raw, aspect_ratio="9:16", total_duration=30)
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self.assertEqual(len(draft["segments"]), 2)
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for seg in draft["segments"]:
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self.assertTrue(seg["narration"], "旁白不应为空")
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self.assertTrue(seg["visual"], "画面不应为空")
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self.assertIn("防晒泛白太尴尬", draft["segments"][0]["narration"])
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self.assertEqual(len(draft["segments"][0]["dialogue"]), 2) # lines→结构化对白
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def test_generic_resolver_covers_unseen_field_names(self):
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"""模型每次换字段名(scene/screenDescription/画面…、dialogue/lines/caption…)。
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通用解析应「优先键 + 关键词模糊匹配」都能填,且跳过 bgMusic/note 等非内容键。"""
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variants = {
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"canonical": {"total_duration": 15, "segments": [{"role": "钩子", "narration": "口播A", "visual": "画面A"}]},
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"scene+dialogue_str+subtitle": {"total_duration": 15, "shots": [{"scene": "画面B", "dialogue": "口播B", "subtitle": "字幕B"}]},
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"scene+lines+caption": {"total_duration": 15, "shots": [{"scene": "画面C", "lines": [{"role": "女主", "content": "口播C"}], "caption": "字幕C"}]},
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"screenDescription+dialogue+bgMusic+note": {"total_duration": 15, "shots": [{"screenDescription": "画面D", "dialogue": "口播D", "bgMusic": "音乐D", "note": "备注D"}]},
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}
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for name, raw in variants.items():
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draft = normalize_draft(json.dumps(raw, ensure_ascii=False), aspect_ratio="9:16", total_duration=15)
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seg = draft["segments"][0]
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self.assertTrue(seg["narration"], f"{name}: 旁白为空")
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self.assertTrue(seg["visual"], f"{name}: 画面为空")
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# bgMusic/note 不得被误当画面/旁白
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d = normalize_draft(json.dumps(variants["screenDescription+dialogue+bgMusic+note"], ensure_ascii=False), aspect_ratio="9:16", total_duration=15)
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self.assertEqual(d["segments"][0]["visual"], "画面D")
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self.assertEqual(d["segments"][0]["narration"], "口播D")
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def test_string_dialogue_not_iterated_as_chars(self):
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"""dialogue 为整句字符串时,要当作旁白而不是逐字符遍历。"""
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raw = json.dumps({
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"segments": [{"role": "钩子", "dialogue": "一句完整口播", "visual": "画面"}],
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"total_duration": 15,
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}, ensure_ascii=False)
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draft = normalize_draft(raw, aspect_ratio="9:16", total_duration=15)
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self.assertEqual(draft["segments"][0]["narration"], "一句完整口播")
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self.assertEqual(draft["segments"][0]["dialogue"], []) # 字符串不进结构化对白
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def test_empty_segments_skeleton_yields_to_richer_scenes(self):
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"""真实回归(全空根因):模型把内容放 scenes/voiceover/visual,却另给一个**空 segments 骨架**。
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必须挑内容最丰富的数组(scenes),而不是见 segments 是 list 就用→落 4 个空镜。"""
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raw = json.dumps({
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"scenes": [
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{"sceneIndex": 1, "sceneTitle": "通勤", "voiceover": "手机一卡效率掉线", "visual": "地铁口拿出亮银色机身"},
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{"sceneIndex": 2, "sceneTitle": "办公", "voiceover": "A19多任务很跟手", "visual": "办公桌俯拍切换应用"},
