Improve docs related to dynamic T5 sequence length selection.

## Summary

This PR adds support for regional prompting with FLUX.

### Example 1
Global prompt: `An architecture rendering of the reception area of a
corporate office with modern decor.`
<img width="1386" alt="image"
src="https://github.com/user-attachments/assets/c8169bdb-49a9-44bc-bd9e-58d98e09094b">

![image](https://github.com/user-attachments/assets/4a426be9-9d7a-4527-b27c-2d2514ee73fe)

## QA Instructions

- [x] Test that there is no slowdown in the base case with a single
global prompt.
- [x] Test image fully covered by regional masks.
- [x] Test image covered by region masks with small gaps.
- [x] Test region masks with large unmasked ‘background’ regions
- [x] Test region masks with significant overlap
- [x] Test multiple global prompts.
- [x] Test no global prompt.
- [x] Test regional negative prompts (It runs... but results are not
great. Needs more tuning to be useful.)
- Test compatibility with:
    - [x] ControlNet
    - [x] LoRA
    - [x] IP-Adapter

## Remaining TODO

- [x] Disable the following UI features for FLUX prompt regions:
negative prompts, reference images, auto-negative.

## Checklist

- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_

docs/contributing/index.md

@@ -38,7 +38,7 @@ This project is a combined effort of dedicated people from across the world. [C
 
 ## Code of Conduct
 
-The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
+The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/docs/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
 
 By making a contribution to this project, you certify that:
 

invokeai/app/invocations/fields.py

@@ -250,6 +250,11 @@ class FluxConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 
     conditioning_name: str = Field(description="The name of conditioning tensor")
+    mask: Optional[TensorField] = Field(
+        default=None,
+        description="The mask associated with this conditioning tensor. Excluded regions should be set to False, "
+        "included regions should be set to True.",
+    )
 
 
 class SD3ConditioningField(BaseModel):

invokeai/app/invocations/flux_denoise.py

@@ -30,6 +30,7 @@ from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlN
 from invokeai.backend.flux.denoise import denoise
 from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterFlux
@@ -42,6 +43,7 @@ from invokeai.backend.flux.sampling_utils import (
     pack,
     unpack,
 )
+from invokeai.backend.flux.text_conditioning import FluxTextConditioning
 from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 from invokeai.backend.lora.lora_patcher import LoRAPatcher
@@ -87,10 +89,10 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
         title="Transformer",
     )
-    positive_text_conditioning: FluxConditioningField = InputField(
+    positive_text_conditioning: FluxConditioningField | list[FluxConditioningField] = InputField(
         description=FieldDescriptions.positive_cond, input=Input.Connection
     )
-    negative_text_conditioning: FluxConditioningField | None = InputField(
+    negative_text_conditioning: FluxConditioningField | list[FluxConditioningField] | None = InputField(
         default=None,
         description="Negative conditioning tensor. Can be None if cfg_scale is 1.0.",
         input=Input.Connection,
@@ -139,36 +141,12 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         name = context.tensors.save(tensor=latents)
         return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
 
-    def _load_text_conditioning(
-        self, context: InvocationContext, conditioning_name: str, dtype: torch.dtype
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(conditioning_name)
-        assert len(cond_data.conditionings) == 1
-        flux_conditioning = cond_data.conditionings[0]
-        assert isinstance(flux_conditioning, FLUXConditioningInfo)
-        flux_conditioning = flux_conditioning.to(dtype=dtype)
-        t5_embeddings = flux_conditioning.t5_embeds
-        clip_embeddings = flux_conditioning.clip_embeds
-        return t5_embeddings, clip_embeddings
-
     def _run_diffusion(
         self,
         context: InvocationContext,
     ):
         inference_dtype = torch.bfloat16
 
-        # Load the conditioning data.
-        pos_t5_embeddings, pos_clip_embeddings = self._load_text_conditioning(
-            context, self.positive_text_conditioning.conditioning_name, inference_dtype
-        )
-        neg_t5_embeddings: torch.Tensor | None = None
-        neg_clip_embeddings: torch.Tensor | None = None
-        if self.negative_text_conditioning is not None:
-            neg_t5_embeddings, neg_clip_embeddings = self._load_text_conditioning(
-                context, self.negative_text_conditioning.conditioning_name, inference_dtype
-            )
-
         # Load the input latents, if provided.
         init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
         if init_latents is not None:
@@ -183,15 +161,45 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             dtype=inference_dtype,
             seed=self.seed,
         )
+        b, _c, latent_h, latent_w = noise.shape
+        packed_h = latent_h // 2
+        packed_w = latent_w // 2
+
+        # Load the conditioning data.
+        pos_text_conditionings = self._load_text_conditioning(
+            context=context,
+            cond_field=self.positive_text_conditioning,
+            packed_height=packed_h,
+            packed_width=packed_w,
+            dtype=inference_dtype,
+            device=TorchDevice.choose_torch_device(),
+        )
+        neg_text_conditionings: list[FluxTextConditioning] | None = None
+        if self.negative_text_conditioning is not None:
+            neg_text_conditionings = self._load_text_conditioning(
+                context=context,
+                cond_field=self.negative_text_conditioning,
+                packed_height=packed_h,
+                packed_width=packed_w,
+                dtype=inference_dtype,
+                device=TorchDevice.choose_torch_device(),
+            )
+        pos_regional_prompting_extension = RegionalPromptingExtension.from_text_conditioning(
+            pos_text_conditionings, img_seq_len=packed_h * packed_w
+        )
+        neg_regional_prompting_extension = (
+            RegionalPromptingExtension.from_text_conditioning(neg_text_conditionings, img_seq_len=packed_h * packed_w)
+            if neg_text_conditionings
+            else None
+        )
 
         transformer_info = context.models.load(self.transformer.transformer)
         is_schnell = "schnell" in transformer_info.config.config_path
 
         # Calculate the timestep schedule.
-        image_seq_len = noise.shape[-1] * noise.shape[-2] // 4
         timesteps = get_schedule(
             num_steps=self.num_steps,
-            image_seq_len=image_seq_len,
+            image_seq_len=packed_h * packed_w,
             shift=not is_schnell,
         )
 
@@ -228,28 +236,17 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         inpaint_mask = self._prep_inpaint_mask(context, x)
 
-        b, _c, latent_h, latent_w = x.shape
         img_ids = generate_img_ids(h=latent_h, w=latent_w, batch_size=b, device=x.device, dtype=x.dtype)
 
-        pos_bs, pos_t5_seq_len, _ = pos_t5_embeddings.shape
-        pos_txt_ids = torch.zeros(
-            pos_bs, pos_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
-        )
-        neg_txt_ids: torch.Tensor | None = None
-        if neg_t5_embeddings is not None:
-            neg_bs, neg_t5_seq_len, _ = neg_t5_embeddings.shape
-            neg_txt_ids = torch.zeros(
-                neg_bs, neg_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
-            )
-
         # Pack all latent tensors.
         init_latents = pack(init_latents) if init_latents is not None else None
         inpaint_mask = pack(inpaint_mask) if inpaint_mask is not None else None
         noise = pack(noise)
         x = pack(x)
 
-        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len correctly.
-        assert image_seq_len == x.shape[1]
+        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len, packed_h, and
+        # packed_w correctly.
+        assert packed_h * packed_w == x.shape[1]
 
         # Prepare inpaint extension.
         inpaint_extension: InpaintExtension | None = None
@@ -338,12 +335,8 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 model=transformer,
                 img=x,
                 img_ids=img_ids,
-                txt=pos_t5_embeddings,
-                txt_ids=pos_txt_ids,
-                vec=pos_clip_embeddings,
-                neg_txt=neg_t5_embeddings,
-                neg_txt_ids=neg_txt_ids,
-                neg_vec=neg_clip_embeddings,
+                pos_regional_prompting_extension=pos_regional_prompting_extension,
+                neg_regional_prompting_extension=neg_regional_prompting_extension,
                 timesteps=timesteps,
                 step_callback=self._build_step_callback(context),
                 guidance=self.guidance,
@@ -357,6 +350,43 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         x = unpack(x.float(), self.height, self.width)
         return x
 
+    def _load_text_conditioning(
+        self,
+        context: InvocationContext,
+        cond_field: FluxConditioningField | list[FluxConditioningField],
+        packed_height: int,
+        packed_width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> list[FluxTextConditioning]:
+        """Load text conditioning data from a FluxConditioningField or a list of FluxConditioningFields."""
+        # Normalize to a list of FluxConditioningFields.
+        cond_list = [cond_field] if isinstance(cond_field, FluxConditioningField) else cond_field
+
+        text_conditionings: list[FluxTextConditioning] = []
+        for cond_field in cond_list:
+            # Load the text embeddings.
+            cond_data = context.conditioning.load(cond_field.conditioning_name)
+            assert len(cond_data.conditionings) == 1
+            flux_conditioning = cond_data.conditionings[0]
+            assert isinstance(flux_conditioning, FLUXConditioningInfo)
+            flux_conditioning = flux_conditioning.to(dtype=dtype, device=device)
+            t5_embeddings = flux_conditioning.t5_embeds
+            clip_embeddings = flux_conditioning.clip_embeds
+
+            # Load the mask, if provided.
+            mask: Optional[torch.Tensor] = None
+            if cond_field.mask is not None:
+                mask = context.tensors.load(cond_field.mask.tensor_name)
+                mask = mask.to(device=device)
+                mask = RegionalPromptingExtension.preprocess_regional_prompt_mask(
+                    mask, packed_height, packed_width, dtype, device
+                )
+
+            text_conditionings.append(FluxTextConditioning(t5_embeddings, clip_embeddings, mask))
+
+        return text_conditionings
+
     @classmethod
     def prep_cfg_scale(
         cls, cfg_scale: float | list[float], timesteps: list[float], cfg_scale_start_step: int, cfg_scale_end_step: int

invokeai/app/invocations/flux_text_encoder.py

@@ -1,11 +1,18 @@
 from contextlib import ExitStack
-from typing import Iterator, Literal, Tuple
+from typing import Iterator, Literal, Optional, Tuple
 
