Merge branch 'main' into JPPhoto-codeowners-update

Author: lstein

invokeai/app/invocations/flux_denoise.py

@@ -47,6 +47,7 @@
     pack,
     unpack,
 )
+from invokeai.backend.flux.schedulers import FLUX_SCHEDULER_LABELS, FLUX_SCHEDULER_MAP, FLUX_SCHEDULER_NAME_VALUES
 from invokeai.backend.flux.text_conditioning import FluxReduxConditioning, FluxTextConditioning
 from invokeai.backend.model_manager.taxonomy import BaseModelType, FluxVariantType, ModelFormat, ModelType
 from invokeai.backend.patches.layer_patcher import LayerPatcher
@@ -63,7 +64,7 @@
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="4.1.0",
+    version="4.2.0",
 )
 class FluxDenoiseInvocation(BaseInvocation):
     """Run denoising process with a FLUX transformer model."""
@@ -132,6 +133,12 @@ class FluxDenoiseInvocation(BaseInvocation):
     num_steps: int = InputField(
         default=4, description="Number of diffusion steps. Recommended values are schnell: 4, dev: 50."
     )
+    scheduler: FLUX_SCHEDULER_NAME_VALUES = InputField(
+        default="euler",
+        description="Scheduler (sampler) for the denoising process. 'euler' is fast and standard. "
+        "'heun' is 2nd-order (better quality, 2x slower). 'lcm' is optimized for few steps.",
+        ui_choice_labels=FLUX_SCHEDULER_LABELS,
+    )
     guidance: float = InputField(
         default=4.0,
         description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
@@ -242,6 +249,12 @@ def _run_diffusion(
             shift=not is_schnell,
         )
 
+        # Create scheduler if not using default euler
+        scheduler = None
+        if self.scheduler in FLUX_SCHEDULER_MAP:
+            scheduler_class = FLUX_SCHEDULER_MAP[self.scheduler]
+            scheduler = scheduler_class(num_train_timesteps=1000)
+
         # Clip the timesteps schedule based on denoising_start and denoising_end.
         timesteps = clip_timestep_schedule_fractional(timesteps, self.denoising_start, self.denoising_end)
 
@@ -426,6 +439,7 @@ def _run_diffusion(
                 img_cond=img_cond,
                 img_cond_seq=img_cond_seq,
                 img_cond_seq_ids=img_cond_seq_ids,
+                scheduler=scheduler,
             )
 
         x = unpack(x.float(), self.height, self.width)

invokeai/app/invocations/z_image_denoise.py

@@ -1,7 +1,9 @@
+import inspect
 import math
 from typing import Callable
 
 import torch
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
 from tqdm import tqdm
 
 from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput, sum_controlnet_flux_outputs
@@ -35,24 +37,199 @@ def denoise(
     # extra img tokens (sequence-wise) - for Kontext conditioning
     img_cond_seq: torch.Tensor | None = None,
     img_cond_seq_ids: torch.Tensor | None = None,
+    # Optional scheduler for alternative sampling methods
+    scheduler: SchedulerMixin | None = None,
 ):
-    # step 0 is the initial state
-    total_steps = len(timesteps) - 1
-    step_callback(
-        PipelineIntermediateState(
-            step=0,
-            order=1,
-            total_steps=total_steps,
-            timestep=int(timesteps[0]),
-            latents=img,
-        ),
-    )
+    # Determine if we're using a diffusers scheduler or the built-in Euler method
+    use_scheduler = scheduler is not None
+
+    if use_scheduler:
+        # Initialize scheduler with timesteps
+        # The timesteps list contains values in [0, 1] range (sigmas)
+        # Some schedulers (like Euler) support custom sigmas, others (like Heun) don't
+        set_timesteps_sig = inspect.signature(scheduler.set_timesteps)
+        if "sigmas" in set_timesteps_sig.parameters:
+            # Scheduler supports custom sigmas - use InvokeAI's time-shifted schedule
+            scheduler.set_timesteps(sigmas=timesteps, device=img.device)
+        else:
+            # Scheduler doesn't support custom sigmas - use num_inference_steps
+            # The schedule will be computed by the scheduler itself
+            num_inference_steps = len(timesteps) - 1
+            scheduler.set_timesteps(num_inference_steps=num_inference_steps, device=img.device)
+
+        # For schedulers like Heun, the number of actual steps may differ
+        # (Heun doubles timesteps internally)
+        num_scheduler_steps = len(scheduler.timesteps)
+        # For user-facing step count, use the original number of denoising steps
+        total_steps = len(timesteps) - 1
+    else:
+        total_steps = len(timesteps) - 1
+        num_scheduler_steps = total_steps
+
     # guidance_vec is ignored for schnell.
     guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
 
