Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

[0.29.0] - 2026-08-15

Generation previews with tiny autoencoders.

Added

• Step-wise generation previews with tiny autoencoders. --stepwise-image-output-dir renders previews with the published tiny decoder for the model's latent space: madebyollin/taef1 for the FLUX.1 latent space (flux, Z-Image) and madebyollin/taef2 for the FLUX.2 latent space (FLUX.2 Klein, ERNIE-Image, Bonsai). Measured on M5 Max at 512x512 with FLUX.2 Klein 4B q8, a frame decodes in 19 ms versus 203 ms through the full VAE, so previewing every step costs about 8% extra wall time instead of about 86% — which makes continuous live preview practical for interactive applications. Other families preview through the full VAE.
• --preview-decoder selects auto (default: tiny decoder when one is published for the family and downloaded, full VAE otherwise), tiny (required, errors when unavailable), or full. Final outputs are always decoded with the full VAE, so a run with previews enabled produces a byte-identical image to the same run without them.
• ImageUtil.to_pil_image(decoded_latents) converts decoded latents to a PIL.Image without generation metadata, for applications rendering live preview frames. docs/python-integration.md documents the PreviewDecoder callback path, and docs/previews.md covers supported families, reproducible tiny-versus-full commands, measured fidelity, and decode costs.

Notes

• Tiny decoders are mapped explicitly per latent space rather than by latent-channel count. Families that share a VAE share a decoder; families without a published decoder are unaffected and continue to preview through their own VAE.
• Preview decoding is approximate by design: fidelity against the full VAE decode of the same latent measures 34.8 dB PSNR (SSIM 0.975) at the final step on Z-Image Turbo, with differences concentrated in fine texture. Previews are not suitable for final-quality decisions.

[0.28.0] - 2026-08-14

Wan default shot length, SeedVR2 image-restoration geometry fidelity, and documentation-site release.

Fixed

• SeedVR2 image restoration returns the exact requested geometry. Previously the shortest edge was silently floored to a multiple of 16 before scaling, so --resolution 1x resampled the whole image (for example 1451x1600 came back as 1440x1586, displacing every feature toward the top-left), and internal padding filled with black, bleeding a dark edge into the restored content near the padded border. 1x is now a pixel-exact identity on geometry (output size equals input size, zero shift at every corner), integer shorter-edge targets keep the exact aspect ratio, and internal padding is reflective and cropped away after decoding. Video restoration keeps the official center-crop-to-16 behavior.

Added

• Automatic step selection for SeedVR2 image restoration, with a --steps override. On flat or dark content such as astrophotography or night scenes, the official one-step estimate retains a measurable residue of the sampling noise that decodes as a regular 8-pixel mesh texture and washes out the faintest structure, while on detail-rich content extra steps instead synthesize texture that is not in the source. The default is now adaptive: MLX-Gen runs the official single step, measures the mesh signature of the decoded output against the source, and re-runs at 4 steps only when the artifact is present (validated across astrophoto, portrait, super-resolution, and landscape content: only the astrophoto triggers refinement). The decision is recorded in metadata (steps_mode, one_step_residue_pct), and --steps 1-4 forces a fixed count. See "Steps for Flat or Dark Content" in docs/upscaling.md.
• Documentation site on GitHub Pages: every published release now builds and deploys the documentation with MkDocs (.github/workflows/docs.yml), staging the doc set with validation media linked on GitHub instead of shipped in the site.

Changed

• Wan generation defaults to 81 frames across the family (previously TI2V-5B and the shared Python-API default used 121). The A14B models train at 81 frames, and longer single shots drift toward a ping-pong ending that returns to the first frame; 81 keeps default shots inside every model's stable range. TI2V-5B's native 121-frame shots stay available by passing --frames 121 (or num_frames=121) explicitly. docs/wan-video.md and the FAQ document the recommendation and the continuation routes for longer clips.

[0.27.0] - 2026-08-13

Bernini-R 1.3B BF16 package and Qwen image-edit geometry-stability release.

Added

• Bernini-R 1.3B BF16 package (bernini-r-1.3b-bf16): new catalog entry for AbstractFramework/bernini-r-1.3B-diffusers-bf16, a runtime-dtype repack of the pinned ByteDance source (BF16 text encoder and transformer with their FP32 keep-sets preserved, FP32 VAE). The download is ~15.6 GiB instead of ~27 GiB, revision-pinned with byte-count preflights, and produces bit-identical output to the FP32 source package (verified on image and video use cases at identical settings and seed). Aliases: bernini-r-1.3b-bf16, bernini-bf16.

Fixed

• Image-edit geometry drift across iterative edit chains (Qwen edit and FLUX.2 Klein edit): the edited source image is now conditioned at the resolved generation canvas in both families, so the reference and target position grids always match and no source pixels are lost per pass. Qwen edit previously conditioned at the area-normalized ~1MP size regardless of the requested output size; any mismatch pulled every edit toward a zoom-crop of its own input (measured up to 18% horizontal warp in a single 614x512 edit). FLUX.2 Klein edit previously conditioned at source-derived floor-16 dimensions with a center crop, losing a sliver of the source every pass (+0.5% horizontal drift per iteration in edit chains). Iterative edits on both families are now registration-stable (scale 1.000, 0px translation across three-edit chains). Automatic-canvas single edits are unchanged (Qwen bit-identical), inpaint paths keep their existing contracts, and additional reference images keep per-image sizing. The Qwen change is a deliberate geometry-stability deviation from the upstream pipeline, which keeps the mismatch at explicit sizes.

Changed

• docs/bernini.md recommends the BF16 package for download and documents both packages' sizes and runtime precision; README.md and llms-full.txt updated to match.
• docs/image-edit-modes.md documents the iterative-edit geometry-stability contract.

[0.26.0] - 2026-08-13

Bernini-R 1.3B renderer and Wan conditioning release. Ships the experimental Bernini reference-to-video / video-edit renderer (0105), Wan A14B multi-frame context head conditioning (0102), and Wan A14B SVI 2.0 Pro chain conditioning (0103). Capabilities schema_version advances 5 -> 7 for the new Wan fields.

Added

• Bernini-R 1.3B reference video renderer (0105): the unified mlxgen generate command and Python runtime support reference-to-video with 1-8 ordinary --reference-image inputs, prompt-guided video-to-video, and reference-guided video editing with --video plus references. The dedicated Wan-family runtime matches Bernini's packed source-aware RoPE, target-only extraction, source ordering, independent VAE conditioning, official UniPC schedule, task-specific chained APG/CFG semantics, and the official upstream task taxonomy (t2i, i2i, t2v, r2v, rv2v, v2v, mv2v, ads2v) used by the public-case parity harness. Setup uses a bounded, revision-pinned factored download from the Wan2.1 base components and official 1.3B renderer; --all-files, prepare, runtime q4/q8, and LoRA are rejected because only the BF16 source route is numerically credible. The route ships as EXPERIMENTAL and is not yet promoted: the historical 2026-08-04 schema-v3 bundle still fails at its bounded 17-frame profile, but seven official public 1.3B rows plus ads2v at mid profile are qualitatively accepted with committed proof (full prompts and contact sheets in docs/assets/validation/bernini-r-1.3b-2026-08-11/). Oracle-dispositioned rows r2v_case2 and v2v_case3 fail at their official recipes in both implementations; documented tuned recovery recipes are bundled separately. See Bernini-R 1.3B for commands, capacity limits, the official example catalog, and the parity matrix.

• Wan A14B i2v SVI 2.0 Pro conditioning (0103): --svi-anchor-image, --svi-motion-latent, --svi-motion-latent-count, --svi-lora-high, --svi-lora-low (Python: generate_video(svi_anchor_image_path=..., svi_motion_latent_path=..., svi_motion_latent_count=..., svi_motion_latent_export_path=...) plus the svi_lora_high_path/ svi_lora_low_path constructor pair) bring Stable Video Infinity 2.0 Pro (ICLR'26 Oral, vita-epfl svi_wan22 branch) chain conditioning to the Wan 2.2 A14B image-to-video route: every clip conditions on [anchor_latent, motion_latent?, zero-latents] — one PERSISTENT identity anchor re-injected into every clip, plus the previous clip's final denoised latent (handed over losslessly through an exported *.svi_latent.safetensors sidecar, never a pixel round-trip), padded with true zero latents (NOT the stock VAE-encoded zero frames — the two conventions are mutually unintelligible). The SVI error-recycling LoRA pair loads at scale 1.0 under a strict key-match contract (unmatched_key_count == 0 per file, verified 800/800 on the official vita-video-gen/svi-model v2.0 Pro pack) and is re-fused on per-item high-noise expert reloads; PEFT adapter-infix keys (lora_A.default.weight) now normalize for single-adapter files. SVI mode and the pack gate each other loudly in BOTH directions (running either without the other produces garbage per the upstream warning), and the mode conflicts explicitly with --image-path, --last-image, --context-frames, and --video-path; TI2V-5B and VACE reject before weight load. Continuation clips >65 frames print a trained-length advisory. Metadata records the full SVI truth (svi_anchor_image_path, svi_motion_latent_path/_count, svi_motion_latent_export, svi_assembly_trim_frames = 1 + 4x count for continuation clips, and the per-expert LoRA key-match reports), replays through --config-from-metadata, and is advertised as the additive supports_svi field on the wan.first-frame capability row (capabilities schema_version 6 -> 7). Use a unique seed per clip (author guidance: identical seeds accumulate artifacts).

• Wan A14B i2v multi-frame context head conditioning (0102): --context-frames <png>... (Python: generate_video(context_image_paths=[...])) extends the conditioned head of a Wan A14B image-to-video clip with the ordered frames that FOLLOW --image-path in the motion being continued — the SkyReels-V2/SVI-class multi-frame handover that lets a storyboard continuation inherit the predecessor's real momentum instead of restarting from one frozen frame. The head [image, *context] must fill whole 4x VAE latent groups (4/8/12 context frames = heads 5/9/13; the misuse of passing all K frames to --context-frames fails loudly on that count check), needs frames >= head + 4, composes with --last-image, and maps every frame through the same canvas and --resize-mode as the first frame. Fails loudly on every route without the A14B 36-channel i2v conditioning layout: text/video-to-video, TI2V-5B (expand_timesteps), and Wan VACE — the CLI rejects before the multi-minute weight load. --context-noise <0-1000> (Python: context_noise=...) optionally perturbs the conditioned head in latent space (SkyReels addnoise_condition precedent, ~20), deterministic per seed and outside the condition cache. Recorded in metadata (context_image_paths, context_noise), replayed by --config-from-metadata, recorded in failure manifests, and advertised as the additive supports_context_frames field on the wan.first-frame capability row (capabilities schema_version 5 -> 6). Ships EXPERIMENTAL with a measured zero-shot probe (backlog 0102): on a Lightning 4-step continuation pair the K=5 head carried the source clip's motion speed across the seam (magnitude ratio 0.90 vs the single-frame baseline's 1.90 double-speed restart; direction cosine 0.999) at the cost of a mild ~2-frame flare/exposure step at the conditioned-to-free boundary (luma delta ~3.2/255 against the source clip's own 1.15 max; structurally clean in stills). K=1 behavior is bitwise unchanged.

[0.25.0] - 2026-07-25

Load-path performance and Wan conditioning release. The performance wave (backlog 0093/0094/0095) makes page-cold weight loads fault in at sequential SSD speed, caps the previously unbounded MLX buffer cache with a machine-derived default, and makes the flux2 prompt and compiled-predict caches real. The Wan wave (backlog 0097/0098/0099) adds EXPERIMENTAL A14B first+last bracket conditioning (--last-image; the capabilities contract bumps schema_version 4 -> 5 with the additive supports_last_image field), an honest prompt-truncation warning with token-count metadata, and explicit denoising step grids for on-grid Lightning sampling.

Release validation: same-seed TI2V-5B output is bitwise identical to 0.24.0 under identical dependency versions (raw-YUV frame comparison; evidence in backlog 0101).

Added

• Wan A14B i2v first+last bracket conditioning (0097): --last-image <path> (Python: generate_video(last_image_path=...)) pins the END of a Wan A14B image-to-video clip to a second anchor image, porting the diffusers WanImageToVideoPipeline last_image variant exactly (condition video [first, zeros x (frames-2), last], mask endpoints kept at 1). The last image maps through the same canvas and --resize-mode as the first frame. Recorded in metadata (last_image_path), replayed by --config-from-metadata, and advertised as an additive supports_last_image capability field on the wan.first-frame row (capabilities schema_version 5). Fails loudly on every route without the A14B 36-channel i2v conditioning layout: text/video-to-video, TI2V-5B (expand_timesteps), and Wan VACE. Official first+last training exists for Wan 2.1 (FLF2V); on Wan 2.2 A14B this ships as EXPERIMENTAL with a measured probe: on the preserved storyboard case (Lightning 4-step, 480x240x33f, target = a real future frame of the same shot), the bracketed clip ended at final-frame MAE 4.6/255 from the target (NCC 0.995, sharpness preserved at 711 vs target 713) against the first-frame-only baseline's MAE 56.1 (NCC 0.335, drift-to-whiteout sharpness 55), with no mid-clip exposure jumps (max brightness step 5.9 vs baseline 17.5). One probe pair; see backlog item 0097 for bounds before relying on exact end-frame adherence.
• Wan prompt-truncation warning (0098): Wan prompt encoding silently right-truncated prompts beyond --max-sequence-length (512) UMT5 tokens; the trailing text simply never conditioned the video. Encoding now measures the real token counts, prints one stderr warning per truncated prompt naming the counts ("prompt truncated: 547 -> 512 UMT5 tokens"), and records prompt_tokens / prompt_truncated (plus negative_prompt_tokens / negative_prompt_truncated when CFG actually encodes the negative) in the metadata sidecar so hosts can surface headroom.

• Wan explicit denoising step grids (0099): --denoising-step-list 1000 750 500 250 (Python: generate_video(denoising_step_list=[...])) runs the exact timestep grid the lightx2v distill contract specifies (Wan22StepDistillScheduler denoising_step_list) instead of a count-derived schedule, on both the unipc and euler solvers. The transformer sees exactly the requested timesteps; sigma follows the flow-matching identity t / 1000 (with the count path's same leading 1e-6 guard on the UniPC solver). Grid entries are final, already-shifted timesteps, so the flag is mutually exclusive with --steps and --flow-shift (clear errors, no silent reconciliation) and rejected for video-to-video (strength truncation would drop grid points) and on Wan VACE. Metadata records the grid (steps becomes the grid length, flow_shift is recorded as null) and --config-from-metadata replays it. This enables on-grid 4/8-step Lightning variants and honest Lightning-vs-base comparisons.

• Sequential weight prefetch at load (0093): weight files selected for a load from HF-repo layouts are sequentially read into the OS page cache before first use, so page-cold weights fault in at sequential SSD speed instead of random-access speed (measured mechanism: an 8 GB page-cold component served random-order access at ~157 MB/s effective; after a 0.9 s sequential prefetch at ~8.8 GB/s the same access pattern ran ~12x faster, bytes identical). Prepared MLX-Gen packages are deliberately NOT prefetched: they are written in module-tree order, materialize near-sequentially on their own, and the prefetch measurably regressed their cold loads (+1.7-2.1 s end-to-end on the Klein 9B q8 package, reproduced twice against page-cold verified 0.000 residency). Already-resident files are detected via mincore and skipped (~0.03 s/GB probe cost on warm reloads, zero bytes re-read), and the prefetch is skipped when the selected files exceed half of physical RAM (low-RAM protection) or when MFLUX_NO_WEIGHT_PREFETCH=1 is set.

• Default MLX buffer-cache limit (0094): when no cache limit is set, model load now applies a machine-derived default — total RAM / 8, clamped to [1 GiB, 8 GiB] (the ceiling matches the 8 GiB cap validated for Wan A14B transient buffers in the embedding-host precedent; the floor matches the long-standing low-RAM default) — exactly once per process, announced with one stderr line. Previously a bare Python-API session ran uncapped (measured 32.9 GB MLX free cache after two Klein 9B q8 generations, evicting page cache system-wide). --mlx-cache-limit-gb and low-RAM behavior are unchanged and win over the default; MFLUX_MLX_CACHE_LIMIT_GB serves Python-API hosts; -1 (flag or env) is the explicit unlimited opt-out. Hosts that cap the cache directly through mx.set_cache_limit before loading are detected (a pre-existing limit at or below half of physical RAM is treated as deliberate, preserved, and announced) — the default never overrides a host-managed limit.
• FLUX.2 prompt cache and compiled-predict reuse (0095): the flux2 family now actually reads its prompt_cache (it was write-only) — identical prompts on a resident instance skip the ~1 s Qwen3 re-encode across txt2img, edit, inpaint, and outpaint, with a bounded 8-entry LRU (~100 MB worst case at Klein 9B embeds). The per-call mx.compile of the denoise predict is now cached per instance and reused across same-structure calls, and invalidated on transformer replacement, LoRA application, training preview, assistant LoRA scale toggling (training preview context exit), LoRA bake at save_model, and low-RAM transformer release (compiled callables must not keep freed weights alive or replay stale baked constants).