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, UIComponent
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    FluxConditioningField,
+    Input,
+    InputField,
+    TensorField,
+    UIComponent,
+)
 from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import FluxConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -22,7 +29,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Condit
     title="FLUX Text Encoding",
     tags=["prompt", "conditioning", "flux"],
     category="conditioning",
-    version="1.1.0",
+    version="1.2.0",
     classification=Classification.Prototype,
 )
 class FluxTextEncoderInvocation(BaseInvocation):
@@ -41,9 +48,14 @@ class FluxTextEncoderInvocation(BaseInvocation):
     t5_max_seq_len: Literal[256, 512] = InputField(
         description="Max sequence length for the T5 encoder. Expected to be 256 for FLUX schnell models and 512 for FLUX dev models."
     )
-    prompt: str = InputField(
-        description="Text prompt to encode.",
-        ui_component=UIComponent.Textarea,
+    use_short_t5_seq_len: bool = InputField(
+        description="Use a shorter sequence length for the T5 encoder if a short prompt is used. This can improve "
+        + "performance and reduce peak memory, but may result in slightly different image outputs.",
+        default=True,
+    )
+    prompt: str = InputField(description="Text prompt to encode.", ui_component=UIComponent.Textarea)
+    mask: Optional[TensorField] = InputField(
+        default=None, description="A mask defining the region that this conditioning prompt applies to."
     )
 
     @torch.no_grad()
@@ -57,7 +69,9 @@ class FluxTextEncoderInvocation(BaseInvocation):
         )
 
         conditioning_name = context.conditioning.save(conditioning_data)
-        return FluxConditioningOutput.build(conditioning_name)
+        return FluxConditioningOutput(
+            conditioning=FluxConditioningField(conditioning_name=conditioning_name, mask=self.mask)
+        )
 
     def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
         t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
@@ -65,6 +79,12 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
         prompt = [self.prompt]
 
+        valid_seq_lens = [self.t5_max_seq_len]
+        if self.use_short_t5_seq_len:
+            # We allow a minimum sequence length of 128. Going too short results in more significant image chagnes.
+            valid_seq_lens = list(range(128, self.t5_max_seq_len, 128))
+            valid_seq_lens.append(self.t5_max_seq_len)
+
         with (
             t5_text_encoder_info as t5_text_encoder,
             t5_tokenizer_info as t5_tokenizer,
@@ -72,10 +92,10 @@ class FluxTextEncoderInvocation(BaseInvocation):
             assert isinstance(t5_text_encoder, T5EncoderModel)
             assert isinstance(t5_tokenizer, T5Tokenizer)
 
-            t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
+            t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False)
 
             context.util.signal_progress("Running T5 encoder")
-            prompt_embeds = t5_encoder(prompt)
+            prompt_embeds = t5_encoder(prompt, valid_seq_lens)
 
         assert isinstance(prompt_embeds, torch.Tensor)
         return prompt_embeds
@@ -113,10 +133,10 @@ class FluxTextEncoderInvocation(BaseInvocation):
                 # There are currently no supported CLIP quantized models. Add support here if needed.
                 raise ValueError(f"Unsupported model format: {clip_text_encoder_config.format}")
 
-            clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True, 77)
+            clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True)
 
             context.util.signal_progress("Running CLIP encoder")
-            pooled_prompt_embeds = clip_encoder(prompt)
+            pooled_prompt_embeds = clip_encoder(prompt, [77])
 
         assert isinstance(pooled_prompt_embeds, torch.Tensor)
         return pooled_prompt_embeds

invokeai/backend/flux/custom_block_processor.py

@@ -1,9 +1,10 @@
 import einops
 import torch
 
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.math import attention
-from invokeai.backend.flux.modules.layers import DoubleStreamBlock
+from invokeai.backend.flux.modules.layers import DoubleStreamBlock, SingleStreamBlock
 
 
 class CustomDoubleStreamBlockProcessor:
@@ -13,7 +14,12 @@ class CustomDoubleStreamBlockProcessor:
 
     @staticmethod
     def _double_stream_block_forward(
-        block: DoubleStreamBlock, img: torch.Tensor, txt: torch.Tensor, vec: torch.Tensor, pe: torch.Tensor
+        block: DoubleStreamBlock,
+        img: torch.Tensor,
+        txt: torch.Tensor,
+        vec: torch.Tensor,
+        pe: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """This function is a direct copy of DoubleStreamBlock.forward(), but it returns some of the intermediate
         values.
@@ -40,7 +46,7 @@ class CustomDoubleStreamBlockProcessor:
         k = torch.cat((txt_k, img_k), dim=2)
         v = torch.cat((txt_v, img_v), dim=2)
 
-        attn = attention(q, k, v, pe=pe)
+        attn = attention(q, k, v, pe=pe, attn_mask=attn_mask)
         txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
 
         # calculate the img bloks
@@ -63,11 +69,15 @@ class CustomDoubleStreamBlockProcessor:
         vec: torch.Tensor,
         pe: torch.Tensor,
         ip_adapter_extensions: list[XLabsIPAdapterExtension],
+        regional_prompting_extension: RegionalPromptingExtension,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """A custom implementation of DoubleStreamBlock.forward() with additional features:
         - IP-Adapter support
         """
-        img, txt, img_q = CustomDoubleStreamBlockProcessor._double_stream_block_forward(block, img, txt, vec, pe)
+        attn_mask = regional_prompting_extension.get_double_stream_attn_mask(block_index)
+        img, txt, img_q = CustomDoubleStreamBlockProcessor._double_stream_block_forward(
+            block, img, txt, vec, pe, attn_mask=attn_mask
+        )
 
         # Apply IP-Adapter conditioning.
         for ip_adapter_extension in ip_adapter_extensions:
@@ -81,3 +91,48 @@ class CustomDoubleStreamBlockProcessor:
             )
 
         return img, txt
+
+
+class CustomSingleStreamBlockProcessor:
+    """A class containing a custom implementation of SingleStreamBlock.forward() with additional features (masking,
+    etc.)
+    """
+
+    @staticmethod
+    def _single_stream_block_forward(
+        block: SingleStreamBlock,
+        x: torch.Tensor,
+        vec: torch.Tensor,
+        pe: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """This function is a direct copy of SingleStreamBlock.forward()."""
+        mod, _ = block.modulation(vec)
+        x_mod = (1 + mod.scale) * block.pre_norm(x) + mod.shift
+        qkv, mlp = torch.split(block.linear1(x_mod), [3 * block.hidden_size, block.mlp_hidden_dim], dim=-1)
+
+        q, k, v = einops.rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=block.num_heads)
+        q, k = block.norm(q, k, v)
+
+        # compute attention
+        attn = attention(q, k, v, pe=pe, attn_mask=attn_mask)
+        # compute activation in mlp stream, cat again and run second linear layer
+        output = block.linear2(torch.cat((attn, block.mlp_act(mlp)), 2))
+        return x + mod.gate * output
+
+    @staticmethod
+    def custom_single_block_forward(
+        timestep_index: int,
+        total_num_timesteps: int,
+        block_index: int,
+        block: SingleStreamBlock,
+        img: torch.Tensor,
+        vec: torch.Tensor,
+        pe: torch.Tensor,
+        regional_prompting_extension: RegionalPromptingExtension,
+    ) -> torch.Tensor:
+        """A custom implementation of SingleStreamBlock.forward() with additional features:
+        - Masking
+        """
+        attn_mask = regional_prompting_extension.get_single_stream_attn_mask(block_index)
+        return CustomSingleStreamBlockProcessor._single_stream_block_forward(block, img, vec, pe, attn_mask=attn_mask)

invokeai/backend/flux/denoise.py

@@ -7,6 +7,7 @@ from tqdm import tqdm
 from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput, sum_controlnet_flux_outputs
 from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.model import Flux
@@ -18,14 +19,8 @@ def denoise(
     # model input
     img: torch.Tensor,
     img_ids: torch.Tensor,
-    # positive text conditioning
-    txt: torch.Tensor,
-    txt_ids: torch.Tensor,
-    vec: torch.Tensor,
-    # negative text conditioning
-    neg_txt: torch.Tensor | None,
-    neg_txt_ids: torch.Tensor | None,
-    neg_vec: torch.Tensor | None,
+    pos_regional_prompting_extension: RegionalPromptingExtension,
+    neg_regional_prompting_extension: RegionalPromptingExtension | None,
     # sampling parameters
     timesteps: list[float],
     step_callback: Callable[[PipelineIntermediateState], None],
@@ -61,9 +56,9 @@ def denoise(
                     total_num_timesteps=total_steps,
                     img=img,
                     img_ids=img_ids,
-                    txt=txt,
-                    txt_ids=txt_ids,
-                    y=vec,
+                    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,
                 )
@@ -78,9 +73,9 @@ def denoise(
         pred = model(
             img=img,
             img_ids=img_ids,
-            txt=txt,
-            txt_ids=txt_ids,
-            y=vec,
+            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=step_index,
@@ -88,6 +83,7 @@ def denoise(
             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,
         )
 
         step_cfg_scale = cfg_scale[step_index]
@@ -97,15 +93,15 @@ def denoise(
             # TODO(ryand): Add option to run positive and negative predictions in a single batch for better performance
             # on systems with sufficient VRAM.
 
