Merge branch 'main' into feat/z-image-starter-models

Author: blessedcoolant

invokeai/app/api/routers/workflows.py

@@ -223,6 +223,15 @@ async def get_workflow_thumbnail(
         raise HTTPException(status_code=404)
 
 
+@workflows_router.get("/tags", operation_id="get_all_tags")
+async def get_all_tags(
+    categories: Optional[list[WorkflowCategory]] = Query(default=None, description="The categories to include"),
+) -> list[str]:
+    """Gets all unique tags from workflows"""
+
+    return ApiDependencies.invoker.services.workflow_records.get_all_tags(categories=categories)
+
+
 @workflows_router.get("/counts_by_tag", operation_id="get_counts_by_tag")
 async def get_counts_by_tag(
     tags: list[str] = Query(description="The tags to get counts for"),

invokeai/app/invocations/z_image_control.py

@@ -0,0 +1,112 @@
+# Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
+"""Z-Image Control invocation for spatial conditioning."""
+
+from pydantic import BaseModel, Field
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    InputField,
+    OutputField,
+)
+from invokeai.app.invocations.model import ModelIdentifierField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType
+
+
+class ZImageControlField(BaseModel):
+    """A Z-Image control conditioning field for spatial control (Canny, HED, Depth, Pose, MLSD)."""
+
+    image_name: str = Field(description="The name of the preprocessed control image")
+    control_model: ModelIdentifierField = Field(description="The Z-Image ControlNet adapter model")
+    control_context_scale: float = Field(
+        default=0.75,
+        ge=0.0,
+        le=2.0,
+        description="The strength of the control signal. Recommended range: 0.65-0.80.",
+    )
+    begin_step_percent: float = Field(
+        default=0.0,
+        ge=0.0,
+        le=1.0,
+        description="When the control is first applied (% of total steps)",
+    )
+    end_step_percent: float = Field(
+        default=1.0,
+        ge=0.0,
+        le=1.0,
+        description="When the control is last applied (% of total steps)",
+    )
+
+
+@invocation_output("z_image_control_output")
+class ZImageControlOutput(BaseInvocationOutput):
+    """Z-Image Control output containing control configuration."""
+
+    control: ZImageControlField = OutputField(description="Z-Image control conditioning")
+
+
+@invocation(
+    "z_image_control",
+    title="Z-Image ControlNet",
+    tags=["image", "z-image", "control", "controlnet"],
+    category="control",
+    version="1.1.0",
+    classification=Classification.Prototype,
+)
+class ZImageControlInvocation(BaseInvocation):
+    """Configure Z-Image ControlNet for spatial conditioning.
+
+    Takes a preprocessed control image (e.g., Canny edges, depth map, pose)
+    and a Z-Image ControlNet adapter model to enable spatial control.
+
+    Supports 5 control modes: Canny, HED, Depth, Pose, MLSD.
+    Recommended control_context_scale: 0.65-0.80.
+    """
+
+    image: ImageField = InputField(
+        description="The preprocessed control image (Canny, HED, Depth, Pose, or MLSD)",
+    )
+    control_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.controlnet_model,
+        title="Control Model",
+        ui_model_base=BaseModelType.ZImage,
+        ui_model_type=ModelType.ControlNet,
+    )
+    control_context_scale: float = InputField(
+        default=0.75,
+        ge=0.0,
+        le=2.0,
+        description="Strength of the control signal. Recommended range: 0.65-0.80.",
+        title="Control Scale",
+    )
+    begin_step_percent: float = InputField(
+        default=0.0,
+        ge=0.0,
+        le=1.0,
+        description="When the control is first applied (% of total steps)",
+    )
+    end_step_percent: float = InputField(
+        default=1.0,
+        ge=0.0,
+        le=1.0,
+        description="When the control is last applied (% of total steps)",
+    )
+
+    def invoke(self, context: InvocationContext) -> ZImageControlOutput:
+        return ZImageControlOutput(
+            control=ZImageControlField(
+                image_name=self.image.image_name,
+                control_model=self.control_model,
+                control_context_scale=self.control_context_scale,
+                begin_step_percent=self.begin_step_percent,
+                end_step_percent=self.end_step_percent,
+            )
+        )

invokeai/app/invocations/z_image_denoise.py

@@ -1,8 +1,11 @@
+import math
 from contextlib import ExitStack
 from typing import Callable, Iterator, Optional, Tuple
 
