* fix(ui): improve DyPE field ordering and add 'On' preset option
- Add ui_order to DyPE fields (100, 101, 102) to group them at bottom of node
- Change DyPEPreset from Enum to Literal type for proper frontend dropdown support
- Add ui_choice_labels for human-readable dropdown options
- Add new 'On' preset to enable DyPE regardless of resolution
- Fix frontend input field sorting to respect ui_order (unordered first, then ordered)
- Bump flux_denoise node version to 4.4.0
* Chore Ruff check fix
* fix(flux): remove .value from dype_preset logging
DyPEPreset is now a Literal type (string) instead of an Enum,
so .value is no longer needed.
* fix(tests): update DyPE tests for Literal type change
Update test imports and assertions to use string constants
instead of Enum attributes since DyPEPreset is now a Literal type.
* feat(flux): add DyPE scale and exponent controls to Linear UI
- Add dype_scale (λs) and dype_exponent (λt) sliders to generation settings
- Add Zod schemas and parameter types for DyPE scale/exponent
- Pass custom values from Linear UI to flux_denoise node
- Fix bug where DyPE was enabled even when preset was "off"
- Add enhanced logging showing all DyPE parameters when enabled
* fix(flux): apply DyPE scale/exponent and add metadata recall
- Fix DyPE scale and exponent parameters not being applied in frequency
computation (compute_vision_yarn_freqs, compute_yarn_freqs now call
get_timestep_mscale)
- Add metadata handlers for dype_scale and dype_exponent to enable
recall from generated images
- Add i18n translations referencing existing parameter labels
* fix(flux): apply DyPE scale/exponent and add metadata recall
- Fix DyPE scale and exponent parameters not being applied in frequency
computation (compute_vision_yarn_freqs, compute_yarn_freqs now call
get_timestep_mscale)
- Add metadata handlers for dype_scale and dype_exponent to enable
recall from generated images
- Add i18n translations referencing existing parameter labels
* feat(ui): show DyPE scale/exponent only when preset is "on"
- Hide scale/exponent controls in UI when preset is not "on"
- Only parse/recall scale/exponent from metadata when preset is "on"
- Prevents confusion where custom values override preset behavior
* fix(dype): only allow custom scale/exponent with 'on' preset
Presets (auto, 4k) now use their predefined values and ignore
any custom_scale/custom_exponent parameters. Only the 'on' preset
allows manual override of these values.
This matches the frontend UI behavior where the scale/exponent
fields are only shown when 'On' is selected.
* refactor(dype): rename 'on' preset to 'manual'
Rename the 'on' DyPE preset to 'manual' to better reflect its purpose:
allowing users to manually configure scale and exponent values.
Updated in:
- Backend presets (DYPE_PRESET_ON -> DYPE_PRESET_MANUAL)
- Frontend UI labels and options
- Redux slice type definitions
- Zod schema validation
- Tests
* refactor(dype): rename 'on' preset to 'manual'
Rename the 'on' DyPE preset to 'manual' to better reflect its purpose:
allowing users to manually configure scale and exponent values.
Updated in:
- Backend presets (DYPE_PRESET_ON -> DYPE_PRESET_MANUAL)
- Frontend UI labels and options
- Redux slice type definitions
- Zod schema validation
- Tests
* fix(dype): update remaining 'on' references to 'manual'
- Update docstrings, comments, and error messages to use 'manual' preset name
- Simplify FLUX graph builder to always send dype_scale/dype_exponent
- Fix UI condition to show DyPE controls for 'manual' preset
---------
Co-authored-by: Jonathan <34005131+JPPhoto@users.noreply.github.com>
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
* Update flux_model_loader.py
Added nodal points for inputs to the model loader since we should be able to use a model selection node and pass in for Flux models.
* typegen
* Fixed existing ruff error
---------
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
* docs: add DyPE implementation plan for FLUX high-resolution generation
Add detailed plan for porting ComfyUI-DyPE (Dynamic Position Extrapolation)
to InvokeAI, enabling 4K+ image generation with FLUX models without
training. Estimated effort: 5-7 developer days.
