* 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>
* 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>
Each of these was a bit off:
- The SD callback started at `-1` and ended at `i`. Combined w/ the weird math on the previous `calc_percentage` util, this caused the progress bar to never finish.
- The MultiDiffusion callback had the same problems as SD.
- The FLUX callback didn't emit a pre-denoising step 0 image. It also reported total_steps as 1 higher than the actual step count.
Each of these now emit the expected events to the frontend:
- The initial latents at 0%
- Progress at each step, ending at 100%