-            if neg_txt is None or neg_txt_ids is None or neg_vec is None:
+            if neg_regional_prompting_extension is None:
                 raise ValueError("Negative text conditioning is required when cfg_scale is not 1.0.")
 
             neg_pred = model(
                 img=img,
                 img_ids=img_ids,
-                txt=neg_txt,
-                txt_ids=neg_txt_ids,
-                y=neg_vec,
+                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=step_index,
@@ -113,6 +109,7 @@ def denoise(
                 controlnet_double_block_residuals=None,
                 controlnet_single_block_residuals=None,
                 ip_adapter_extensions=neg_ip_adapter_extensions,
+                regional_prompting_extension=neg_regional_prompting_extension,
             )
             pred = neg_pred + step_cfg_scale * (pred - neg_pred)
 

invokeai/backend/flux/extensions/regional_prompting_extension.py

@@ -0,0 +1,276 @@
+from typing import Optional
+
+import torch
+import torchvision
+
+from invokeai.backend.flux.text_conditioning import FluxRegionalTextConditioning, FluxTextConditioning
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Range
+from invokeai.backend.util.devices import TorchDevice
+from invokeai.backend.util.mask import to_standard_float_mask
+
+
+class RegionalPromptingExtension:
+    """A class for managing regional prompting with FLUX.
+
+    This implementation is inspired by https://arxiv.org/pdf/2411.02395 (though there are significant differences).
+    """
+
+    def __init__(
+        self,
+        regional_text_conditioning: FluxRegionalTextConditioning,
+        restricted_attn_mask: torch.Tensor | None = None,
+    ):
+        self.regional_text_conditioning = regional_text_conditioning
+        self.restricted_attn_mask = restricted_attn_mask
+
+    def get_double_stream_attn_mask(self, block_index: int) -> torch.Tensor | None:
+        order = [self.restricted_attn_mask, None]
+        return order[block_index % len(order)]
+
+    def get_single_stream_attn_mask(self, block_index: int) -> torch.Tensor | None:
+        order = [self.restricted_attn_mask, None]
+        return order[block_index % len(order)]
+
+    @classmethod
+    def from_text_conditioning(cls, text_conditioning: list[FluxTextConditioning], img_seq_len: int):
+        """Create a RegionalPromptingExtension from a list of text conditionings.
+
+        Args:
+            text_conditioning (list[FluxTextConditioning]): The text conditionings to use for regional prompting.
+            img_seq_len (int): The image sequence length (i.e. packed_height * packed_width).
+        """
+        regional_text_conditioning = cls._concat_regional_text_conditioning(text_conditioning)
+        attn_mask_with_restricted_img_self_attn = cls._prepare_restricted_attn_mask(
+            regional_text_conditioning, img_seq_len
+        )
+        return cls(
+            regional_text_conditioning=regional_text_conditioning,
+            restricted_attn_mask=attn_mask_with_restricted_img_self_attn,
+        )
+
+    # Keeping _prepare_unrestricted_attn_mask for reference as an alternative masking strategy:
+    #
+    # @classmethod
+    # def _prepare_unrestricted_attn_mask(
+    #     cls,
+    #     regional_text_conditioning: FluxRegionalTextConditioning,
+    #     img_seq_len: int,
+    # ) -> torch.Tensor:
+    #     """Prepare an 'unrestricted' attention mask. In this context, 'unrestricted' means that:
+    #     - img self-attention is not masked.
+    #     - img regions attend to both txt within their own region and to global prompts.
+    #     """
+    #     device = TorchDevice.choose_torch_device()
+
+    #     # Infer txt_seq_len from the t5_embeddings tensor.
+    #     txt_seq_len = regional_text_conditioning.t5_embeddings.shape[1]
+
+    #     # In the attention blocks, the txt seq and img seq are concatenated and then attention is applied.
+    #     # Concatenation happens in the following order: [txt_seq, img_seq].
+    #     # There are 4 portions of the attention mask to consider as we prepare it:
+    #     # 1. txt attends to itself
+    #     # 2. txt attends to corresponding regional img
+    #     # 3. regional img attends to corresponding txt
+    #     # 4. regional img attends to itself
+
+    #     # Initialize empty attention mask.
+    #     regional_attention_mask = torch.zeros(
+    #         (txt_seq_len + img_seq_len, txt_seq_len + img_seq_len), device=device, dtype=torch.float16
+    #     )
+
+    #     for image_mask, t5_embedding_range in zip(
+    #         regional_text_conditioning.image_masks, regional_text_conditioning.t5_embedding_ranges, strict=True
+    #     ):
+    #         # 1. txt attends to itself
+    #         regional_attention_mask[
+    #             t5_embedding_range.start : t5_embedding_range.end, t5_embedding_range.start : t5_embedding_range.end
+    #         ] = 1.0
+
+    #         # 2. txt attends to corresponding regional img
+    #         # Note that we reshape to (1, img_seq_len) to ensure broadcasting works as desired.
+    #         fill_value = image_mask.view(1, img_seq_len) if image_mask is not None else 1.0
+    #         regional_attention_mask[t5_embedding_range.start : t5_embedding_range.end, txt_seq_len:] = fill_value
+
+    #         # 3. regional img attends to corresponding txt
+    #         # Note that we reshape to (img_seq_len, 1) to ensure broadcasting works as desired.
+    #         fill_value = image_mask.view(img_seq_len, 1) if image_mask is not None else 1.0
+    #         regional_attention_mask[txt_seq_len:, t5_embedding_range.start : t5_embedding_range.end] = fill_value
+
+    #     # 4. regional img attends to itself
+    #     # Allow unrestricted img self attention.
+    #     regional_attention_mask[txt_seq_len:, txt_seq_len:] = 1.0
+
+    #     # Convert attention mask to boolean.
+    #     regional_attention_mask = regional_attention_mask > 0.5
+
+    #     return regional_attention_mask
+
+    @classmethod
+    def _prepare_restricted_attn_mask(
+        cls,
+        regional_text_conditioning: FluxRegionalTextConditioning,
+        img_seq_len: int,
+    ) -> torch.Tensor | None:
+        """Prepare a 'restricted' attention mask. In this context, 'restricted' means that:
+        - img self-attention is only allowed within regions.
+        - img regions only attend to txt within their own region, not to global prompts.
+        """
+        # Identify background region. I.e. the region that is not covered by any region masks.
+        background_region_mask: None | torch.Tensor = None
+        for image_mask in regional_text_conditioning.image_masks:
+            if image_mask is not None:
+                if background_region_mask is None:
+                    background_region_mask = torch.ones_like(image_mask)
+                background_region_mask *= 1 - image_mask
+
+        if background_region_mask is None:
+            # There are no region masks, short-circuit and return None.
+            # TODO(ryand): We could restrict txt-txt attention across multiple global prompts, but this would
+            # is a rare use case and would make the logic here significantly more complicated.
+            return None
+
+        device = TorchDevice.choose_torch_device()
+
+        # Infer txt_seq_len from the t5_embeddings tensor.
+        txt_seq_len = regional_text_conditioning.t5_embeddings.shape[1]
+
+        # In the attention blocks, the txt seq and img seq are concatenated and then attention is applied.
+        # Concatenation happens in the following order: [txt_seq, img_seq].
+        # There are 4 portions of the attention mask to consider as we prepare it:
+        # 1. txt attends to itself
+        # 2. txt attends to corresponding regional img
+        # 3. regional img attends to corresponding txt
+        # 4. regional img attends to itself
+
+        # Initialize empty attention mask.
+        regional_attention_mask = torch.zeros(
+            (txt_seq_len + img_seq_len, txt_seq_len + img_seq_len), device=device, dtype=torch.float16
+        )
+
+        for image_mask, t5_embedding_range in zip(
+            regional_text_conditioning.image_masks, regional_text_conditioning.t5_embedding_ranges, strict=True
+        ):
+            # 1. txt attends to itself
+            regional_attention_mask[
+                t5_embedding_range.start : t5_embedding_range.end, t5_embedding_range.start : t5_embedding_range.end
+            ] = 1.0
+
+            if image_mask is not None:
+                # 2. txt attends to corresponding regional img
+                # Note that we reshape to (1, img_seq_len) to ensure broadcasting works as desired.
+                regional_attention_mask[t5_embedding_range.start : t5_embedding_range.end, txt_seq_len:] = (
+                    image_mask.view(1, img_seq_len)
+                )
+
+                # 3. regional img attends to corresponding txt
+                # Note that we reshape to (img_seq_len, 1) to ensure broadcasting works as desired.
+                regional_attention_mask[txt_seq_len:, t5_embedding_range.start : t5_embedding_range.end] = (
+                    image_mask.view(img_seq_len, 1)
+                )
+
+                # 4. regional img attends to itself
+                image_mask = image_mask.view(img_seq_len, 1)
+                regional_attention_mask[txt_seq_len:, txt_seq_len:] += image_mask @ image_mask.T
+            else:
+                # We don't allow attention between non-background image regions and global prompts. This helps to ensure
+                # that regions focus on their local prompts. We do, however, allow attention between background regions
+                # and global prompts. If we didn't do this, then the background regions would not attend to any txt
+                # embeddings, which we found experimentally to cause artifacts.
+
+                # 2. global txt attends to background region
+                # Note that we reshape to (1, img_seq_len) to ensure broadcasting works as desired.
+                regional_attention_mask[t5_embedding_range.start : t5_embedding_range.end, txt_seq_len:] = (
+                    background_region_mask.view(1, img_seq_len)
+                )
+
+                # 3. background region attends to global txt
+                # Note that we reshape to (img_seq_len, 1) to ensure broadcasting works as desired.
+                regional_attention_mask[txt_seq_len:, t5_embedding_range.start : t5_embedding_range.end] = (
+                    background_region_mask.view(img_seq_len, 1)
+                )
+
+        # Allow background regions to attend to themselves.
+        regional_attention_mask[txt_seq_len:, txt_seq_len:] += background_region_mask.view(img_seq_len, 1)
+        regional_attention_mask[txt_seq_len:, txt_seq_len:] += background_region_mask.view(1, img_seq_len)
+
+        # Convert attention mask to boolean.
+        regional_attention_mask = regional_attention_mask > 0.5
+
+        return regional_attention_mask
+
+    @classmethod
+    def _concat_regional_text_conditioning(
+        cls,
+        text_conditionings: list[FluxTextConditioning],
+    ) -> FluxRegionalTextConditioning:
+        """Concatenate regional text conditioning data into a single conditioning tensor (with associated masks)."""
+        concat_t5_embeddings: list[torch.Tensor] = []
+        concat_t5_embedding_ranges: list[Range] = []
+        image_masks: list[torch.Tensor | None] = []
+
+        # Choose global CLIP embedding.
+        # Use the first global prompt's CLIP embedding as the global CLIP embedding. If there is no global prompt, use
+        # the first prompt's CLIP embedding.
+        global_clip_embedding: torch.Tensor = text_conditionings[0].clip_embeddings
+        for text_conditioning in text_conditionings:
+            if text_conditioning.mask is None:
+                global_clip_embedding = text_conditioning.clip_embeddings
+                break
+
+        cur_t5_embedding_len = 0
+        for text_conditioning in text_conditionings:
+            concat_t5_embeddings.append(text_conditioning.t5_embeddings)
+
+            concat_t5_embedding_ranges.append(
+                Range(start=cur_t5_embedding_len, end=cur_t5_embedding_len + text_conditioning.t5_embeddings.shape[1])
+            )
+
+            image_masks.append(text_conditioning.mask)
+
+            cur_t5_embedding_len += text_conditioning.t5_embeddings.shape[1]
+
+        t5_embeddings = torch.cat(concat_t5_embeddings, dim=1)
+
+        # Initialize the txt_ids tensor.
+        pos_bs, pos_t5_seq_len, _ = t5_embeddings.shape
+        t5_txt_ids = torch.zeros(
+            pos_bs, pos_t5_seq_len, 3, dtype=t5_embeddings.dtype, device=TorchDevice.choose_torch_device()
+        )
+
+        return FluxRegionalTextConditioning(
+            t5_embeddings=t5_embeddings,
+            clip_embeddings=global_clip_embedding,
+            t5_txt_ids=t5_txt_ids,
+            image_masks=image_masks,
+            t5_embedding_ranges=concat_t5_embedding_ranges,
+        )
+
+    @staticmethod
+    def preprocess_regional_prompt_mask(
+        mask: Optional[torch.Tensor], packed_height: int, packed_width: int, dtype: torch.dtype, device: torch.device
+    ) -> torch.Tensor:
+        """Preprocess a regional prompt mask to match the target height and width.
+        If mask is None, returns a mask of all ones with the target height and width.
+        If mask is not None, resizes the mask to the target height and width using 'nearest' interpolation.
+
+        packed_height and packed_width are the target height and width of the mask in the 'packed' latent space.
+
+        Returns:
+            torch.Tensor: The processed mask. shape: (1, 1, packed_height * packed_width).
+        """
+
+        if mask is None:
+            return torch.ones((1, 1, packed_height * packed_width), dtype=dtype, device=device)
+
+        mask = to_standard_float_mask(mask, out_dtype=dtype)
+
+        tf = torchvision.transforms.Resize(
+            (packed_height, packed_width), interpolation=torchvision.transforms.InterpolationMode.NEAREST
+        )
+
+        # Add a batch dimension to the mask, because torchvision expects shape (batch, channels, h, w).
+        mask = mask.unsqueeze(0)  # Shape: (1, h, w) -> (1, 1, h, w)
+        resized_mask = tf(mask)
+
+        # Flatten the height and width dimensions into a single image_seq_len dimension.
+        return resized_mask.flatten(start_dim=2)

