## Summary
Fix Z-Image LoRA/DoRA model detection failing during installation.
Z-Image LoRAs use different key patterns than SD/SDXL LoRAs. The base
`LoRA_LyCORIS_Config_Base` class only checked for key suffixes like
`lora_A.weight` and `lora_B.weight`, but Z-Image LoRAs (especially those
in DoRA format) use:
- `lora_down.weight` / `lora_up.weight` (standard LoRA format)
- `dora_scale` (DoRA weight decomposition)
This PR overrides `_validate_looks_like_lora` in
`LoRA_LyCORIS_ZImage_Config` to recognize Z-Image specific patterns:
- Keys starting with `diffusion_model.layers.` (Z-Image S3-DiT
architecture)
- Keys ending with `lora_down.weight`, `lora_up.weight`,
`lora_A.weight`, `lora_B.weight`, or `dora_scale`
## Related Issues / Discussions
Fixes installation of Z-Image LoRAs trained with DoRA (Weight-Decomposed
Low-Rank Adaptation).
## QA Instructions
1. Download a Z-Image LoRA in DoRA format (e.g., from CivitAI with keys
like `diffusion_model.layers.X.attention.to_k.lora_down.weight`)
2. Try to install the LoRA via Model Manager
3. Verify the model is recognized as a Z-Image LoRA and installs
successfully
4. Verify the LoRA can be applied when generating with Z-Image
## Merge Plan
Standard merge, no special considerations.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _❗Changes to a redux slice have a corresponding migration_
- [ ] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
Two fixes for Z-Image LoRA support:
1. Override _validate_looks_like_lora in LoRA_LyCORIS_ZImage_Config to
recognize Z-Image specific LoRA formats that use different key patterns
than SD/SDXL LoRAs. Z-Image LoRAs use lora_down.weight/lora_up.weight
and dora_scale suffixes instead of lora_A.weight/lora_B.weight.
2. Fix _group_by_layer in z_image_lora_conversion_utils.py to correctly
group LoRA keys by layer name. The previous logic used rsplit with
maxsplit=2 which incorrectly grouped keys like:
- "to_k.alpha" -> layer "diffusion_model.layers.17.attention"
- "lora_down.weight" -> layer "diffusion_model.layers.17.attention.to_k"
Now uses suffix matching to ensure all keys for a layer are grouped
together (alpha, dora_scale, lora_down.weight, lora_up.weight).
Override _validate_looks_like_lora in LoRA_LyCORIS_ZImage_Config to
recognize Z-Image specific LoRA formats that use different key patterns
than SD/SDXL LoRAs.
Z-Image LoRAs (including DoRA format) use keys like:
- diffusion_model.layers.X.attention.to_k.lora_down.weight
- diffusion_model.layers.X.attention.to_k.dora_scale
The base LyCORIS config only checked for lora_A.weight/lora_B.weight
suffixes, missing the lora_down.weight/lora_up.weight and dora_scale
patterns used by Z-Image LoRAs.
* feat: Add Regional Guidance support for Z-Image model
Implements regional prompting for Z-Image (S3-DiT Transformer) allowing
different prompts to affect different image regions using attention masks.
Backend changes:
- Add ZImageRegionalPromptingExtension for mask preparation
- Add ZImageTextConditioning and ZImageRegionalTextConditioning data classes
- Patch transformer forward to inject 4D regional attention masks
- Use additive float mask (0.0 attend, -inf block) in bfloat16 for compatibility
- Alternate regional/full attention layers for global coherence
Frontend changes:
- Update buildZImageGraph to support regional conditioning collectors
- Update addRegions to create z_image_text_encoder nodes for regions
- Update addZImageLoRAs to handle optional negCond when guidance_scale=0
- Add Z-Image validation (no IP adapters, no autoNegative)
* @Pfannkuchensack
Fix windows path again
* ruff check fix
* ruff formating
* fix(ui): Z-Image CFG guidance_scale check uses > 1 instead of > 0
Changed the guidance_scale check from > 0 to > 1 for Z-Image models.
Since Z-Image uses guidance_scale=1.0 as "no CFG" (matching FLUX convention),
negative conditioning should only be created when guidance_scale > 1.
---------
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
* (bugfix)(mm) work around Windows being unable to rmtree tmp directories after GGUF install
* (style) fix ruff error
* (fix) add workaround for Windows Permission Denied on GGUF file move() call
* (fix) perform torch copy() in GGUF reader to avoid deletion failures on Windows
* (style) fix ruff formatting issues
Add support for loading Flux LoRA models in the xlabs format, which uses
keys like `double_blocks.X.processor.{qkv|proj}_lora{1|2}.{down|up}.weight`.
