Merge remote-tracking branch 'origin/diffusion-auto-badges' into fold-integration

# Conflicts:
#	studio/backend/models/inference.py
#	studio/frontend/src/features/images/images-page.tsx

studio/backend/core/inference/diffusion_inference_info.py

@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""Pure, torch-free per-family footprint summary for the inference info endpoint.
+
+The Advanced Dtype selector offers int8/fp8/nvfp4/mxfp8 dense transformer-quant
+schemes. This module turns the auto-policy's per-family bf16 component table plus
+its per-scheme steady factors into a static "what would this cost resident" summary
+the frontend can show BEFORE anything is loaded, so a user can size the tradeoff.
+
+Estimates here are the STEADY resident footprint (transformer * factor + companions),
+not the transient build peak -- the same quantity ``estimate_dense_quant`` reports as
+``steady_total`` -- so the numbers match what stays on the card during generation.
+
+Pure by design: only imports the auto-policy tables (themselves torch-free), does no
+GPU probing, so it unit-tests on CPU-only hosts.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from .diffusion_auto_policy import _FAMILY_BF16_GB, _QUANT_STEADY_FACTOR
+
+
+def _round1(value: float) -> float:
+    return round(value, 1)
+
+
+def family_inference_infos() -> list[dict[str, Any]]:
+    """Per-family bf16 component sizes + estimated resident footprint per quant scheme.
+
+    One dict per family in the auto-policy bf16 table (registry order), each carrying the
+    bf16-resident component sizes and the estimated resident GB under bf16 and each dense
+    quant scheme. bf16's estimate is the un-quantised transformer + companions; each
+    scheme scales the transformer by its steady factor and adds the same companions.
+    """
+    infos: list[dict[str, Any]] = []
+    for name, (transformer_gb, text_encoders_gb, vae_gb) in _FAMILY_BF16_GB.items():
+        companions_gb = text_encoders_gb + vae_gb
+        estimated = {"bf16": _round1(transformer_gb + companions_gb)}
+        for scheme, factor in _QUANT_STEADY_FACTOR.items():
+            estimated[scheme] = _round1(transformer_gb * factor + companions_gb)
+        infos.append(
+            {
+                "family": name,
+                "transformer_bf16_gb": _round1(transformer_gb),
+                "text_encoders_bf16_gb": _round1(text_encoders_gb),
+                "vae_bf16_gb": _round1(vae_gb),
+                "estimated_resident_gb": estimated,
+            }
+        )
+    return infos