* docs: update DyPE plan with design decisions
- Integrate DyPE directly into FluxDenoise (no separate node)
- Add 4K preset and "auto" mode for automatic activation
- Confirm FLUX Schnell support (same base resolution as Dev)
* docs: add activation threshold for DyPE auto mode
FLUX can handle resolutions up to ~1.5x natively without artifacts.
Set activation_threshold=1536 so DyPE only kicks in above that.
* feat(flux): implement DyPE for high-resolution generation
Add Dynamic Position Extrapolation (DyPE) support to FLUX models,
enabling artifact-free generation at 4K+ resolutions.
New files:
- invokeai/backend/flux/dype/base.py: DyPEConfig and scaling calculations
- invokeai/backend/flux/dype/rope.py: DyPE-enhanced RoPE functions
- invokeai/backend/flux/dype/embed.py: DyPEEmbedND position embedder
- invokeai/backend/flux/dype/presets.py: Presets (off, auto, 4k)
- invokeai/backend/flux/extensions/dype_extension.py: Pipeline integration
Modified files:
- invokeai/backend/flux/denoise.py: Add dype_extension parameter
- invokeai/app/invocations/flux_denoise.py: Add UI parameters
UI parameters:
- dype_preset: off | auto | 4k
- dype_scale: Custom magnitude override (0-8)
- dype_exponent: Custom decay speed override (0-1000)
Auto mode activates DyPE for resolutions > 1536px.
Based on: https://github.com/wildminder/ComfyUI-DyPE
* feat(flux): add DyPE preset selector to Linear UI
Add Linear UI integration for FLUX DyPE (Dynamic Position Extrapolation):
- Add ParamFluxDypePreset component with Off/Auto/4K options
- Integrate preset selector in GenerationSettingsAccordion for FLUX models
- Add state management (paramsSlice, types) for fluxDypePreset
- Add dype_preset to FLUX denoise graph builder and metadata
- Add translations for DyPE preset label and popover
- Add zFluxDypePresetField schema definition
Fix DyPE frequency computation:
- Remove incorrect mscale multiplication on frequencies
- Use only NTK-aware theta scaling for position extrapolation
* feat(flux): add DyPE preset to metadata recall
- Add FluxDypePreset handler to ImageMetadataHandlers
- Parse dype_preset from metadata and dispatch setFluxDypePreset on recall
- Add translation key metadata.dypePreset
* chore: remove dype-implementation-plan.md
Remove internal planning document from the branch.
* chore(flux): bump flux_denoise version to 4.3.0
Version bump for dype_preset field addition.
* chore: ruff check fix
* chore: ruff format
* Fix truncated DyPE label in advanced options UI
Shorten the label from "DyPE (High-Res)" to "DyPE" to prevent text truncation in the sidebar. The high-resolution context is preserved in the informational popover tooltip.
* Add DyPE preset to recall parameters in image viewer
The dype_preset metadata was being saved but not displayed in the Recall Parameters tab. Add FluxDypePreset handler to ImageMetadataActions so users can see and recall this parameter.
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Jonathan <34005131+JPPhoto@users.noreply.github.com>
* WIP: feat(flux2): add FLUX 2 Kontext model support
- Add new invocation nodes for FLUX 2:
- flux2_denoise: Denoising invocation for FLUX 2
- flux2_klein_model_loader: Model loader for Klein architecture
- flux2_klein_text_encoder: Text encoder for Qwen3-based encoding
- flux2_vae_decode: VAE decoder for FLUX 2
- Add backend support:
- New flux2 module with denoise and sampling utilities
- Extended model manager configs for FLUX 2 models
- Updated model loaders for Klein architecture
- Update frontend:
- Extended graph builder for FLUX 2 support
- Added FLUX 2 model types and configurations
- Updated readiness checks and UI components
* fix(flux2): correct VAE decode with proper BN denormalization
FLUX.2 VAE uses Batch Normalization in the patchified latent space
(128 channels). The decode must:
1. Patchify latents from (B, 32, H, W) to (B, 128, H/2, W/2)
2. Apply BN denormalization using running_mean/running_var
3. Unpatchify back to (B, 32, H, W) for VAE decode
Also fixed image normalization from [-1, 1] to [0, 255].
