Source: https://ailoot.ai/models/en/google-gemini-nano-banana-2-1/ Canonical: https://ailoot.ai/models/en/google-gemini-nano-banana-2-1/ Language: en # Google: Nano Banana 2.1 [Markdown](https://ailoot.ai/models/en/google-gemini-nano-banana-2-1/index.md) · [JSON](https://ailoot.ai/models/en/google-gemini-nano-banana-2-1/facts.json) ## [Google: Nano Banana 2.1](https://ailoot.ai/models/en/google-gemini-nano-banana-2-1/) Gemini Nano Banana 2.1 generates and edits images conversationally from text and visual material. It improves text rendering, infographic layout and multi-turn consistency, with 1K–4K output and up to 14 reference images for a defined visual brief. - [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `google/gemini-nano-banana-2.1`](https://ailoot.ai/models/en/google-gemini-nano-banana-2-1/#method-0) - [**Ailoot Ultra · Images** `POST /v1/images/generations` `google/gemini-nano-banana-2.1`](https://ailoot.ai/models/en/google-gemini-nano-banana-2-1/#method-1) - [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `google/gemini-nano-banana-2.1`](https://ailoot.ai/models/en/google-gemini-nano-banana-2-1/#method-2) About the model This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below. Gemini Nano Banana 2.1 is Google’s image-generation and conversational-editing model. It is an update to Nano Banana 2, with improvements in visual realism, adherence to instructions, text rendering and character consistency across successive edits. Its input table includes text, images, video and PDF, while the output is image and text. This makes it useful for a product scene, an infographic or a visual series that develops through several specific revisions. The model offers 1K, 2K and 4K output, with 1K as the default. It does not inherit Nano Banana 2’s 0.5K option. The documented aspect ratios include 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9, plus the tall or panoramic formats 1:4, 4:1, 1:8 and 8:1. Google identifies fixes to tiling artifacts in the latter wide and tall formats at 2K and 4K. Resolution labels are size tiers, not one fixed rectangle: a square 4K image is 4096 × 4096, while the documented 16:9 4K output is 5504 × 3072. Text is a substantial part of the visual workflow. The model is designed to render legible styled wording and organise infographic elements more accurately. Prepare the exact headline, labels and reading order, then request the image with that approved text. For a menu, diagram or instructional graphic, name the relationship between a caption and its visual element. Google recommends creating the wording first and then asking for the image, so the visual composition starts from a settled text brief. Reference-image fusion supports up to 14 images. The documented profile covers consistency for up to four characters and fidelity for up to ten objects. Assign each reference a role: the product that must stay recognisable, the character appearance, the intended background or the visual style. An illustrative product brief is: “Use image A for the bottle shape and label. Use image B for the surface and lighting. Keep the product unchanged, place it beside a folded towel and leave clear space above it for the supplied headline.” Google Web Search and Image Search grounding can provide current information and visual context for generation. Image Search supplies retrieved web images as references; standard web grounding supplies information about the subject. This is useful for a factual graphic or a scene whose appearance depends on a real object. The image-search workflow does not support retrieving real-world images of people for this purpose. Grounded responses carry source and attribution metadata in Google’s documented workflow, so an application should preserve it with the generated result. Thinking is configurable at minimal, medium and high levels, with medium as the default for this exact model. It helps the model work through composition and more complex instructions before final output. The image guide describes interim composition images within that process; they are distinct from the intended final deliverable. Choose the required final layout, aspect ratio and resolution explicitly rather than expecting the prompt’s description alone to fix every output setting. Conversational editing lets a follow-up change one part of an existing result: adjust the light, move an object or translate the labels while preserving the rest of the composition. Carry the earlier image or interaction into the next turn and state both the requested change and the features to retain. For a character series, keep appearance references consistent and use a pose reference when a new angle or complex pose matters. A practical revision brief can say: “Preserve the product, labels and layout; make only the background a warmer grey.” The model table gives an input limit of 131,072 tokens and an output limit of 32,768 tokens. Image generation and search grounding are supported, while audio generation, code execution, function calling, structured outputs, caching, file search, URL context and Live API are not listed as supported for this edition. Video can inform a generated still image; it does not