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],
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"segments": [{"index": 0, "role": "钩子", "narration": "", "visual": ""}, {"index": 1, "role": "痛点", "narration": "", "visual": ""}],
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"total_duration": 30,
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}, ensure_ascii=False)
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draft = normalize_draft(raw, aspect_ratio="9:16", total_duration=30)
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self.assertEqual(draft["segments"][0]["narration"], "手机一卡效率掉线")
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self.assertEqual(draft["segments"][0]["visual"], "地铁口拿出亮银色机身")
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def test_script_key_with_visual_object_is_flattened(self):
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"""真实回归:数组键叫 script、visual 写成 {setting,camera,key_shots} 对象。
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必须认出 script 数组并把 visual 对象拍平成一句,而非留空。"""
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raw = json.dumps({
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"script": [
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{"scene_id": 1, "scene_title": "通勤", "voiceover": "选手机看重流畅好看",
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"visual": {"setting": "地铁口", "camera": "竖屏手持跟拍", "key_shots": ["拿出亮银色机身"]}},
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],
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"total_duration": 15,
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}, ensure_ascii=False)
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draft = normalize_draft(raw, aspect_ratio="9:16", total_duration=15)
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seg = draft["segments"][0]
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self.assertEqual(seg["narration"], "选手机看重流畅好看")
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self.assertIn("地铁口", seg["visual"])
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self.assertIn("竖屏手持跟拍", seg["visual"])
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def test_audio_field_fills_narration(self):
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"""真实回归:shots 用 audio 放口播(GPT 变体),旁白曾因 audio 不在词典而留空。"""
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raw = json.dumps({
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"shots": [{"scene": 1, "visual": "咖啡厅办公把玩机身", "audio": "这款亮银色是我的高光决定"}],
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"total_duration": 15,
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}, ensure_ascii=False)
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draft = normalize_draft(raw, aspect_ratio="9:16", total_duration=15)
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self.assertEqual(draft["segments"][0]["narration"], "这款亮银色是我的高光决定")
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self.assertEqual(draft["segments"][0]["visual"], "咖啡厅办公把玩机身")
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def test_canonical_flat_schema_still_works(self):
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"""契约内的扁平 schema(segments/narration/visual)不受兼容改动影响。"""
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raw = json.dumps({
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"total_duration": 30,
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"segments": [
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{"role": "钩子", "narration": "口播1", "visual": "画面1"},
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{"role": "CTA", "narration": "口播2", "visual": "画面2"},
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],
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}, ensure_ascii=False)
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draft = normalize_draft(raw, aspect_ratio="9:16", total_duration=30)
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self.assertEqual(len(draft["segments"]), 2)
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self.assertEqual(draft["segments"][0]["narration"], "口播1")
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self.assertEqual(draft["segments"][1]["visual"], "画面2")
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from io import BytesIO
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from unittest.mock import patch