     # Store original sequence length for slicing predictions
     original_seq_len = img.shape[1]
 
+    # Track the actual step for user-facing progress (accounts for Heun's double steps)
+    user_step = 0
+
+    if use_scheduler:
+        # Use diffusers scheduler for stepping
+        for step_index in tqdm(range(num_scheduler_steps)):
+            timestep = scheduler.timesteps[step_index]
+            # Convert scheduler timestep (0-1000) to normalized (0-1) for the model
+            t_curr = timestep.item() / scheduler.config.num_train_timesteps
+            t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
+
+            # For Heun scheduler, track if we're in first or second order step
+            is_heun = hasattr(scheduler, "state_in_first_order")
+            in_first_order = scheduler.state_in_first_order if is_heun else True
+
+            # Run ControlNet models
+            controlnet_residuals: list[ControlNetFluxOutput] = []
+            for controlnet_extension in controlnet_extensions:
+                controlnet_residuals.append(
+                    controlnet_extension.run_controlnet(
+                        timestep_index=user_step,
+                        total_num_timesteps=total_steps,
+                        img=img,
+                        img_ids=img_ids,
+                        txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
+                        txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
+                        y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
+                        timesteps=t_vec,
+                        guidance=guidance_vec,
+                    )
+                )
+
+            merged_controlnet_residuals = sum_controlnet_flux_outputs(controlnet_residuals)
+
+            # Prepare input for model
+            img_input = img
+            img_input_ids = img_ids
+
+            if img_cond is not None:
+                img_input = torch.cat((img_input, img_cond), dim=-1)
+
+            if img_cond_seq is not None:
+                assert img_cond_seq_ids is not None
+                img_input = torch.cat((img_input, img_cond_seq), dim=1)
+                img_input_ids = torch.cat((img_input_ids, img_cond_seq_ids), dim=1)
+
+            pred = model(
+                img=img_input,
+                img_ids=img_input_ids,
+                txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
+                txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
+                y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
+                timesteps=t_vec,
+                guidance=guidance_vec,
+                timestep_index=user_step,
+                total_num_timesteps=total_steps,
+                controlnet_double_block_residuals=merged_controlnet_residuals.double_block_residuals,
+                controlnet_single_block_residuals=merged_controlnet_residuals.single_block_residuals,
+                ip_adapter_extensions=pos_ip_adapter_extensions,
+                regional_prompting_extension=pos_regional_prompting_extension,
+            )
+
+            if img_cond_seq is not None:
+                pred = pred[:, :original_seq_len]
+
+            # Get CFG scale for current user step
+            step_cfg_scale = cfg_scale[min(user_step, len(cfg_scale) - 1)]
+
+            if not math.isclose(step_cfg_scale, 1.0):
+                if neg_regional_prompting_extension is None:
+                    raise ValueError("Negative text conditioning is required when cfg_scale is not 1.0.")
+
+                neg_img_input = img
+                neg_img_input_ids = img_ids
+
+                if img_cond is not None:
+                    neg_img_input = torch.cat((neg_img_input, img_cond), dim=-1)
+
+                if img_cond_seq is not None:
+                    neg_img_input = torch.cat((neg_img_input, img_cond_seq), dim=1)
+                    neg_img_input_ids = torch.cat((neg_img_input_ids, img_cond_seq_ids), dim=1)