invokeai/backend/flux/math.py

@@ -5,10 +5,10 @@ from einops import rearrange
 from torch import Tensor
 
 
-def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, attn_mask: Tensor | None = None) -> Tensor:
     q, k = apply_rope(q, k, pe)
 
-    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+    x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
     x = rearrange(x, "B H L D -> B L (H D)")
 
     return x

invokeai/backend/flux/model.py

@@ -5,7 +5,11 @@ from dataclasses import dataclass
 import torch
 from torch import Tensor, nn
 
-from invokeai.backend.flux.custom_block_processor import CustomDoubleStreamBlockProcessor
+from invokeai.backend.flux.custom_block_processor import (
+    CustomDoubleStreamBlockProcessor,
+    CustomSingleStreamBlockProcessor,
+)
+from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.modules.layers import (
     DoubleStreamBlock,
@@ -95,6 +99,7 @@ class Flux(nn.Module):
         controlnet_double_block_residuals: list[Tensor] | None,
         controlnet_single_block_residuals: list[Tensor] | None,
         ip_adapter_extensions: list[XLabsIPAdapterExtension],
+        regional_prompting_extension: RegionalPromptingExtension,
     ) -> Tensor:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -117,7 +122,6 @@ class Flux(nn.Module):
             assert len(controlnet_double_block_residuals) == len(self.double_blocks)
         for block_index, block in enumerate(self.double_blocks):
             assert isinstance(block, DoubleStreamBlock)
-
             img, txt = CustomDoubleStreamBlockProcessor.custom_double_block_forward(
                 timestep_index=timestep_index,
                 total_num_timesteps=total_num_timesteps,
@@ -128,6 +132,7 @@ class Flux(nn.Module):
                 vec=vec,
                 pe=pe,
                 ip_adapter_extensions=ip_adapter_extensions,
+                regional_prompting_extension=regional_prompting_extension,
             )
 
             if controlnet_double_block_residuals is not None:
@@ -140,7 +145,17 @@ class Flux(nn.Module):
             assert len(controlnet_single_block_residuals) == len(self.single_blocks)
 
         for block_index, block in enumerate(self.single_blocks):
-            img = block(img, vec=vec, pe=pe)
+            assert isinstance(block, SingleStreamBlock)
+            img = CustomSingleStreamBlockProcessor.custom_single_block_forward(
+                timestep_index=timestep_index,
+                total_num_timesteps=total_num_timesteps,
+                block_index=block_index,
+                block=block,
+                img=img,
+                vec=vec,
+                pe=pe,
+                regional_prompting_extension=regional_prompting_extension,
+            )
 
             if controlnet_single_block_residuals is not None:
                 img[:, txt.shape[1] :, ...] += controlnet_single_block_residuals[block_index]

invokeai/backend/flux/modules/conditioner.py

@@ -1,32 +1,53 @@
 # Initially pulled from https://github.com/black-forest-labs/flux
 
+
 from torch import Tensor, nn
 from transformers import PreTrainedModel, PreTrainedTokenizer
 
 
 class HFEncoder(nn.Module):
-    def __init__(self, encoder: PreTrainedModel, tokenizer: PreTrainedTokenizer, is_clip: bool, max_length: int):
+    def __init__(self, encoder: PreTrainedModel, tokenizer: PreTrainedTokenizer, is_clip: bool):
         super().__init__()
-        self.max_length = max_length
         self.is_clip = is_clip
         self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"
         self.tokenizer = tokenizer
         self.hf_module = encoder
         self.hf_module = self.hf_module.eval().requires_grad_(False)
 
-    def forward(self, text: list[str]) -> Tensor:
+    def forward(self, text: list[str], valid_seq_lens: list[int]) -> Tensor:
+        """Encode text into a tensor.
+
+        Args:
+            text: A list of text prompts to encode.
+            valid_seq_lens: A list of valid sequence lengths. The shortest valid sequence length that can contain the
+                text will be used. If the largest valid sequence length cannot contain the text, the encoding will be
+                truncated.
+        """
+        valid_seq_lens = sorted(valid_seq_lens)
+
+        # Perform initial encoding with the maximum valid sequence length.
         batch_encoding = self.tokenizer(
             text,
             truncation=True,
-            max_length=self.max_length,
-            return_length=False,
+            max_length=max(valid_seq_lens),
+            return_length=True,
             return_overflowing_tokens=False,
             padding="max_length",
             return_tensors="pt",
         )
 
+        # Find selected_seq_len, the minimum valid sequence length that can contain all of the input tokens.
+        seq_len: int = batch_encoding["length"][0].item()
+        selected_seq_len = valid_seq_lens[-1]
+        for len in valid_seq_lens:
+            if len >= seq_len:
+                selected_seq_len = len
+                break
+
+        input_ids = batch_encoding["input_ids"][..., :selected_seq_len]
+
         outputs = self.hf_module(
-            input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
+            input_ids=input_ids.to(self.hf_module.device),
             attention_mask=None,
             output_hidden_states=False,
         )

invokeai/backend/flux/text_conditioning.py

@@ -0,0 +1,36 @@
+from dataclasses import dataclass
+
+import torch
+
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Range
+
+
+@dataclass
+class FluxTextConditioning:
+    t5_embeddings: torch.Tensor
+    clip_embeddings: torch.Tensor
+    # If mask is None, the prompt is a global prompt.
+    mask: torch.Tensor | None
+
+
+@dataclass
+class FluxRegionalTextConditioning:
+    # Concatenated text embeddings.
+    # Shape: (1, concatenated_txt_seq_len, 4096)
+    t5_embeddings: torch.Tensor
+    # Shape: (1, concatenated_txt_seq_len, 3)
+    t5_txt_ids: torch.Tensor
+
+    # Global CLIP embeddings.
+    # Shape: (1, 768)
+    clip_embeddings: torch.Tensor
+
+    # A binary mask indicating the regions of the image that the prompt should be applied to. If None, the prompt is a
+    # global prompt.
+    # image_masks[i] is the mask for the ith prompt.
+    # image_masks[i] has shape (1, image_seq_len) and dtype torch.bool.
+    image_masks: list[torch.Tensor | None]
+
+    # List of ranges that represent the embedding ranges for each mask.
+    # t5_embedding_ranges[i] contains the range of the t5 embeddings that correspond to image_masks[i].
+    t5_embedding_ranges: list[Range]