[0.24.0] - 2026-07-23

Embedded-host performance and generation-geometry release. The first wave (backlog 0086/0087) removes the dominant per-video fixed costs — the per-prompt UMT5 text-encoder load and the post-save health re-decode. The second wave (backlog 0088/0089/0090) trims the import graph, changes Wan decode memory behavior, and adds the geometry contract (--resize-mode, Wan --canvas-policy) plus opt-in step-loop performance options.

Release validation: same-seed TI2V-5B output is bitwise identical to 0.23.1 under identical dependency versions (raw-YUV frame comparison; evidence in backlog 0091), disk-cache hits reproduce their miss runs exactly, and the local Wan parity suite passes 6/6. Same-seed outputs can shift when the host's torch/transformers versions change, because UMT5 text-encoder kernels differ across those versions; that is upstream behavior, not an mlx-gen change.

Added

• Wan UMT5 prompt-embed disk cache (0086): identical (encoder snapshot, tokenized prompt, sequence length, precision) encodes are now served from a small exact safetensors cache under the user cache dir (LRU-bounded, 64 entries) instead of reloading the ~11 GB torch text encoder. A cache hit does not import torch at all (tokenization moved to numpy tensors). Corrupt entries are dropped loudly and re-encoded. Opt out with --no-prompt-cache (CLI) or prompt_embed_disk_cache=False (constructor).
• Wan resident text encoder (0086): --keep-text-encoder / keep_text_encoder_resident=True keeps the UMT5 encoder alive between generations in one process, for hosts that chain new-prompt scene generations. Default remains load-and-release.
• load_generation_model(..., model_kwargs={...}): the Python runtime wrapper now forwards host-provided constructor extras to the resolved model class, so embedding apps can reach model-specific controls (such as the two options above) through the public wrapper.
• --no-validate-health (0087) on Wan video generation and SeedVR2 video restore: skips the post-save full-file health re-decode for embedded hosts that probe the saved file themselves. Default stays ON. The skip is recorded as health_check: "skipped" in the video metadata and the save runtime event.
• Save-event metadata completeness (0087): the Wan --json-events save event now carries fps, width, height, and total_frames of the saved output, so hosts can build artifact metadata with zero probe decodes.

Import diet, Wan streamed decode, opt-in Wan compile, video color tags

Second wave of the same audit cycle, covering backlog 0088/0089/0090 plus one cross-repo color bug the audit measured.

Changed

• Import-graph diet (0088): import mflux no longer pulls huggingface_hub (with httpx+rich, ~0.4-0.6 s), PIL (~0.2 s of eager plugin registration via a module-scope PIL.Image.init() that is now deleted - PIL self-registers on first open/save, test-verified), or numpy. Measured on M5 Max: warm import mflux 237-274 ms -> 57-60 ms wall (cold-ish shell measurements were 1.34-1.62 s before); the remaining cost is mlx.core (~28 ms). The pure output-path logic moved from ImageUtil into cli/output_paths.py (public ImageUtil.resolve_output_path delegates, surface unchanged), huggingface_hub imports became function-local at all six module-scope sites, and tests/test_import_hygiene.py now gates dependency creep in CI (torch/transformers/tokenizers/matplotlib/httpx/huggingface_hub/cv2/av/rich/PIL must stay off the import). Note: the 0088 document's "module-scope mx.compile ~1.7 s" claim was re-measured as wrong (~0.2 ms; compilation happens on first call) and corrected in place.
• matplotlib is now the concept extra (0088): it was a hard install dependency used only for concept-attention heatmaps. Heatmap rendering without it now fails loudly with pip install mlx-gen[concept]; the dev extra still includes it. uv.lock regenerated.
• Wan streamed VAE decode is the default (0089 e3): Wan2_2_TI2V now always decodes through the per-slice streaming path behind a frame-batches factory; the full-tensor decode branch is gone. Frames are BITWISE identical (test-pinned against the non-streamed decoder, which WanVace still uses), and the default 121f@1280x704 bf16 run no longer materializes a ~650 MB decoded tensor - python_runtime.generate_outputs results now retain only latents (~3.5 MB) per item until save. release_denoisers_before_decode semantics are unchanged; per-slice cache flushes follow --low-ram. All runs now carry the wan_decode_mode: streamed_vae_slices / generation_time_scope: pre-save metadata extras (generation_time is recorded before the decode, which runs at save), and a non-finite decode surfaces at frame materialization through the per-frame finite checks.
• Video writer color metadata (cross-repo bug): the ffmpeg writer emitted untagged yuv420p that ffmpeg had coded with the BT.601 matrix at every resolution (probed: pure-red luma 81 at 64p/720p/1080p), so players assuming BT.709 for >=720p (AVFoundation/Quick Look) visibly shifted colors on HD clips while in-app chains stayed consistent. The writer now pins the conversion it already performs (scale=out_color_matrix=bt601) and tags the stream truthfully (colorspace=smpte170m, primaries/transfer bt709 for the sRGB-origin frames). Pixel data verified bitwise identical at 64x64 and 1280x720; seed stability preserved. Switching HD to a real BT.709 encode is recorded as a non-bitwise follow-up in backlog 0089.
• Wan A14B per-item transformer release + reload (0089 e4): the release default now lives in the model. generate_video(release_inactive_denoiser=None) auto-releases the ~14 GB high-noise expert after its per-item denoise phase whenever the model is dual-expert AND loaded from a disk-prequantized package, and lazily REBUILDS it at the next item's first high-noise timestep (per-component weight reload + deterministic LoRA re-fusion, pinned bitwise by tiny-weights tests). This closes two gaps at once: the Python API (generate_outputs) previously set no release at all - embedding hosts kept both experts resident even single-seed - and multi-seed CLI batches pinned ~28 GB for the whole run. Runtime quantization over bf16 (and bf16-on-disk) is gated OUT of the auto default because each reload would re-quantize or re-read 14B parameters; explicit intent via --release-inactive-denoiser / --no-release-inactive-denoiser (or the kwarg) always wins. Compiled runs (--compile-transformer) re-trace the reloaded expert instead of reusing the popped callable. Metadata records released_inactive_denoiser: true and high_noise_reloads: N when the behavior actually fired. release_denoisers_before_decode stays terminal (the low expert is never reloaded). Real-checkpoint validation (multi-seed peak-RSS, Lightning-LoRA storyboard re-fusion identity) is queued in backlog 0089. Correction: SINGLE-seed CLI runs keep the pre-0089 rule and release the inactive expert regardless of checkpoint quantization — the process exits after one item, so no reload is ever paid and bf16 checkpoints lose nothing (the first cut had silently dropped that release for bf16/runtime-quantized single-seed runs).

Added

• --compile-transformer for Wan (0090 d12, opt-in): runs each Wan denoiser as a compiled MLX graph (compile_transformer=True on the Python API), with an honest expected gain of ~2-6% per step. Output is NOT bit-identical to eager (compiled kernel fusion differs by ~5e-4), so it will never become a silent default. Runs that need per-block introspection or mid-graph cache flushes (--low-ram, --tensor-health-check-interval, MFLUX_WAN_BLOCK_HEALTH) stay eager and print one notice naming the blocker. Compiled runs record compile_transformer: true in metadata, and the compiled callable for the A14B high-noise expert is dropped before release_inactive_denoiser frees it so weight memory is actually released. Compiled-vs-eager parity (both timestep conventions), eligibility notice, and CLI wiring are test-pinned.
• --resize-mode (resize | crop | pad), orthogonal to --canvas-policy: the canvas policy picks the output canvas; the resize mode picks how source pixels map onto it. resize stretches to fill (the unchanged default), crop center-crops without distortion, and the new pad mode letterboxes the full source onto the canvas (aspect-preserving, black fill by default and configurable via ImageUtil.scale_to_dimensions(fill_color=...); the Wan VACE reference-image canvas keeps its own upstream-parity white/BILINEAR path). Exposed as a generate_image kwarg and CLI flag on the latent image-to-image families (Qwen incl. native masked edit, FLUX.2 Klein, Z-Image incl. native inpaint, ERNIE, FIBO, FLUX.1) and on Wan generate_video (i2v first frame, v2v source frames, VACE conditioning). Masks always map through the SAME source-to-canvas geometry as the pixels (shared letterbox math; pad borders binarize to 0 = preserved), so inpaint/VACE alignment cannot drift. Routes with reference-pinned conditioning geometry (edit/reference, controlnet, outpaint) reject the flag loudly instead of ignoring it. Image and video metadata record resize_mode next to canvas_policy; Wan condition caches key on it. Defaults are bit-identical everywhere.
• Wan i2v exact-canvas support (--canvas-policy on the wan route): image-to-video previously ALWAYS replaced the requested canvas with a source-ratio canvas. generate_video(canvas_policy=...) / wan --canvas-policy now accept exact-resize to honor the requested (validated, multiple-snapped) dimensions with the source mapped per --resize-mode, and source-aspect on video-to-video to derive a source-ratio canvas from the clip. Default (None) keeps each route's existing behavior bit-identically. The i2v resolution print names the active policy and the escape hatch, both values replay via --config-from-metadata, and Wan start runtime events (plus ProgressEvent) now carry the RESOLVED width/height so embedding hosts learn final geometry before container probing (0087 save-event pattern).
• Wan VACE ratio-mismatch warning: VACE video conditioning used to stretch mismatched sources silently; it now prints the same >2% aspect-ratio warning as plain video-to-video. Both warnings fire only when the mapping actually stretches (resize); crop/pad are aspect-preserving by construction.

Changed (performance, output-identical)

• Wan A14B i2v condition builds in model precision (0089 F2): the first-frame video condition concatenated first_frame + zero_frames in float32 before casting (a ~1 GB f32 transient at 1280x720x81, ~3 GB at 1920x1080x121 that the cast immediately halved). The padded VAE-encode input is now built directly in the model precision; normalization stays in f32, so the encoded tensor is BITWISE identical (test-pinned through a tiny random-weight VAE encode).
• Wan RoPE per-shape cache (0090 F7): WanRotaryPosEmbed rebuilt the rotary embeds on every forward (~33 ms at A14B-121f token counts, ~11 ms TI2V, x2 with CFG) although shapes are constant within a run. The embeds now cache per (frames, height, width) token grid on the embed instance (underscore-prefixed: excluded from parameter/quantization traversal; bounded to 2 grids — a grid pair measures ~114 MB f32 at A14B 121f@1280x720 per expert, a run only ever uses one grid, and a rebuild costs ~33 ms). Pure shape-keyed compute: cached vs fresh embeds are bitwise identical (test-pinned).

Fixed

• Wan text-to-video now validates canvas_policy loudly: the no-source branch skipped policy normalization entirely, so invalid values AND the contradictory source-aspect request (no source to derive an aspect from) were silently ignored. Both now raise; exact-resize stays accepted because it names what text-to-video already does (regression-tested).
• Image CLIs replay canvas_policy/resize_mode from metadata: the wan route replayed both via --config-from-metadata, but the image parsers replayed neither (canvas_policy had been recorded since 0022 and never replayed), so a faithful -C replay could silently reproduce different geometry. Both fields now replay on parsers that define the flags; explicit arguments still win (regression-tested).
• Capabilities advertise resize_modes per route: mlxgen capabilities and resolve_generation_plan now expose which source-to-canvas mapping modes each route's handler actually accepts (additive field, schema version unchanged): latent image-to-image rows, Qwen native masked edit, Z-Image native inpaint, and Wan i2v/v2v advertise resize | crop | pad; reference-pinned routes (edit/reference, controlnet incl. the Qwen control-inpaint sidecar, outpaint/reframe) and text-only routes advertise none. Wan i2v/v2v rows also advertise their canvas_policies and per-route default (i2v: source-aspect; v2v and VACE: exact-resize, with VACE exact-only), which they previously did not.

[0.23.1] - 2026-07-18

Documentation and verification patch; no behavior changes.

Changed

• Wan2.2 A14B video-to-video: re-verified the documented proof command end to end on 0.23.0 (bit-identical output to the archived 0.18.24 clip across all 17 frames - the route is seed-stable across releases). New proof card at docs/assets/examples/spaceship-v2v/README.md with the cross-release verification record; the Wan video page now cites the measured runtime (298.7 s, 13.8 GiB peak RSS with --low-ram) instead of a stale 90-second estimate.

[0.23.0] - 2026-07-17

Performance release: three fixes from a structured adversarial audit, each independently re-verified on a clean checkout before release (deterministic memory numbers reproduced exactly; timing ratios confirmed; quality parity confirmed metric-by-metric and visually).

Changed

• Denoise-loop dtype fix: masked-edit and warm-start latent blends no longer let float32 scheduler sigmas (and float32 latent masks) promote bf16 latents, so the Z-Image and FLUX.2 Klein masked loops now run at bf16 like their txt2img paths. Measured on the Z-Image Turbo q8 masked route: MLX peak memory 21.8 GB -> 13.1 GB (-40%) and the published native-inpaint recommendation profile (768x432, 9 steps) drops from 18.11 GiB to 10.57 GiB peak RSS; Klein 4B q8 masked 11.4 GB -> 10.4 GB. Wall-clock is neutral to slightly positive (q8 inference is weight-streaming bound). Reproducibility note: same-seed outputs on the Z-Image and Klein masked routes change imperceptibly (measured mean abs diff 0.19-0.30/255 with outside-mask preservation unchanged); Qwen routes, Z-Image latent img2img, and training numerics are bit-identical (training pins its historical float32 forward pass explicitly). Dtype contracts are pinned by new tests.
• Video save speedup: frame conversion now uses buffer-protocol np.asarray instead of per-pixel getdata() in the encoder and health validator: 33-frame 1280x704 save 7.75 s -> 0.86 s measured, ~9-12x independently confirmed (the old path also relied on an API removed in Pillow 14). Byte-parity with the old conversion is test-pinned across image modes.
• CLI startup speedup: transformers/torch no longer import at module scope on any of the 22 console-script paths (annotation-only imports deferred via TYPE_CHECKING; tokenizer class lookup deferred to first use): importing the qwen backend CLI drops from ~1.4 s to ~0.3 s (~4x, independently confirmed). A new test imports every [project.scripts] module and asserts the heavy libraries stay unloaded.
• Model recommendations page: the Z-Image Turbo native-inpaint benchmark row reflects the new post-fix measurement with the pre-fix value retained for context.

[0.22.1] - 2026-07-15

Documentation-truth patch; no behavior changes.

Changed

• Masked-edit docs: a post-release seed sweep (source bf16 seeds 42-45, q4 seeds 42-44) resolved the 0.22.0 single-case source-row --mask-strength 0.95 recolor miss as per-seed variance, not a precision difference. The 0.95 guidance now reads uniformly across all three qwen.base-inpaint proof rows, with a retry-another-seed note for per-seed color fidelity (sweep sheet added to the matrix bundle).

[0.22.0] - 2026-07-15

This release grades the masked-edit routes shipped in 0.20.0/0.21.0 with a standardized multi-case visual-QA matrix, makes the base-Qwen warm start user-tunable, and withdraws the one route the matrix measured as failing.

Added

• Masked-edit 5x5 validation matrix: standardized multi-case visual QA (object insertion, lens recolor, arm retexture, sticker removal, plus an unscored partial-object-removal limitation demonstration; one source, same seed) for the masked-edit routes shipped in 0.20.0/0.21.0. Results: both FLUX.2 Klein 4B q8 rows PASS all scored cases; the qwen.base-inpaint q4/2512-q8 rows are PARTIAL at default settings (warm-start anchoring keeps opaque full-region recolors incomplete). New registry profile masked_edit_matrix_5x5_2026_07_15 for mlxgen validation, published bundle in docs/assets/validation/masked-edit-matrix-2026-07-15/, proof-grade updates across docs/masked-editing.md and docs/edit-capabilities.md, and the documented mask-design rule that removals need full-object masks. The source Qwen/Qwen-Image bf16 checkpoint ran the same four scored cases (PARTIAL aggregate with the identical recolor signature), closing the last wiring-shared gap on the qwen.base-inpaint matrix.
• --mask-strength for native base-Qwen masked edit (qwen.base-inpaint only): exposes the upstream QwenImageInpaintPipeline inpaint strength scoped to masked runs. The default 0.85 anchors repainted content to the source (best for retexture/removal); the measured 0.95 setting turns the matrix recolor cells from PARTIAL into complete recolors on both prepared rows (q4 and 2512-q8), at the measured cost of a weaker structural anchor (masks crossing thin connected structures detached on both prepared rows at 0.95 - s095 regression sheet in the matrix bundle). On the source bf16 row the same setting repaints the full region but misplaces the color (single-case observation; stated in the bundle). The metadata key for the applied warm start is now mask_strength (was masked_warm_start_strength in 0.21.0; metadata_schema_version bumped to 2 for the rename, and --config-from-metadata reads both spellings); mask_strength and executed effective_steps are recorded in metadata; the option is rejected without a mask and on the control-inpaint sidecar row.

Changed

• Non-turbo Z-Image masked editing withdrawn for the moment: the matrix measured a reproducible geometry artifact (across seeds and with CFG on or off) when masks cross thin connected structures on AbstractFramework/z-image-8bit, while Z-Image Turbo renders the same case cleanly. z-image.inpaint is Turbo-only again; non-turbo masked requests fail before model load with an actionable error, and the withdrawal evidence stays published in the matrix bundle and registry.
• Backlog hygiene: the three 2026-06-30 root-level bug_*.md reports moved into the backlog as completed items 0083-0085 with resolution records; completed-ledger rows 0077-0080 added and lifecycle counts recounted.