make this model a video generator. Google also notes that a request for an exact number of images may not produce that count. All generated images include a SynthID watermark. For a useful first brief, give the image’s purpose, subject, composition, lighting, camera view, required text and output shape. For example, request a portrait infographic with three labelled steps, a readable heading and a specified colour hierarchy, then review the wording and relative placement before the next edit. The result can become a product illustration, editorial graphic or design reference, with the chosen image and its explanatory text retained together. Practical task ### Refine an approved poster background Technical example in English ``` Refine an approved poster background Edit the supplied event poster. Preserve every word, the headline hierarchy, illustration placement and portrait composition. Change only the background from cool white to warm ivory, keeping dark text readable and retaining the original accent color. Do not add shapes, badges, textures or extra captions. Return the revised poster image. ``` #### Expected result ``` One edited poster with the original wording and layout preserved over a warm ivory background. ``` #### Check the result ``` Compare every word and element position against the supplied poster, then inspect text contrast. ``` **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `google/gemini-nano-banana-2.1` [`google/gemini-nano-banana-2.1`](https://ailoot.ai/models/en/#m=google%2Fgemini-nano-banana-2.1&route=Ultra&caller=google%2Fgemini-nano-banana-2.1&fmt=chat) Google /`google/gemini-nano-banana-2.1` ### Overview Gemini Nano Banana 2.1 generates and edits images conversationally from text and visual material. It improves text rendering, infographic layout and multi-turn consistency, with 1K–4K output and up to 14 reference images for a defined visual brief. Accepted inputs Text Results Text ### Available in Ailoot · inputs & methods **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` Input types `text` Output types `text` Output format json or SSE | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits google/gemini-nano-banana-2.1 | Default— | | Field`messages` | Type`message[]` | RequirementRequired | Values & limits Minimum items: 1 | Default— | | Field`messages[].role` | Type`string` | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— | | Field`messages[].content` | Type`string\|content_part[]\|null` | RequirementOptional | Values & limits | Default— | | Field`messages[].tool_calls` | Type`tool_call[]` | RequirementOptional | Values & limits | Default— | | Field`messages[].tool_call_id` | Type`string` | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— | | Field`messages[].name` | Type`string` | RequirementOptional | Values & limits | Default— | | Field`messages[].reasoning_content` | Type`string` | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— | | Field`messages[].reasoning_details` | Type`object\|array` | RequirementOptional | Values & limits Model-specific | Default— | | Field`max_tokens` | Type`integer` | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— | | Field`max_completion_tokens` | Type`integer` | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse | | Field`stream_options.include_usage` | Type`boolean` | RequirementOptional | Values & limits Model-specific | Default— | | Field`temperature` | Type`number` | RequirementOptional | Values & limits Model-specific | Default— | | Field`top_p` | Type`number` | RequirementOptional | Values & limits Model-specific | Default— | | Field`stop` | Type`string\|string[]` | RequirementOptional | Values & limits Model-specific | Default— | | Field`tools` | Type`function_tool[]` | RequirementOptional | Values & limits Model-specific | Default— | | Field`tools[].type` | Type`string` | RequirementOptional | Values & limits function Model-specific | Default— | | Field`tools[].function.name` | Type`string` | RequirementRequired | Values & limits Applies when: function tool supplied | Default— | | Field`tools[].function.description` | Type`string` | RequirementOptional | Values & limits | Default— | | Field`tools[].function.parameters` | Type`JSON_schema` | RequirementOptional | Values & limits | Default— | | Field`tool_choice` | Type`string\|object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`parallel_tool_calls` | Type`boolean` | RequirementOptional | Values & limits Model-specific | Default— | | Field`response_format` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`reasoning_effort` | Type`string` | RequirementOptional | Values & limits Model-specific | Default— | | Field`reasoning` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`service_tier` | Type`string` | RequirementOptional | Values & limits Model-specific | Default— | | Field`instructions` | Type`string` | RequirementOptional | Values & limits Compatibility field | Default— | | Field`functions` | Type`function[]` | RequirementOptional | Values & limits Model-specific | Default— | | Field`function_call` | Type`string\|object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`prompt_cache_options` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`modalities` | Type`string[]` | RequirementOptional | Values & limits text | Default— | | Field`provider` | Type`object` | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— | | Field`cache_control.ttl` | Type`string` | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— | Input material combinations **Text** · UTF8 text `messages[].content` Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages | | Parameter`material_types` | Contracttext | | Parameter`messages` | Contractmessage array with role/content shape | | Parameter`file_material` | Contractactual UTF-8 document inserted as literal text; no file ID inferred | | Parameter`optional_controls` | Contractstream, temperature, top\_p, stop, max\_tokens, max\_completion\_tokens, reasoning, response\_format, tools, tool\_choice | | Parameter`control_scope` | Contractmodel-specific; user function tools differ from hosted tools | | Parameter`output_modalities` | Contracttext | | Parameter`hosted_operations` | Contractfalse | | Parameter`multimodal_input_binding` | Contractnull | Lifecycle: `synchronous_or_streamed` Send exact model ID with messages. Context length is not an output cap. A text file may supply literal text; image/audio/file-object inputs need exact binding. ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Standard rate When fast mode is selected Flex mode Standard rate When flex mode is selected Maximum reasoning effort Standard rate When maximum reasoning effort uses the default adjustment Reasoning effort · high Standard rate When reasoning effort is high Reasoning effort · minimal Standard rate When reasoning effort is minimal Explicit cache pricing Not enabled When explicit cache is requested #### Ailoot rates #### Prices by configuration Match each charge to the conditions shown. Use one rate for each set of alternative configurations. | Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge | | --- | --- | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens **$0.003** / 1K tokens Output · Output tokens **$0.015** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens Output · Image output tokens **$0.06** / 1K image tokens | | ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens **$0.003** / 1K tokens Output · Output tokens **$0.015** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | #### Reference tariff details Model-level reference schedules do not change method selection, quotes or charges. Active charges use the selected Ailoot method rates. Google: Nano Banana 2.1 #### Prices by configuration | Configuration | Billing conditions | Charge | | --- | --- | --- | | ConfigurationGoogle: Nano Banana 2.1 · Default configuration | Billing conditionsDefault configuration | Charge Input · Input tokens **$0.003** / 1K tokens Output · Output tokens **$0.015** / 1K tokens Output · Output image **$0.06** / 1K tokens Output · Web search **$0.028** per search | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · OpenAI Chat curlPython SDKPython stream ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model": "google/gemini-nano-banana-2.1", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}') Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path r=json.loads(Path("result.json").read_text()) for x in r.get("choices",[]): if x.get("message",{}).get("content"): print(x["message"]["content"]) PY_RESULT ``` ```python from openai import OpenAI import os if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) result=client.chat.completions.create(model='google/gemini-nano-banana-2.1',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) save_result(result.to_dict(mode="json")) ``` ```python from openai import OpenAI import os from pathlib import Path if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) with client.chat.completions.stream(model='google/gemini-nano-banana-2.1',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream: with Path("stream-events.jsonl").open("w",encoding="utf-8") as log: for event in stream: payload=event.to_dict(mode="json") log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush() print(json.dumps(payload,ensure_ascii=False)) final=stream.get_final_completion() save_result(final.to_dict(mode="json"),"stream-result") ``` ### Receive and save results #### Ailoot Ultra · OpenAI Chat json or SSE ##### Keep all completed results Keep every complete returned response, file and content part. Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage. ##### If the original response needs checking Keep request/response IDs and headers. No generic text job retrieval URL is supplied. Response fields | Field | Type | | --- | --- | | Field`choices[].index` | Typeinteger | | Field`choices[].message.content` | Typestring\|null | | Field`choices[].message.tool_calls` | Typetool\_call\[\] | | Field`choices[].message.reasoning_content` | Typestring | | Field`choices[].message.reasoning_details` | Typeobject\|array | | Field`choices[].finish_reason` | Typestring\|null | | Field`usage` | Typeobject | Errors and next steps | Response | Condition | Next step | | --- | --- | --- | | Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. | ### Aliases #### Model aliases **Ailoot