This fixes washed-out colors in generated FLUX.2 Klein images.
* feat(flux2): add FLUX.2 Klein model support with ComfyUI checkpoint compatibility
- Add FLUX.2 transformer loader with BFL-to-diffusers weight conversion
- Fix AdaLayerNorm scale-shift swap for final_layer.adaLN_modulation weights
- Add VAE batch normalization handling for FLUX.2 latent normalization
- Add Qwen3 text encoder loader with ComfyUI FP8 quantization support
- Add frontend components for FLUX.2 Klein model selection
- Update configs and schema for FLUX.2 model types
* Chore Ruff
* Fix Flux1 vae probing
* Fix Windows Paths schema.ts
* Add 4B und 9B klein to Starter Models.
* feat(flux2): add non-commercial license indicator for FLUX.2 Klein 9B
- Add isFlux2Klein9BMainModelConfig and isNonCommercialMainModelConfig functions
- Update MainModelPicker and InitialStateMainModelPicker to show license icon
- Update license tooltip text to include FLUX.2 Klein 9B
* feat(flux2): add Klein/Qwen3 variant support and encoder filtering
Backend:
- Add klein_4b/klein_9b variants for FLUX.2 Klein models
- Add qwen3_4b/qwen3_8b variants for Qwen3 encoder models
- Validate encoder variant matches Klein model (4B↔4B, 9B↔8B)
- Auto-detect Qwen3 variant from hidden_size during probing
Frontend:
- Show variant field for all model types in ModelView
- Filter Qwen3 encoder dropdown to only show compatible variants
- Update variant type definitions (zFlux2VariantType, zQwen3VariantType)
- Remove unused exports (isFluxDevMainModelConfig, isFlux2Klein9BMainModelConfig)
* Chore Ruff
* feat(flux2): add Klein 9B Base (undistilled) variant support
Distinguish between FLUX.2 Klein 9B (distilled) and Klein 9B Base (undistilled)
models by checking guidance_embeds in diffusers config or guidance_in keys in
safetensors. Klein 9B Base requires more steps but offers higher quality.
* feat(flux2): improve diffusers compatibility and distilled model support
Backend changes:
- Update text encoder layers from [9,18,27] to (10,20,30) matching diffusers
- Use apply_chat_template with system message instead of manual formatting
- Change position IDs from ones to zeros to match diffusers implementation
- Add get_schedule_flux2() with empirical mu computation for proper schedule shifting
- Add txt_embed_scale parameter for Qwen3 embedding magnitude control
- Add shift_schedule toggle for base (28+ steps) vs distilled (4 steps) models
- Zero out guidance_embedder weights for Klein models without guidance_embeds
UI changes:
- Clear Klein VAE and Qwen3 encoder when switching away from flux2 base
- Clear Qwen3 encoder when switching between different Klein model variants
- Add toast notification informing user to select compatible encoder
* feat(flux2): fix distilled model scheduling with proper dynamic shifting
- Configure scheduler with FLUX.2 Klein parameters from scheduler_config.json
(use_dynamic_shifting=True, shift=3.0, time_shift_type="exponential")
- Pass mu parameter to scheduler.set_timesteps() for resolution-aware shifting
- Remove manual shift_schedule parameter (scheduler handles this automatically)
- Simplify get_schedule_flux2() to return linear sigmas only
- Remove txt_embed_scale parameter (no longer needed)
This matches the diffusers Flux2KleinPipeline behavior where the
FlowMatchEulerDiscreteScheduler applies dynamic timestep shifting
based on image resolution via the mu parameter.
Fixes 4-step distilled Klein 9B model quality issues.
* fix(ui): fix FLUX.1 graph building with posCondCollect node lookup
The posCondCollect node was created with getPrefixedId() which generates
a random suffix (e.g., 'pos_cond_collect:abc123'), but g.getNode() was
called with the plain string 'pos_cond_collect', causing a node lookup
failure.
Fix by declaring posCondCollect as a module-scoped variable and
referencing it directly instead of using g.getNode().