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from django.test import TestCase
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from rest_framework.test import APIClient
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from apps.accounts.models import Team, TeamMember, User
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from apps.ai.models import AITask, ImageConversation, ModelConfig
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from apps.ai.providers.volcano import VolcanoArkProvider
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from apps.ai.services import enqueue_standalone_images
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from apps.assets.models import Asset, AssetFile
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from apps.billing.models import CreditAccount
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from apps.products.models import Product
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class StandaloneImageReferenceTests(TestCase):
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"""独立生图(平台套图 / 模特上身图)必须把商品真实主图(+ 模特图)作为参考图走 image_edit,
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而不是纯文生图——回归保护 #图片生成没参考商品主图# 这个 bug。
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(图像默认模型由迁移 seed 的 tokenssr:gpt-image-2 提供,get_image_provider 全程 mock。)"""
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def setUp(self):
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self.user = User.objects.create_user(username="owner", password="pass")
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self.team = Team.objects.create(name="T", owner=self.user)
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CreditAccount.objects.create(team=self.team, balance="100.0000")
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# 商品 + 主图(带可访问 preview_url)
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self.product = Product.objects.create(team=self.team, created_by=self.user, title="南卡 Lite Pro")
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cover = Asset.objects.create(
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team=self.team, created_by=self.user, name="主图", asset_type=Asset.Type.IMAGE,
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source=Asset.Source.UPLOAD, category=Asset.Category.PRODUCT_IMAGE,
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)
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AssetFile.objects.create(asset=cover, object_key="c.png", bucket="b", content_type="image/png", preview_url="http://x/cover.png", is_primary=True)
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self.product.cover_asset = cover
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self.product.save(update_fields=["cover_asset"])
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def _patch_provider(self):
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# image_edit 返回值 + 媒体落库链路全部 mock,聚焦验证「传了哪些参考图」
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provider = patch("apps.ai.services.get_image_provider").start()
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prov = provider.return_value
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prov.image_edit.return_value = {"data": [{"url": "http://x/out.png"}]}
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prov.image_generation.return_value = {"data": [{"url": "http://x/out.png"}]}
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prov.extract_first_media_url.return_value = "http://x/out.png"
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media = patch("apps.ai.services.VolcanoArkProvider.media_to_bytes").start()
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media.return_value = (BytesIO(b"img"), "image/png")
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store = patch("apps.ai.services.TosStorage").start()
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stored = store.return_value.upload_fileobj.return_value
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stored.object_key, stored.bucket, stored.content_type, stored.size_bytes = "o.png", "b", "image/png", 3
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self.addCleanup(patch.stopall)
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return prov
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def test_cover_mode_references_product_main_image(self):
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prov = self._patch_provider()