+import einops
 import torch
 import torchvision.transforms as tv_transforms
+from PIL import Image
 from torchvision.transforms.functional import resize as tv_resize
 from tqdm import tqdm
 
@@ -16,8 +19,10 @@
     LatentsField,
     ZImageConditioningField,
 )
-from invokeai.app.invocations.model import TransformerField
+from invokeai.app.invocations.model import TransformerField, VAEField
 from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.invocations.z_image_control import ZImageControlField
+from invokeai.app.invocations.z_image_image_to_latents import ZImageImageToLatentsInvocation
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat
 from invokeai.backend.patches.layer_patcher import LayerPatcher
@@ -27,6 +32,11 @@
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ZImageConditioningInfo
 from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.z_image.z_image_control_adapter import ZImageControlAdapter
+from invokeai.backend.z_image.z_image_controlnet_extension import (
+    ZImageControlNetExtension,
+    z_image_forward_with_control,
+)
 
 
 @invocation(
@@ -59,18 +69,31 @@ class ZImageDenoiseInvocation(BaseInvocation):
     negative_conditioning: Optional[ZImageConditioningField] = InputField(
         default=None, description=FieldDescriptions.negative_cond, input=Input.Connection
     )
-    # Z-Image-Turbo uses guidance_scale=0.0 by default (no CFG)
+    # Z-Image-Turbo works best without CFG (guidance_scale=1.0)
     guidance_scale: float = InputField(
-        default=0.0,
-        ge=0.0,
-        description="Guidance scale for classifier-free guidance. Use 0.0 for Z-Image-Turbo.",
+        default=1.0,
+        ge=1.0,
+        description="Guidance scale for classifier-free guidance. 1.0 = no CFG (recommended for Z-Image-Turbo). "
+        "Values > 1.0 amplify guidance.",
         title="Guidance Scale",
     )
     width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
     height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
     # Z-Image-Turbo uses 8 steps by default
     steps: int = InputField(default=8, gt=0, description="Number of denoising steps. 8 recommended for Z-Image-Turbo.")
     seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
+    # Z-Image Control support
+    control: Optional[ZImageControlField] = InputField(
+        default=None,
+        description="Z-Image control conditioning for spatial control (Canny, HED, Depth, Pose, MLSD).",
+        input=Input.Connection,
+    )
+    # VAE for encoding control images (required when using control)
+    vae: Optional[VAEField] = InputField(
+        default=None,
+        description=FieldDescriptions.vae + " Required for control conditioning.",
+        input=Input.Connection,
+    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
@@ -206,12 +229,17 @@ def _run_diffusion(self, context: InvocationContext) -> torch.Tensor:
             device=device,
         )
 
-        # Load negative conditioning if provided and guidance_scale > 0
+        # Load negative conditioning if provided and guidance_scale != 1.0
+        # CFG formula: pred = pred_uncond + cfg_scale * (pred_cond - pred_uncond)
+        # At cfg_scale=1.0: pred = pred_cond (no effect, skip uncond computation)
+        # This matches FLUX's convention where 1.0 means "no CFG"
         neg_prompt_embeds: torch.Tensor | None = None
-        do_classifier_free_guidance = self.guidance_scale > 0.0 and self.negative_conditioning is not None
+        do_classifier_free_guidance = (
+            not math.isclose(self.guidance_scale, 1.0) and self.negative_conditioning is not None
+        )
         if do_classifier_free_guidance:
             if self.negative_conditioning is None:
-                raise ValueError("Negative conditioning is required when guidance_scale > 0")
+                raise ValueError("Negative conditioning is required when guidance_scale != 1.0")
             neg_prompt_embeds = self._load_text_conditioning(
                 context=context,
                 conditioning_name=self.negative_conditioning.conditioning_name,
@@ -293,9 +321,6 @@ def _run_diffusion(self, context: InvocationContext) -> torch.Tensor:
         )
 
         with ExitStack() as exit_stack:
-            # Load transformer and apply LoRA patches
-            (cached_weights, transformer) = exit_stack.enter_context(transformer_info.model_on_device())
-
             # Get transformer config to determine if it's quantized
             transformer_config = context.models.get_config(self.transformer.transformer)
 
