Seedream 4.5 is a refinement of ByteDance Seed’s combined image-generation and editing system. The producer scales both the model and its training data, with particular attention to reference consistency, fine visual details, dense text and identifying the intended subject across multiple images. Its useful distinction from Seedream 4.0 is more precise execution of a visual brief, especially when the result must remain recognisably tied to the supplied person, product or design.
Consistency with the reference image The manufacturer emphasises preserving facial features, lighting, colour tone and other source details while making the requested change. Demonstrations include retaining one marked person while removing others, translating text into Chinese with the rest of the design retained, changing a highlighted word’s appearance, and replacing a garment’s material while keeping its pose and flowing shape. These are different kinds of editing: subject selection, typography, material appearance and illumination.
For an edit, separate what changes from what must remain. “Change the jacket material to translucent glass; retain the pose, silhouette and folds” gives a more useful target than a broad restyling request. A material transformation can also require a lighting change: the producer’s example distinguishes metallic reflection from transparent refraction. Compare the output with the source in the regions you intended to preserve, including the face, pose, small accessories and surrounding colour treatment.
Poster layout, logos and dense text Seedream 4.5’s model page places graphic design next, focusing on composition and readable small text. Examples combine a dominant title, artwork and secondary information in an exhibition poster; others explore invitations, fragrance-detail visuals and advertising compositions. The model is useful for testing an entire visual hierarchy rather than treating text as an afterthought. Describe where the main image belongs, give the exact heading and supporting copy, and distinguish font style, alignment, emphasis and spacing.
For a typographic revision, identify the text region and the new wording or treatment. A request to make one heading blue and italic is more specific than asking for a new poster style. Review all rendered text at its intended display size, including small labels and secondary lines. A generated poster or logo concept is visual artwork; the documented image workflows do not establish an editable vector or layered-design output.
Accurate editing across multiple images The producer highlights selecting target elements from different inputs and combining them coherently. The manufacturer’s 4.5 release describes multi-image compositions using up to 10 reference images. Official examples use one image for a person, another for a setting or clothing material, and another for a product or accessory. This supports lifestyle product visuals, coordinated advertisements, interior-style concepts and character or product variations. The model can also generate related outputs: demonstrated tasks include a small advertisement set and separate illustrations for steps in a recipe, with a shared visual style.
Prepare references with explicit roles. For example, assign one image to the subject, one to the bag and one to the location, then specify the composition and which features to retain. If two references disagree about colour or style, name the one that should control that feature. Inspect the resulting scale, placement, lighting and identity across the whole set rather than accepting a convincing single image as evidence that all outputs match.
What the 4.5 refinement adds The technical report singles out stronger editing consistency, preservation of identity-related features and fine details, target-subject handling in multi-image compositions, and typography in dense poster layouts. The producer’s release also describes clearer small faces, improved spatial proportions, realistic lighting and 4K image output. These strengths make 4.5 particularly useful for product catalogues, campaign variations, packaging concepts, pre-production art and graphic design with substantial text.
Text briefs guide new visuals; source images and edit instructions guide reference-based changes. Use the model for creative image production, then review the result against the actual brief: exact copy, recognisable product or person, preserved details, coherent scale and consistent treatment across variants. The edition’s improved preservation is not a guarantee that every source detail survives every complex edit. Reference-count and output-count limits also depend on the documented interface; the 4.0 reference budget should not be assumed for every 4.5 workflow.
", "aboutText": "Seedream 4.5 is a refinement of ByteDance Seed’s combined image-generation and editing system. The producer scales both the model and its training data, with particular attention to reference consistency, fine visual details, dense text and identifying the intended subject across multiple images. Its useful distinction from Seedream 4.0 is more precise execution of a visual brief, especially when the result must remain recognisably tied to the supplied person, product or design.\n\nConsistency with the reference image\nThe manufacturer emphasises preserving facial features, lighting, colour tone and other source details while making the requested change. Demonstrations include retaining one marked person while removing others, translating text into Chinese with the rest of the design retained, changing a highlighted word’s appearance, and replacing a garment’s material while keeping its pose and flowing shape. These are different kinds of editing: subject selection, typography, material appearance and illumination.\n\nFor an edit, separate what changes from what must remain. “Change the jacket material to translucent glass; retain the pose, silhouette and folds” gives a more useful target than a broad restyling request. A material transformation can also require a lighting change: the producer’s example distinguishes metallic reflection from transparent refraction. Compare the output with the source in the regions you intended to preserve, including the face, pose, small accessories and surrounding colour treatment.\n\nPoster layout, logos and dense text\nSeedream 4.5’s model page places graphic design next, focusing on composition and readable small text. Examples combine a dominant title, artwork and secondary information in an exhibition poster; others explore invitations, fragrance-detail visuals and advertising compositions. The model is useful for testing an entire visual hierarchy rather than treating text as an afterthought. Describe where the main image belongs, give the exact heading and supporting copy, and distinguish font style, alignment, emphasis and spacing.