[0.21.0] - 2026-07-15

This release completes the masked-edit expansion started in 0.20.0: every currently supported image family with a viable upstream or in-repo inpaint mechanism now accepts --mask-path.

Added

• Native base-Qwen masked edit (qwen.base-inpaint): --image + --mask-path now works on trusted base Qwen rows without a ControlNet sidecar (source Qwen/Qwen-Image, AbstractFramework/qwen-image-4bit, and the exact proven AbstractFramework/qwen-image-2512-8bit row), ported from the diffusers QwenImageInpaintPipeline. The masked region warm-starts from the re-noised source (upstream-example strength 0.85) so repainted content anchors to the surrounding structure; output metadata records the executed effective_steps and masked_warm_start_strength (metadata key renamed to mask_strength in the next release when the strength became the tunable --mask-strength option). The exact validated AbstractFramework/qwen-image-8bit row keeps control-inpaint as its masked route, unchanged. Visual-smoke proof bundle with outside-mask preservation measurements in docs/assets/validation/masked-edit-2026-07-15/.
• Z-Image non-turbo native inpaint: z-image.inpaint now covers trusted non-turbo Z-Image rows (Tongyi-MAI/Z-Image, AbstractFramework/z-image-8bit) and the mflux-generate-z-image command accepts --mask-path. Pass an explicit --guidance on non-turbo masked runs; the non-turbo default runs guidance-free. (Withdrawn in the next release after the validation matrix measured reproducible geometry artifacts on the non-turbo row; see the Unreleased section.)
• docs/masked-editing.md: canonical masked-edit page consolidating the request contract, the per-model route matrix with proof grades, and per-family behavior; the API, FAQ, image-edit-modes, and Qwen pages now summarize and link there.

Changed

• Masked-capable routes selected without --mask-path (for example --i2i-mode edit on a base Qwen or Z-Image row) now fail at plan time with an actionable "mask-path is required" error instead of failing after model load. Maskless single-image requests on AbstractFramework/qwen-image-8bit now report "image-strength is required" instead of a generic ambiguity error.
• Shell completions for mflux-generate-qwen, mflux-generate-z-image, and mflux-generate-z-image-turbo now mirror the real parsers (mask and ControlNet options were missing).

[0.20.0] - 2026-07-15

This release ships two image/video editing expansions: the natively ported Wan2.1-VACE-1.3B conditioning route and FLUX.2 Klein masked edit.

Added

• FLUX.2 Klein masked edit / inpaint (--mask-path on all FLUX.2 Klein image-to-image routes, distilled and base): localized edits that repaint white mask pixels and preserve black pixels through per-step latent compositing, ported from the diffusers Flux2KleinInpaintPipeline semantics (clean source latents ride along as conditioning tokens at reserved grid coordinates; the mask is binarized at pixel resolution and then bilinear-interpolated to the packed latent grid with torch-F.interpolate parity). Unified mlxgen generate --image ... --mask-path ... selects the new flux2.inpaint capability (one source image; --image-strength stays rejected with a mask). The backend mflux-generate-flux2-edit command and the Python Flux2KleinInpaint runtime additionally accept extra images as masked-area references (diffusers image_reference parity: source conditioning at t=10, references at t=20+). Base Klein models default to guidance 4.0 with true CFG on this route; distilled Klein models stay at guidance 1.0. The route is smoke-validated on the exact AbstractFramework/flux.2-klein-4b-8bit and flux.2-klein-base-4b-8bit packages (masked lens recolor: mean abs pixel diff 2.37/255 outside the mask vs 55.93/255 inside, plus a reference-conditioned fill case); a published visual-QA proof row remains follow-up work, so mlxgen validation does not yet list masked-edit rows for Klein packages.

• Native Wan2.1-VACE-1.3B port (Wan-AI/Wan2.1-VACE-1.3B-diffusers, alias wan-vace - VACE only exists as a Wan2.1 release; this is the runtime's first Wan2.1 model): reference-image-guided generation (--reference-image, repeatable - inject a pictured object/subject into a new scene) and learned masked source-video editing (--video-path + --video-mask-path), with --conditioning-scale controlling the VACE branch strength. Single-transformer Wan2.1 backbone with 15 VACE control blocks, shared UMT5/wan21-VAE/UniPC runtime; --video-strength and guidance_2 are rejected on VACE models (no SDEdit warm start, no boundary routing), and the planner reports the capability accordingly. --vace-masked-region selects the masked-edit semantics: generate (default) gray-fills the editable region per the official VACE inpainting convention so the model synthesizes new structure there; repaint keeps the source content as conditioning for restyle-in-place edits (both recorded in metadata and replayed). Ported stage-by-stage against the diffusers reference: mask-channel preparation and the UniPC schedule are bit-exact, transformer deltas sit at the model's intrinsic fp32 sensitivity floor (verified by a noise-injection probe), and the bounded CFG-loop error matches the analytic guidance amplification model. Capability proof bundle in docs/assets/validation/wan-vace-2026-07-06/ with request/inputs/output panels and controls: a masked object REPLACEMENT judged against strict vision criteria (generate mode: silhouette IoU vs source 0.16-0.20 where repaints measure 0.73-0.88, in-mask change 63.8 vs a 3.8-3.9 codec-floor background; repaint mode restyles in place at IoU 0.80-0.88; the upstream pipeline fed un-blanked inputs fails identically to repaint while drifting the whole frame, 2161 s CPU), a reference-injection identity proof via a same-seed no-reference control (segmented subject on white transfers; crops and full-scene references measurably fail - documented as user guidance), a flag-free defaults run (832x480x81f/30 steps: 1 h 56 min, 31.7 GiB peak), and the bf16 + fp32 parity comparisons; parity tools in tools/wan_vace_parity_export.py / tools/wan_vace_parity_compare.py.

[0.19.0] - 2026-07-06

This release folds in the never-published 0.18.26 truth patch (its corrections are listed under Fixed below) and ships the video-to-video temporal/audio features, the router and Wan structural cleanup, and the measured motion-fidelity ladder.

Added

• Measured motion-fidelity ladder for Wan video-to-video in docs/wan-video.md: a strength-vs-gesture-preservation table backed by a same-seed 20-step proof matrix (0.5 / 0.6 / 0.7 / 0.8) with per-run metrics, contact sheets, and a null-row metric floor, plus a paired Lightning-point control run showing prompt gesture language recovers the class of motion but not its timing. Proof assets in docs/assets/validation/motion-ladder-2026-07-05/. The FAQ's strength advice now cites the measured band (gestures survive at 0.5-0.6, r 0.86-0.90; the 0.8 default re-synthesizes them, r 0.20), and the docs state plainly that the 4-step Lightning fast path and the motion-preserving band are mutually exclusive.

• metadata_schema_version in image and video metadata (sidecar and embedded), starting at 1, with a documented additive-only evolution policy in docs/api.md. Consumers can now detect structural metadata changes without parsing mflux_version.

• Wan video-to-video audio copy-through: when the source clip has audio, the matching segment (trimmed to the output duration) is copied onto the saved MP4, in both regular and --low-ram batch save paths. Best-effort by design: on failure the video is saved silent, a warning prints the reason and a manual ffmpeg remux command, and the sidecar records audio_present / audio_copied / audio_copy_mode / audio_copy_reason. Unlike the strict SeedVR2 restore contract, a failed mux never discards a finished generation (documented in the README and docs/wan-video.md).

Changed

• Router option surface single-sourced: the mlxgen generate parser and its re-emission of consumed flags are now driven by one descriptor table (router_options.py). A completeness test maps every parser action to exactly one descriptor with a declared forwarding fate, and a round-trip test asserts each consumed flag reaches the backend (from argv and from metadata), so a future consumed-but-unforwarded flag fails CI instead of silently running defaults. Backend parsers, planner constraints, and metadata replay are not unified in this pass.
• Shell completions coverage tested against pyproject scripts: completions now exist for mflux-generate-wan/mlxgen-generate-wan, mflux-generate-ernie-image, and mflux-generate-bonsai (generated from the real entrypoint parsers); a truth test fails when a new console script ships without a completion or a documented exclusion. The mlxgen router aliases are excluded until subcommand-aware completion exists.
• User-mask loading centralized in MaskUtil: Qwen edit inpaint, Qwen control inpaint, Z-Image inpaint, and Wan masked video-to-video now share one loader with an explicit, documented resampling policy (reference-ported surfaces keep their reference's resampling - NEAREST for the diffusers-ported paths; in-house Wan masked V2V uses BOX). Pixel behavior is unchanged. Each surface now warns once per generation when a mask carries an alpha channel (previously only Wan did).
• Wan runtime decomposition, phase 1: the generate_video validation/resolution head moved into WanVideoRequest.resolve and the twin per-branch metadata blocks collapsed into one shared builder, removing the duplication where the 0.18.25 steps replay bug lived. No behavior change; all helpers remain on the model class.
• Wan video-to-video temporal contract: the source clip is now resampled onto the requested --fps timeline at decode, so the output keeps real-time speed regardless of the source frame rate (previously the first --frames source frames were consumed as-is and re-timed, changing playback speed on fps mismatch - a limitation the truth-patch band documented and this release removes). Matching source/requested fps passes frames through untouched (bit-identical with the 0.18.25 decode behavior). Downsampling prints an informational note; upsampling above the source fps duplicates frames and prints a warning. Metadata gains source_video_resampled; the V2V latent cache key now includes fps. Frame-exact first-N extraction is available by re-encoding the source to the target fps beforehand.

Fixed

• mlxgen generate --debug was silently dropped: the router consumed the flag without re-emitting it, so backends never enabled debug logging (the help text claimed otherwise). The flag is now re-emitted to all routes, and the Wan CLI gained --debug support wired to LoRA debug logging. Third instance of the consumed-but-not-forwarded router bug class, now structurally closed (see below).
• Metadata-sourced video_strength validated late: a video_strength out of (0, 1] inside --config-from-metadata used to fail only after the multi-minute Wan weight load. The router now backfills and re-emits it like video_mask_path, so the backend parser rejects invalid values at parse time.
• mflux-completions crashed on every invocation: a duplicated add_lora_arguments call in the mflux-upscale-controlnet completion branch raised ArgumentError: conflicting option string --lora-style during generation, so completion install and --print both failed. The duplicate is removed and a truth test now generates every command's parser in CI.
• mflux-generate-z-image shell completion was empty: the command was listed in the completions generator without a parser branch; it now completes the real z-image options.
• Removed the "motion anchor" overclaim from video-to-video docs: prose that promised the source clip anchors "motion" (gesture-level) is corrected across docs/wan-video.md, docs/getting-started.md, and docs/faq.md - camera path, framing, and layout survive; subject gestures and timing are re-synthesized at the default strength (the measured ladder below quantifies where the transition happens), and the FAQ example prompt no longer suggests "keep the same motion" can force motion through.
• CI test gate: CI and the release workflow now run the full no-weights band (-m "not slow and not high_memory_requirement", 1244+ tests) instead of -m fast (425 tests). 57% of tests - including every video-to-video contract test - previously ran in no CI band, and v0.18.25 shipped with a statically red release-date pin test that CI never executed. The red pin is also fixed (PACKAGED_RELEASE_DATE now matches the changelog date of the packaged version).
• Wan q8 memory truth: docs/quantization.md now states that since the 2026-06-12 runtime-precision fix, Wan q8 packages (A14B and TI2V-5B) dequantize all transformer-block linears to BF16 at load - q8 is a storage/download saving only, and runtime memory matches the BF16 packages. The stale pre-fix rows are annotated with a dated correction and a re-measured MLX-peak value at the exact documented profile (27.8 GiB q8 vs 27.7 GiB BF16); the same correction propagates to README, FAQ, recommendations tiers (Wan A14B moves from the 24/32 GB tiers to 64 GB), and the llms context files. Separately, runtime metadata gains a darwin_peak_physical_footprint_bytes lifetime high-water field via proc_pid_rusage for future runs, and the rusage helper struct was completed to the full v4 layout (the previous truncated layout under-allocated the flavor-4 buffer).
• V2V temporal and audio truth: removed the false claim that video-to-video keeps "clip timing"; docs/wan-video.md and docs/faq.md now document the exact contract (first-N frames re-timed to --fps, no temporal resampling, audio track dropped). The runtime now warns with the exact speed factor on fps mismatch and warns when the source has an audio track; metadata records source_video_audio_present.
• Lightning V2V contract: the documented recipe now uses the copy-pasteable owner/repo:subdir/file.safetensors adapter form; wan.video-video has a LoRA validation registry row (lora_wan_a14b_q8_lightning_v2v_2026_07_04), so mlxgen capabilities reports the documented recipe as validated instead of mapped-unvalidated; the matrix proof bundle is included in-repo under docs/assets/validation/lightning-v2v-2026-07-04/.
• Doc drift: README no longer advertises LoRA-guide artifacts that were removed in the 0.18.24 docs refresh; public docs no longer link proof bundles inside the git-ignored validation_outputs/ folder.

[0.18.25] - 2026-07-05

Added

• Masked Wan video-to-video: new --video-mask-path role on the Wan2.2-T2V-A14B video-to-video route. White mask regions are regenerated under the prompt; black regions are locked to the source video at every denoising step (including UniPC corrector state) and match the source up to VAE round-trip precision after the final clean composite. Static image masks, binarized at 50% on the latent grid; all-black masks fail before model load; recorded in metadata and replayed by --config-from-metadata. Capabilities schema bumped to version 4 with supports_video_mask.

• Lightning fast video-to-video recipe: bounded validation of the lightx2v/Wan2.2-Lightning T2V-A14B 4-step LoRA pairs (Seko-V1.1 and Seko-V2.0) on the public video-to-video route via the on-grid recipe (--steps 4 --video-strength 0.75 --guidance 1 --flow-shift 5 --solver unipc), including the masked combination whose preserved regions stay at the H.264 re-encode floor while cutting the denoise loop from 28 to 3 transformer forwards. Documented with the strength-lattice and inert-negative-prompt caveats.

• Wan plain video-to-video route: public prompt-guided source-video editing on Wan2.2-T2V-A14B through mlxgen generate --video-path ... with --video-strength (default 0.8), unipc-only solver enforcement, fail-closed rejection on TI2V-5B and I2V-A14B, and included reproducible proof artifacts under docs/assets/examples/spaceship-v2v/.

• V2V observability: saved metadata records requested steps plus effective_steps, video_strength, high_noise_stage_skipped, and source-clip frame count, duration, and fps; the runtime warns when a low video_strength skips the A14B high-noise stage (making --guidance inactive) and when source frames are stretched to a mismatched canvas.

Fixed

• Router --video-strength forwarding: mlxgen generate consumed --video-strength for validation but never forwarded it to the Wan backend, so every V2V run silently used the 0.8 default; the router now re-emits the flag and rejects out-of-range values before any model work.
• V2V metadata replay: metadata previously recorded the strength-truncated step count as steps, so --config-from-metadata replays shrank the schedule on every round trip; steps now records the requested count.
• Early source-video validation: unreadable or too-short --video-path inputs now fail before the multi-minute A14B weight load instead of after prompt encoding.
• Source latent cache staleness: image/video conditioning cache keys now include file mtime and size, so overwriting a source file inside one Python session re-encodes instead of silently reusing stale latents.

Changed

• Dependency hygiene: accelerate moved from runtime dependencies to the dev extra; it is only needed by the upstream Diffusers reference probe in tools/, not by the MLX runtime. ftfy stays in runtime dependencies because Wan prompt cleaning uses it for upstream-parity text normalization.

[0.18.24] - 2026-06-30

Added

• Python runtime loading and multi-output reuse: add public resolve_generation_runtime(...), load_generation_model(...), and loaded-runtime generate_output(...) / generate_outputs(...) helpers for the unified mlxgen generate families, with shared output naming, overwrite-safe collision handling, and published reuse-vs-reload validation across Qwen masked edit, FLUX.2 multi-reference, Wan A14B image-to-video, and large Z-Image generation.
• Machine-readable runtime events: add --json-events for mlxgen generate and mlxgen upscale, with structured runtime progress, saved-artifact terminal events, diagnostics paths, and remediation objects for actionable failures.
• Model recommendations guide: add a memory-tier recommendation page for 18 GB, 24 GB, 32 GB, 64 GB, and 128+ GB Macs based on published MLX-Gen memory measurements.

Changed

• Core documentation refresh: rewrite the main user-facing docs so they describe the shipped CLI and Python capabilities directly, including seed-driven multi-output generation, metadata save behavior, Python runtime ownership, and conservative model recommendations by memory tier.

Fixed

• Image finalization memory tail: default image save is now one-pass and metadata-light, --embed-metadata is explicit opt-in, and the published 4096x4096 save-phase probe measured peak sampled RSS -51.2316% and peak Darwin physical footprint -54.1496% versus the legacy three-pass path.
• Runtime contract correctness: fix progress terminal semantics, Qwen control/control-inpaint no-LoRA completion, Z-Image CFG math, and variant-sensitive capability spoofing for local/custom model identities.

