- 🆕 Meta's image model for generation, editing, and composition [2]
- 🔧 Offered here as an image edit (inpainting) endpoint
- 🧠 Described as a reasoning model that plans layout first [2]
- 🎯 Anchored composition aims to keep edits consistent [2]
- 👁️ Accepts a source image plus a text instruction
- 💬 Supports iterative refinement of an existing image
- 🏢 Available through Meta's Model API [2]
- 📏 No published parameter count, license, or context details
Meta Platforms is an American multinational technology company headquartered in Menlo Park, California. Best known for operating Facebook, Instagram, WhatsApp, and Threads, Meta has become one of the most influential players in open-weight AI research through its Llama family of…
Explore 6 more models by Meta Platforms →Muse Image is Meta's image generation and editing model, listed in this catalog in its edit form: you supply a source image together with a text instruction, and the model returns a modified version. In catalog terms this is an inpaint-type endpoint, aimed at altering an existing picture rather than creating one from scratch.
What Meta emphasises in its own developer materials is that the model treats image creation as a reasoning task rather than a single-shot render. Meta's developer blog describes Muse Image as a reasoning model that plans layout before drawing, and highlights anchored composition intended to keep results consistent as an image is refined. The same source presents the model as covering generation, editing, and composition within one system. [2]
For editing specifically, that planning-then-drawing approach is the stated basis for changes that stay coherent with the rest of the frame across successive requests. Practical use follows the usual image-to-image pattern: provide the base image, describe the change, then iterate on the output.
Distribution is through Meta's Model API. [2] Public technical details such as parameter count, training data, and licensing are not disclosed in the sources available here, so this entry lists none; no benchmark figures are cited because no primary or independent-evaluator results were available.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · Muse Image · Organic sellers
| Seller | Rep↓ | Sales | Images | Routing | $ / img | Save |
|---|---|---|---|---|---|---|
| Surplus-backed seller 0x0e49…8927 | 0.00 | $0.04 | 1 | gated | $0.038 | — |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.