\n\nFor a typographic revision, identify the text region and the new wording or treatment. A request to make one heading blue and italic is more specific than asking for a new poster style. Review all rendered text at its intended display size, including small labels and secondary lines. A generated poster or logo concept is visual artwork; the documented image workflows do not establish an editable vector or layered-design output.\n\nAccurate editing across multiple images\nThe producer highlights selecting target elements from different inputs and combining them coherently. The manufacturer’s 4.5 release describes multi-image compositions using up to 10 reference images. Official examples use one image for a person, another for a setting or clothing material, and another for a product or accessory. This supports lifestyle product visuals, coordinated advertisements, interior-style concepts and character or product variations. The model can also generate related outputs: demonstrated tasks include a small advertisement set and separate illustrations for steps in a recipe, with a shared visual style.\n\nPrepare references with explicit roles. For example, assign one image to the subject, one to the bag and one to the location, then specify the composition and which features to retain. If two references disagree about colour or style, name the one that should control that feature. Inspect the resulting scale, placement, lighting and identity across the whole set rather than accepting a convincing single image as evidence that all outputs match.\n\nWhat the 4.5 refinement adds\nThe technical report singles out stronger editing consistency, preservation of identity-related features and fine details, target-subject handling in multi-image compositions, and typography in dense poster layouts. The producer’s release also describes clearer small faces, improved spatial proportions, realistic lighting and 4K image output. These strengths make 4.5 particularly useful for product catalogues, campaign variations, packaging concepts, pre-production art and graphic design with substantial text.\n\nText briefs guide new visuals; source images and edit instructions guide reference-based changes. Use the model for creative image production, then review the result against the actual brief: exact copy, recognisable product or person, preserved details, coherent scale and consistent treatment across variants. The edition’s improved preservation is not a guarantee that every source detail survives every complex edit. Reference-count and output-count limits also depend on the documented interface; the 4.0 reference budget should not be assumed for every 4.5 workflow.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "2ea5f4a73003f88fa4b115fb33430daa0c6b4e7f185e87a3acd71a87507c90f7", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "bytedance-seed/seedream-4.5Seedream 4.5 refines ByteDance Seed’s image generation and editing with stronger reference-detail preservation, small-text typography and multi-image composition. It suits product visuals, posters and coordinated creative sets where identity and layout matter.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Seedream 4.5 is a refinement of ByteDance Seed’s combined image-generation and editing system. The producer scales both the model and its training data, with particular attention to reference consistency, fine visual details, dense text and identifying the intended subject across multiple images. Its useful distinction from Seedream 4.0 is more precise execution of a visual brief, especially when the result must remain recognisably tied to the supplied person, product or design.
Consistency with the reference image The manufacturer emphasises preserving facial features, lighting, colour tone and other source details while making the requested change. Demonstrations include retaining one marked person while removing others, translating text into Chinese with the rest of the design retained, changing a highlighted word’s appearance, and replacing a garment’s material while keeping its pose and flowing shape. These are different kinds of editing: subject selection, typography, material appearance and illumination.
For an edit, separate what changes from what must remain. “Change the jacket material to translucent glass; retain the pose, silhouette and folds” gives a more useful target than a broad restyling request. A material transformation can also require a lighting change: the producer’s example distinguishes metallic reflection from transparent refraction. Compare the output with the source in the regions you intended to preserve, including the face, pose, small accessories and surrounding colour treatment.
Poster layout, logos and dense text Seedream 4.5’s model page places graphic design next, focusing on composition and readable small text. Examples combine a dominant title, artwork and secondary information in an exhibition poster; others explore invitations, fragrance-detail visuals and advertising compositions. The model is useful for testing an entire visual hierarchy rather than treating text as an afterthought. Describe where the main image belongs, give the exact heading and supporting copy, and distinguish font style, alignment, emphasis and spacing.
For a typographic revision, identify the text region and the new wording or treatment. A request to make one heading blue and italic is more specific than asking for a new poster style. Review all rendered text at its intended display size, including small labels and secondary lines. A generated poster or logo concept is visual artwork; the documented image workflows do not establish an editable vector or layered-design output.
Accurate editing across multiple images The producer highlights selecting target elements from different inputs and combining them coherently. The manufacturer’s 4.5 release describes multi-image compositions using up to 10 reference images. Official examples use one image for a person, another for a setting or clothing material, and another for a product or accessory. This supports lifestyle product visuals, coordinated advertisements, interior-style concepts and character or product variations. The model can also generate related outputs: demonstrated tasks include a small advertisement set and separate illustrations for steps in a recipe, with a shared visual style.
Prepare references with explicit roles. For example, assign one image to the subject, one to the bag and one to the location, then specify the composition and which features to retain. If two references disagree about colour or style, name the one that should control that feature. Inspect the resulting scale, placement, lighting and identity across the whole set rather than accepting a convincing single image as evidence that all outputs match.