@@ -309,6 +334,102 @@ def _run_diffusion(self, context: InvocationContext) -> torch.Tensor:
             else:
                 raise ValueError(f"Unsupported Z-Image model format: {transformer_config.format}")
 
+            # Load transformer - always use base transformer, control is handled via extension
+            (cached_weights, transformer) = exit_stack.enter_context(transformer_info.model_on_device())
+
+            # Prepare control extension if control is provided
+            control_extension: ZImageControlNetExtension | None = None
+
+            if self.control is not None:
+                # Load control adapter using context manager (proper GPU memory management)
+                control_model_info = context.models.load(self.control.control_model)
+                (_, control_adapter) = exit_stack.enter_context(control_model_info.model_on_device())
+                assert isinstance(control_adapter, ZImageControlAdapter)
+
+                # Get control_in_dim from adapter config (16 for V1, 33 for V2.0)
+                adapter_config = control_adapter.config
+                control_in_dim = adapter_config.get("control_in_dim", 16)
+                num_control_blocks = adapter_config.get("num_control_blocks", 6)
+
+                # Log control configuration for debugging
+                version = "V2.0" if control_in_dim > 16 else "V1"
+                context.util.signal_progress(
+                    f"Using Z-Image ControlNet {version} (Extension): control_in_dim={control_in_dim}, "
+                    f"num_blocks={num_control_blocks}, scale={self.control.control_context_scale}"
+                )
+
+                # Load and prepare control image - must be VAE-encoded!
+                if self.vae is None:
+                    raise ValueError("VAE is required when using Z-Image Control. Connect a VAE to the 'vae' input.")
+
+                control_image = context.images.get_pil(self.control.image_name)
+
+                # Resize control image to match output dimensions
+                control_image = control_image.convert("RGB")
+                control_image = control_image.resize((self.width, self.height), Image.Resampling.LANCZOS)
+
+                # Convert to tensor format for VAE encoding
+                from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+
+                control_image_tensor = image_resized_to_grid_as_tensor(control_image)
+                if control_image_tensor.dim() == 3:
+                    control_image_tensor = einops.rearrange(control_image_tensor, "c h w -> 1 c h w")
+
+                # Encode control image through VAE to get latents
+                vae_info = context.models.load(self.vae.vae)
+                control_latents = ZImageImageToLatentsInvocation.vae_encode(
+                    vae_info=vae_info,
+                    image_tensor=control_image_tensor,
+                )
+
+                # Move to inference device/dtype
+                control_latents = control_latents.to(device=device, dtype=inference_dtype)
+
+                # Add frame dimension: [B, C, H, W] -> [C, 1, H, W] (single image)
+                control_latents = control_latents.squeeze(0).unsqueeze(1)
+
+                # Prepare control_cond based on control_in_dim
+                # V1: 16 channels (just control latents)
+                # V2.0: 33 channels = 16 control + 16 reference + 1 mask
+                #   - Channels 0-15: control image latents (from VAE encoding)
+                #   - Channels 16-31: reference/inpaint image latents (zeros for pure control)
+                #   - Channel 32: inpaint mask (1.0 = don't inpaint, 0.0 = inpaint region)
+                # For pure control (no inpainting), we set mask=1 to tell model "use control, don't inpaint"
+                c, f, h, w = control_latents.shape
+                if c < control_in_dim:
+                    padding_channels = control_in_dim - c
+                    if padding_channels == 17:
+                        # V2.0: 16 reference channels (zeros) + 1 mask channel (ones)
+                        ref_padding = torch.zeros(
+                            (16, f, h, w),
+                            device=device,
+                            dtype=inference_dtype,
+                        )
+                        # Mask channel = 1.0 means "don't inpaint this region, use control signal"
+                        mask_channel = torch.ones(
+                            (1, f, h, w),
+                            device=device,
+                            dtype=inference_dtype,
+                        )
+                        control_latents = torch.cat([control_latents, ref_padding, mask_channel], dim=0)
+                    else:
+                        # Generic padding with zeros for other cases
+                        zero_padding = torch.zeros(
+                            (padding_channels, f, h, w),
+                            device=device,
+                            dtype=inference_dtype,
+                        )
+                        control_latents = torch.cat([control_latents, zero_padding], dim=0)
+
+                # Create control extension (adapter is already on device from model_on_device)
+                control_extension = ZImageControlNetExtension(
+                    control_adapter=control_adapter,
+                    control_cond=control_latents,
+                    weight=self.control.control_context_scale,
+                    begin_step_percent=self.control.begin_step_percent,
+                    end_step_percent=self.control.end_step_percent,
+                )
+
             # Apply LoRA models to the transformer.
             # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
             exit_stack.enter_context(
@@ -340,25 +461,48 @@ def _run_diffusion(self, context: InvocationContext) -> torch.Tensor:
                 latent_model_input = latent_model_input.unsqueeze(2)  # Add frame dimension
                 latent_model_input_list = list(latent_model_input.unbind(dim=0))
 