[0.18.23] - 2026-06-28

Added

• Runtime memory telemetry: generated image/video metadata and video failure manifests now include MLX allocator, process RSS, Darwin physical-footprint, cache-policy, and timing records when available. Set MFLUX_RUNTIME_MEMORY_TELEMETRY=0 to disable metadata collection.
• Generation memory benchmarks: add repeatable local benchmark tooling and profiles for prompt-materialization release, callback/output retention, telemetry overhead, Wan low-RAM profiles, and SeedVR2 image/video memory behavior.
• Qwen route matrix: add a first-class Qwen route matrix page that maps MLX-Gen capability ids to the upstream Diffusers Qwen pipelines and to the exact accepted proof surfaces already shipped in the docs.

Changed

• Low-RAM generation behavior: --low-ram now applies a default MLX cache limit and clears cache at more model-family denoise/decode boundaries while preserving the same generation surfaces and documented quality profiles.
• CLI and prepare defaults: centralize model inference-step defaults, reject non-positive step counts before loading weights, and resolve mlxgen prepare backends from model configuration instead of only from string matching.
• LoRA exact-row completion: close the remaining production-support gaps for the current Qwen and FLUX.2 image LoRA surface. The exact validated public rows now also include AbstractFramework/qwen-image-8bit on qwen.latent, AbstractFramework/qwen-image-edit-2511-8bit on qwen.multi-reference, qwen.reframe, and qwen.outpaint, AbstractFramework/flux.2-klein-9b-8bit on flux2.multi-reference, and AbstractFramework/flux.2-klein-base-4b-8bit on flux2.outpaint. The route-expansion proof bundle now covers the full accepted June 22 exact-row set with contact sheets, command logs, and loader metrics.
• Public contract wording: remove stale "experimental" wording from the exact validated Qwen/FLUX.2 LoRA rows and from the shipped reframe/outpaint route documentation. The docs now describe a narrower but honest production-supported surface: exact validated rows are public support, mapped-unvalidated rows are not.
• LoRA proof bundle cleanup: tighten the published June 22 route-expansion bundle after a stricter review. The exploratory seed-sweep sheet was removed from docs/assets/validation, the Qwen 2511 A/B sheets now use prompt-matched Lightning baselines, and every accepted contact sheet now shows readable route/model labels plus the exact prompt and key generation parameters.
• Z-Image latent LoRA proof: restore the exact AbstractFramework/z-image-turbo-8bit z-image.latent row with a new same-source A/B proof bundle using ostris/z_image_turbo_childrens_drawings. The published bundle now shows a readable source / baseline / with-LoRA sheet, the exact reproduction commands, and the exact scope of the accepted q8 latent style-transfer row.

Fixed

• SeedVR2 video temporal continuity: SeedVR2 video restore now rejects multi-chunk profiles below 29 source frames and 8 overlap frames on both the CLI and Python API, keeps restored output frame count/FPS aligned with the requested source window, and preserves the current quality-first 29/8 video profile for both 3B and 7B.
• Release publishing order: the GitHub release workflow now publishes to PyPI first and only creates or updates the GitHub Release after PyPI succeeds, so failed package publication does not leave a misleading GitHub release.
• Distribution hygiene: keep twine out of default user installs and in the dev/release extras, and make the package build cleanup target match mlx-gen artifacts.

[0.18.22] - 2026-06-22

Added

• Qwen base control-inpaint proof: add the first exact qwen.control-inpaint public row on AbstractFramework/qwen-image-8bit, inject the exact InstantX/Qwen-Image-ControlNet-Inpainting sidecar through unified mlxgen generate, validate the route with two same-source same-mask same-seed q8 Lightning rows, and publish a dedicated contact sheet, command log, and M5 Max timings.
• Z-Image Turbo native inpaint proof: add the first exact z-image.inpaint public row on AbstractFramework/z-image-turbo-8bit, validate it with a same-prompt same-seed engine-thruster comparison against the old latent route, and publish a full-sheet plus masked-area crop proof.

Changed

• Masked-route docs and help: document the new base-Qwen control-inpaint and Z-Image Turbo native-inpaint routes across the README, API reference, image-edit capability guide, FAQ, Qwen localized-edit guide, and top-level mlxgen generate --help, so --mask-path now describes the current shipped route surface instead of the earlier planned-only state.
• Qwen and Z-Image masked-route runtime: tighten the shipped localized-edit routes without expanding the public API. Base-Qwen control-inpaint now skips inactive negative-prompt work on the exact guidance=1 Lightning proof path and records only the effective negative prompt in metadata. Base-Qwen and Z-Image native inpaint now also invalidate cached source/mask conditions when those files change in place. The published proof bundles were refreshed with the accepted current timings: Qwen control-inpaint engine/repair at 17.29s / 24.65s and 17.74s / 23.86s, and Z-Image Turbo native inpaint at 21.00s / 26.86s on the documented M5 Max proof rows.
• Secondary CLI contract alignment: unified mlxgen generate now forwards --controlnet-strength to the exact base-Qwen qwen.control-inpaint route and accepts an explicit --controlnet-model only when it matches the validated inpainting sidecar. The direct non-turbo mflux-generate-z-image command no longer advertises --mask-path; native z-image.inpaint remains the Turbo-only public masked route.

[0.18.21] - 2026-06-21

Changed

• SeedVR2 video audio contract: SeedVR2 restored MP4s now preserve the matching source audio segment by default when the source clip has audio. If copied audio cannot be proven safe, MLX-Gen fails the run instead of silently publishing a muted output. mlxgen upscale now exposes --drop-audio as the explicit opt-out for intentionally silent restored MP4s. The shared post-write path records audio_copied, audio_copy_mode, and audio_copy_reason, and the published Air France 25s–35s proof bundle remains the release evidence for the copied-audio path.

[0.18.20] - 2026-06-21

Changed

• SeedVR2 video restoration: mlxgen upscale accepts --video-path in addition to --image-path, preserves source FPS by default, trims padded frames back to the requested clip length, records source-video and chunk metadata in .metadata.json, documents the current explicit audio contract (audio_copied=false), and rejects --vae-tiling on video input. Public docs now center the June 21 five-second Eiffel proof bundle: safe bounded 1x 29/8 3B and 7B runs plus explicit enlarged 2x 29/8 3B and 7B comparison runs, each with restored MP4s, comparison MP4s, motion strips, contact sheets, readable labels, and reproduction commands. Older exploratory SeedVR2 video artifacts were removed from the published docs assets so the versioned validation surface only reflects the accepted current proof set.
• SeedVR2 video host safety: the public SeedVR2 video CLI path is now conservative by default: it enables --low-ram automatically, defaults omitted video resolution to 1x, uses --mlx-cache-limit-gb 8 as part of the MLX cache policy in safe mode, runs video restore through sequential temporal chunking with a fresh model per video seed, serializes video work through a runtime lock, and fails closed on enlarged video output unless --force-unsafe-video-memory is passed explicitly. The planner now reserves resident-weight headroom up front so the safe preflight chunk budget agrees with the runtime per-chunk budget. The CLI now also rejects streamed SeedVR2 chunk profiles below 9 frames because they can preserve frame count while still breaking temporal continuity.
• SeedVR2 7B route hardening: add a first-class seedvr2-7b-sharp source route for the official seedvr2_ema_7b_sharp.pth checkpoint, apply the SeedVR2 image --color-correction flag end-to-end, record SeedVR2 checkpoint provenance in output metadata, and fail closed if the loaded SeedVR2 transformer or VAE weights do not exactly cover the runtime parameter tree.
• SeedVR2 temporal VAE and chunk repair: align the streamed video planner with real model windows, restore clip-global streamed noise continuity, implement the official temporal-upsample contract, and enable SeedVR2 VAE causal slicing with temporal memory handoff. On the accepted Eiffel 2x 29/8 proof clip, the path reduced peak MLX usage to 34.40 GB for 3B and 44.27 GB for 7B, while the accepted 1x 29/8 proof shows correct frame count, FPS, and late-tail continuity for both models.
• Qwen localized-edit docs: add a dedicated public guide that explains Qwen masked edit, Qwen structured control, and the planned control-inpaint slice in plain language, including what ControlNet means, what “sidecar” means, when control-inpaint is likely to help, and why the extra control model is not a LoRA.
• Legacy FLUX.2 CLI guidance: make the legacy mflux-generate-flux2 and mflux-generate-flux2-edit entry points identify themselves as compatibility commands, point new integrations to mlxgen generate, document the FLUX.2 negative-prompt exception more clearly, and add a dedicated troubleshooting migration note for packages that still shell out to the legacy commands.

[0.18.19] - 2026-06-15

Added

• Qwen masked edit / inpaint proof: add first-class --mask-path support on the Qwen edit route, plus a validated AbstractFramework/qwen-image-edit-2511-8bit q8 masked-edit proof row with a two-condition contact sheet, command log, and M5 Max timings for the regular 20-step path versus the dedicated lightx2v/Qwen-Image-Edit-2511-Lightning 4-step path.
• Qwen masked edit control sheet: add a same-prompt same-seed no-mask-versus-mask Lightning control sheet, showing that the dedicated mask is what keeps the Qwen 2511 q8 edit localized.
• Qwen structured control proof: add the first exact public --controlnet-image-path route on AbstractFramework/qwen-image-8bit, with the exact InstantX union ControlNet sidecar, a dedicated qwen.control capability row, a same-seed no-control-versus-control contact sheet, command log, and M5 Max timings using lightx2v/Qwen-Image-Lightning as the recommended 4-step path.

Changed

• Qwen image-edit docs: document masked edit / inpaint in the image-edit guide, the capability matrix, the API reference, the FAQ, and the LoRA guide, and surface the dedicated Qwen 2511 Lightning adapter as the recommended fast public path for masked edits on the validated q8 row.
• Qwen structured-control docs: document the exact qwen.control route, make the --image versus --controlnet-image-path workflow boundary explicit, and publish the accepted base Qwen q8 structured-control proof in the capability guide, API reference, FAQ, and LoRA guide.
• Adapter guidance boundary: clarify that MLX-Gen owns exact route truth, capability reporting, and fail-closed adapter checks, while higher-level model-to-adapter convenience and curation belong in higher-level integrations. The LoRA and quantization docs now also make the boundary between validated MLX-Gen q8 packages and arbitrary third-party FP8 checkpoint guidance explicit.

[0.18.18] - 2026-06-13

Changed

• Wan LightX2V public adapter syntax: document the supported A14B adapter forms more clearly, including the stable repo:subdir/file.safetensors syntax, equivalent absolute local file paths after download, and the requirement to pass paired A14B adapter files as separate --lora-paths arguments.

Fixed

• Wan mixed-base LoRA compatibility checks: accept the official lightx2v/Wan2.2-Lightning I2V and T2V subpath references on the matching A14B routes instead of rejecting them when the adapter repository declares several compatible Wan base models.
• Nested LoRA file resolution: resolve Hugging Face adapter references with subdirectories such as owner/repo:subdir/file.safetensors correctly in the local LoRA cache, so the documented LightX2V Wan commands work as written.

[0.18.17] - 2026-06-12

Added

• LightX2V Wan Lightning 4-step proof: add exact q8 A14B text-to-video and first-frame image-to-video same-seed A/B contact sheets using the official lightx2v/Wan2.2-Lightning paired high-noise and low-noise LoRAs at 4 steps, flow_shift=5.0, guidance=1.0, and guidance_2=1.0.
• LightX2V Wan longer-run timing evidence: add 81-frame, 20 fps speed comparisons against the current practical original A14B profiles, showing 8.27x faster text-to-video and 6.13x faster first-frame image-to-video runs with the explicit LightX2V 4-step recipe.
• LightX2V Wan compact proof matrix: add 41-frame M5 Max progress contact sheets for prepared BF16/q8 T2V plus q8 I2V, along with wall-time and max-RSS measurements for the current LightX2V 4-step recipe and the current practical original q8 profiles.
• LightX2V T2V quality-envelope follow-up: add an M5 Max T2V step sweep (4, 6, 8 steps) plus a 832x480 probe, and document that the weaker 240p T2V look is mainly a fast low-resolution tradeoff rather than a LoRA loader or scheduler bug.
• LightX2V Wan public proof refresh: add a readable 720p q8-versus-BF16 keyframe sheet and a working-only 240p-versus-480p T2V sweep for the current public docs.

Changed

• Wan A14B route-level LoRA proof ids: the public A14B capability rows now surface the accepted LightX2V 4-step validation profiles instead of the earlier effect-specific A/B examples.
• Wan q8 LightX2V 720p stability: improve the documented 720p A14B q8 Lightning profile and add a same-seed q8-versus-BF16 comparison sheet for the 1280x720, 41-frame, 4-step route.

[0.18.16] - 2026-06-11

Added

• Wan video LoRA runtime: add Wan-specific LoRA mapping, explicit Wan CLI support for --lora-paths, --lora-scales, and --lora-target-roles, generated-video metadata for applied Wan adapters, and focused tests for TI2V-5B single-transformer plus A14B dual-transformer LoRA routing.
• Original Qwen Image Edit q8 LoRA proof: add an accepted single-image edit A/B contact sheet for AbstractFramework/qwen-image-edit-8bit using the current Ghibli-style adapter profile.
• Wan TI2V-5B q8 text-to-video LoRA proof: add an exact q8 text-to-video A/B contact sheet using AlekseyCalvin/HSToric_Color_Wan2.2_5B_LoRA_BySilverAgePoets.
• Wan video LoRA proof assets: add exact q8 proof contact sheets for TI2V I2V, T2V-A14B, and I2V-A14B, plus a combined Wan route matrix built from the final A/B artifacts.

Changed

• LoRA support snapshot: Wan video LoRA is now documented as exact-route validated on all current Wan q8 public rows: TI2V-5B text-to-video, TI2V-5B first-frame image-to-video, T2V-A14B text-to-video, and I2V-A14B first-frame image-to-video. Original Qwen Image Edit q8 is now also treated as an accepted exact qwen.edit proof row.
• LoRA validation command surface: mlxgen validation --profile <lora_validation_profile> now resolves the current exact LoRA proof rows exposed by mlxgen capabilities.
• Backlog status: close the Wan video LoRA item as completed, keep Wan follow-up work on parity/performance/extra package variants separate, and narrow the remaining LoRA backlog to unfinished image-family proofs plus Bonsai deferral.

[0.18.15] - 2026-06-11

Added

• LoRA capability reporting: mlxgen capabilities now reports route-level LoRA support, validation status, target roles, and validation-profile ids for applications that need to decide whether to expose adapter controls.
• LoRA documentation: add a dedicated LoRA guide covering explicit adapter download, strict scale matching, adapter/base-model compatibility, and source/no-LoRA/with-LoRA validation.
• Qwen Image Edit 2511 LoRA proof: add a Qwen 2511 q8 multiple-angle LoRA A/B contact sheet and command log using fal/Qwen-Image-Edit-2511-Multiple-Angles-LoRA.
• Qwen 2509, Qwen 2512, Z-Image Turbo, and ERNIE LoRA proofs: add exact q8 proof rows, contact sheets, and public command examples for AbstractFramework/qwen-image-edit-2509-8bit, AbstractFramework/qwen-image-2512-8bit, AbstractFramework/z-image-turbo-8bit, and AbstractFramework/ernie-image-turbo-8bit.
• ERNIE Image Turbo LoRA support: add public-route LoRA loading, capability surfacing, strict metadata reporting, and an exact q8 anime-style A/B proof for AbstractFramework/ernie-image-turbo-8bit.
• Wan practical video examples: add a dedicated Wan video guide with 101-frame, 20 fps M5 Max comparison clips for TI2V-5B at 832x480 and 1280x704 plus A14B T2V at 480x240.
• Wan TI2V-5B parity backlog: add a planned item for source-model TI2V-5B math and behavior comparison against official Wan plus local Diffusers/Transformers references.
• Wan flow-shift control: add --flow-shift and Python flow_shift= support for Wan video generation, with metadata recording and tests for explicit overrides, so lower-resolution TI2V-5B runs can use the 3.0 schedule shift recommended by Wan references for 480p-class profiles while native TI2V-5B keeps its model default.

Changed

• Experimental feature wording: mark LoRA and reframe/outpaint documentation as experimental while preserving the existing fail-closed adapter and capability contracts.
• LoRA support snapshot: document the exact validated q8 rows, keep base Qwen Image experimental, and explicitly deprioritize Bonsai packed-runtime LoRA work.
• Wan guidance: document that A14B at 480x240 or 240x480, 101 frames, 20 fps, and 20-25 steps is the preferred practical M5 Max profile for the recorded starship prompt, and document --flow-shift 3 for new 480p-class TI2V-5B checks.
• Wan video LoRA planning: refine backlog and docs to distinguish the single-transformer TI2V-5B path from dual-transformer A14B routes, and record named public proof candidates for future Wan LoRA validation.