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enqueue_standalone_images(team=self.team, user=self.user, prompt="平台套图", mode="cover", count=1, product_id=str(self.product.id), ratio="4:5")
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prov.image_edit.assert_called_once()
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self.assertEqual(prov.image_edit.call_args.kwargs["images"], ["http://x/cover.png"])
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prov.image_generation.assert_not_called()
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def test_model_tryon_combines_product_and_model_images(self):
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prov = self._patch_provider()
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# 模特资产(PERSON)带 preview_url
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model_asset = Asset.objects.create(
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team=self.team, created_by=self.user, name="模特", asset_type=Asset.Type.IMAGE,
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source=Asset.Source.AI_GENERATED, category=Asset.Category.PERSON,
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)
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AssetFile.objects.create(asset=model_asset, object_key="m.png", bucket="b", content_type="image/png", preview_url="http://x/model.png", is_primary=True)
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enqueue_standalone_images(team=self.team, user=self.user, prompt="上身图", mode="model", count=1, product_id=str(self.product.id), model_id=str(model_asset.id), ratio="4:5")
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prov.image_edit.assert_called_once()
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self.assertEqual(prov.image_edit.call_args.kwargs["images"], ["http://x/cover.png", "http://x/model.png"])
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prov.image_generation.assert_not_called()
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def test_cover_mode_falls_back_to_t2i_without_main_image(self):
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prov = self._patch_provider()
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self.product.cover_asset = None
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self.product.save(update_fields=["cover_asset"])
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enqueue_standalone_images(team=self.team, user=self.user, prompt="平台套图", mode="cover", count=1, product_id=str(self.product.id), ratio="4:5")
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prov.image_generation.assert_called_once()
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prov.image_edit.assert_not_called()
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def test_image_mode_uses_uploaded_reference_images(self):
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"""图片创作自由模式:用户上传的参考图必须作为 image_edit 的参考图传进去,
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而不是被忽略走纯文生图——回归保护 #图片创作没参考我上传的素材# 这个最致命的 bug。"""
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prov = self._patch_provider()
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ref = Asset.objects.create(
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team=self.team, created_by=self.user, name="背心参考", asset_type=Asset.Type.IMAGE,
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source=Asset.Source.UPLOAD, category=Asset.Category.UPLOAD,
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)
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AssetFile.objects.create(asset=ref, object_key="r.png", bucket="b", content_type="image/png", preview_url="http://x/ref.png", is_primary=True)
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enqueue_standalone_images(
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team=self.team, user=self.user, prompt="按这件背心生成不同场景的穿搭",
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mode="image", count=1, ratio="1:1", reference_image_ids=[str(ref.id)],
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)
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prov.image_edit.assert_called_once()
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self.assertEqual(prov.image_edit.call_args.kwargs["images"], ["http://x/ref.png"])
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# 提示词必须显式钉住「保留参考图主体」+ 带上用户原话(否则图生图不真的还原上传素材)
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used_prompt = prov.image_edit.call_args.kwargs["prompt"]
