Move Scheduler handler after MainModel in ImageMetadataHandlers so that
base-dependent recall logic (z-image scheduler) works correctly. The
Scheduler handler checks `base === 'z-image'` before dispatching the
z-image scheduler action, but this check failed when Scheduler ran
before MainModel was recalled.
* 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
* fix(flux): 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(flux): remove initial step-0 callback for consistent step count
Remove the initial step_callback at step=0 to match SD/SDXL behavior.
Previously Flux showed N+1 steps (step 0 + N denoising steps), while
SD/SDXL showed only N steps. Now all models display N steps consistently.
* feat(flux): 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 Flux denoise node in graph builder
- Add model-aware metadata recall for Flux scheduler
---------
Co-authored-by: Jonathan <34005131+JPPhoto@users.noreply.github.com>
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
* 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
When using GGUF-quantized models on MPS (Apple Silicon), the
dequantized tensors could end up on a different device than the
other operands in math operations, causing "Expected all tensors
to be on the same device" errors.
This fix ensures that after dequantization, tensors are moved to
the same device as the other tensors in the operation.
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
Add local_files_only fallback for Qwen3 tokenizer loading in both
Checkpoint and GGUF loaders. This ensures Z-Image models can generate
images offline after the initial tokenizer download.
The tokenizer is now loaded with local_files_only=True first, falling
back to network download only if files aren't cached yet.
Fixes#8716
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
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>
* Fix an issue with multiple quick-queued generations after moving bbox
After moving the canvas bbox we still handed out the previous regional-guidance mask because only two parts of the system knew anything had changed. The adapter’s
cache key doesn’t include the bbox, so the next few graph builds reused the stale mask from before the move; if the user queued several runs back‑to‑back, every
background enqueue except the last skipped rerasterizing altogether because another raster job was still in flight. The fix makes the canvas manager invalidate each
region adapter’s cached mask whenever the bbox (or a related setting) changes, and—if a reraster is already running—queues up and waits instead of bailing. Now the
first run after a bbox edit forces a new mask, and rapid-fire enqueues just wait their turn, so every queued generation gets the correct regional prompt.
* (fix) Update invokeai/frontend/web/src/features/controlLayers/konva/CanvasStateApiModule.ts
Fixes race condition identified during copilot review.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update invokeai/frontend/web/src/features/controlLayers/konva/CanvasStateApiModule.ts
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* fix(ui): make Z-Image model selects mutually exclusive
VAE and Qwen3 Encoder selects are disabled when Qwen3 Source is selected,
and vice versa. This prevents invalid model combinations.
* feat(ui): auto-select Z-Image component models on model change
When switching to a Z-Image model, automatically set valid defaults
if no configuration exists:
- Prefers Qwen3 Source (Diffusers model) if available
- Falls back to Qwen3 Encoder + FLUX VAE combination
This ensures the generate button is enabled immediately after selecting
a Z-Image model, without requiring manual configuration.
* fix(ui): save and restore Qwen3 Source model in metadata
Qwen3 Source (Diffusers Z-Image) model was not being saved to image
metadata or restored during Remix. This adds:
- Saving qwen3_source to metadata in buildZImageGraph
- ZImageQwen3SourceModel metadata handler for parsing and recall
- i18n translation for qwen3Source
Changes image self-attention from restricted (region-isolated) to unrestricted
(all image tokens can attend to each other), similar to the FLUX approach.
This fixes the issue where ZImage-Turbo with multiple regional guidance layers
would generate two separate/disconnected images instead of compositing them
into a single unified image.
The regional text-image attention remains restricted so that each region still
responds to its corresponding prompt.
Fixes#8715
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.