+
+                neg_pred = model(
+                    img=neg_img_input,
+                    img_ids=neg_img_input_ids,
+                    txt=neg_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
+                    txt_ids=neg_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
+                    y=neg_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
+                    timesteps=t_vec,
+                    guidance=guidance_vec,
+                    timestep_index=user_step,
+                    total_num_timesteps=total_steps,
+                    controlnet_double_block_residuals=None,
+                    controlnet_single_block_residuals=None,
+                    ip_adapter_extensions=neg_ip_adapter_extensions,
+                    regional_prompting_extension=neg_regional_prompting_extension,
+                )
+
+                if img_cond_seq is not None:
+                    neg_pred = neg_pred[:, :original_seq_len]
+                pred = neg_pred + step_cfg_scale * (pred - neg_pred)
+
+            # Use scheduler.step() for the update
+            step_output = scheduler.step(model_output=pred, timestep=timestep, sample=img)
+            img = step_output.prev_sample
+
+            # Get t_prev for inpainting (next sigma value)
+            if step_index + 1 < len(scheduler.sigmas):
+                t_prev = scheduler.sigmas[step_index + 1].item()
+            else:
+                t_prev = 0.0
+
+            if inpaint_extension is not None:
+                img = inpaint_extension.merge_intermediate_latents_with_init_latents(img, t_prev)
+
+            # For Heun, only increment user step after second-order step completes
+            if is_heun:
+                if not in_first_order:
+                    # Second order step completed
+                    user_step += 1
+                    # Only call step_callback if we haven't exceeded total_steps
+                    if user_step <= total_steps:
+                        preview_img = img - t_curr * pred
+                        if inpaint_extension is not None:
+                            preview_img = inpaint_extension.merge_intermediate_latents_with_init_latents(
+                                preview_img, 0.0
+                            )
+                        step_callback(
+                            PipelineIntermediateState(
+                                step=user_step,
+                                order=2,
+                                total_steps=total_steps,
+                                timestep=int(t_curr * 1000),
+                                latents=preview_img,
+                            ),
+                        )
+            else:
+                # For Euler, LCM and other first-order schedulers
+                user_step += 1
+                # Only call step_callback if we haven't exceeded total_steps
+                # (LCM scheduler may have more internal steps than user-facing steps)
+                if user_step <= total_steps:
+                    preview_img = img - t_curr * pred
+                    if inpaint_extension is not None:
+                        preview_img = inpaint_extension.merge_intermediate_latents_with_init_latents(preview_img, 0.0)
+                    step_callback(
+                        PipelineIntermediateState(
+                            step=user_step,
+                            order=1,
+                            total_steps=total_steps,
+                            timestep=int(t_curr * 1000),
+                            latents=preview_img,
+                        ),
+                    )
+
+        return img
+
+    # Original Euler implementation (when scheduler is None)
     for step_index, (t_curr, t_prev) in tqdm(list(enumerate(zip(timesteps[:-1], timesteps[1:], strict=True)))):
         t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
 