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self.assertIn("按这件背心生成不同场景的穿搭", used_prompt)
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self.assertIn("参考图", used_prompt)
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self.assertIn("严格保留", used_prompt)
|
|
prov.image_generation.assert_not_called()
|
|
|
|
def test_refs_force_image_edit_model_when_volcano_selected(self):
|
|
"""用户选了火山 Seedream(只有图生图、不会真锁主体)却传了参考图时,必须自动切到支持
|
|
image_edit 的 gpt-image-2——否则参考图形同虚设。回归保护「选火山+传参考图=完全不参考」。"""
|
|
from apps.ai.models import ModelProvider
|
|
|
|
vp = ModelProvider.objects.create(name="volcengine", display_name="火山")
|
|
ModelConfig.objects.create(provider=vp, name="seedream-4", display_name="Seedream", capability=ModelConfig.Capability.IMAGE)
|
|
self._patch_provider()
|
|
ref = Asset.objects.create(
|
|
team=self.team, created_by=self.user, name="背心", asset_type=Asset.Type.IMAGE,
|
|
source=Asset.Source.UPLOAD, category=Asset.Category.UPLOAD,
|
|
)
|
|
AssetFile.objects.create(asset=ref, object_key="r.png", bucket="b", content_type="image/png", preview_url="http://x/ref.png", is_primary=True)
|
|
tasks = enqueue_standalone_images(
|
|
team=self.team, user=self.user, prompt="不同场景", mode="image", count=1,
|
|
image_model="volcano", reference_image_ids=[str(ref.id)],
|
|
)
|
|
# 任务最终落在支持 image_edit 的模型(gpt-image),而不是用户选的火山 Seedream
|
|
self.assertIn("gpt-image", tasks[0].model_config.name)
|
|
|
|
|
|
class ImageConversationTests(TestCase):
|
|
"""图片创作「对话」实体:CRUD + 团队隔离 + 软删 + 生图自动归属对话 + 任务回填。"""
|
|
|
|
def setUp(self):
|
|
self.user = User.objects.create_user(username="convowner", password="pass")
|
|
self.team = Team.objects.create(name="ConvT", owner=self.user)
|
|
TeamMember.objects.create(team=self.team, user=self.user, role="owner", status="active")
|
|
self.client = APIClient()
|
|
self.client.force_authenticate(self.user)
|
|
|
|
def test_crud_and_listing(self):
|
|
# 新建
|
|
r = self.client.post("/api/ai/image-conversations/", {"mode": "image", "title": "测试创作"}, format="json")
|
|
self.assertEqual(r.status_code, 201, r.content)
|
|
conv_id = r.json()["id"]
|
|
# 列出(只 image 模式)
|
|
r = self.client.get("/api/ai/image-conversations/?mode=image")
|
|
self.assertEqual(r.status_code, 200)
|
|
self.assertEqual(len(r.json()["results"]), 1)
|
|
# 重命名
|
|
r = self.client.patch(f"/api/ai/image-conversations/{conv_id}/", {"title": "改后"}, format="json")
|
|
self.assertEqual(r.status_code, 200)
|
|
self.assertEqual(r.json()["title"], "改后")
|
|
# 软删:列表消失,但 DB 记录仍在(is_deleted=True)
|
|
r = self.client.delete(f"/api/ai/image-conversations/{conv_id}/")
|
|
self.assertIn(r.status_code, (204, 200))
|
|
self.assertEqual(self.client.get("/api/ai/image-conversations/?mode=image").json()["results"], [])
|
|
self.assertTrue(ImageConversation.objects.get(id=conv_id).is_deleted)
|
|
|
|
def test_team_isolation(self):
|
|
other = User.objects.create_user(username="other", password="pass")
|
|
other_team = Team.objects.create(name="Other", owner=other)
|
|
ImageConversation.objects.create(team=other_team, created_by=other, title="别人的")
|
|
r = self.client.get("/api/ai/image-conversations/?mode=image")
|
|
self.assertEqual(r.json()["results"], [])
|
|
|
|
def test_generate_auto_creates_and_binds_conversation(self):
|
|
# 余额 + 默认图像模型(迁移已 seed),provider/worker 全 mock,聚焦验证「任务挂到对话」
|
|
CreditAccount.objects.create(team=self.team, balance="100.0000")
|
|
patch("apps.ai.tasks.generate_standalone_image_task.delay").start()
|
|
self.addCleanup(patch.stopall)
|
|
conv = ImageConversation.objects.create(team=self.team, created_by=self.user, title="目标对话")
|
|
tasks = enqueue_standalone_images(team=self.team, user=self.user, prompt="一只猫", mode="image", count=2, conversation=conv)
|
|
self.assertEqual(len(tasks), 2)
|
|
self.assertTrue(all(t.conversation_id == conv.id for t in tasks))
|
|
self.assertEqual(conv.tasks.count(), 2)
|
|
|
|
def test_tasks_endpoint_returns_grouped_history(self):
|
|
conv = ImageConversation.objects.create(team=self.team, created_by=self.user, title="历史")
|
|
mc = ModelConfig.objects.filter(capability=ModelConfig.Capability.IMAGE).first()
|
|
AITask.objects.create(
|
|
team=self.team, created_by=self.user, conversation=conv, task_type=AITask.Type.PRODUCT_IMAGE,
|
|
status=AITask.Status.SUCCEEDED, model_config=mc, idempotency_key="conv-test-1",
|
|
request_payload={"prompt": "猫", "batch_id": "bx", "ratio": "1:1"},
|
|
)
|
|
r = self.client.get(f"/api/ai/image-conversations/{conv.id}/tasks/")
|
|