-                # Transformer returns (List[torch.Tensor], dict) - we only need the tensor list
-                model_output = transformer(
-                    x=latent_model_input_list,
-                    t=timestep,
-                    cap_feats=[pos_prompt_embeds],
-                )
-                model_out_list = model_output[0]  # Extract list of tensors from tuple
+                # Determine if control should be applied at this step
+                apply_control = control_extension is not None and control_extension.should_apply(step_idx, total_steps)
+
+                # Run forward pass - use custom forward with control if extension is active
+                if apply_control:
+                    model_out_list, _ = z_image_forward_with_control(
+                        transformer=transformer,
+                        x=latent_model_input_list,
+                        t=timestep,
+                        cap_feats=[pos_prompt_embeds],
+                        control_extension=control_extension,
+                    )
+                else:
+                    model_output = transformer(
+                        x=latent_model_input_list,
+                        t=timestep,
+                        cap_feats=[pos_prompt_embeds],
+                    )
+                    model_out_list = model_output[0]  # Extract list of tensors from tuple
+
                 noise_pred_cond = torch.stack([t.float() for t in model_out_list], dim=0)
                 noise_pred_cond = noise_pred_cond.squeeze(2)  # Remove frame dimension
                 noise_pred_cond = -noise_pred_cond  # Z-Image uses v-prediction with negation
 
                 # Apply CFG if enabled
                 if do_classifier_free_guidance and neg_prompt_embeds is not None:
-                    model_output_uncond = transformer(
-                        x=latent_model_input_list,
-                        t=timestep,
-                        cap_feats=[neg_prompt_embeds],
-                    )
-                    model_out_list_uncond = model_output_uncond[0]  # Extract list of tensors from tuple
+                    if apply_control:
+                        model_out_list_uncond, _ = z_image_forward_with_control(
+                            transformer=transformer,
+                            x=latent_model_input_list,
+                            t=timestep,
+                            cap_feats=[neg_prompt_embeds],
+                            control_extension=control_extension,
+                        )
+                    else:
+                        model_output_uncond = transformer(
+                            x=latent_model_input_list,
+                            t=timestep,
+                            cap_feats=[neg_prompt_embeds],
+                        )
+                        model_out_list_uncond = model_output_uncond[0]  # Extract list of tensors from tuple
+
                     noise_pred_uncond = torch.stack([t.float() for t in model_out_list_uncond], dim=0)
                     noise_pred_uncond = noise_pred_uncond.squeeze(2)
                     noise_pred_uncond = -noise_pred_uncond