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

@@ -176,7 +176,8 @@
         "reset": "Reset",
         "none": "None",
         "new": "New",
-        "generating": "Generating"
+        "generating": "Generating",
+        "warnings": "Warnings"
     },
     "hrf": {
         "hrf": "High Resolution Fix",
@@ -1040,6 +1041,7 @@
             "noNodesInGraph": "No nodes in graph",
             "systemDisconnected": "System disconnected",
             "layer": {
+                "unsupportedModel": "layer not supported for selected base model",
                 "controlAdapterNoModelSelected": "no Control Adapter model selected",
                 "controlAdapterIncompatibleBaseModel": "incompatible Control Adapter base model",
                 "t2iAdapterIncompatibleBboxWidth": "$t(parameters.invoke.layer.t2iAdapterRequiresDimensionsToBeMultipleOf) {{multiple}}, bbox width is {{width}}",
@@ -1050,7 +1052,10 @@
                 "ipAdapterIncompatibleBaseModel": "incompatible IP Adapter base model",
                 "ipAdapterNoImageSelected": "no IP Adapter image selected",
                 "rgNoPromptsOrIPAdapters": "no text prompts or IP Adapters",
-                "rgNoRegion": "no region selected"
+                "rgNegativePromptNotSupported": "negative prompt not supported for selected base model",
+                "rgReferenceImagesNotSupported": "regional reference images not supported for selected base model",
+                "rgAutoNegativeNotSupported": "auto-negative not supported for selected base model",
+                "emptyLayer": "empty layer"
             }
         },
         "maskBlur": "Mask Blur",

invokeai/frontend/web/src/features/controlLayers/components/CanvasAddEntityButtons.tsx

@@ -63,7 +63,7 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addRegionalGuidance}
-              isDisabled={isFLUX || isSD3}
+              isDisabled={isSD3}
             >
               {t('controlLayers.regionalGuidance')}
             </Button>

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListGlobalActionBarAddLayerMenu.tsx

@@ -49,7 +49,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
           <MenuItem icon={<PiPlusBold />} onClick={addInpaintMask}>
             {t('controlLayers.inpaintMask')}
           </MenuItem>
-          <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance} isDisabled={isFLUX || isSD3}>
+          <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance} isDisabled={isSD3}>
             {t('controlLayers.regionalGuidance')}
           </MenuItem>
           <MenuItem icon={<PiPlusBold />} onClick={addRegionalReferenceImage} isDisabled={isFLUX || isSD3}>

invokeai/frontend/web/src/features/controlLayers/components/RegionalGuidance/RegionalGuidanceDeletePromptButton.tsx

@@ -1,27 +1,28 @@
-import { IconButton, Tooltip } from '@invoke-ai/ui-library';
+import type { IconButtonProps } from '@invoke-ai/ui-library';
+import { IconButton } from '@invoke-ai/ui-library';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiTrashSimpleFill } from 'react-icons/pi';
+import { PiXBold } from 'react-icons/pi';
 
-type Props = {
+type Props = Omit<IconButtonProps, 'aria-label'> & {
   onDelete: () => void;
 };
 
-export const RegionalGuidanceDeletePromptButton = memo(({ onDelete }: Props) => {
+export const RegionalGuidanceDeletePromptButton = memo(({ onDelete, ...rest }: Props) => {
   const { t } = useTranslation();
   return (
-    <Tooltip label={t('controlLayers.deletePrompt')}>
-      <IconButton
-        variant="link"
-        aria-label={t('controlLayers.deletePrompt')}
-        icon={<PiTrashSimpleFill />}
-        onClick={onDelete}
-        flexGrow={0}
-        size="sm"
-        p={0}
-        colorScheme="error"
-      />
-    </Tooltip>
+    <IconButton
+      tooltip={t('common.delete')}
+      variant="link"
+      aria-label={t('common.delete')}
+      icon={<PiXBold />}
+      onClick={onDelete}
+      flexGrow={0}
+      size="sm"
+      p={0}
+      colorScheme="error"
+      {...rest}
+    />
   );
 });
 

invokeai/frontend/web/src/features/controlLayers/components/RegionalGuidance/RegionalGuidanceIPAdapterSettingsEmptyState.tsx

@@ -1,8 +1,10 @@
 import { Button, Flex, Text } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { useImageUploadButton } from 'common/hooks/useImageUploadButton';
+import { RegionalGuidanceDeletePromptButton } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceDeletePromptButton';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
+import { rgIPAdapterDeleted } from 'features/controlLayers/store/canvasSlice';
 import type { SetRegionalGuidanceReferenceImageDndTargetData } from 'features/dnd/dnd';
 import { setRegionalGuidanceReferenceImageDndTarget } from 'features/dnd/dnd';
 import { DndDropTarget } from 'features/dnd/DndDropTarget';
@@ -31,6 +33,9 @@ export const RegionalGuidanceIPAdapterSettingsEmptyState = memo(({ referenceImag
   const onClickGalleryButton = useCallback(() => {
     dispatch(activeTabCanvasRightPanelChanged('gallery'));
   }, [dispatch]);
+  const onDeleteIPAdapter = useCallback(() => {
+    dispatch(rgIPAdapterDeleted({ entityIdentifier, referenceImageId }));
+  }, [dispatch, entityIdentifier, referenceImageId]);
 
   const dndTargetData = useMemo<SetRegionalGuidanceReferenceImageDndTargetData>(
     () =>
@@ -43,6 +48,7 @@ export const RegionalGuidanceIPAdapterSettingsEmptyState = memo(({ referenceImag
 
   return (
     <Flex flexDir="column" gap={3} position="relative" w="full" p={4}>
+      <RegionalGuidanceDeletePromptButton onDelete={onDeleteIPAdapter} position="absolute" top={0} insetInlineEnd={0} />
       <Text textAlign="center" color="base.300">
         <Trans
           i18nKey="controlLayers.referenceImageEmptyState"

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityHeaderCommonActions.tsx

@@ -1,6 +1,7 @@
 import { Flex } from '@invoke-ai/ui-library';
 import { CanvasEntityDeleteButton } from 'features/controlLayers/components/common/CanvasEntityDeleteButton';
 import { CanvasEntityEnabledToggle } from 'features/controlLayers/components/common/CanvasEntityEnabledToggle';
+import { CanvasEntityHeaderWarnings } from 'features/controlLayers/components/common/CanvasEntityHeaderWarnings';
 import { CanvasEntityIsBookmarkedForQuickSwitchToggle } from 'features/controlLayers/components/common/CanvasEntityIsBookmarkedForQuickSwitchToggle';
 import { CanvasEntityIsLockedToggle } from 'features/controlLayers/components/common/CanvasEntityIsLockedToggle';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
@@ -11,6 +12,7 @@ export const CanvasEntityHeaderCommonActions = memo(() => {
 
   return (
     <Flex alignSelf="stretch">
+      <CanvasEntityHeaderWarnings />
       <CanvasEntityIsBookmarkedForQuickSwitchToggle />
       {entityIdentifier.type !== 'reference_image' && <CanvasEntityIsLockedToggle />}
       <CanvasEntityEnabledToggle />

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityHeaderWarnings.tsx

@@ -0,0 +1,101 @@
+import { Flex, IconButton, ListItem, Text, UnorderedList } from '@invoke-ai/ui-library';
+import { createSelector } from '@reduxjs/toolkit';
+import { EMPTY_ARRAY } from 'app/store/constants';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
+import { useEntityIsEnabled } from 'features/controlLayers/hooks/useEntityIsEnabled';
+import { selectModel } from 'features/controlLayers/store/paramsSlice';
+import { selectCanvasSlice, selectEntityOrThrow } from 'features/controlLayers/store/selectors';
+import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
+import {
+  getControlLayerWarnings,
+  getGlobalReferenceImageWarnings,
+  getInpaintMaskWarnings,
+  getRasterLayerWarnings,
+  getRegionalGuidanceWarnings,
+} from 'features/controlLayers/store/validators';
+import type { TFunction } from 'i18next';
+import { upperFirst } from 'lodash-es';
+import { memo, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiWarningBold } from 'react-icons/pi';
+import type { Equals } from 'tsafe';
+import { assert } from 'tsafe';
+
+const buildSelectWarnings = (entityIdentifier: CanvasEntityIdentifier, t: TFunction) => {
+  return createSelector(selectCanvasSlice, selectModel, (canvas, model) => {
+    // This component is used within a <CanvasEntityStateGate /> so we can safely assume that the entity exists.
+    // Should never throw.
+    const entity = selectEntityOrThrow(canvas, entityIdentifier, 'CanvasEntityHeaderWarnings');
+
+    let warnings: string[] = [];
+
+    const entityType = entity.type;
+
+    if (entityType === 'control_layer') {
+      warnings = getControlLayerWarnings(entity, model);
+    } else if (entityType === 'regional_guidance') {
+      warnings = getRegionalGuidanceWarnings(entity, model);
+    } else if (entityType === 'inpaint_mask') {
+      warnings = getInpaintMaskWarnings(entity, model);
+    } else if (entityType === 'raster_layer') {
+      warnings = getRasterLayerWarnings(entity, model);
+    } else if (entityType === 'reference_image') {
+      warnings = getGlobalReferenceImageWarnings(entity, model);
+    } else {
+      assert<Equals<typeof entityType, never>>(false, 'Unexpected entity type');
+    }
+
+    // Return a stable reference if there are no warnings
+    if (warnings.length === 0) {
+      return EMPTY_ARRAY;
+    }
+
+    return warnings.map((w) => t(w)).map(upperFirst);
+  });
+};
+
+export const CanvasEntityHeaderWarnings = memo(() => {
+  const entityIdentifier = useEntityIdentifierContext();
+  const { t } = useTranslation();
+  const isEnabled = useEntityIsEnabled(entityIdentifier);
+  const selectWarnings = useMemo(() => buildSelectWarnings(entityIdentifier, t), [entityIdentifier, t]);
+  const warnings = useAppSelector(selectWarnings);
+
+  if (warnings.length === 0) {
+    return null;
+  }
+
+  return (
+    // Using IconButton here bc it matches the styling of the actual buttons in the header without any fanagling, but
+    // it's not a button
+    <IconButton
+      as="span"
+      size="sm"
+      variant="link"
+      alignSelf="stretch"
+      aria-label="warnings"
+      tooltip={<TooltipContent warnings={warnings} />}
+      icon={<PiWarningBold />}
+      colorScheme="warning"
+      isDisabled={!isEnabled}
+    />
+  );
+});
+
+CanvasEntityHeaderWarnings.displayName = 'CanvasEntityHeaderWarnings';
+
+const TooltipContent = memo((props: { warnings: string[] }) => {
+  const { t } = useTranslation();
+  return (
+    <Flex flexDir="column">
+      <Text>{t('common.warnings')}:</Text>
+      <UnorderedList>
+        {props.warnings.map((warning, index) => (
+          <ListItem key={index}>{warning}</ListItem>
+        ))}
+      </UnorderedList>
+    </Flex>
+  );
+});
+TooltipContent.displayName = 'TooltipContent';