self.assertEqual(r.status_code, 200, r.content)
|
|
data = r.json()["tasks"]
|
|
self.assertEqual(len(data), 1)
|
|
self.assertEqual(data[0]["prompt"], "猫")
|
|
self.assertEqual(data[0]["batch_id"], "bx")
|
|
|
|
def test_generate_with_refs_persists_and_tasks_endpoint_returns_them(self):
|
|
"""HTTP 全链路:带 reference_image_ids 提交 → 任务 payload 落 ids → tasks 接口把参考图解析回 {name,url}。
|
|
守护「上传的参考图被带进生成 + 切换/刷新后批次头仍能回显参考图」。"""
|
|
CreditAccount.objects.create(team=self.team, balance="100.0000")
|
|
patch("apps.ai.tasks.generate_standalone_image_task.delay").start()
|
|
self.addCleanup(patch.stopall)
|
|
ref = Asset.objects.create(
|
|
team=self.team, created_by=self.user, name="背心参考", asset_type=Asset.Type.IMAGE,
|
|
source=Asset.Source.UPLOAD, category=Asset.Category.UPLOAD,
|
|
)
|
|
AssetFile.objects.create(asset=ref, object_key="r.png", bucket="b", content_type="image/png", preview_url="http://x/ref.png", is_primary=True)
|
|
r = self.client.post(
|
|
"/api/ai/generate-image/",
|
|
{"prompt": "按这件背心生成不同场景", "mode": "image", "count": 1, "reference_image_ids": [str(ref.id)]},
|
|
format="json",
|
|
)
|
|
self.assertEqual(r.status_code, 202, r.content)
|
|
conv_id = r.json()["conversation_id"]
|
|
# 任务 payload 落了参考图 id
|
|
task = AITask.objects.filter(conversation_id=conv_id).first()
|
|
self.assertEqual(task.request_payload.get("reference_image_ids"), [str(ref.id)])
|
|
# tasks 接口把参考图解析成 {name,url} 回显
|
|
data = self.client.get(f"/api/ai/image-conversations/{conv_id}/tasks/").json()["tasks"]
|
|
self.assertEqual(data[0]["reference_images"], [{"name": "背心参考", "url": "http://x/ref.png"}])
|
|
|
|
|
|
class StandaloneCategoryTests(TestCase):
|
|
"""图片趴三类归类(期2):模特上身图→model_tryon / 平台套图→platform_kit / 自由创作→free_create;
|
|
生成演员(model 无 product)仍→person(视频角色)。直接跑 worker 函数避开异步 .delay。"""
|
|
|
|
def setUp(self):
|
|
self.user = User.objects.create_user(username="catowner", password="pass")
|
|
self.team = Team.objects.create(name="CT", owner=self.user)
|
|
CreditAccount.objects.create(team=self.team, balance="100.0000")
|
|
self.product = Product.objects.create(team=self.team, created_by=self.user, title="测试商品")
|
|
cover = Asset.objects.create(
|
|
team=self.team, created_by=self.user, name="主图", asset_type=Asset.Type.IMAGE,
|
|
source=Asset.Source.UPLOAD, category=Asset.Category.PRODUCT_IMAGE,
|
|
)
|
|
AssetFile.objects.create(asset=cover, object_key="c.png", bucket="b", content_type="image/png", preview_url="http://x/cover.png", is_primary=True)
|
|
self.product.cover_asset = cover
|
|
self.product.save(update_fields=["cover_asset"])
|
|
|
|
def _patch_provider(self):
|
|
provider = patch("apps.ai.services.get_image_provider").start()
|
|
prov = provider.return_value
|
|
prov.image_edit.return_value = {"data": [{"url": "http://x/out.png"}]}
|
|
prov.image_generation.return_value = {"data": [{"url": "http://x/out.png"}]}
|
|
prov.extract_first_media_url.return_value = "http://x/out.png"
|
|
media = patch("apps.ai.services.VolcanoArkProvider.media_to_bytes").start()
|
|
media.return_value = (BytesIO(b"img"), "image/png")
|
|
store = patch("apps.ai.services.TosStorage").start()
|
|
stored = store.return_value.upload_fileobj.return_value
|
|
stored.object_key, stored.bucket, stored.content_type, stored.size_bytes = "o.png", "b", "image/png", 3
|
|
self.addCleanup(patch.stopall)
|
|
return prov
|
|
|
|
def _run(self, mode, *, product_id=None, model_id=None):
|
|
from apps.ai.services import run_standalone_image_task
|
|
|
|
self._patch_provider()
|
|
tasks = enqueue_standalone_images(
|
|
team=self.team, user=self.user, prompt="x", mode=mode, count=1,
|
|
product_id=product_id, model_id=model_id, model_entity_id="ent-1", ratio="4:5",
|
|
)
|
|
run_standalone_image_task(task_id=str(tasks[0].id))
|
|
return Asset.objects.filter(origin_task=tasks[0]).first()
|
|
|
|
def test_model_tryon_category(self):
|
|
a = self._run("model", product_id=str(self.product.id))
|
|
self.assertEqual(a.category, Asset.Category.MODEL_TRYON)
|
|
self.assertEqual(a.metadata.get("mode"), "model")
|
|
self.assertTrue(a.metadata.get("batch_id")) # 成组
|
|
self.assertEqual(a.metadata.get("model_entity_id"), "ent-1") # 溯源模特库
|
|
|
|
def test_platform_kit_category(self):
|
|
a = self._run("cover", product_id=str(self.product.id))
|
|
self.assertEqual(a.category, Asset.Category.PLATFORM_KIT)
|
|
|
|
def test_free_create_category(self):
|
|
a = self._run("image")
|
|
self.assertEqual(a.category, Asset.Category.FREE_CREATE)
|
|
|
|
def test_generate_actor_stays_person(self):