Fixed

• LoRA fail-closed behavior: requested LoRA adapters now fail on missing files, unreadable files, zero matched keys, zero applied layers, incompatible matrix shapes, and incompatible cached model-card base metadata instead of continuing without the requested adapter.
• Qwen Image Edit 2511 LoRA mapping: accept the Diffusers transformer.transformer_blocks.* lora_A/lora_B adapter key format, including Qwen modulation layers, so compatible Qwen 2511 LoRAs apply instead of being rejected as zero-match adapters.
• Qwen 2509 and Qwen 2512 LoRA mappings: accept the public Diffusers modulation-key variants used by current Qwen 2509 edit and Qwen 2512 text adapters, allowing exact matched-key application on the validated q8 routes.
• FLUX.2-dev LoRA routing: black-forest-labs/FLUX.2-dev is not inferred as a supported FLUX.2 Klein route, and FLUX.2-dev adapters such as lovis93/Flux-2-Multi-Angles-LoRA-v2 are rejected for FLUX.2 Klein models.
• Bonsai LoRA fail-closed boundary: Bonsai capability surfacing and planning now stay explicit about LoRA being unsupported on the packed ternary runtime instead of implying that ordinary adapter injection should work there.

[0.18.14] - 2026-06-08

Added

• Generative reframe and canvas-guided outpaint: add --reframe-padding and --outpaint-padding support for validated FLUX.2 Klein 4B/9B and Qwen Image Edit original, 2509, and 2511 routes, including model capability reporting, router validation, generated metadata, source/q8/q4 proof contact sheets, and a mlxgen validation profile.

Changed

• Outpaint source blending: canvas-guided outpaint now uses edge-extended conditioning and an adaptive source blend. MLX-Gen blends source detail back only when the generated source window still matches the original source; otherwise it keeps the generated canvas to avoid ghosted fragments.
• I2I documentation: clarify the difference between latent I2I, edit-reference I2I, multi-reference I2I, generative reframe, canvas-guided outpaint, and future native fill/inpaint outpaint.

Fixed

• SeedVR2 large-output decode: keep small upscales on the untiled VAE path while automatically using tiled VAE decode for large outputs, preventing invalid full-frame decodes without requiring users to opt into tiled VAE encoding.

[0.18.13] - 2026-06-07

Added

• SeedVR2 official source and packages: route seedvr2 and seedvr2-3b to the official ByteDance-Seed/SeedVR2-3B checkpoint, add mlxgen prepare support for reusable SeedVR2 packages, and document the AbstractFramework/seedvr2-3b-8bit and AbstractFramework/seedvr2-3b-4bit package profiles.
• Official SeedVR2 7B route and package validation: route seedvr2-7b to the official ByteDance-Seed/SeedVR2-7B checkpoint, add official 7B source loading, validate local q8/q4 7B packages, and document storage, timing, Max RSS, and 5x contact-sheet outputs.
• SeedVR2 3B/7B comparison sheet: add a single side-by-side documentation asset stacking 3B and 7B source/q8/q4 outputs so users can compare the same 5x upscale profile directly.
• Unified SeedVR2 upscale command: add mlxgen upscale as the public SeedVR2 image super-resolution command while keeping mflux-upscale-seedvr2 available for compatibility.

Fixed

• SeedVR2 package resolution: reject unsupported Hugging Face-style SeedVR2 model handles before loading weights and preserve recognized AbstractFramework/seedvr2-* package handles through weight resolution. Missing q8/q4 packages now fail with the explicit download/prepare guidance instead of resolving to another SeedVR2 source.

[0.18.12] - 2026-06-07

Added

• SeedVR2 upscaling guide: add a dedicated image-upscaling documentation page with a real 5x SeedVR2 comparison, including the original 133x113 source enlarged to the generated 658x560 output size for direct quality assessment.

Changed

• SeedVR2 upscale quality default: SeedVR2 now defaults to untiled VAE encode/decode for image quality, with --vae-tiling available as an explicit memory-saving opt-in for very large upscales. The docs now recommend --softness 0.25 to 0.5 for visibly noisy sources.
• Acknowledgements: refresh model-family credits for the routed MLX-Gen model surface, including FLUX, Qwen, Wan, Z-Image, ERNIE, FIBO, Bonsai, SeedVR2, and inherited mflux routes.

Fixed

• SeedVR2 output metadata: mflux-upscale-seedvr2 --metadata now writes JSON sidecars, records source image path/dimensions, and reports final even output dimensions for non-16-multiple upscale targets.

[0.18.11] - 2026-06-06

Added

• Image-edit examples: add a current Qwen Image Edit 2511 source/q8/q4 parity sheet with exact commands for pencil sketch, hard-landing edit, and multi-reference composition, and document the existing Qwen Image Edit 2509 plus FLUX.2 Klein 4B/9B proof matrices.
• Wan video health metadata: saved Wan MP4 metadata now includes decoded frame/file health measurements such as frame count, output size, luma range, and mean temporal delta.
• Wan failure diagnostics: mlxgen generate can pass --failure-diagnostics to include runtime memory and tensor-health details in Wan failure manifests.

Changed

• FIBO Edit public status: keep FIBO Edit unavailable through unified mlxgen generate capability discovery until source-model parity and release-quality edit validation pass.
• Prepared local folder routing: Hugging Face model handles such as AbstractFramework/wan2.2-i2v-a14b-diffusers-8bit can resolve to a complete matching ./models/<repo-name> local MLX-Gen package before requiring a Hugging Face cache snapshot.
• Wan image-to-video sizing: image-to-video now treats width and height as a size target, resolves the actual output canvas from the input image aspect ratio and model spatial multiples, and records requested, source, and resolved dimensions in MP4 metadata.

Fixed

• Qwen Image/Edit FlowMatch scheduler parity: use the model-config dynamic-shift scheduler values and terminal sigma stretch for Qwen Image/Edit FlowMatch runs, matching the upstream Diffusers scheduler contract and fixing Qwen Image Edit 2511 edit/composition adherence.
• mlxgen alias imports: importing mlxgen.models.* no longer replaces the mflux.models parent package with an alias module, keeping mixed mlxgen and mflux imports stable in the same Python process.
• Wan q8 default generation path: tensor-health diagnostics are opt-in so ordinary Wan q8 generation keeps the default lazy MLX execution path. The published A14B q8 T2V and I2V packages passed the 480x240-target, 41-frame, 15-step release profile documented in docs/quantization.md.

[0.18.10] - 2026-06-04

Added

• Taskless generation planning: add public model capabilities and generation-plan resolution for text-to-image, image-to-image, text-to-video, and image-to-video. mlxgen capabilities now reports supported public tasks, internal modes, image-count contracts, route handlers, and option support before weights are loaded.
• Image-to-image mode routing: route one-image FLUX.2 requests to edit/reference I2I by default, route --image-strength requests to latent img2img, and route repeated --image inputs to multi-reference I2I where the model supports it.
• Reproducible multimodal example: add a documented spaceship-in-snow workflow with real generated assets for T2I, I2I edit, multi-reference I2I, Wan A14B T2V, and Wan A14B I2V.

Changed

• Public task cleanup: edit is now a compatibility alias for image-to-image plus an internal I2I mode. New integrations should use media-direction tasks and inspect the resolved generation plan when they need routing details.
• Progress event consistency: edit and fill image pipelines now report image-to-image progress through the shared progress callback contract instead of relying on latent-img2img inference.
• Local model routing safety: local paths and custom repository names now fail earlier when model family or base-model hints are insufficient or contradictory.
• README and docs: rewrite the README around the current mlxgen command surface, published AbstractFramework model repositories, Wan A14B memory measurements, and AbstractVision / AbstractFramework / AbstractFlow usage.

Fixed

• Metadata replay routing: generation metadata that includes image_strength now resolves to latent img2img instead of routing to edit/reference I2I.
• Unsupported option handling: mask, outpaint, image-strength, and model-family contradictions are rejected before model execution when the selected model capability does not support them.

[0.18.9] - 2026-06-03

Added

• Shared progress callbacks: expose mflux.callbacks.ProgressEvent and CallbackRegistry.subscribe_progress(...) so text-to-image, image-to-image, text-to-video, and image-to-video callers can subscribe to one denoise-step progress contract.
• Wan progress API cleanup: Wan video generation now emits the shared progress event and still supports a direct generate_video(progress_callback=...) handler for one-shot callers.
• Wan A14B memory modes: add runtime lifecycle paths that can release inactive A14B denoisers and lower MLX/Metal memory pressure during long T2V/I2V runs.

Changed

• Wan CLI progress semantics: the Wan CLI now reports denoising-step progress instead of presenting step progress as an output-frame counter.
• Wan q8 policy: Wan prepared q8 folders now use a mixed q8/BF16 policy. Transformer block linears are quantized at q8, while conditioning/output projection paths, VAE, text encoder, scheduler metadata, tokenizer files, norms, convolutions, and other sensitive or non-quantizable paths stay BF16.
• Wan video memory behavior: MP4 frame conversion now avoids full-video NumPy temporaries, reducing avoidable memory pressure most noticeably for larger 81/121-frame outputs.
• Generated model cards: prepared BF16 and quantized model cards now describe their saved-weight layout and Wan mixed q8/BF16 policy more precisely.

Documentation

• Document shared progress callbacks for Python integrations, Wan A14B guidance behavior, and Wan q8 storage/runtime-memory measurements.
• Clarify that Wan mixed q8/BF16 improves storage and measured usage memory in validation, but is not currently claimed as a speed improvement.
• Link Wan follow-up validation work from the backlog without making unsupported full-size claims in user-facing docs.

[0.18.8] - 2026-05-31

Added

• Wan2.2 A14B support: add T2V and I2V model configs, dynamic transformer_2 weight loading, Wan2.1-style A14B VAE support, Diffusers-compatible high/low-noise boundary routing, scalar A14B timesteps, A14B concatenated image-condition latents, and optional --guidance-2 low-noise guidance.
• Wan A14B docs and tests: document TI2V-5B versus A14B defaults, local snapshot requirements, and add focused tests for A14B config, VAE mapping, I2V conditioning shape, CLI defaults, optional low-noise guidance, and guidance routing.
• Wan console scripts: expose mlxgen-generate-wan and mflux-generate-wan entrypoints for direct Wan CLI checks.

Fixed

• Wan model identity safety: unknown or generic Wan names no longer infer the TI2V-5B runtime from the generic wan substring. Wan requests now require an exact supported repo or a local MLX-Gen package with a specific Wan alias.
• Wan prompt and default handling: Wan generation now applies model-specific default negative prompts, spatial defaults, guidance defaults, and optional A14B low-noise guidance consistently across CLI and Python generation.
• Wan low-cost runs: valid low-resolution or short Wan runs no longer emit runtime quality warnings; docs now describe those settings as quick command checks rather than final quality settings.
• Wan source/runtime mismatch guard: Wan initialization now compares available Diffusers source configs against the selected MLX-Gen runtime before loading weights, and transformer calls fail with a clear channel mismatch before entering MLX convolution.

[0.18.7] - 2026-05-27

Added

• Bonsai Image ternary 2-bit support: mlxgen generate can now run prism-ml/bonsai-image-ternary-4B-mlx-2bit directly as a FLUX.2-derived text-to-image model with Prism's pre-packed low-bit transformer, a 4-bit Qwen3 text encoder, and the Flux2 VAE.
• Bonsai validation docs: Add Bonsai versus FLUX.2 Klein 4B q8 quality/speed/memory documentation, validation imagery, and comparison with MLX-Gen's mixed q4/q8 policies.

Changed

• Bonsai binary 1-bit handling: prism-ml/bonsai-image-binary-4B-mlx-1bit is detected and rejected with an explicit unsupported-runtime message. The latest published stock MLX checked for this release, 0.31.2, still does not support the required bits=1, group_size=128 packed affine matmul.

[0.18.6] - 2026-05-26

Added

• Wan local parity fixtures: Add opt-in full-model Wan component-parity tests for the MLX transformer, VAE encoder/decoder, prompt-embedding paths, and a tiny 3-step CFG latent denoise loop against Diffusers-generated fixtures.
• Wan smoke-setting warnings: Warn when Wan generation uses tiny resolution, frame, step, or fps settings that are useful only for routing/MP4 smoke tests and not visual quality assessment.
• Wan video progress: Show a frame-based CLI progress bar during Wan text-to-video and image-to-video generation, and expose structured WanProgressEvent callbacks for Python callers.

Documentation

• Replace low-resolution Wan example panels with upstream TI2V-5B recommended settings plus 1280x704 spatial-scale examples, and document tiny runs as command checks rather than final quality settings.

[0.18.5] - 2026-05-26

Added

• ERNIE q8/q4 preparation and generation: mlxgen prepare --model baidu/ERNIE-Image-Turbo --quantize 8|4 now creates loadable MLX-Gen packages, and ERNIE generation can run from those q8/q4 packages.
• ERNIE Prompt Enhancer: mflux-generate-ernie-image / mlxgen generate can now run ERNIE's optional Prompt Enhancer when a full source snapshot with pe/ and pe_tokenizer/ files is available.

Changed

• Output replacement default: Generation saves now replace the requested output path by default. Use --replace false or --no-replace to preserve existing files and write a suffixed output.
• ERNIE q4 policy: ERNIE q4 now uses a model-specific mixed q4/q8 policy. q8 is used for Mistral3 text/Prompt Enhancer linears plus ERNIE transformer V/O attention, conditioning/output linears, and VAE attention, while transformer Q/K and feed-forward modules remain q4.

Documentation

• Advertise the current quantized-model compatibility surface and document that Qwen and ERNIE q4 checkpoints use model-specific mixed q4/q8 policies validated for generation quality.

[0.18.4] - 2026-05-25

Added

• ERNIE Image Turbo text-to-image: Add an initial MLX-native port of baidu/ERNIE-Image-Turbo, including Mistral3 text encoding, ERNIE transformer weights, FlowMatch Euler scheduling, Flux2-style VAE decode, mlxgen generate routing, and mlxgen download / mlxgen prepare support.
• ERNIE model-card support: Generated Hugging Face cards now recognize ERNIE Image Turbo as Apache 2.0, use ERNIE-specific 512px / 8-step / guidance-1 examples, and describe BF16 prepared weights when no quantization level is used.

Changed

• ERNIE quantization boundary: ERNIE --quantize requests now fail explicitly because q4/q8 layouts are not validated yet.
• Small ERNIE outputs: ERNIE CLI generation warns when width or height is below 384px because very small outputs can crop or truncate subjects.

Documentation

• Document ERNIE routing, BF16-only prepare/generation, model-card licensing, and recommended validation dimensions.

---

[0.18.3] - 2026-05-25

Changed

• Generated model-card licenses: mlxgen prepare now writes Apache 2.0 license metadata and a license section for Qwen, Z-Image, and FLUX.2 Klein 4B prepared checkpoints.
• FLUX.2 Klein 9B publishing safety: Generated cards for FLUX.2 Klein 9B and base-9B derivatives now use license: other, license_name: flux-non-commercial-license, source license links, and Hugging Face gated-access prompts.
• Z-Image usage defaults: Generated Z-Image cards now show model-specific generation defaults: Turbo examples use --steps 8 --guidance 0, and base Z-Image examples use --steps 50 --guidance 4.

Documentation

• Add backlog tracking for model-integration priorities, publication audit guardrails, and Hugging Face collection follow-up work.

---

[0.18.2] - 2026-05-25

Changed

• Generated model-card namespace: Hugging Face model cards created by mlxgen prepare now default usage examples to AbstractFramework/<repo-name>.
• Copy/paste model-card usage: Generated cards now include python -m pip install -U mlx-gen before the mlxgen download and mlxgen generate commands.

Removed

• Generated collection recommendation: Generated cards no longer emit a "Recommended Hugging Face collection" sentence. Collection membership remains a separate Hugging Face publishing step.

Documentation

• Clarify that public generated cards use pip for baseline installation while repository development and release workflows continue to use uv.

---

[0.18.1] - 2026-05-25

Changed

• CLI discoverability: mlxgen and mlxgen --help now show the top-level generate, download, and prepare workflows instead of dropping directly into generation arguments.
• Prepare command naming: mlxgen prepare --help now presents the command as mlxgen prepare and describes the generated local model folder and Hugging Face model card.
• Generated model-card tracking: Hugging Face model cards created by mlxgen prepare now record the exact mlx-gen version that generated them.

Fixed

• Generate/prepare confusion: mlxgen generate --path ... now fails with an actionable message that points to mlxgen prepare --model ... --path ... --quantize ... and explains that image outputs use --output.

Documentation

• Clarify that mlxgen prepare is the public MLX-Gen workflow for creating reusable local quantized model folders and generated Hugging Face cards, and prefer long-form flags such as --quantize in public examples.
• Generated model cards now link to the MLX-Gen project and quantization documentation when describing quantized checkpoint policies.

---

[0.18.0] - 2026-05-25

Added

• Generated Hugging Face model cards: mlxgen prepare now writes a README.md model card into MLX-Gen model packages with source-model attribution, mflux and MLX-Gen compatibility notes, quantization details, contributor attribution, and collection guidance.

Changed

• Download command hints: Missing-artifact remediation now shows plain mlxgen download and mlxgen prepare commands. HF_HUB_ENABLE_HF_TRANSFER=1 remains optional acceleration for explicit Hugging Face downloads, not the download authorization mechanism.

Documentation

• Document Qwen q4 mixed q4/q8 policy, q8 behavior, Hugging Face model-card generation, and collection publishing workflow.

---

[0.17.5.post3] - 2026-05-25

Changed

• Explicit model preparation: Runtime generation and Python model construction no longer download missing model, tokenizer, LoRA, or Depth Pro files. Missing artifacts now raise DownloadRequiredError with the exact mlxgen download or mlxgen prepare command to run.
• Smart MLX-Gen commands: Add mlxgen download and mlxgen prepare as explicit preparation flows. mlxgen download --model depth-pro handles the direct Apple Depth Pro weights.
• LoRA handling: User-requested LoRAs are required; missing LoRA files now fail instead of being ignored.