* Remove Flux2 Klein Base from Starter Models
* Remove Logging
* Add Default Values for Flux2 Klein and add variant as additional info to from_base
* Add migrations for the z-image qwen3 encoder without a variant value
* Add img2img, inpainting and outpainting support for FLUX.2 Klein
- Add flux2_vae_encode invocation for encoding images to FLUX.2 latents
- Integrate inpaint_extension into FLUX.2 denoise loop for proper mask handling
- Apply BN normalization to init_latents and noise for consistency in inpainting
- Use manual Euler stepping for img2img/inpaint to preserve exact timestep schedule
- Add flux2_img2img, flux2_inpaint, flux2_outpaint generation modes
- Expand starter models with FP8 variants, standalone transformers, and separate VAE/encoders
- Fix outpainting to always use full denoising (0-1) since strength doesn't apply
- Improve error messages in model loader with clear guidance for standalone models
* Add GGUF quantized model support and Diffusers VAE loader for FLUX.2 Klein
- Add Main_GGUF_Flux2_Config for GGUF-quantized FLUX.2 transformer models
- Add VAE_Diffusers_Flux2_Config for FLUX.2 VAE in diffusers format
- Add Flux2GGUFCheckpointModel loader with BFL-to-diffusers conversion
- Add Flux2VAEDiffusersLoader for AutoencoderKLFlux2
- Add FLUX.2 Klein 4B/9B hardware requirements to documentation
- Update starter model descriptions to clarify dependencies install together
- Update frontend schema for new model configs
* Fix FLUX.2 model detection and add FP8 weight dequantization support
- Improve FLUX.2 variant detection for GGUF/checkpoint models (BFL format keys)
- Fix guidance_embeds logic: distilled=False, undistilled=True
- Add FP8 weight dequantization for ComfyUI-style quantized models
- Prevent FLUX.2 models from being misidentified as FLUX.1
- Preserve user-editable fields (name, description, etc.) on model reidentify
- Improve Qwen3Encoder detection by variant in starter models
- Add defensive checks for tensor operations
* Chore ruff format
* Chore Typegen
* Fix FLUX.2 Klein 9B model loading by detecting hidden_size from weights
Previously num_attention_heads was hardcoded to 24, which is correct for
Klein 4B but causes size mismatches when loading Klein 9B checkpoints.
Now dynamically calculates num_attention_heads from the hidden_size
dimension of context_embedder weights:
- Klein 4B: hidden_size=3072 → num_attention_heads=24
- Klein 9B: hidden_size=4096 → num_attention_heads=32
Fixes both Checkpoint and GGUF loaders for FLUX.2 models.
* Only clear Qwen3 encoder when FLUX.2 Klein variant changes
Previously the encoder was cleared whenever switching between any Klein
models, even if they had the same variant. Now compares the variant of
the old and new model and only clears the encoder when switching between
different variants (e.g., klein_4b to klein_9b).
This allows users to switch between different Klein 9B models without
having to re-select the Qwen3 encoder each time.
* Add metadata recall support for FLUX.2 Klein parameters
The scheduler, VAE model, and Qwen3 encoder model were not being
recalled correctly for FLUX.2 Klein images. This adds dedicated
metadata handlers for the Klein-specific parameters.
* Fix FLUX.2 Klein denoising scaling and Z-Image VAE compatibility
- Apply exponential denoising scaling (exponent 0.2) to FLUX.2 Klein,
matching FLUX.1 behavior for more intuitive inpainting strength
- Add isFlux1VAEModelConfig type guard to filter FLUX 1.0 VAEs only
- Restrict Z-Image VAE selection to FLUX 1.0 VAEs, excluding FLUX.2
Klein 32-channel VAEs which are incompatible
* chore pnpm fix
* Add FLUX.2 Klein to starter bundles and documentation
- Add FLUX.2 Klein hardware requirements to quick start guide
- Create flux2_klein_bundle with GGUF Q4 model, VAE, and Qwen3 encoder
- Add "What's New" entry announcing FLUX.2 Klein support
* Add FLUX.2 Klein built-in reference image editing support
FLUX.2 Klein has native multi-reference image editing without requiring
a separate model (unlike FLUX.1 which needs a Kontext model).