The xlabs format maps:
- lora1 -> img_attn (image attention stream)
- lora2 -> txt_attn (text attention stream)
- qkv -> query/key/value projection
- proj -> output projection
Changes:
- Add FluxLoRAFormat.XLabs enum value
- Add flux_xlabs_lora_conversion_utils.py with detection and conversion
- Update formats.py to detect xlabs format
- Update lora.py loader to handle xlabs format
- Update model probe to accept recognized Flux LoRA formats
- Add unit tests for xlabs format detection and conversion
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
* Feature: Add Tag System for user made Workflows
* feat(ui): display tags on workflow library tiles
Show workflow tags at the bottom of each tile in the workflow browser,
making it easier to identify workflow categories at a glance.
---------
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
* feat(nodes): add Prompt Template node
Add a new node that applies Style Preset templates to prompts in workflows.
The node takes a style preset ID and positive/negative prompts as inputs,
then replaces {prompt} placeholders in the template with the provided prompts.
This makes Style Preset templates accessible in Workflow mode, enabling
users to apply consistent styling across their workflow-based generations.
* feat(nodes): add StylePresetField for database-driven preset selection
Adds a new StylePresetField type that enables dropdown selection of
style presets from the database in the workflow editor.
Changes:
- Add StylePresetField to backend (fields.py)
- Update Prompt Template node to use StylePresetField instead of string ID
- Add frontend field type definitions (zod schemas, type guards)
- Create StylePresetFieldInputComponent with Combobox
- Register field in InputFieldRenderer and nodesSlice
- Add translations for preset selection
* fix schema.ts on windows.
* chore(api): regenerate schema.ts after merge
---------
Co-authored-by: Claude <noreply@anthropic.com>
* fix(model-install): support multi-subfolder downloads for Z-Image Qwen3 encoder
The Z-Image Qwen3 text encoder requires both text_encoder and tokenizer
subfolders from the HuggingFace repo, but the previous implementation
only downloaded the text_encoder subfolder, causing model identification
to fail.
Changes:
- Add subfolders property to HFModelSource supporting '+' separated paths
- Extend filter_files() and download_urls() to handle multiple subfolders
- Update _multifile_download() to preserve subfolder structure
- Make Qwen3Encoder probe check both nested and direct config.json paths
- Update Qwen3EncoderLoader to handle both directory structures
- Change starter model source to text_encoder+tokenizer
* ruff format
* fix schema description
* fix schema description
---------
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
* feat(ui): add model path update for external models
Add ability to update file paths for externally managed models (models with
absolute paths). Invoke-controlled models (with relative paths in the models
directory) are excluded from this feature to prevent breaking internal
model management.
- Add ModelUpdatePathButton component with modal dialog
- Only show button for external models (absolute path check)
- Add translations for path update UI elements
* Added support for Windows UNC paths in ModelView.tsx:38-41. The isExternalModel function now detects:
Unix absolute paths: /home/user/models/...
Windows drive paths: C:\Models\... or D:/Models/...
Windows UNC paths: \\ServerName\ShareName\... or //ServerName/ShareName/...
* fix(ui): validate path format in Update Path modal to prevent invalid paths
When updating an external model's path, the new path is now validated to ensure
it follows an absolute path format (Unix, Windows drive, or UNC). This prevents
users from accidentally entering invalid paths that would cause the Update Path
button to disappear, leaving them unable to correct the mistake.
* fix(ui): extract isExternalModel to separate file to fix circular dependency
Moves the isExternalModel utility function to its own file to break the
circular dependency between ModelView.tsx and ModelUpdatePathButton.tsx.
---------
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
## Summary
Add Z-Image Turbo and related models to the starter models list for easy
installation via the Model Manager:
- **Z-Image Turbo** - Full precision Diffusers format (~13GB)
- **Z-Image Turbo (quantized)** - GGUF Q4_K format (~4GB)
- **Z-Image Qwen3 Text Encoder** - Full precision (~8GB)
- **Z-Image Qwen3 Text Encoder (quantized)** - GGUF Q6_K format (~3.3GB)
- **Z-Image ControlNet Union** - Unified ControlNet supporting Canny,
HED, Depth, Pose, MLSD, and Inpainting modes
The quantized Turbo model includes the quantized Qwen3 encoder as a
dependency for automatic installation.