Documentation

• Document that generation runs from local model files, plus Python integration expectations, AbstractVision usage context, and explicit model-management workflows.

---

[0.17.5.post2] - 2026-05-25

🐛 Bug Fixes

• Qwen Image q4 saving: Save Qwen q4 transformers as mixed q4/q8 by using q8 for image-stream modulation (*.img_mod_linear) and q4 for the rest of the quantizable transformer linears. This preserves coherent Qwen q4 outputs while making newly saved q4 checkpoints smaller than both q8 and the 0.17.5.post1 BF16 mixed-q4 layout.

📝 Compatibility

• Existing all-q4 and 0.17.5.post1 Qwen BF16 mixed-q4 checkpoints continue to load. Re-save Qwen Image / Qwen Image Edit q4 checkpoints with 0.17.5.post2 to get the smaller q4/q8 layout.

🧰 DX & Maintenance

• Package distribution: Rename this fork's PyPI distribution to mlx-gen while preserving the mflux Python module and CLI command names, and add mlxgen as a lightweight import alias.

---

[0.17.5.post1] - 2026-05-24

🐛 Bug Fixes

• Qwen Image Edit conditioning resolution: Encode the transformer’s image-conditioning latents at the edit target resolution, not the vision-language conditioning resolution (≈384px by area), preventing patchy/tiled artifacts in edit outputs.
• Qwen Image Edit default dimensions: Preserve the first input image dimensions by default; explicit --width/--height values or scale factors still opt into resizing.
• Qwen Image Edit CLI scheduler: Forward --scheduler to the Qwen edit pipeline (previously ignored).
• Qwen Image Edit q4 saving: Use mixed q4 quantization for Qwen transformers by keeping conditioning, modulation, and output projections at higher precision while quantizing the bulk attention and feed-forward layers.
• FLUX.2 Klein Edit guidance: Allow --guidance > 1.0 for FLUX.2 Klein edits by checking the resolved FLUX.2 model config instead of requiring a base model name; defaults remain unchanged.

🧰 DX & Maintenance

• AbstractVision package: Publish this fork as abstractvision-mflux on PyPI while preserving the mflux Python module and CLI command names.

---

[0.17.5] - 2026-04-10

🐛 Bug Fixes

• Qwen Image Edit mflux-save: Route Qwen edit model names to QwenImageEdit and save through the same path as inference so VisionTransformer (encoder.visual) weights are written. Saving with QwenImage previously omitted those weights and led to random vision encoders after reload.
• Battery saver callback: Harden Apple Silicon battery detection when system_profiler is missing and resolve the helper script via absolute paths.

📝 Documentation

• Related projects: Clarify that MindCraft Studio is a macOS app built on mflux.

🧰 DX & Maintenance

• Dependencies: Relax the protobuf upper bound to allow current 7.x releases while keeping a safe ceiling below 8.0.

👩‍💻 Contributors

• @anthonywu
• @f-gibellini
• @JiwaniZakir

---

[0.17.4] - 2026-03-28

🐛 Bug Fixes

• Z-Image PEFT/ModelScope LoRA keys: Extend the Z-Image LoRA mapping with .default tensor name variants so adapters in PEFT/ModelScope layouts (for example Tongyi-MAI exports) resolve and apply correctly instead of matching zero weights.

👩‍💻 Contributors

• @filipstrand

---

[0.17.3] - 2026-03-27

🐛 Bug Fixes

• FLUX.2 edit guidance metadata: Preserve the requested guidance value for FLUX.2 Klein base image-edit runs so mflux-info and saved metadata report the actual guidance used instead of always showing 1.0.

👩‍💻 Contributors

• @filipstrand

---

[0.17.2] - 2026-03-23

🐛 Bug Fixes

• Shared tokenizer cache resolution: Fix Hugging Face tokenizer resolution when a repo is only partially cached locally, preserving offline-first behavior for valid cached layouts while retrying ambiguous cached primaries once before surfacing real load errors.

🧰 DX & Maintenance

• Tokenizer resolution coverage: Expand shared tokenizer-resolution regression tests to cover root-layout tokenizers, fallback edge cases, and refresh failure handling.

👩‍💻 Contributors

• @filipstrand

---

[0.17.1] - 2026-03-22

🐛 Bug Fixes

• Hugging Face tokenizer dependencies: Declare protobuf so minimal installs (including uv tool install mflux) include packages Transformers may require when loading tokenizers, fixing failures such as mflux-generate-fibo when the tokenizer falls back off the fast path.

👩‍💻 Contributors

• @filipstrand

---

[0.17.0] - 2026-03-20

🎨 New Model Support

• FIBO Edit: Add image-editing support for the FIBO model family.
• FIBO Edit remove-background workflow: Support the dedicated remove-background edit path for FIBO.

✨ Improvements

• Training image scaling: Scale training images by area rather than longest side for more consistent preprocessing.
• MLX 0.31.x: Allow MLX 0.31.x in dependency ranges.
• FLUX.2 LoRA mapping: Expand LoRA key mapping coverage for FLUX.2.

🐛 Bug Fixes

• Training optimizer state: Evaluate optimizer state after each training step as intended.
• Local tokenizer loading: Fix loading tokenizers from local paths.
• Dynamic-resolution image edit: Restore correct behavior for image edit when using dynamic resolution.

👩‍💻 Contributors

• @filipstrand
• @icelaglace
• @TheOrsa
• @waldheinz

---

[0.16.9] - 2026-03-07

✨ Improvements

• Broader LoRA compatibility for FLUX.2 and Z-Image: Expand LoRA mapping coverage so more adapter key layouts resolve cleanly for FLUX.2 and Z-Image models.

👩‍💻 Contributors

• @filipstrand

---

[0.16.8] - 2026-03-06

✨ Improvements

• Local-model LoRA training: Allow LoRA training to work when the base model is supplied from a local path, including the FLUX.2 and Z-Image training adapters.

📝 Documentation

• Distilled-model step defaults: Clarify CLI guidance so examples prefer model default inference steps unless the user intentionally overrides them.

👩‍💻 Contributors

• @waldheinz

---

[0.16.7] - 2026-03-02

🎨 New Model Support

• FIBO-Lite support: Add support for the FIBO-Lite model variant.

🐛 Bug Fixes

• FLUX.2 edit downsampling extents: Fix downsampling in FLUX.2 edit paths so image extents are preserved.

👩‍💻 Contributors

• @filipstrand

---

[0.16.6] - 2026-02-20

✨ Improvements

• SeedVR2 7B support: Add support for the SeedVR2 7B upscaler variant.
• Qwen-Image parity with diffusers: Align Qwen-Image behavior more closely with the diffusers reference implementation.
• FIBO scheduler default: Default FIBO generate_image to flow_match_euler_discrete.

🧰 DX & Maintenance

• Repo tooling cleanup: Remove unused Cursor command wrappers from the repository.
• SeedVR2 7B test coverage: Add image test support for the new SeedVR2 7B path.

👩‍💻 Contributors

• @ciaranbor
• @icelaglace
• @filipstrand

---

[0.16.5] - 2026-02-17

✨ Improvements

• FLUX.2 Klein img2img CLI parity: Add --image-path and --image-strength to mflux-generate-flux2, enabling init-image driven generation with the same CLI pattern used in other generators.
• MLX cache control: Add --mlx-cache-limit-gb to cap MLX cache usage without requiring full --low-ram mode.

📝 Documentation

• Common CLI docs: Document --mlx-cache-limit-gb behavior and usage in the shared model README.

👩‍💻 Contributors

• @terribilissimo
• @icelaglace

---

[0.16.4] - 2026-02-15

🐛 Bug Fixes

• Training preview stability: Always offload optimizer state during preview generation to avoid memory pressure and improve preview reliability.
• Apple Silicon compile guard: Narrow the M1/M2 compile fallback so it excludes Max and Ultra variants, preserving expected optimized behavior on those chips.

---

[0.16.3] - 2026-02-14

🐛 Bug Fixes

• Z-Image training preview guidance: Fix Z-Image (non-turbo) training previews so they use the configured guidance value instead of defaulting to 0.0, ensuring preview quality matches actual CFG behavior.
• FLUX.2 training preview guidance: Fix FLUX.2 training previews (txt2img and edit) so they use the configured guidance value instead of forcing 1.0.

---

[0.16.2] - 2026-02-12

🐛 Bug Fixes

• Edit training preview fallback: Fix edit auto-discovery runs (*_in/*_out) with monitoring enabled so fallback preview prompts use an available input image instead of requiring explicit data/preview.* files.

📝 Documentation

• FLUX.2 training guide: Expand the FLUX.2 LoRA training example documentation with richer guidance and examples.

---

[0.16.1] - 2026-02-11

🐛 Performance regression fixes

• M1/M2 inference performance fallback: Disable model-level mx.compile prediction wrappers for Z-Image and FLUX.2 on Apple M1/M2 to avoid observed 0.16 regressions on older Apple Silicon while preserving compiled paths on newer chips.

---

[0.16.0] - 2026-02-11

✨ Improvements

• Completely rewritten training system: Rebuild LoRA training end-to-end, replacing the DreamBooth-specific implementation with a new common training stack (dataset, state, optimizer, runner, and statistics) shared across model families.
• New base-model support for training and inference: Add support for flux2-klein-base-4b, flux2-klein-base-9b, and z-image (in addition to z-image-turbo) with dedicated FLUX.2 and Z-Image training adapters.
• Performance tuning: Improve core scheduler/model execution paths used by FLUX.2 and Z-Image.

🐛 Bug Fixes

• FLUX.2 Klein 9B text encoder overrides: Fix override resolution/application in the FLUX.2 initializer/config flow.

🧰 DX & Maintenance

• FLUX.1 legacy cleanup: Remove legacy FLUX.1 image-generation tests/resources and retire unused helper tools.
• Dependency alignment: Update install guidance for stable transformers 5.0 and refresh lockfile/dependency metadata.

📝 Documentation

• Training docs refresh: Expand and update training docs/README sections for common training, FLUX.2, and Z-Image.
• Install troubleshooting: Add troubleshooting guidance for hf_transfer installation issues.

👩‍💻 Contributors

• Filip Strand (@filipstrand)
• Xin (@q3g)

---

[0.15.5] - 2026-01-26

✨ Improvements

• SeedVR2 directory input: Allow passing a folder to --image-path to upscale all images inside.

🧰 DX & Maintenance

• Model porting guidance: Require model README entries in the porting workflow.

📝 Documentation

• SeedVR2 usage: Document directory upscaling with CLI and Python API examples.
• CLI docs: Add Python API sections and improve Z-Image Turbo entry-point links.

---

[0.15.4] - 2026-01-20

✨ Improvements

• Flux2 LoRA aliasing: Add key aliases for base_model prefixes to improve LoRA resolution across configs.

📝 Documentation

• Agent guidance: Clarify skill references for Cursor agents.

---

[0.15.3] - 2026-01-19

🐛 Bug Fixes

• Flux2 Klein local path: Fix errors when using a local FLUX.2-klein-9B path in mflux-save and mflux-generate-flux2.

---

[0.15.2] - 2026-01-19

🐛 Bug Fixes

• Flux2 edit (low-ram): Normalize tiled VAE latents to 4D before patchifying to avoid shape errors.

---

[0.15.1] - 2026-01-18

🐛 Bug Fixes

• PyPI metadata: Removed invalid architecture classifier that blocked uploads (Architecture :: AArch64).

---

[0.15.0] - 2026-01-18

🎨 New Model Support

• Flux2 Klein (4B/9B): Full MLX port of Flux2 Klein (including multi-image editing support).
• New command: mflux-generate-flux2 for Flux2 Klein image generation.
• New command: mflux-generate-flux2-edit for Flux2 Klein image editing.

🔧 Improvements

• Qwen3-VL shared module: Extracted qwen3_vl into models/common_models/ for reuse across model families (Flux2 and Fibo etc).
• Experimental CUDA support: Added initial CUDA backend support as an experimental feature.
• Test Infrastructure: Image tests are pinned to MLX v0.30.3.

📝 Documentation

• README reorganization: Reorganized the main README for better structure and readability.

---

[0.14.2] - 2026-01-13

📊 Improved Metadata Handling

• Enhanced IPTC & XMP Support: Significant improvements to metadata reading and writing, ensuring better compatibility with professional image editing tools.
• Robust Metadata Extraction: Refined logic for extracting generation parameters from previously generated images.
• New Metadata Tests: Added comprehensive test suite for IPTC metadata building and original image info utilities.

🤖 DX & Maintenance

• Cursor AI Workflows: Introduced standardized Cursor commands and agent rules in .cursor/ for improved development consistency and automation.
• SeedVR2 & ControlNet Tweaks: Minor refinements to SeedVR2 and ControlNet model implementations.
• Documentation Updates: Updated README and added AGENTS.md for better contributor onboarding.

---

[0.14.1] - 2026-01-01

🔧 SeedVR2 Improvements

• Enhanced Color Correction: Implemented precise LAB histogram matching with wavelet reconstruction for superior color consistency between input and upscaled images.
• Configurable Softness: Added a new --softness parameter (0.0 to 1.0) to control input pre-downsampling, allowing for smoother upscaling results when desired.
• RoPE Alignment: Fixed RoPE dimension mismatch (increased to 128) to perfectly match the reference 3B transformer architecture.

🤖 DX & Maintenance

• Updated .cursorrules: Added standard procedure for test output preservation and release management.
• Updated Test Infrastructure: Updated SeedVR2 reference images and fixed dimension-related test failures.

---

[0.14.0] - 2025-12-31

🎨 New Model Support

• SeedVR2 Diffusion Upscaler: Added initial SeedVR2 image upscaling support.
• New command: mflux-upscale-seedvr2 for high-quality image upscaling.
• Tiling support: Tiling is enabled by default for SeedVR2 to support high-resolution upscaling on standard memory configurations.

🔧 Improvements

• Global VAE Tiling Support: Introduced a unified VAE tiling system (VAETiler) that supports both tiled encoding and decoding.
• Low-RAM Mode Enhancements: Enabling --low-ram now automatically activates VAE tiling across all model families (Flux, Qwen, FIBO, Z-Image), significantly reducing memory pressure for high-resolution generation on Apple Silicon.
• Robust Offline Cache Handling: Improved logic for detecting complete cached models on HuggingFace Hub, handling symlinks and missing files more reliably to prevent runtime errors during offline use.
• Selective Weight Loading: Support for loading specific weight files, enabling more flexible model configurations and better resource sharing between related models.
• CLI UX Improvements:
• Multi-image generation (multiple seeds or input images) now automatically appends suffixes (_seed_{seed} or _{image_name}) to output filenames to prevent accidental overwrites.
• Better model configuration resolution with a priority-based system for resolving ambiguous model names.
• Enhanced Shell Completions: Significant updates to shell completion generation to support new commands and properly handle positional arguments and subparsers.
• Qwen Test Hardening: Updated Qwen image generation and edit tests to use 8-bit quantization for more robust and faster testing.
• Test Infrastructure: Added automatic MLX version pinning (v0.29.2) in make test-fast to ensure consistent test environments across different development setups.

📝 Documentation

• Added information about pre-quantized models available on HuggingFace for easier access.

---

[0.13.3] - 2025-12-06

🐛 Bug Fixes

• LoRA save bloat prevention: Bake and strip LoRA wrappers before sharding to avoid exploding shard counts/sizes when saving quantized models with multiple/mismatched LoRAs (see issue #217 comment).
• Regression test hardening: LoRA model-saving tests now include size guardrails (5% tolerance) while using the bundled local LoRA fixtures to catch shard bloat regressions early.

---

[0.13.2] - 2025-12-05

✨ Improvements

• Better error messages for multi-file LoRA repos: When a HuggingFace LoRA repo contains multiple .safetensors files, the error message now displays copy-paste ready options instead of a raw list
• Z-Image LoRA format support: Added support for Kohya and ComfyUI LoRA naming conventions, enabling compatibility with more community LoRAs.

---

[0.13.1] - 2025-12-03

🐛 Bug Fixes

• FIBO VLM chat template not loaded: Fixed issue where the FIBO VLM tokenizer's chat template was not being loaded with transformers v5, causing apply_chat_template() to fail. The tokenizer loader now properly extracts and sets the chat template from the tokenizer config.