invokeai/frontend/web/src/features/controlLayers/store/validators.ts

@@ -0,0 +1,133 @@
+import type {
+  CanvasControlLayerState,
+  CanvasInpaintMaskState,
+  CanvasRasterLayerState,
+  CanvasReferenceImageState,
+  CanvasRegionalGuidanceState,
+} from 'features/controlLayers/store/types';
+import type { ParameterModel } from 'features/parameters/types/parameterSchemas';
+
+export const getRegionalGuidanceWarnings = (
+  entity: CanvasRegionalGuidanceState,
+  model: ParameterModel | null
+): string[] => {
+  const warnings: string[] = [];
+
+  if (entity.objects.length === 0) {
+    // Layer is in empty state - skip other checks
+    warnings.push('parameters.invoke.layer.emptyLayer');
+  } else {
+    if (entity.positivePrompt === null && entity.negativePrompt === null && entity.referenceImages.length === 0) {
+      // Must have at least 1 prompt or IP Adapter
+      warnings.push('parameters.invoke.layer.rgNoPromptsOrIPAdapters');
+    }
+
+    if (model) {
+      if (model.base === 'sd-3' || model.base === 'sd-2') {
+        // Unsupported model architecture
+        warnings.push('parameters.invoke.layer.unsupportedModel');
+      } else if (model.base === 'flux') {
+        // Some features are not supported for flux models
+        if (entity.negativePrompt !== null) {
+          warnings.push('parameters.invoke.layer.rgNegativePromptNotSupported');
+        }
+        if (entity.referenceImages.length > 0) {
+          warnings.push('parameters.invoke.layer.rgReferenceImagesNotSupported');
+        }
+        if (entity.autoNegative) {
+          warnings.push('parameters.invoke.layer.rgAutoNegativeNotSupported');
+        }
+      } else {
+        entity.referenceImages.forEach(({ ipAdapter }) => {
+          if (!ipAdapter.model) {
+            // No model selected
+            warnings.push('parameters.invoke.layer.ipAdapterNoModelSelected');
+          } else if (ipAdapter.model.base !== model.base) {
+            // Supported model architecture but doesn't match
+            warnings.push('parameters.invoke.layer.ipAdapterIncompatibleBaseModel');
+          }
+
+          if (!ipAdapter.image) {
+            // No image selected
+            warnings.push('parameters.invoke.layer.ipAdapterNoImageSelected');
+          }
+        });
+      }
+    }
+  }
+
+  return warnings;
+};
+
+export const getGlobalReferenceImageWarnings = (
+  entity: CanvasReferenceImageState,
+  model: ParameterModel | null
+): string[] => {
+  const warnings: string[] = [];
+
+  if (!entity.ipAdapter.model) {
+    // No model selected
+    warnings.push('parameters.invoke.layer.ipAdapterNoModelSelected');
+  } else if (model) {
+    if (model.base === 'sd-3' || model.base === 'sd-2') {
+      // Unsupported model architecture
+      warnings.push('parameters.invoke.layer.unsupportedModel');
+    } else if (entity.ipAdapter.model.base !== model.base) {
+      // Supported model architecture but doesn't match
+      warnings.push('parameters.invoke.layer.ipAdapterIncompatibleBaseModel');
+    }
+  }
+
+  if (!entity.ipAdapter.image) {
+    // No image selected
+    warnings.push('parameters.invoke.layer.ipAdapterNoImageSelected');
+  }
+
+  return warnings;
+};
+
+export const getControlLayerWarnings = (entity: CanvasControlLayerState, model: ParameterModel | null): string[] => {
+  const warnings: string[] = [];
+
+  if (entity.objects.length === 0) {
+    // Layer is in empty state - skip other checks
+    warnings.push('parameters.invoke.layer.emptyLayer');
+  } else {
+    if (!entity.controlAdapter.model) {
+      // No model selected
+      warnings.push('parameters.invoke.layer.controlAdapterNoModelSelected');
+    } else if (model) {
+      if (model.base === 'sd-3' || model.base === 'sd-2') {
+        // Unsupported model architecture
+        warnings.push('parameters.invoke.layer.unsupportedModel');
+      } else if (entity.controlAdapter.model.base !== model.base) {
+        // Supported model architecture but doesn't match
+        warnings.push('parameters.invoke.layer.controlAdapterIncompatibleBaseModel');
+      }
+    }
+  }
+
+  return warnings;
+};
+
+export const getRasterLayerWarnings = (entity: CanvasRasterLayerState, _model: ParameterModel | null): string[] => {
+  const warnings: string[] = [];
+
+  if (entity.objects.length === 0) {
+    // Layer is in empty state - skip other checks
+    warnings.push('parameters.invoke.layer.emptyLayer');
+  }
+
+  return warnings;
+};
+
+export const getInpaintMaskWarnings = (entity: CanvasInpaintMaskState, _model: ParameterModel | null): string[] => {
+  const warnings: string[] = [];
+
+  if (entity.objects.length === 0) {
+    // Layer is in empty state - skip other checks
+    warnings.push('parameters.invoke.layer.emptyLayer');
+  }
+
+  return warnings;
+};

invokeai/frontend/web/src/features/nodes/util/graph/generation/addControlAdapters.ts

@@ -1,35 +1,41 @@
 import { logger } from 'app/logging/logger';
 import { withResultAsync } from 'common/util/result';
 import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
-import type {
-  CanvasControlLayerState,
-  ControlNetConfig,
-  Rect,
-  T2IAdapterConfig,
-} from 'features/controlLayers/store/types';
+import type { CanvasControlLayerState, Rect } from 'features/controlLayers/store/types';
+import { getControlLayerWarnings } from 'features/controlLayers/store/validators';
 import type { Graph } from 'features/nodes/util/graph/generation/Graph';
+import type { ParameterModel } from 'features/parameters/types/parameterSchemas';
 import { serializeError } from 'serialize-error';
-import type { BaseModelType, ImageDTO, Invocation } from 'services/api/types';
+import type { ImageDTO, Invocation } from 'services/api/types';
 import { assert } from 'tsafe';
 
 const log = logger('system');
 
+type AddControlNetsArg = {
+  manager: CanvasManager;
+  entities: CanvasControlLayerState[];
+  g: Graph;
+  rect: Rect;
+  collector: Invocation<'collect'>;
+  model: ParameterModel;
+};
+
 type AddControlNetsResult = {
   addedControlNets: number;
 };
 
-export const addControlNets = async (
-  manager: CanvasManager,
-  layers: CanvasControlLayerState[],
-  g: Graph,
-  rect: Rect,
-  collector: Invocation<'collect'>,
-  base: BaseModelType
-): Promise<AddControlNetsResult> => {
-  const validControlLayers = layers
-    .filter((layer) => layer.isEnabled)
-    .filter((layer) => isValidControlAdapter(layer.controlAdapter, base))
-    .filter((layer) => layer.controlAdapter.type === 'controlnet');
+export const addControlNets = async ({
+  manager,
+  entities,
+  g,
+  rect,
+  collector,
+  model,
+}: AddControlNetsArg): Promise<AddControlNetsResult> => {
+  const validControlLayers = entities
+    .filter((entity) => entity.isEnabled)
+    .filter((entity) => entity.controlAdapter.type === 'controlnet')
+    .filter((entity) => getControlLayerWarnings(entity, model).length === 0);
 
   const result: AddControlNetsResult = {
     addedControlNets: 0,
@@ -54,22 +60,31 @@ export const addControlNets = async (
   return result;
 };
 
+type AddT2IAdaptersArg = {
+  manager: CanvasManager;
+  entities: CanvasControlLayerState[];
+  g: Graph;
+  rect: Rect;
+  collector: Invocation<'collect'>;
+  model: ParameterModel;
+};
+
 type AddT2IAdaptersResult = {
   addedT2IAdapters: number;
 };
 