Backend changes:
- Add Flux2RefImageExtension for encoding reference images with FLUX.2 VAE
- Apply BN normalization to reference image latents for correct scaling
- Use T-coordinate offset scale=10 like diffusers (T=10, 20, 30...)
- Concatenate reference latents with generated image during denoising
- Extract only generated portion in step callback for correct preview
Frontend changes:
- Add flux2_reference_image config type without model field
- Hide model selector for FLUX.2 reference images (built-in support)
- Add type guards to handle configs without model property
- Update validators to skip model validation for FLUX.2
- Add 'flux2' to SUPPORTS_REF_IMAGES_BASE_MODELS
* Chore windows path fix
* Add reference image resizing for FLUX.2 Klein
Resize large reference images to match BFL FLUX.2 sampling.py limits:
- Single reference: max 2024² pixels (~4.1M)
- Multiple references: max 1024² pixels (~1M)
Uses same scaling approach as BFL's cap_pixels() function.
* feat(z-image): add Seed Variance Enhancer node and Linear UI integration
Add a new conditioning node for Z-Image models that injects seed-based
noise into text embeddings to increase visual variation between seeds.
Backend:
- New invocation: z_image_seed_variance_enhancer.py
- Parameters: strength (0-2), randomize_percent (1-100%), seed
Frontend:
- State management in paramsSlice with selectors and reducers
- UI components in SeedVariance/ folder with toggle and sliders
- Integration in GenerationSettingsAccordion (Advanced Options)
- Graph builder integration in buildZImageGraph.ts
- Metadata recall handlers for remix functionality
- Translations and tooltip descriptions
Based on: github.com/Pfannkuchensack/invokeai-z-image-seed-variance-enhancer
* chore: ruff and typegen fix
* chore: ruff and typegen fix
* Revise seedVarianceStrength explanation
Updated description for seedVarianceStrength.
* Update description for seedVarianceStrength
* fix(z-image): correct noise range comment from [-1, 1] to [-1, 1)
torch.rand() generates [0, 1), so the scaled range excludes 1.
* feat(z-image): add `add_noise` option to Z-Image Denoise
Add the same `add_noise` option that exists in FLUX Denoise to Z-Image Denoise.
When set to false, no noise is added to the input latents during image-to-image,
allowing for more controlled transformations.
* feat(flux): add scheduler selection for Flux models
Add support for alternative diffusers Flow Matching schedulers:
- Euler (default, 1st order)
- Heun (2nd order, better quality, 2x slower)
- LCM (optimized for few steps)
Backend:
- Add schedulers.py with scheduler type definitions and class mapping
- Modify denoise.py to accept optional scheduler parameter
- Add scheduler InputField to flux_denoise invocation (v4.2.0)
Frontend:
- Add fluxScheduler to Redux state and paramsSlice
- Create ParamFluxScheduler component for Linear UI
- Add scheduler to buildFLUXGraph for generation
* feat(z-image): add scheduler selection for Z-Image models
Add support for alternative diffusers Flow Matching schedulers for Z-Image:
- Euler (default) - 1st order, optimized for Z-Image-Turbo (8 steps)
- Heun (2nd order) - Better quality, 2x slower
- LCM - Optimized for few-step generation
Backend:
- Extend schedulers.py with Z-Image scheduler types and mapping
- Add scheduler InputField to z_image_denoise invocation (v1.3.0)
- Refactor denoising loop to support diffusers schedulers
Frontend:
- Add zImageScheduler to Redux state in paramsSlice
- Create ParamZImageScheduler component for Linear UI
- Add scheduler to buildZImageGraph for generation
* fix ruff check
* fix(schedulers): prevent progress percentage overflow with LCM scheduler
LCM scheduler may have more internal timesteps than user-facing steps,
causing user_step to exceed total_steps. This resulted in progress
percentage > 1.0, which caused a pydantic validation error.
Fix: Only call step_callback when user_step <= total_steps.
* Ruff format
* fix(schedulers): remove initial step-0 callback for consistent step count
Remove the initial step_callback at step=0 to match SD/SDXL behavior.
Previously Flux/Z-Image showed N+1 steps (step 0 + N denoising steps),
while SD/SDXL showed only N steps. Now all models display N steps
consistently in the server log.