---

[0.13.0] - 2025-12-03

MFLUX v.0.13.0 Release Notes

🎨 New Model Support

• Z-Image Turbo Support: Added support for Z-Image Turbo, a fast distilled Z-Image variant optimized for speed
• New command: mflux-generate-z-image-turbo for rapid image generation (with LoRA support, img2img, and quantization)

✨ New Features

• FIBO VLM Quantization Support: The FIBO VLM commands (mflux-fibo-inspire, mflux-fibo-refine) now support quantization via the -q flag (3, 4, 5, 6, or 8-bit)

• Unified --model argument: The --model flag now accepts local paths, HuggingFace repos, or predefined model names

• Local paths: --model /Users/me/models/fibo-4bit or --model ~/my-model
• HuggingFace repos: --model briaai/Fibo-mlx-4bit
• Predefined names: --model dev, --model schnell, --model fibo

• This mirrors how LoRA paths work for a consistent UX

• Scale Factor Dimensions for Img2Img: Generalized the scale factor feature (e.g., 2x, 0.5x, auto) from upscaling to all img2img commands

• Specify output dimensions relative to input image: --width 2x --height 2x
• Use auto to match input image dimensions: --width auto --height auto
• Mix scale factors with absolute values: --width 2x --height 512
• Supported in: mflux-generate, mflux-generate-z-image-turbo, mflux-generate-fibo, mflux-generate-kontext, mflux-generate-qwen
• DimensionResolver utility: New DimensionResolver.resolve() for consistent dimension handling across commands

🔧 Architecture Improvements

• Unified Resolution System: New resolution/ module for consistent parameter resolution across all models
• PathResolution: Resolves model paths from local paths, HuggingFace repos, or predefined names
• LoRAResolution: Handles LoRA path resolution from all supported formats
• ConfigResolution: Centralizes configuration resolution logic
• QuantizationResolution: Determines quantization from saved models or CLI args
• Unified Weight Loading System: Complete rewrite of weight handling with declarative mappings
• New WeightLoader with single load(model_path) interface
• WeightDefinition classes define model structure per model family
• WeightMapping declarative mappings replace imperative weight handlers
• Removed all per-model weight_handler_*.py files in favor of unified system
• Unified Tokenizer System: New common tokenizer module
• TokenizerLoader.load_all() with unified model_path interface
• Removed model-specific tokenizer handlers (clip_tokenizer.py, t5_tokenizer.py, etc.)
• Unified LoRA API: Simplified LoRA loading to a single lora_paths parameter
• All LoRA formats now resolved through LoRALibrary.resolve_paths():
  • Local paths: /path/to/lora.safetensors
  • Registry names: my-lora (from LORA_LIBRARY_PATH)
  • HuggingFace repos: author/model
  • New: HuggingFace collections: repo_id:filename.safetensors
• Simplified model initialization: just pass lora_paths and everything resolves automatically
• Unified Latent Creator Interface: Standardized unpack_latents(latents, height, width) signature across all model families
• FluxLatentCreator, ZImageLatentCreator, FiboLatentCreator, and QwenLatentCreator now share the same interface
• Moved FIBO._unpack_latents to FiboLatentCreator.unpack_latents for consistency
• StepwiseHandler Refactor: Fixed StepwiseHandler to work with all model types by accepting a latent_creator parameter
• Previously hardcoded to FluxLatentCreator, now model-agnostic
• Each command passes its appropriate latent creator to CallbackManager.register_callbacks()
• CLI Reorganization: Moved CLI entry points to model-specific directories (e.g., mflux/models/flux/cli/)

🔄 Breaking Changes

• Simplified generate_image() API (programmatic users only):
• Removed Config class - parameters are now passed directly to generate_image()
• Removed RuntimeConfig class - internal complexity eliminated
• Added Flux1 export to main mflux module for cleaner imports
• LoRA API simplified (programmatic users only):
• Removed lora_names and lora_repo_id parameters from all model classes (Flux1, QwenImage, QwenImageEdit, etc.)
• Removed --lora-name and --lora-repo-id CLI arguments
• Removed LoRAHuggingFaceDownloader class

🔄 Breaking Changes (CLI)

• --path flag removed: The deprecated --path flag for loading models has been removed. Use --model instead for local paths, HuggingFace repos, or predefined model names.

📦 Dependency Updates

• Updated huggingface-hub from >=0.24.5,<1.0 to >=1.1.6,<2.0
• v1.1.6 includes fix for incomplete file listing in snapshot_download which could cause cache corruption
• Removed explicit accelerate and filelock dependencies (pulled in as transitive dependencies)
• Updated transformers from >=4.57,<5.0 to >=5.0.0rc0,<6.0
• Required for huggingface-hub 1.x compatibility
• Added workaround for Qwen2Tokenizer bug in transformers 5.0.0rc0 where vocab/merges files are not loaded correctly via from_pretrained()

🐛 Bug Fixes

• Qwen empty negative prompt crash: Fixed crash when running Qwen models without a --negative-prompt argument. Empty prompts now use a space as fallback to ensure valid tokenization.

• --model flag not working: Fixed bug where the --model argument wasn't being used for loading models from HuggingFace or local paths. All CLI commands now correctly use --model for model path resolution.

• Model Saving Index File: Fixed issue where locally saved models (via mflux-save) would fail to load when uploaded to HuggingFace, due to missing model.safetensors.index.json. The model saver now generates this index file alongside the safetensor shards, ensuring compatibility with both mflux and standard HuggingFace loading paths. (see #285)

🧪 Test Infrastructure

• Test markers: Added fast and slow pytest markers to categorize tests
• Fast tests: Unit tests that don't generate images (parsers, schedulers, resolution, utilities)
• Slow tests: Integration tests that generate actual images and compare to references
• New Makefile targets:
• make test-fast - Run fast tests only (quick feedback during development)
• make test-slow - Run slow tests only (image generation tests)
• make test - Run all tests (unchanged)
• Run specific test categories: pytest -m fast or pytest -m slow
• GitHub Actions CI: Fast tests now run automatically on PRs and pushes to main

🔧 Internal Changes

• Simplified WeightLoader.load() to take a single model_path parameter instead of separate repo_id and local_path
• Simplified TokenizerLoader.load_all() with the same unified model_path interface
• Renamed local_path parameter to model_path in all model constructors for clarity
• Removed quantization_util.py - quantization now handled through QuantizationResolution
• Removed lora_huggingface_downloader.py - downloading integrated into LoRAResolution
• Added comprehensive test coverage for resolution modules

👩‍💻 Contributors

• Filip Strand (@filipstrand): Z-Image Turbo support, architecture improvements, core development

---

[0.12.1] - 2025-11-27

🐛 Bug Fixes

• FIBO VLM Tokenizer Download: Fixed an issue where the FIBO VLM tokenizer files would not download automatically when the model weights were cached but tokenizer files were missing. The initializer now properly checks for tokenizer file existence and downloads them if needed.

---

[0.12.0] - 2025-11-27

MFLUX v.0.12.0 Release Notes

🎨 New Model Support

• Bria FIBO Support: Added support for FIBO, the first open-source JSON-native text-to-image model from Bria.ai
• Three operation modes: Generate (text-to-image with VLM expansion), Refine (structured prompt editing), and Inspire (image-to-prompt extraction)
• New commands:
• mflux-generate-fibo - Generate images from text prompts with VLM-guided JSON expansion
• mflux-refine-fibo - Refine images using structured JSON prompts for targeted attribute editing
• mflux-inspire-fibo - Extract structured prompts from reference images for style transfer and remixing
• VLM-guided JSON prompting: Automatically expands short text prompts into 1,000+ word structured schemas using a fine-tuned Qwen3-VL model

🔧 Restructure and 🔄 Breaking Changes

• Common module reorganization: Moved shared functionality to models/common/ for better code reuse
• Unified latent creators across model families
• Centralized scheduler implementations
• Common quantization utilities
• Shared model saving functionality

👩‍💻 Contributors

• Filip Strand (@filipstrand): FIBO model implementation, architecture, core development

---

[0.11.1] - 2025-11-13

MFLUX v.0.11.1 Release Notes

🎨 New Model Support

• Qwen Image Edit Support: Added support for the Qwen Image Edit model, enabling natural language image editing capabilities
• New command: mflux-generate-qwen-edit for image editing with text instructions
• Multiple image support: Edit images using multiple reference images via --image-paths parameter
• Model: Uses Qwen/Qwen-Image-Edit-2509 for high-quality image editing
• Quantization support: Full support for quantized models (8-bit recommended for optimal quality)

🔧 Improvements

• Dedicated Qwen Image command: Added mflux-generate-qwen as a dedicated command for Qwen Image model generation. The mflux-generate command now only supports Flux models.
• Image comparison utility refactoring: Refactored image_compare.py into a cleaner class-based structure with static methods
• Error handling: Moved ReferenceVsOutputImageError to the main exceptions module for better organization

🔄 Breaking Changes

⚠️ Qwen Image Command Change: The Qwen Image model now requires using the dedicated mflux-generate-qwen command instead of mflux-generate --model qwen. This provides better separation between Flux and Qwen model families and improves command clarity.

👩‍💻 Contributors

• Filip Strand (@filipstrand): Qwen Image Edit model implementation, code refactoring

---

[0.11.0] - 2025-10-14

MFLUX v.0.11.0 Release Notes

🎨 New Model Support

• Qwen Image Support: Added support for the Qwen Image text-to-image model, enabling a new generation of visual content creation
• New command: mflux-generate now supports Qwen models for image generation
• Qwen-specific features: Full LoRA support with Qwen naming conventions, img2img support, and optimized weight handling
• Qwen-Image-mflux-6bit Model: Added filipstrand/Qwen-Image-mflux-6bit quantized model to HF

🏗️ Major Architecture Improvements

• Package Restructure: Complete reorganization of the codebase to support multiple model architectures
• Moved from flat structure to organized models/ hierarchy (models/flux/, models/qwen/, models/depth_pro/)
• Better separation of concerns with dedicated model, variant, tokenizer, and weight handler modules
• Improved maintainability and extensibility for future model additions
• Namespace Package: Converted mflux to a namespace package (in preparation for mflux.mcp extension)
• Common Module: Extracted shared functionality into models/common/ for better code reuse
• Unified LoRA handling across different model types
• Shared attention utilities
• Common download and weight management utilities

📊 Metadata Enhancements

• XMP/IPTC Metadata Support: Added comprehensive metadata support for professional workflows
• Write XMP and IPTC metadata to generated images
• Industry-standard metadata formats for better compatibility with professional image tools
• Enhanced metadata reading and writing capabilities
• New mflux-info command: Display detailed metadata information from generated images
• View generation parameters, model information, and settings
• Extract metadata from any mflux-generated image
• Professional-grade metadata inspection

🔧 Scheduler System

• Scheduler Interface: Introduced a new scheduler abstraction for better extensibility
• Clean interface for implementing custom sampling schedulers
• Foundation for future scheduler additions (Euler, DPM++, etc.)
• Current implementation: Linear scheduler (existing behavior preserved)
• Scheduler Selection: Added --scheduler command-line argument for choosing schedulers

🐛 Bug Fixes

• Non-Quantized Model Loading: Fixed critical bug where locally saved non-quantized models failed to load properly
• Model Weight Handling: Improved weight loading reliability for edge cases

🔧 Developer Experience

• MLX 0.29.2 Support: Updated MLX dependency to support the latest version (mlx>=0.27.0,<0.30.0)
• Python 3.13 Support: Unblocked sentencepiece and torch dependencies for Python 3.13
• Updated dependency specifications for better Python 3.13 compatibility
• Ensured smooth experience on latest Python versions
• Test Improvements: Enhanced image comparison logic to allow similar images that are "close enough"
• More robust test suite that accommodates minor numerical differences
• Reduced false positives in image generation tests
• CI Updates: Removed Claude CI agent (replacement coming soon)

🔄 Breaking Changes

⚠️ Import Path Changes: Due to the package restructure, some internal import paths have changed. If you're using mflux as a library and importing internal modules directly, you may need to update your imports: - Flux modules moved from mflux.flux.* to mflux.models.flux.* - Common utilities moved to mflux.models.common.* - CLI tools remain unchanged and fully backward compatible

👩‍💻 Contributors

• Filip Strand (@filipstrand): Qwen model support, package restructure, core development
• Alessandro Rizzo (@azrahello): XMP/IPTC metadata support, info command implementation
• Anthony Wu (@anthonywu): Scheduler interface, namespace package conversion, Python 3.13 improvements, bug fixes

---

[0.10.0] - 2025-08-04

MFLUX v.0.10.0 Release Notes

🎨 Model Improvements

• FLUX.1 Krea [dev] Support!
• FLUX.1-Krea-dev-mflux-4bit Model: Added filipstrand/FLUX.1-Krea-dev-mflux-4bit quantized model to HF
• FLUX.1-Kontext-dev-mflux-4bit Model: Added akx/FLUX.1-Kontext-dev-mflux-4bit quantized model to HF, contributed by @akx

✨ New Features

• 5-bit Quantization Support: Added support for 5-bit quantization as a new option alongside existing 3, 4, 6, and 8-bit quantization levels

🔧 Improvements

• Enhanced Default Inference Steps: Increased default inference steps for dev models from 14 to 25 for improved image quality
• Multiple Model Aliases Support: Improved model configuration system to properly support multiple aliases per model, making model selection more flexible and robust

🐛 Bug Fixes

• LoRA Resume Training: Fixed critical bug where adapters created after training interruption would fail to load for generation with AttributeError: 'list' object has no attribute 'weight'. The issue occurred because the resume loading logic wasn't properly handling layers that are legitimately lists in the transformer architecture (like attn.to_out). (see #224)

🔧 Technical Requirements

• MLX Compatibility: This release assumes MLX 0.27.0 and upwards for optimal performance and compatibility
• MLX Compatibility for test: Fix MLX version to 0.27.1 for image generation tests
• Non-strict Weight Updates: Explicitly added non-strict mode (strict=False) for weight updates to maintain compatibility with later MLX versions that enforce stricter weight validation by default

👩‍💻 Developer Experience

• Streamlined Release Process: Removed TestPyPi publishing step from release workflow for simplified deployment

🙏 Contributors

• @filipstrand - FLUX.1 Krea [dev] model support, 5-bit quantization, enhanced defaults, and various improvements
• @akx - Added 4-bit quantized Kontext model to HF

---

[0.9.6] - 2025-07-20

MFLUX v.0.9.6 Release Notes

🔧 Technical Details

• Cap the upper MLX dependency to a known working version (0.26.1) to avoid compatibility issues with newer MLX releases that enforce stricter weight validation (see #238)

[0.9.5] - 2025-07-17

MFLUX v.0.9.5 Release Notes

🐛 Bug Fixes

• Fixed faulty imports: Corrected import issues in the mflux module to ensure proper package initialization and functionality

[0.9.4] - 2025-07-17

MFLUX v.0.9.4 Release Notes

🛠️ Dependency Updates

• Expanded MLX dependency range from mlx>=0.22.0,<=0.26.1 to mlx>=0.22.0,<0.27.0 to support newer MLX versions

🔧 Developer Experience

• Refactor the release script into a reusable Python module for better maintainability

[0.9.3] - 2025-07-08

MFLUX v.0.9.3 Release Notes

😖 Revert "Offline Resilience" change

On a "cold start" where user has not previously downloaded the requested model, the workflow does not successfully request the download of all the expected files, blocking the image generation workflow for first time users. The feature will be re-evaluated carefully after this hot fix.

[0.9.2] - 2025-07-08

MFLUX v.0.9.2 Release Notes

🏗️ Build System Improvements

• Updated build backend: Migrated from setuptools to modern uv build backend for faster and more reliable package builds
• Enhanced artifact exclusion: Optimized distribution packages by excluding documentation assets (~27MB) and example images (~5MB) from published packages
• New make build command: Added development build command for testing distribution packages and validating sizes

🗃️ Offline Resilience

• Local-first behavior: Implemented cache-first downloading to improve resilience when HuggingFace Hub or network connectivity is unavailable
• Graceful fallback: System automatically uses cached model files when available, falling back to downloads only when necessary
• Improved reliability: Enhanced model loading reliability in environments with unstable internet connections

🔧 Developer Experience

• Release script improvements: Enhanced release automation with better error handling and duplicate version detection
• Build system fixes: Fixed minor typos in Makefile that could cause build issues

Contributors

• Anthony Wu (@anthonywu): Build system modernization, offline resilience implementation
• Filip Strand (@filipstrand): Release automation improvements, build fixes

---

[0.9.1] - 2025-07-04

MFLUX v.0.9.1 Release Notes

🛠️ Dependency Fixes

• Restricted MLX dependency upper bound to 0.26.1 (mlx>=0.22.0,<=0.26.1) to prevent incompatibility issues with MLX 0.26.2.

🎨 Inpaint Mask Tool Improvements

• Enhanced interactive inpaint masking tool with additional shape options (ellipse, rectangle, and free-hand drawing).
• Added eraser mode for precise mask corrections.
• Implemented undo/redo history for non-destructive editing when crafting masks.

👩‍💻 Developer Experience

• Introduced initial mypy static-type checking configuration and performed a first round of type-hint clean-up across the codebase.
• Upgraded pre-commit hooks and addressed newly surfaced lint warnings for a cleaner commit experience.