-export const addT2IAdapters = async (
-  manager: CanvasManager,
-  layers: CanvasControlLayerState[],
-  g: Graph,
-  rect: Rect,
-  collector: Invocation<'collect'>,
-  base: BaseModelType
-): Promise<AddT2IAdaptersResult> => {
-  const validControlLayers = layers
-    .filter((layer) => layer.isEnabled)
-    .filter((layer) => isValidControlAdapter(layer.controlAdapter, base))
-    .filter((layer) => layer.controlAdapter.type === 't2i_adapter');
+export const addT2IAdapters = async ({
+  manager,
+  entities,
+  g,
+  rect,
+  collector,
+  model,
+}: AddT2IAdaptersArg): Promise<AddT2IAdaptersResult> => {
+  const validControlLayers = entities
+    .filter((entity) => entity.isEnabled)
+    .filter((entity) => entity.controlAdapter.type === 't2i_adapter')
+    .filter((entity) => getControlLayerWarnings(entity, model).length === 0);
 
   const result: AddT2IAdaptersResult = {
     addedT2IAdapters: 0,
@@ -145,11 +160,3 @@ const addT2IAdapterToGraph = (
 
   g.addEdge(t2iAdapter, 't2i_adapter', collector, 'item');
 };
-
-const isValidControlAdapter = (controlAdapter: ControlNetConfig | T2IAdapterConfig, base: BaseModelType): boolean => {
-  // Must be have a model
-  const hasModel = Boolean(controlAdapter.model);
-  // Model must match the current base model
-  const modelMatchesBase = controlAdapter.model?.base === base;
-  return hasModel && modelMatchesBase;
-};

invokeai/frontend/web/src/features/nodes/util/graph/generation/addIPAdapters.ts

@@ -1,19 +1,23 @@
 import type { CanvasReferenceImageState } from 'features/controlLayers/store/types';
+import { getGlobalReferenceImageWarnings } from 'features/controlLayers/store/validators';
 import type { Graph } from 'features/nodes/util/graph/generation/Graph';
-import type { BaseModelType, Invocation } from 'services/api/types';
+import type { ParameterModel } from 'features/parameters/types/parameterSchemas';
+import type { Invocation } from 'services/api/types';
 import { assert } from 'tsafe';
 
 type AddIPAdaptersResult = {
   addedIPAdapters: number;
 };
 
-export const addIPAdapters = (
-  ipAdapters: CanvasReferenceImageState[],
-  g: Graph,
-  collector: Invocation<'collect'>,
-  base: BaseModelType
-): AddIPAdaptersResult => {
-  const validIPAdapters = ipAdapters.filter((entity) => isValidIPAdapter(entity, base));
+type AddIPAdaptersArg = {
+  entities: CanvasReferenceImageState[];
+  g: Graph;
+  collector: Invocation<'collect'>;
+  model: ParameterModel;
+};
+
+export const addIPAdapters = ({ entities, g, collector, model }: AddIPAdaptersArg): AddIPAdaptersResult => {
+  const validIPAdapters = entities.filter((entity) => getGlobalReferenceImageWarnings(entity, model).length === 0);
 
   const result: AddIPAdaptersResult = {
     addedIPAdapters: 0,
@@ -76,11 +80,3 @@ const addIPAdapter = (entity: CanvasReferenceImageState, g: Graph, collector: In
 
   g.addEdge(ipAdapterNode, 'ip_adapter', collector, 'item');
 };
-
-const isValidIPAdapter = ({ isEnabled, ipAdapter }: CanvasReferenceImageState, base: BaseModelType): boolean => {
-  // Must be have a model that matches the current base and must have a control image
-  const hasModel = Boolean(ipAdapter.model);
-  const modelMatchesBase = ipAdapter.model?.base === base;
-  const hasImage = Boolean(ipAdapter.image);
-  return isEnabled && hasModel && modelMatchesBase && hasImage;
-};

invokeai/frontend/web/src/features/nodes/util/graph/generation/addRegions.ts

@@ -3,15 +3,12 @@ import { deepClone } from 'common/util/deepClone';
 import { withResultAsync } from 'common/util/result';
 import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
 import { getPrefixedId } from 'features/controlLayers/konva/util';
-import type {
-  CanvasRegionalGuidanceState,
-  IPAdapterConfig,
-  Rect,
-  RegionalGuidanceReferenceImageState,
-} from 'features/controlLayers/store/types';
+import type { CanvasRegionalGuidanceState, Rect } from 'features/controlLayers/store/types';
+import { getRegionalGuidanceWarnings } from 'features/controlLayers/store/validators';
 import type { Graph } from 'features/nodes/util/graph/generation/Graph';
+import type { ParameterModel } from 'features/parameters/types/parameterSchemas';
 import { serializeError } from 'serialize-error';
-import type { BaseModelType, Invocation } from 'services/api/types';
+import type { Invocation } from 'services/api/types';
 import { assert } from 'tsafe';
 
 const log = logger('system');
@@ -23,19 +20,26 @@ type AddedRegionResult = {
   addedIPAdapters: number;
 };
 
-const isValidRegion = (rg: CanvasRegionalGuidanceState, base: BaseModelType) => {
-  const isEnabled = rg.isEnabled;
-  const hasTextPrompt = Boolean(rg.positivePrompt || rg.negativePrompt);
-  const hasIPAdapter = rg.referenceImages.filter(({ ipAdapter }) => isValidIPAdapter(ipAdapter, base)).length > 0;
-  return isEnabled && (hasTextPrompt || hasIPAdapter);
+type AddRegionsArg = {
+  manager: CanvasManager;
+  regions: CanvasRegionalGuidanceState[];
+  g: Graph;
+  bbox: Rect;
+  model: ParameterModel;
+  posCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder'>;
+  negCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder'> | null;
+  posCondCollect: Invocation<'collect'>;
+  negCondCollect: Invocation<'collect'> | null;
+  ipAdapterCollect: Invocation<'collect'>;
 };
 
 /**
  * Adds regional guidance to the graph
+ * @param manager The canvas manager
  * @param regions Array of regions to add
  * @param g The graph to add the layers to
- * @param base The base model type
- * @param denoise The main denoise node
+ * @param bbox The bounding box
+ * @param model The main model
  * @param posCond The positive conditioning node
  * @param negCond The negative conditioning node
  * @param posCondCollect The positive conditioning collector
@@ -44,22 +48,28 @@ const isValidRegion = (rg: CanvasRegionalGuidanceState, base: BaseModelType) =>
  * @returns A promise that resolves to the regions that were successfully added to the graph
  */
 
-export const addRegions = async (
-  manager: CanvasManager,
-  regions: CanvasRegionalGuidanceState[],
-  g: Graph,
-  bbox: Rect,
-  base: BaseModelType,
-  denoise: Invocation<'denoise_latents'>,
-  posCond: Invocation<'compel'> | Invocation<'sdxl_compel_prompt'>,
-  negCond: Invocation<'compel'> | Invocation<'sdxl_compel_prompt'>,
-  posCondCollect: Invocation<'collect'>,
-  negCondCollect: Invocation<'collect'>,
-  ipAdapterCollect: Invocation<'collect'>
-): Promise<AddedRegionResult[]> => {
-  const isSDXL = base === 'sdxl';
+export const addRegions = async ({
+  manager,
+  regions,
+  g,
+  bbox,
+  model,
+  posCond,
+  negCond,
+  posCondCollect,
+  negCondCollect,
+  ipAdapterCollect,
+}: AddRegionsArg): Promise<AddedRegionResult[]> => {
+  const isSDXL = model.base === 'sdxl';
+  const isFLUX = model.base === 'flux';
+
+  const validRegions = regions.filter((rg) => {
+    if (!rg.isEnabled) {
+      return false;
+    }
+    return getRegionalGuidanceWarnings(rg, model).length === 0;
+  });
 
-  const validRegions = regions.filter((rg) => isValidRegion(rg, base));
   const results: AddedRegionResult[] = [];
 
   for (const region of validRegions) {
@@ -94,20 +104,27 @@ export const addRegions = async (
     if (region.positivePrompt) {
       // The main positive conditioning node
       result.addedPositivePrompt = true;
-      const regionalPosCond = g.addNode(
-        isSDXL
-          ? {
-              type: 'sdxl_compel_prompt',
-              id: getPrefixedId('prompt_region_positive_cond'),
-              prompt: region.positivePrompt,
-              style: region.positivePrompt, // TODO: Should we put the positive prompt in both fields?
-            }
-          : {
-              type: 'compel',
-              id: getPrefixedId('prompt_region_positive_cond'),
-              prompt: region.positivePrompt,
-            }
-      );
+      let regionalPosCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder'>;
+      if (isSDXL) {
+        regionalPosCond = g.addNode({
+          type: 'sdxl_compel_prompt',
+          id: getPrefixedId('prompt_region_positive_cond'),
+          prompt: region.positivePrompt,
+          style: region.positivePrompt, // TODO: Should we put the positive prompt in both fields?
+        });
+      } else if (isFLUX) {
+        regionalPosCond = g.addNode({
+          type: 'flux_text_encoder',
+          id: getPrefixedId('prompt_region_positive_cond'),
+          prompt: region.positivePrompt,
+        });
+      } else {
+        regionalPosCond = g.addNode({
+          type: 'compel',
+          id: getPrefixedId('prompt_region_positive_cond'),
+          prompt: region.positivePrompt,
+        });
+      }
       // Connect the mask to the conditioning
       g.addEdge(maskToTensor, 'mask', regionalPosCond, 'mask');
       // Connect the conditioning to the collector
@@ -115,38 +132,55 @@ export const addRegions = async (
       // Copy the connections to the "global" positive conditioning node to the regional cond
       if (posCond.type === 'compel') {
         for (const edge of g.getEdgesTo(posCond, ['clip', 'mask'])) {
-          // Clone the edge, but change the destination node to the regional conditioning node
+          const clone = deepClone(edge);
+          clone.destination.node_id = regionalPosCond.id;
+          g.addEdgeFromObj(clone);
+        }
+      } else if (posCond.type === 'sdxl_compel_prompt') {
+        for (const edge of g.getEdgesTo(posCond, ['clip', 'clip2', 'mask'])) {
+          const clone = deepClone(edge);
+          clone.destination.node_id = regionalPosCond.id;
+          g.addEdgeFromObj(clone);
+        }
+      } else if (posCond.type === 'flux_text_encoder') {
+        for (const edge of g.getEdgesTo(posCond, ['clip', 't5_encoder', 't5_max_seq_len', 'mask'])) {
           const clone = deepClone(edge);
           clone.destination.node_id = regionalPosCond.id;
           g.addEdgeFromObj(clone);
         }
       } else {
-        for (const edge of g.getEdgesTo(posCond, ['clip', 'clip2', 'mask'])) {
-          // Clone the edge, but change the destination node to the regional conditioning node
-          const clone = deepClone(edge);
-          clone.destination.node_id = regionalPosCond.id;
-          g.addEdgeFromObj(clone);
-        }
+        assert(false, 'Unsupported positive conditioning node type.');
       }
     }
 