Ultra** [`google/gemini-nano-banana-2.1`](https://ailoot.ai/models/en/#m=google%2Fgemini-nano-banana-2.1&route=Ultra&caller=google%2Fgemini-nano-banana-2.1&fmt=chat) #### Method path aliases `/v1/images` #### Related caller IDs [`google/gemini-nano-banana-2.1`](https://ailoot.ai/models/en/#m=google%2Fgemini-nano-banana-2.1&route=Ultra&caller=google%2Fgemini-nano-banana-2.1&fmt=chat) **Ailoot Ultra · Images** `POST /v1/images/generations` `google/gemini-nano-banana-2.1` [`google/gemini-nano-banana-2.1`](https://ailoot.ai/models/en/#m=google%2Fgemini-nano-banana-2.1&route=Ultra&caller=google%2Fgemini-nano-banana-2.1&fmt=images) Google /`google/gemini-nano-banana-2.1` ### Overview Gemini Nano Banana 2.1 generates and edits images conversationally from text and visual material. It improves text rendering, infographic layout and multi-turn consistency, with 1K–4K output and up to 14 reference images for a defined visual brief. Accepted inputs Text · Images Results Images ### Available in Ailoot · inputs & methods **Ailoot Ultra · Images** `POST /v1/images/generations` Input types `text`, `image` Output types `image` | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits google/gemini-nano-banana-2.1 | Default— | | Field`prompt` | Type`string` | RequirementRequired | Values & limits Minimum length: 1 | Default— | | Field`resolution` | Type`string` | RequirementOptional | Values & limits 1K, 2K, 4K | Default— | | Field`aspect_ratio` | Type`string` | RequirementOptional | Values & limits 1:1, 1:4, 1:8, 2:3, 3:2, 3:4, 4:1, 4:3, 4:5, 5:4, 8:1, 9:16, 16:9, 21:9 | Default— | | Field`n` | Type`integer` | RequirementOptional | Values & limits Minimum: 1 Maximum: 1 | Default— | | Field`input_references` | Type`array` | RequirementOptional | Values & limits Minimum items: 0 Maximum items: 14 | Default— | | Field`provider` | Type`object` | RequirementOptional | Values & limits | Default— | Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, prompt | | Parameter`material_types` | Contracttext, image | Lifecycle: `synchronous_result_then_owned_library` `GET /v1/media/tasks/{task_id}` · actual returned task\_id or media library record `GET /v1/media/assets/{asset_id}/content` `GET /v1/media/assets` `GET /v1/media/assets/{asset_id}` ### Pricing ##### How the final charge is calculated The displayed rates estimate the cost before generation. The final charge is calculated from the cost recorded for the original generation. Measured output duration and forecast quantities are not the final billable quantity. Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Estimated rates for comparison #### Prices by configuration Match each charge to the conditions shown. Use one rate for each set of alternative configurations. | Configuration | Rate schedule | Forecast quantity | Charge | | --- | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Forecast quantity— | Charge Input · Input tokens · Estimate for comparison only **—** / 1K tokens Output · Output tokens · Estimate for comparison only **—** / 1K tokens Input · Cache read · Estimate for comparison only **—** / 1K tokens Input · Cache write · Estimate for comparison only **—** / 1K tokens Input · Cache write, 1 hour · Estimate for comparison only **—** / 1K tokens | | ConfigurationEstimated generation cost | Rate schedule— | Forecast quantityone generation estimate | Charge Output · Estimated generation cost · Estimate for comparison only **$0.24576** estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Images curl ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" export AILOOT_API_KEY python3 - <<'PY_REQUEST' import json,os from pathlib import Path p={'model': 'google/gemini-nano-banana-2.1', 'prompt': 'a red panda astronaut floating in space, studio lighting'} Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/images/generations \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import base64, json, os, subprocess from pathlib import Path from urllib.parse import urlsplit, urljoin def save_images(items): if not items: raise SystemExit("No completed images; retain the full response") for index, item in enumerate(items): encoded=item.get("b64_json") uri=item.get("url", "") if not encoded and uri.startswith("data:image/"): header, separator, encoded=uri.partition(",") if not separator or ";base64" not in header: raise SystemExit("Unsupported image data URI") if encoded: raw=base64.b64decode(encoded,validate=True) elif uri: uri=urljoin("https://api.ailoot.ai",uri) parsed=urlsplit(uri) if parsed.scheme!="https" or not parsed.netloc or parsed.username or parsed.password: raise SystemExit("Invalid returned image URL") owned=parsed.netloc=="api.ailoot.ai" and parsed.path.startswith("/v1/media/assets/") and parsed.path.endswith("/content") args=["curl","--fail-with-body","--silent","--show-error",uri] if owned: args += ["-H","Authorization: Bearer "+os.environ["AILOOT_API_KEY"]] temporary=Path(f"image-{index}.bin") subprocess.run(args+["-o",str(temporary)],check=True) raw=temporary.read_bytes() temporary.unlink() else: raise SystemExit("Image has neither bytes nor URL") if not raw: raise SystemExit("Empty image result") ext="png" if raw.startswith(b"\x89PNG") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else "svg" if raw.lstrip().startswith((b"