Contributors

• Filip Strand (@filipstrand)
• Anthony Wu (@anthonywu)

---

[0.9.0] - 2025-06-28

MFLUX v.0.9.0 Release Notes

Major New Features

📸 FLUX.1 Kontext

• Added FLUX.1 Kontext support: Official Black Forest Labs model for character consistency, local editing, and style reference
• New command: mflux-generate-kontext for image-guided generation with text instructions
• Advanced image editing capabilities: Sequential editing, style transfer, character consistency, and local modifications
• Comprehensive documentation: Detailed prompting guide with tips, templates, and best practices
• Automatic model handling: Uses dev-kontext model configuration with optimized defaults

🖼️ Scale Factor Support for Image Upscaling

• Enhanced upscaling dimensions: Added support for scale factors (e.g., 2x, 1.5x) in addition to absolute pixel values
• Mixed dimension types: Ability to combine scale factors and absolute values (e.g., --height 2x --width 1024)
• Auto dimension handling: Use auto to preserve original image dimensions
• Safety warnings: Automatic warnings when requested dimensions exceed recommended limits
• Pixel-perfect scaling: Scale factors automatically align to 16-pixel boundaries for optimal results

⌨️ Shell Completions

• ZSH completion support: Full tab completion for all mflux CLI commands and arguments
• Smart completions: Context-aware completions for model names, quantization levels, LoRA styles, and file paths
• Easy installation: Simple mflux-completions command for automatic setup
• Dynamic generation: Completions stay in sync with code changes and new commands
• Comprehensive coverage: Supports all 15+ mflux commands with proper argument validation

🗂️ Cache Management Improvements

• Platform-native caching: Uses platformdirs for macOS-idiomatic cache locations (~/Library/Caches/mflux/)
• Automatic migration: Seamless migration from legacy ~/.cache/mflux to new platform-appropriate locations
• Environment variable support: MFLUX_CACHE_DIR for custom cache locations
• Improved organization: Separate cache directories for different types of data (models, LoRAs, etc.)
• Backward compatibility: Automatic symlink creation for legacy path compatibility

Breaking Changes

🔧 Python API Class Naming Standardization

• Class rename: FluxInContextFill is now Flux1InContextFill to follow consistent naming convention
• Class rename: FluxConceptFromImage is now Flux1ConceptFromImage to follow consistent naming convention
• Breaking change for library users: If you import these classes directly in Python code, you may need to update your imports
• CLI tools unaffected: All command-line tools (mflux-generate-*) continue to work without changes

Contributors

Contributors: - Anthony Wu (@anthonywu): Scale factor support, shell completions, cache refactor - Filip Strand (@filipstrand): Kontext support, class naming standardization, core development

[0.8.0] - 2025-06-14

MFLUX v.0.8.0 Release Notes

Experimental AI Features

👗 CatVTON (Virtual Try-On)

• [EXPERIMENTAL] Added virtual try-on capabilities using in-context learning via mflux-generate-in-context-catvton
• Support for person image, person mask, and garment image inputs for comprehensive virtual clothing try-on
• Automatic prompting for virtual try-on scenarios with optimized default prompts
• Side-by-side generation showing garment product shot alongside styled result
• AI-powered virtual clothing fitting with realistic lighting and fabric properties

✏️ IC-Edit (In-Context Editing)

• [EXPERIMENTAL] Added natural language image editing capabilities via mflux-generate-in-context-edit
• Natural language image editing using simple text instructions like "make the hair black" or "add sunglasses"
• Automatic diptych template formatting for optimal editing results
• Optimal resolution auto-sizing for 512px width (the resolution IC-Edit was trained on)
• Specialized LoRA automatically downloaded and applied for enhanced editing capabilities

Enhanced Generation Control

🔎 Image Upscaling

• Built-in upscaling capabilities: Enhanced image quality and resolution enhancement for generated images
• Seamless integration with existing generation workflow
• Professional-grade upscaling for production-ready outputs

Interpretability research

🧠 Concept Attention

• Enhanced image generation control: Fine-grained control over image generation focus areas using attention-based concepts
• Improved composition and subject handling for more precise artistic direction
• Advanced attention mechanisms for better understanding of prompt concepts

Workflow & Performance Improvements

🪫 Battery Saver

• Power management: Automatic power optimization during extended generation sessions
• Configurable power-saving modes specifically designed for laptop users
• Smart resource management for long-running batch operations

📝 Prompt File Support

• File-based prompt input: Batch operations via --prompt-file for large-scale generation projects
• Dynamic prompt updates for large batch generation workflows
• Support for external prompt management and automation systems

🔄 Redux Function Balancing

• Enhanced Redux capabilities: Improved control over image-to-image transformation strength
• Better quality variations with adjustable parameters for more predictable results
• Refined Redux algorithm for more natural image variations

📥 Stdin Prompt Support

• LLM Integration Ready: Added support for providing prompts via stdin using --prompt -
• Enables seamless integration with LLMs and other text generation tools
• Supports both single-line and multi-line prompts through stdin
• Perfect for automation workflows and dynamic prompt generation
• Example usage: echo "A beautiful landscape" | mflux-generate --prompt -

Developer Experience

🔧 LORA_LIBRARY_PATH Improvements

• Unix-style resource discovery: Enhanced LoRA library path handling for better organization
• Improved path handling for LoRA weight discovery across multiple directories
• Better cross-platform compatibility for LoRA management

🧪 Testing & Documentation

• New command-line arguments for both experimental features with comprehensive help
• Comprehensive argument parser tests for new functionality
• Updated documentation with experimental feature warnings and usage guidelines
• Added note about upcoming FLUX.1 Kontext model from Black Forest Labs

Architecture Improvements

📚 Documentation Structure

• Refactored "In-Context LoRA" section to "In-Context Generation" with clear subcategories
• Enhanced documentation structure for better organization and user navigation
• Improved categorization of experimental vs stable features

🔄 Code Architecture Changes

• Class rename: Flux1InContextLora is now Flux1InContextDev to better reflect the dev model variant
• Module reorganization: Moved from mflux.community.in_context_lora.flux_in_context_lora to mflux.community.in_context.flux_in_context_dev
• Breaking change for library users: If you import the class directly, update your imports accordingly

⚡ Performance Optimizations

• Updated MLX dependency to latest version for improved performance and stability
• Removed PyTorch dependency for DepthPro model, significantly reducing installation requirements
• Streamlined dependencies for faster installation and reduced disk usage

Experimental Notice

⚠️ Important: CatVTON and IC-Edit features are experimental and may be removed or significantly changed in future updates. These features represent cutting-edge AI capabilities that are still under active development.

Contributors

Special thanks to the following contributors for their exceptional work since v0.7.1: - Anthony Wu (@anthonywu): Battery Saver implementation, Prompt File Support, Stdin Prompt Support, LORA_LIBRARY_PATH improvements - Alessandro (@azrahello): Redux Function Balancing enhancements - Filip Strand (@filipstrand): Core development, experimental features integration, infrastructure improvements

[0.7.1] - 2025-05-06

MFLUX v.0.7.1 Release Notes

New Features

🎭 Multi-LoRA Support

• Multiple LoRA Loading: Added support for loading multiple LoRA adapters simultaneously when using the in-context feature
• Enhanced creative flexibility by combining multiple artistic styles in a single generation
• Reference: GitHub Issue #178

[0.7.0] - 2025-04-25

MFLUX v.0.7.0 Release Notes

Major New Features

🖌️ FLUX.1 Tools | Fill

• Added support for the FLUX.1-Fill model for inpainting and outpainting
• Introduced mflux-generate-fill command-line tool for selective image editing
• Implemented interactive mask creation tool to easily mark areas for regeneration
• Added outpainting capabilities with customizable canvas expansion
• Includes helper tools for creating outpaint image canvases and masks

🔍 FLUX.1 Tools | Depth

• Added support for the FLUX.1-Depth model for depth-conditioned image generation
• Implemented Apple's ML Depth Pro model in MLX for state-of-the-art depth map extraction
• Added mflux-generate-depth and mflux-save-depth command-line tools
• Added ability to use either auto-generated depth maps or custom depth maps

🔄 FLUX.1 Tools | Redux

• Added Redux tool as a new image variation technique
• Implemented a different approach compared to standard image-to-image generation
• Uses image embedding joined with T5 text encodings for more natural variations
• Added Redux-specific weight handlers and initialization

New Models

🔎 Apple ML Depth Pro

• Added native MLX implementation of Apple's ML Depth Pro model for both separate use, and as a part of the Depth tool functionality

🖼️ Google SigLIP Vision Transformer

• Added SigLIP vision model for the Redux functionality

Architecture Improvements

💾 Weight Management Improvements

• Added support for saving MFLUX version information in model metadata

🧠 Memory Optimization

• Additional improvements to the --low-ram option
• Better memory management for image generation models

Contributors

• @anthonywu
• @ssakar
• @akx

[0.6.2] - 2025-03-13

MFLUX v.0.6.2 Release Notes

Bug Fixes

💾 Model Saving Fix

• Fixed local model saving: Resolved bug preventing users from saving models locally with mflux-save
• Restored full functionality for local model storage and management

[0.6.1] - 2025-03-11

MFLUX v.0.6.1 Release Notes

Bug Fixes

🛑 Image Generation Interruption

• Fixed interruption flow: Properly handles interruptions during image generation, ensuring graceful stops even when no callbacks are registered
• Keyboard interrupt handling: Ensures image generation can be stopped via Ctrl+C in all diffusion model variants (standard Flux, ControlNet, and In-Context LoRA)
• Relocated StopImageGenerationException from stepwise handler to main generation functions for more robust interruption system

Test Stability Improvements

🧪 Test Reliability

• Fixed sporadic test failures: Resolved intermittent failures in auto-seeds test case when using random seed count of 1
• Improved test consistency and reliability

Code Quality Improvements

🔧 Code Standards

• Formatting and linting fixes: Fixed various formatting issues that were missed in the v0.6.0 release
• Enhanced code consistency and maintainability

[0.6.0] - 2025-03-05

MFLUX v.0.6.0 Release Notes

Major New Features

🌐 Third-Party HuggingFace Model Support

• Comprehensive ModelConfig refactor to support compatible HuggingFace dev/schnell models
• Added ability to use models like Freepik/flux.1-lite-8B-alpha and shuttleai/shuttle-3-diffusion
• New --base-model parameter to specify which base architecture (dev or schnell) a third-party model is derived from
• Maintains backward compatibility while opening up the ecosystem to community-created models

🎭 In-Context LoRA

• Added support for In-Context LoRA, a powerful technique that allows you to generate images in a specific style based on a reference image without requiring model fine-tuning
• Introduced a new command-line tool: mflux-generate-in-context
• Includes 10 pre-defined styles from the Hugging Face ali-vilab/In-Context-LoRA repository
• Detailed documentation on how to use this feature effectively with prompting tips and best practices

🔌 Automatic LoRA Downloads

• Added ability to automatically download LoRAs from Hugging Face when specified by repository ID
• Simplifies workflow by eliminating the need to manually download LoRA files before use

🧠 Memory Optimizations

• Added --low-ram option to reduce GPU memory usage by constraining the MLX cache size and releasing text encoders and transformer components after use
• Implemented memory saver for ControlNet to reduce RAM requirements
• General memory usage optimizations throughout the codebase

🗜️ Enhanced Quantization Options

• Added support for 3-bit and 6-bit quantization (requires mlx > v0.21.0)
• Expanded quantization options now include 3, 4, 6, and 8-bit precision

⚠️Breaking changes

Previously saved quantized models will not work for v.0.6.0 and later. See #149 for more details.

Interface Improvements

🔧 Modified Parameters

• The previous --init-image-path parameter is now --image-path
• The previous --init-image-strength parameter is now --image-strength

🖼️ Image Generation Enhancements

• Added --auto-seeds option to generate multiple images with random seeds in a single command
• Added option to override previously saved test images
• Added --controlnet-save-canny option to save the Canny edge detection reference image used by ControlNet
• Improved handling of edge cases for img2img generation

🔄 Callback System

• Implemented a general callback mechanism for more flexible image generation pipelines
• Added support for before-loop callbacks to accept latents
• Enhanced StepwiseHandler to include initial latent

Architecture Improvements

🏗️ Code Refactoring

• Removed 'init' prefix for a more general interface
• Removed ConfigControlnet - the controlnet_strength attribute is now on Config
• Simplified quantization by removing unnecessary class predicates
• Refactored model configuration system
• Refactored transformer blocks for better maintainability
• Unified attention mechanism in single and joint attention blocks
• Added support for variable numbers of transformer blocks
• Optimized with fast SDPA (Scaled Dot-Product Attention)
• Added PromptCache for small optimization when generating with repeated prompts

🧰 Developer Tools

• Added Batch Image Renamer tool as an isolated uv run script
• Added descriptive comments for attention computations

Compatibility Updates

• Updated to support the latest mlx version
• Fixed compatibility issues with HuggingFace dev/schnell models

Bug Fixes

• Fixed handling of edge cases for img2img generation
• Various small fixes and improvements throughout the codebase

Contributors

• @anthonywu
• @ssakar
• @azrahello
• @DanaCase

[0.5.1] - 2024-12-23

MFLUX v.0.5.1 Release Notes

Bug Fixes

🔧 LoRA Loading Fix

• Quantized model LoRA compatibility: Fixed critical bug where locally saved quantized models failed to set LoRA weights
• Users can now successfully combine local quantized models with external LoRA adapters
• Improved reliability for advanced workflows combining quantization and LoRA fine-tuning

[0.5.0] - 2024-12-22

MFLUX v.0.5.0 Release Notes

Major New Features

🎛️ DreamBooth Fine-tuning

• DreamBooth support: Introduced V1 of fine-tuning support in MFLUX
• Enables custom model training for personalized image generation
• Full fine-tuning pipeline with training configuration options

Architecture Improvements

🔧 Weight Management Overhaul

• Rewritten LoRA handling: Completely rewritten LoRA weight handling system
• Improved performance and reliability for LoRA operations
• Better support for complex LoRA workflows

Developer Experience

🧪 Testing & Quality

• Enhanced test coverage: Added comprehensive tests for new and existing features
• Multi-LoRA testing support
• Local model saving test coverage

📊 New Dependencies

• Matplotlib integration: Added matplotlib for visualizing training loss during fine-tuning
• TOML support: Added TOML library for better handling of MFLUX version metadata
• Enhanced configuration management

[0.4.1] - 2024-10-29

MFLUX v.0.4.1 Release Notes

Bug Fixes

🐛 Image Generation Fixes

• Img2img resolution fix: Fixed img2img functionality for non-square image resolutions
• Improved compatibility with various aspect ratios

[0.4.0] - 2024-10-28

MFLUX v.0.4.0 Release Notes

Major New Features

🖼️ Image-to-Image Generation

• Img2Img Support: Introduced the ability to generate images based on an initial reference image
• Transform existing images using AI-powered generation techniques
• Control the strength of transformation to balance between original image preservation and creative generation
• Perfect for iterating on designs and creating variations of existing artwork

📊 Metadata-Driven Generation

• Image Generation from Metadata: Added support to generate images directly from provided metadata files
• Streamlined workflow for recreating images with specific parameters
• Enhanced reproducibility for professional and research workflows
• Automated parameter loading from previously generated images

🔍 Real-time Generation Monitoring

• Progressive Step Output: Optionally output each step of the image generation process for real-time monitoring
• Visual feedback during generation for better understanding of the AI process
• Debug and fine-tune generation parameters by observing intermediate steps
• Educational tool for understanding diffusion model progression

Developer Experience Improvements

🛠️ Enhanced Command-Line Interface

• Improved argument handling: Enhanced parsing and validation for command-line arguments
• Better error messages and user guidance for parameter configuration
• More intuitive command structure for complex generation workflows

🧪 Testing & Quality Assurance

• Automated Testing: Added comprehensive automatic tests for image generation and command-line argument handling
• Improved reliability and stability for all generation modes
• Continuous integration testing for better code quality

🔧 Development Workflow

• Pre-Commit Hooks: Integrated pre-commit hooks with ruff, isort, and typo checks for better code consistency
• Enhanced developer experience with automated code quality checks
• Streamlined contribution process for open source development

[0.3.0] - 2024-09-24

MFLUX v.0.3.0 Release Notes

Major New Features

🕹️ ControlNet Support

• ControlNet Canny support: Added Canny edge detection ControlNet functionality for precise image control
• Enhanced control over image generation with edge-guided conditioning

Model Export Improvements

📦 Advanced Model Export

• Quantized model export with LoRA: Added ability to export quantized models with LoRA weights baked in
• Streamlined deployment for fine-tuned models

Developer Experience

🛠️ Development Tools

• Enhanced development workflow: Improved developer experience with uv, ruff, makefile, pre-commit hooks
• Better code quality tools and automated checks
• Streamlined contribution process

Legal & Licensing

⚖️ Open Source License

• Official MIT license: Established clear open source licensing for the project
• Legal clarity for users and contributors

[0.2.1] - 2024-09-14

MFLUX v.0.2.1 Release Notes

Improvements

🔧 LoRA Enhancements

• Enhanced LoRA support: Improved compatibility and performance for LoRA weight loading
• Better integration with existing workflows
• Refined handling of LoRA adapters

[0.2.0] - 2024-09-07

MFLUX v.0.2.0 Release Notes

Major Milestone

🚀 Official PyPI Release

• First official PyPI release: pip install mflux - making MFLUX easily installable for everyone
• Big thanks to @deto for letting us have the "mflux" name on PyPI!

New Features

🎨 Core Image Generation

• Command-line tools: Introduced dedicated commands for better user experience
• mflux-generate for generating images
• mflux-save for saving quantized models to disk
• 🗜️ Quantization support: Added support for quantized models with 4-bit and 8-bit precision
• LoRA weights: Added support for loading trained LoRA (Low-Rank Adaptation) weights
• Automatic metadata: Images now automatically save metadata when generated

Developer Experience

📦 Distribution

• Official packaging and distribution through PyPI
• Simplified installation process for end users
• Professional project structure and naming