* feat(z-image): add scheduler support with metadata recall
- Handle LCM scheduler by using num_inference_steps instead of custom sigmas
- Fix progress bar to show user-facing steps instead of internal scheduler steps
- Pass scheduler parameter to Z-Image denoise node in graph builder
- Add model-aware metadata recall for Flux and Z-Image schedulers
---------
Co-authored-by: Jonathan <34005131+JPPhoto@users.noreply.github.com>
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
Add ZImageDenoiseMetaInvocation that extends ZImageDenoiseInvocation
with metadata output for image recall. Captures generation parameters
including steps, guidance, scheduler, seed, model, and LoRAs.
- Handle LCM scheduler by using num_inference_steps instead of custom sigmas
- Fix progress bar to show user-facing steps instead of internal scheduler steps
- Pass scheduler parameter to Z-Image denoise node in graph builder
- Add model-aware metadata recall for Flux and Z-Image schedulers
Remove the initial step_callback at step=0 to match SD/SDXL behavior.
Previously Flux/Z-Image showed N+1 steps (step 0 + N denoising steps),
while SD/SDXL showed only N steps. Now all models display N steps
consistently in the server log.
* feat: Implement PBR Maps Generation Node
* feat(ui): Add PBR Maps Generation to UI
* chore: fix typegen checks
* chore: possible fix for nvidia 5000 series cards
* fix: Use safetensor models for PBR maps instead of pickles.
* fix: incorrect naming of upconv_block for PBR network
* fix: incorrect naming of displacement map variable
* chore: add relevant docs to the PBR generate function
* fix: clear cuda cache after loading state_dict for PBR maps
* fix: load torch_device only once as multiple models are loaded
* chore(ui): update the filter icon for PBR to CubeBold
More relevant
---------
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
LCM scheduler may have more internal timesteps than user-facing steps,
causing user_step to exceed total_steps. This resulted in progress
percentage > 1.0, which caused a pydantic validation error.
Fix: Only call step_callback when user_step <= total_steps.
Changed the default value of model_cache_keep_alive from 0 (indefinite)
to 5 minutes as requested. This means models will now be automatically
cleared from cache after 5 minutes of inactivity by default, unless
users explicitly configure a different value.
Users can still set it to 0 in their config to get the old behavior
of keeping models indefinitely.
Co-authored-by: lstein <111189+lstein@users.noreply.github.com>
Add support for alternative diffusers Flow Matching schedulers for Z-Image:
- Euler (default) - 1st order, optimized for Z-Image-Turbo (8 steps)
- Heun (2nd order) - Better quality, 2x slower
- LCM - Optimized for few-step generation
Backend:
- Extend schedulers.py with Z-Image scheduler types and mapping
- Add scheduler InputField to z_image_denoise invocation (v1.3.0)
- Refactor denoising loop to support diffusers schedulers
Frontend:
- Add zImageScheduler to Redux state in paramsSlice
- Create ParamZImageScheduler component for Linear UI
- Add scheduler to buildZImageGraph for generation
Add support for alternative diffusers Flow Matching schedulers:
- Euler (default, 1st order)
- Heun (2nd order, better quality, 2x slower)
- LCM (optimized for few steps)
Backend:
- Add schedulers.py with scheduler type definitions and class mapping
- Modify denoise.py to accept optional scheduler parameter
- Add scheduler InputField to flux_denoise invocation (v4.2.0)
Frontend:
- Add fluxScheduler to Redux state and paramsSlice
- Create ParamFluxScheduler component for Linear UI
- Add scheduler to buildFLUXGraph for generation
* 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
* 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>
- Remove all trailing whitespace (W293 errors)
- Add debug logging when timeout fires but activity detected
- Add debug logging when timeout fires but cache is empty
- Only log "Clearing model cache" message when actually clearing
- Prevents misleading timeout messages during active generation
Co-authored-by: lstein <111189+lstein@users.noreply.github.com>
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
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.
The Z-Image denoise node outputs latents, not images, so these mixins
were unnecessary. Metadata and board handling is correctly done in the
L2I (latents-to-image) node. This aligns with how FLUX denoise works.