## Related Issues / Discussions
Builds on the Z-Image Turbo support added in main.
## QA Instructions
1. Open Model Manager → Starter Models
2. Search for "Z-Image"
3. Verify all 5 models appear with correct descriptions
4. Install the quantized version and confirm the Qwen3 encoder
dependency is also installed
## Merge Plan
Standard merge, no special considerations.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _❗Changes to a redux slice have a corresponding migration_
- [ ] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
Add higher quality Q8_0 quantization option for Z-Image Turbo (~6.6GB)
to complement existing Q4_K variant, providing better quality for users
with more VRAM.
Add dedicated Z-Image ControlNet Tile model (~6.7GB) for upscaling and
detail enhancement workflows.
## Summary
Fix shape mismatch when loading GGUF-quantized Z-Image transformer
models.
GGUF Z-Image models store `x_pad_token` and `cap_pad_token` with shape
`[3840]`, but diffusers `ZImageTransformer2DModel` expects `[1, 3840]`
(with batch dimension). This caused a `RuntimeError` on Linux systems
when loading models like `z_image_turbo-Q4_K.gguf`.
The fix:
- Dequantizes GGMLTensors first (since they don't support `unsqueeze`)
- Reshapes the tensors to add the missing batch dimension
## Related Issues / Discussions
Reported by Linux user using:
-
https://huggingface.co/leejet/Z-Image-Turbo-GGUF/resolve/main/z_image_turbo-Q4_K.gguf
-
https://huggingface.co/worstplayer/Z-Image_Qwen_3_4b_text_encoder_GGUF/resolve/main/Qwen_3_4b-Q6_K.gguf
## QA Instructions
1. Install a GGUF-quantized Z-Image model (e.g.,
`z_image_turbo-Q4_K.gguf`)
2. Install a Qwen3 GGUF encoder
3. Run a Z-Image generation
4. Verify no `RuntimeError: size mismatch for x_pad_token` error occurs
## Merge Plan
None, straightforward fix.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _❗Changes to a redux slice have a corresponding migration_
- [ ] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
## Summary
Add support for Z-Image ControlNet V2.0 alongside the existing V1
support.
**Key changes:**
- Auto-detect `control_in_dim` from adapter weights (16 for V1, 33 for
V2.0)
- Auto-detect `n_refiner_layers` from state dict
- Add zero-padding for V2.0's additional control channels (diffusers
approach)
- Use `accelerate.init_empty_weights()` for more efficient model
creation
- Add `ControlNet_Checkpoint_ZImage_Config` to frontend schema
## Related Issues / Discussions
Part of Z-Image feature implementation.
## QA Instructions
1. Load a Z-Image ControlNet V1 model (control_in_dim=16) and verify it
works
2. Load a Z-Image ControlNet V2.0 model (control_in_dim=33) and verify
it works
3. Test with different control types: Canny, Depth, Pose
4. Recommended `control_context_scale`: 0.65-0.80
## Merge Plan
Can be merged after review. No special considerations needed.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _❗Changes to a redux slice have a corresponding migration_
- [ ] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
* chore: localize extraction errors
* chore: rename extract masked area menu item
* chore: rename inpaint mask extract component
* fix: use mask bounds for extraction region
* Prettier format applied to InpaintMaskMenuItemsExtractMaskedArea.tsx
* Fix base64 image import bug in extracted area in InpaintMaskMenuItemsExtractMaskedArea.tsx and removed unused locales entries in en.json
* Fix formatting issue in InpaintMaskMenuItemsExtractMaskedArea.tsx
* Minor comment fix
---------
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
GGUF Z-Image models store x_pad_token and cap_pad_token with shape [dim],
but diffusers ZImageTransformer2DModel expects [1, dim]. This caused a
RuntimeError when loading GGUF-quantized Z-Image models.
The fix dequantizes GGMLTensors first (since they don't support unsqueeze),
then reshapes to add the batch dimension.
* fix(ui): 🐛 `HotkeysModal` and `SettingsModal` initial focus
instead of using the `initialFocusRef` prop, the `Modal` component was focusing on the last available Button. This is a workaround that uses `tabIndex` instead which seems to be working.
Closes#8685
* style: 🚨 satisfy linter
---------
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
Add Z-Image Turbo and related models to the starter models list:
- Z-Image Turbo (full precision, ~13GB)
- Z-Image Turbo quantized (GGUF Q4_K, ~4GB)
- Z-Image Qwen3 Text Encoder (full precision, ~8GB)
- Z-Image Qwen3 Text Encoder quantized (GGUF Q6_K, ~3.3GB)
- Z-Image ControlNet Union (Canny, HED, Depth, Pose, MLSD, Inpainting)
The quantized Turbo model includes the quantized Qwen3 encoder as a
dependency for automatic installation.