What the 4.5 refinement adds The technical report singles out stronger editing consistency, preservation of identity-related features and fine details, target-subject handling in multi-image compositions, and typography in dense poster layouts. The producer’s release also describes clearer small faces, improved spatial proportions, realistic lighting and 4K image output. These strengths make 4.5 particularly useful for product catalogues, campaign variations, packaging concepts, pre-production art and graphic design with substantial text.
Text briefs guide new visuals; source images and edit instructions guide reference-based changes. Use the model for creative image production, then review the result against the actual brief: exact copy, recognisable product or person, preserved details, coherent scale and consistent treatment across variants. The edition’s improved preservation is not a guarantee that every source detail survives every complex edit. Reference-count and output-count limits also depend on the documented interface; the 4.0 reference budget should not be assumed for every 4.5 workflow.
POST /v1/images/generationstext, imageimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits bytedance-seed/seedream-4.5 | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Minimum length: 1 | Default— |
Fieldresolution | Typestring | RequirementOptional | Values & limits 1K, 2K, 4K | Default— |
Fieldaspect_ratio | Typestring | RequirementOptional | Values & limits 1:1, 1:2, 2:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 9:19.5, 19.5:9, 9:20, 20:9, 9:21, 21:9, auto | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Maximum: 10 | Default— |
Fieldinput_references | Typearray | RequirementOptional | Values & limits Minimum items: 0 Maximum items: 14 | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, prompt |
Parametermaterial_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}
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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Forecast quantity | Billing unit | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Forecast quantity— | Billing unit— | 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 |
| Configurationinput image | Rate schedule— | Forecast quantity— | Billing unitper input image | Charge Input · input image · Estimate for comparison only $0 per input image |
| Configurationoutput image | Rate schedule— | Forecast quantity— | Billing unitper output image | Charge Output · output image · Estimate for comparison only $0.08 per output image |
| ConfigurationEstimated generation cost | Rate schedule— | Forecast quantityone generation estimate | Billing unit— | Charge Output · Estimated generation cost · Estimate for comparison only $0.08 estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np={'model': 'bytedance-seed/seedream-4.5', 'prompt': 'a red panda astronaut floating in space, studio lighting'}\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/images/generations \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport base64, json, os, subprocess\nfrom pathlib import Path\nfrom urllib.parse import urlsplit, urljoin\n\ndef save_images(items):\n if not items: raise SystemExit("No completed images; retain the full response")\n for index, item in enumerate(items):\n encoded=item.get("b64_json")\n uri=item.get("url", "")\n if not encoded and uri.startswith("data:image/"):\n header, separator, encoded=uri.partition(",")\n if not separator or ";base64" not in header: raise SystemExit("Unsupported image data URI")\n if encoded:\n raw=base64.b64decode(encoded,validate=True)\n elif uri:\n uri=urljoin("https://api.ailoot.ai",uri)\n parsed=urlsplit(uri)\n if parsed.scheme!="https" or not parsed.netloc or parsed.username or parsed.password:\n raise SystemExit("Invalid returned image URL")\n owned=parsed.netloc=="api.ailoot.ai" and parsed.path.startswith("/v1/media/assets/") and parsed.path.endswith("/content")\n args=["curl","--fail-with-body","--silent","--show-error",uri]\n if owned: args += ["-H","Authorization: Bearer "+os.environ["AILOOT_API_KEY"]]\n temporary=Path(f"image-{index}.bin")\n subprocess.run(args+["-o",str(temporary)],check=True)\n raw=temporary.read_bytes()\n temporary.unlink()\n else: raise SystemExit("Image has neither bytes nor URL")\n if not raw: raise SystemExit("Empty image result")\n 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"<svg",b"<?xml")) else "bin"\n Path(f"image-{index}.{ext}").write_bytes(raw)\n print("Every returned image saved; complete response metadata retained")\nsave_images(json.loads(Path("result.json").read_text())["data"])\nPY_RESULT\nDownload each returned image or save its Base64 contents
GET /v1/media/tasks/{task_id}GET /v1/media/assets/{asset_id}/contentGET /v1/media/assetsGET /v1/media/assets/{asset_id}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o owned-assets.json\n# Preserve every returned asset and follow its exact signed content URL. Do not construct a signed token.\nSave every completed image; retrieve each returned URL or decode its Base64 contents.
Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.
Keep the returned X-Client-Request-ID for reconciliation. Read only actual returned task_id/asset_id values or the owned media library; never build a task URL from the operation ID and never resubmit creation.
| Field | Type |
|---|---|
Fieldcreated | Type |
Fielddata[].url | Type |
Fielddata[].b64_json | Type |
Fieldusage | Type |
Ailoot Ultra bytedance-seed/seedream-4.5
/v1/images