|
|
# 生成演员:mode=model 但无 product → 视频角色,仍 person(送审范围内)
|
|
a = self._run("model")
|
|
self.assertEqual(a.category, Asset.Category.PERSON)
|
|
|
|
|
|
class TriviewAutoEnrollTests(TestCase):
|
|
"""B 路(期3):视频流程新生成的角色立绘 + 三视图成套 → 自动入模特库;幂等不重复建。"""
|
|
|
|
def setUp(self):
|
|
from apps.projects.models import Project
|
|
|
|
self.user = User.objects.create_user(username="trio", password="p")
|
|
self.team = Team.objects.create(name="TR", owner=self.user)
|
|
CreditAccount.objects.create(team=self.team, balance="100.0000")
|
|
self.product = Product.objects.create(team=self.team, created_by=self.user, title="P")
|
|
self.project = Project.objects.create(team=self.team, name="项目甲", product=self.product, created_by=self.user)
|
|
self.portrait = Asset.objects.create(
|
|
team=self.team, created_by=self.user, name="项目甲-person", asset_type=Asset.Type.IMAGE,
|
|
source=Asset.Source.AI_GENERATED, category=Asset.Category.PERSON,
|
|
)
|
|
AssetFile.objects.create(asset=self.portrait, object_key="p.png", bucket="b", content_type="image/png", preview_url="http://x/p.png", is_primary=True)
|
|
|
|
def _patch_provider(self):
|
|
provider = patch("apps.ai.services.get_image_provider").start()
|
|
prov = provider.return_value
|
|
prov.image_edit.return_value = {"data": [{"url": "http://x/tri.png"}]}
|
|
prov.extract_first_media_url.return_value = "http://x/tri.png"
|
|
media = patch("apps.ai.services.VolcanoArkProvider.media_to_bytes").start()
|
|
media.return_value = (BytesIO(b"img"), "image/png")
|
|
store = patch("apps.ai.services.TosStorage").start()
|
|
stored = store.return_value.upload_fileobj.return_value
|
|
stored.object_key, stored.bucket, stored.content_type, stored.size_bytes = "o.png", "b", "image/png", 3
|
|
self.addCleanup(patch.stopall)
|
|
|
|
def _gen_triview(self):
|
|
from apps.ai.services import generate_person_triview, run_triview_task
|
|
|
|
task = generate_person_triview(project=self.project, user=self.user, portrait_asset=self.portrait)
|
|
run_triview_task(task_id=str(task.id))
|
|
|
|
def test_triview_auto_enrolls_model(self):
|
|
from apps.assets.models import Model
|
|
|
|
self._patch_provider()
|
|
self._gen_triview()
|
|
m = Model.objects.filter(portrait_asset=self.portrait).first()
|
|
self.assertIsNotNone(m)
|
|
self.assertIsNotNone(m.triview_asset) # 成套
|
|
self.assertTrue(m.metadata.get("auto_enrolled"))
|
|
self.assertEqual(m.source, Model.Source.AI)
|
|
# 合规(期3):三视图归 tri_view → 落在送审范围内
|
|
self.assertEqual(m.triview_asset.category, Asset.Category.TRI_VIEW)
|
|
self.assertIn(m.triview_asset.category, Asset.REVIEW_CATEGORIES)
|
|
|
|
def test_auto_enroll_is_idempotent(self):
|
|
from apps.assets.models import Model
|
|
|
|
self._patch_provider()
|
|
self._gen_triview()
|
|
self._gen_triview() # 同一立绘再出一版三视图
|
|
self.assertEqual(Model.objects.filter(portrait_asset=self.portrait).count(), 1)
|
|
|
|
|
|
class _FakeStreamResp:
|
|
"""模拟 requests 流式响应:支持 with、raise_for_status、可写 encoding、iter_lines。"""
|
|
status_code = 200
|
|
encoding = None
|
|
|
|
def __init__(self, lines):
|
|
self._lines = lines
|
|
|
|
def __enter__(self):
|
|
return self
|
|
|
|
def __exit__(self, *exc):
|
|
return False
|
|
|
|
def raise_for_status(self):
|
|
return None
|
|
|
|
def iter_lines(self, decode_unicode=True): # noqa: ARG002
|
|
yield from self._lines
|
|
|
|
|
|
class ChatStreamReasoningTests(SimpleTestCase):
|
|
"""推理模型(豆包 seed-pro 等)思考期只发 reasoning_content、不发 content。
|
|
provider 必须把它作为独立 `reasoning` 事件转发——否则脚本 agent 思考期零输出 =
|
|
前端「卡在生成分镜」假死。本测试锁住该转发,防回归。"""
|
|
|
|
def test_reasoning_content_forwarded_as_reasoning_event(self):
|
|
def _chunk(delta):
|
|
return "data: " + json.dumps({"choices": [{"delta": delta}]}, ensure_ascii=False)
|
|
|
|
lines = [
|
|
_chunk({"reasoning_content": "先想想"}),
|
|
_chunk({"reasoning_content": "用户要4镜"}),
|
|
_chunk({"content": "正在生成"}),
|
|
_chunk({"content": "脚本…"}),
|
|
"data: [DONE]",
|
|
]
|
|
prov = VolcanoArkProvider(api_key="k", base_url="http://x")
|
|
with patch("apps.ai.providers.volcano.requests.post", return_value=_FakeStreamResp(lines)):
|
|
events = list(prov.chat_completion_stream(model="m", messages=[{"role": "user", "content": "hi"}]))
|
|
|
|
self.assertEqual([e["type"] for e in events], ["reasoning", "reasoning", "delta", "delta", "done"])
|
|
self.assertEqual([e["text"] for e in events if e["type"] == "reasoning"], ["先想想", "用户要4镜"])
|
|
self.assertEqual("".join(e["text"] for e in events if e["type"] == "delta"), "正在生成脚本…")
|