Implement Z-Image ControlNet as an Extension pattern (similar to FLUX ControlNet)
instead of merging control weights into the base transformer. This provides:
- Lower memory usage (no weight duplication)
- Flexibility to enable/disable control per step
- Cleaner architecture with separate control adapter
Key implementation details:
- ZImageControlNetExtension: computes control hints per denoising step
- z_image_forward_with_control: custom forward pass with hint injection
- patchify_control_context: utility for control image patchification
- ZImageControlAdapter: standalone adapter with control_layers and noise_refiner
Architecture matches original VideoX-Fun implementation:
- Hints computed ONCE using INITIAL unified state (before main layers)
- Hints injected at every other main transformer layer (15 control blocks)
- Control signal added after each designated layer's forward pass
V2.0 ControlNet support (control_in_dim=33):
- Channels 0-15: control image latents
- Channels 16-31: reference image (zeros for pure control)
- Channel 32: inpaint mask (1.0 = don't inpaint, use control signal)
VRAM usage is high.
- Auto-detect control_in_dim from adapter weights (16 for V1, 33 for V2.0)
- Auto-detect n_refiner_layers from state dict
- Add zero-padding for V2.0's additional channels
- Use accelerate.init_empty_weights() for efficient model creation
- Add ControlNet_Checkpoint_ZImage_Config to frontend schema
feat: Add Z-Image ControlNet support with spatial conditioning
Add comprehensive ControlNet support for Z-Image models including:
Backend:
- New ControlNet_Checkpoint_ZImage_Config for Z-Image control adapter models
- Z-Image control key detection (_has_z_image_control_keys) to identify control layers
- ZImageControlAdapter loader for standalone control models
- ZImageControlTransformer2DModel combining base transformer with control layers
- Memory-efficient model loading by building combined state dict
Add comprehensive support for Z-Image-Turbo (S3-DiT) models including:
Backend:
- New BaseModelType.ZImage in taxonomy
- Z-Image model config classes (ZImageTransformerConfig,
Qwen3TextEncoderConfig)
- Model loader for Z-Image transformer and Qwen3 text encoder
- Z-Image conditioning data structures
- Step callback support for Z-Image with FLUX latent RGB factors
Invocations:
- z_image_model_loader: Load Z-Image transformer and Qwen3 encoder
- z_image_text_encoder: Encode prompts using Qwen3 with chat template
- z_image_denoise: Flow matching denoising with time-shifted sigmas
- z_image_image_to_latents: Encode images to 16-channel latents
- z_image_latents_to_image: Decode latents using FLUX VAE
Frontend:
- Z-Image graph builder for text-to-image generation
- Model picker and validation updates for z-image base type
- CFG scale now allows 0 (required for Z-Image-Turbo)
- Clip skip disabled for Z-Image (uses Qwen3, not CLIP)
- Optimal dimension settings for Z-Image (1024x1024)
Technical details:
- Uses Qwen3 text encoder (not CLIP/T5)
- 16 latent channels with FLUX-compatible VAE
- Flow matching scheduler with dynamic time shift
- 8 inference steps recommended for Turbo variant
- bfloat16 inference dtype
## Summary
<!--A description of the changes in this PR. Include the kind of change
(fix, feature, docs, etc), the "why" and the "how". Screenshots or
videos are useful for frontend changes.-->
## Related Issues / Discussions
<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->
## QA Instructions
- Install a Z-Image-Turbo model (e.g., from HuggingFace)
- Select the model in the Model Picker
- Generate a text-to-image with:
- CFG Scale: 0
- Steps: 8
- Resolution: 1024x1024
- Verify the generated image is coherent (not noise)
## Merge Plan
Standard merge, no special considerations needed.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _❗Changes to a redux slice have a corresponding migration_
- [ ] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
The previous mixed-precision optimization for FP32 mode only converted
some VAE decoder layers (post_quant_conv, conv_in, mid_block) to the
latents dtype while leaving others (up_blocks, conv_norm_out) in float32.
This caused "expected scalar type Half but found Float" errors after
recent diffusers updates.
Simplify FP32 mode to consistently use float32 for both VAE and latents,
removing the incomplete mixed-precision logic. This trades some VRAM
usage for stability and correctness.
Also removes now-unused attention processor imports.