invokeai/backend/flux/denoise.py

@@ -0,0 +1,62 @@
+"""Flow Matching scheduler definitions and mapping.
+
+This module provides the scheduler types and mapping for Flow Matching models
+(Flux and Z-Image), supporting multiple schedulers from the diffusers library.
+"""
+
+from typing import Literal, Type
+
+from diffusers import (
+    FlowMatchEulerDiscreteScheduler,
+    FlowMatchHeunDiscreteScheduler,
+)
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+
+# Note: FlowMatchLCMScheduler may not be available in all diffusers versions
+try:
+    from diffusers import FlowMatchLCMScheduler
+
+    _HAS_LCM = True
+except ImportError:
+    _HAS_LCM = False
+
+# Scheduler name literal type for type checking
+FLUX_SCHEDULER_NAME_VALUES = Literal["euler", "heun", "lcm"]
+
+# Human-readable labels for the UI
+FLUX_SCHEDULER_LABELS: dict[str, str] = {
+    "euler": "Euler",
+    "heun": "Heun (2nd order)",
+    "lcm": "LCM",
+}
+
+# Mapping from scheduler names to scheduler classes
+FLUX_SCHEDULER_MAP: dict[str, Type[SchedulerMixin]] = {
+    "euler": FlowMatchEulerDiscreteScheduler,
+    "heun": FlowMatchHeunDiscreteScheduler,
+}
+
+if _HAS_LCM:
+    FLUX_SCHEDULER_MAP["lcm"] = FlowMatchLCMScheduler
+
+
+# Z-Image scheduler types (same schedulers as Flux, both use Flow Matching)
+# Note: Z-Image-Turbo is optimized for ~8 steps with Euler, but other schedulers
+# can be used for experimentation.
+ZIMAGE_SCHEDULER_NAME_VALUES = Literal["euler", "heun", "lcm"]
+
+# Human-readable labels for the UI
+ZIMAGE_SCHEDULER_LABELS: dict[str, str] = {
+    "euler": "Euler",
+    "heun": "Heun (2nd order)",
+    "lcm": "LCM",
+}
+
+# Mapping from scheduler names to scheduler classes (same as Flux)
+ZIMAGE_SCHEDULER_MAP: dict[str, Type[SchedulerMixin]] = {
+    "euler": FlowMatchEulerDiscreteScheduler,
+    "heun": FlowMatchHeunDiscreteScheduler,
+}
+
+if _HAS_LCM:
+    ZIMAGE_SCHEDULER_MAP["lcm"] = FlowMatchLCMScheduler

invokeai/backend/flux/schedulers.py

@@ -2821,8 +2821,10 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "Model Manager: If a model cannot be identified during installation, you can now manually select the model type and architecture.",
-            "Internal: Improved model identification system, making it easier for contributors to add support for new models."
+            "Support for Z-Image-Turbo: InvokeAI now supports the fast and accurate Z-Image-Turbo model. See 'Starter Models' to get started.",
+            "Workflow Editor: You can now organize your custom workflows with tags to facilitate search and retrieval.",
+            "Hotkeys: Add and edit keyboard shortcuts for any of Invoke's major functions.",
+            "Model Manager: The Model Manager user interface now supports bulk tagging and deletion."
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",

invokeai/frontend/web/public/locales/en.json

@@ -69,6 +69,12 @@ const slice = createSlice({
     setScheduler: (state, action: PayloadAction<ParameterScheduler>) => {
       state.scheduler = action.payload;
     },
+    setFluxScheduler: (state, action: PayloadAction<'euler' | 'heun' | 'lcm'>) => {
+      state.fluxScheduler = action.payload;
+    },
+    setZImageScheduler: (state, action: PayloadAction<'euler' | 'heun' | 'lcm'>) => {
+      state.zImageScheduler = action.payload;
+    },
     setUpscaleScheduler: (state, action: PayloadAction<ParameterScheduler>) => {
       state.upscaleScheduler = action.payload;
     },
@@ -449,6 +455,8 @@ export const {
   setCfgRescaleMultiplier,
   setGuidance,
   setScheduler,
+  setFluxScheduler,
+  setZImageScheduler,
   setUpscaleScheduler,
   setUpscaleCfgScale,
   setSeed,
@@ -588,6 +596,8 @@ export const selectModelSupportsOptimizedDenoising = createSelector(
   (model) => !!model && SUPPORTS_OPTIMIZED_DENOISING_BASE_MODELS.includes(model.base)
 );
 export const selectScheduler = createParamsSelector((params) => params.scheduler);
+export const selectFluxScheduler = createParamsSelector((params) => params.fluxScheduler);
+export const selectZImageScheduler = createParamsSelector((params) => params.zImageScheduler);
 export const selectSeamlessXAxis = createParamsSelector((params) => params.seamlessXAxis);
 export const selectSeamlessYAxis = createParamsSelector((params) => params.seamlessYAxis);
 export const selectSeed = createParamsSelector((params) => params.seed);