     if (region.negativePrompt) {
-      result.addedNegativePrompt = true;
+      assert(negCond, 'Negative conditioning node is required if there is a negative prompt');
+      assert(negCondCollect, 'Negative conditioning collector is required if there is a negative prompt');
+
       // The main negative conditioning node
-      const regionalNegCond = g.addNode(
-        isSDXL
-          ? {
-              type: 'sdxl_compel_prompt',
-              id: getPrefixedId('prompt_region_negative_cond'),
-              prompt: region.negativePrompt,
-              style: region.negativePrompt,
-            }
-          : {
-              type: 'compel',
-              id: getPrefixedId('prompt_region_negative_cond'),
-              prompt: region.negativePrompt,
-            }
-      );
+      result.addedNegativePrompt = true;
+      let regionalNegCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder'>;
+      if (isSDXL) {
+        regionalNegCond = g.addNode({
+          type: 'sdxl_compel_prompt',
+          id: getPrefixedId('prompt_region_negative_cond'),
+          prompt: region.negativePrompt,
+          style: region.negativePrompt,
+        });
+      } else if (isFLUX) {
+        regionalNegCond = g.addNode({
+          type: 'flux_text_encoder',
+          id: getPrefixedId('prompt_region_negative_cond'),
+          prompt: region.negativePrompt,
+        });
+      } else {
+        regionalNegCond = g.addNode({
+          type: 'compel',
+          id: getPrefixedId('prompt_region_negative_cond'),
+          prompt: region.negativePrompt,
+        });
+      }
+
       // Connect the mask to the conditioning
       g.addEdge(maskToTensor, 'mask', regionalNegCond, 'mask');
       // Connect the conditioning to the collector
@@ -158,17 +192,27 @@ export const addRegions = async (
           clone.destination.node_id = regionalNegCond.id;
           g.addEdgeFromObj(clone);
         }
-      } else {
+      } else if (negCond.type === 'sdxl_compel_prompt') {
         for (const edge of g.getEdgesTo(negCond, ['clip', 'clip2', 'mask'])) {
           const clone = deepClone(edge);
           clone.destination.node_id = regionalNegCond.id;
           g.addEdgeFromObj(clone);
         }
+      } else if (negCond.type === 'flux_text_encoder') {
+        for (const edge of g.getEdgesTo(negCond, ['clip', 't5_encoder', 't5_max_seq_len', 'mask'])) {
+          const clone = deepClone(edge);
+          clone.destination.node_id = regionalNegCond.id;
+          g.addEdgeFromObj(clone);
+        }
+      } else {
+        assert(false, 'Unsupported negative conditioning node type.');
       }
     }
 
     // If we are using the "invert" auto-negative setting, we need to add an additional negative conditioning node
     if (region.autoNegative && region.positivePrompt) {
+      assert(negCondCollect, 'Negative conditioning collector is required if there is an auto-negative setting');
+
       result.addedAutoNegativePositivePrompt = true;
       // We re-use the mask image, but invert it when converting to tensor
       const invertTensorMask = g.addNode({
@@ -178,20 +222,27 @@ export const addRegions = async (
       // Connect the OG mask image to the inverted mask-to-tensor node
       g.addEdge(maskToTensor, 'mask', invertTensorMask, 'mask');
       // Create the conditioning node. It's going to be connected to the negative cond collector, but it uses the positive prompt
-      const regionalPosCondInverted = g.addNode(
-        isSDXL
-          ? {
-              type: 'sdxl_compel_prompt',
-              id: getPrefixedId('prompt_region_positive_cond_inverted'),
-              prompt: region.positivePrompt,
-              style: region.positivePrompt,
-            }
-          : {
-              type: 'compel',
-              id: getPrefixedId('prompt_region_positive_cond_inverted'),
-              prompt: region.positivePrompt,
-            }
-      );
+      let regionalPosCondInverted: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder'>;
+      if (isSDXL) {
+        regionalPosCondInverted = g.addNode({
+          type: 'sdxl_compel_prompt',
+          id: getPrefixedId('prompt_region_positive_cond_inverted'),
+          prompt: region.positivePrompt,
+          style: region.positivePrompt,
+        });
+      } else if (isFLUX) {
+        regionalPosCondInverted = g.addNode({
+          type: 'flux_text_encoder',
+          id: getPrefixedId('prompt_region_positive_cond_inverted'),
+          prompt: region.positivePrompt,
+        });
+      } else {
+        regionalPosCondInverted = g.addNode({
+          type: 'compel',
+          id: getPrefixedId('prompt_region_positive_cond_inverted'),
+          prompt: region.positivePrompt,
+        });
+      }
       // Connect the inverted mask to the conditioning
       g.addEdge(invertTensorMask, 'mask', regionalPosCondInverted, 'mask');
       // Connect the conditioning to the negative collector
@@ -203,20 +254,26 @@ export const addRegions = async (
           clone.destination.node_id = regionalPosCondInverted.id;
           g.addEdgeFromObj(clone);
         }
-      } else {
+      } else if (posCond.type === 'sdxl_compel_prompt') {
         for (const edge of g.getEdgesTo(posCond, ['clip', 'clip2', 'mask'])) {
           const clone = deepClone(edge);
           clone.destination.node_id = regionalPosCondInverted.id;
           g.addEdgeFromObj(clone);
         }
+      } else if (posCond.type === 'flux_text_encoder') {
+        for (const edge of g.getEdgesTo(posCond, ['clip', 't5_encoder', 't5_max_seq_len', 'mask'])) {
+          const clone = deepClone(edge);
+          clone.destination.node_id = regionalPosCondInverted.id;
+          g.addEdgeFromObj(clone);
+        }
+      } else {
+        assert(false, 'Unsupported positive conditioning node type.');
       }
     }
 
-    const validRGIPAdapters: RegionalGuidanceReferenceImageState[] = region.referenceImages.filter(({ ipAdapter }) =>
-      isValidIPAdapter(ipAdapter, base)
-    );
+    for (const { id, ipAdapter } of region.referenceImages) {
+      assert(!isFLUX, 'Regional IP adapters are not supported for FLUX.');
 
-    for (const { id, ipAdapter } of validRGIPAdapters) {
       result.addedIPAdapters++;
       const { weight, model, clipVisionModel, method, beginEndStepPct, image } = ipAdapter;
       assert(model, 'IP Adapter model is required');
@@ -248,11 +305,3 @@ export const addRegions = async (
 
   return results;
 };
-
-const isValidIPAdapter = (ipAdapter: IPAdapterConfig, base: BaseModelType): boolean => {
-  // Must be have a model that matches the current base and must have a control image
-  const hasModel = Boolean(ipAdapter.model);
-  const modelMatchesBase = ipAdapter.model?.base === base;
-  const hasImage = Boolean(ipAdapter.image);
-  return hasModel && modelMatchesBase && hasImage;
-};

invokeai/frontend/web/src/features/nodes/util/graph/generation/buildFLUXGraph.ts

@@ -11,6 +11,7 @@ import { addImageToImage } from 'features/nodes/util/graph/generation/addImageTo
 import { addInpaint } from 'features/nodes/util/graph/generation/addInpaint';
 import { addNSFWChecker } from 'features/nodes/util/graph/generation/addNSFWChecker';
 import { addOutpaint } from 'features/nodes/util/graph/generation/addOutpaint';
+import { addRegions } from 'features/nodes/util/graph/generation/addRegions';
 import { addTextToImage } from 'features/nodes/util/graph/generation/addTextToImage';
 import { addWatermarker } from 'features/nodes/util/graph/generation/addWatermarker';
 import { Graph } from 'features/nodes/util/graph/generation/Graph';
@@ -79,7 +80,10 @@ export const buildFLUXGraph = async (
     id: getPrefixedId('flux_text_encoder'),
     prompt: positivePrompt,
   });
-
+  const posCondCollect = g.addNode({
+    type: 'collect',
+    id: getPrefixedId('pos_cond_collect'),
+  });
   const denoise = g.addNode({
     type: 'flux_denoise',
     id: getPrefixedId('flux_denoise'),
@@ -104,13 +108,12 @@ export const buildFLUXGraph = async (
   g.addEdge(modelLoader, 'clip', posCond, 'clip');
   g.addEdge(modelLoader, 't5_encoder', posCond, 't5_encoder');
   g.addEdge(modelLoader, 'max_seq_len', posCond, 't5_max_seq_len');
+  g.addEdge(posCond, 'conditioning', posCondCollect, 'item');
+  g.addEdge(posCondCollect, 'collection', denoise, 'positive_text_conditioning');
+  g.addEdge(denoise, 'latents', l2i, 'latents');
 
   addFLUXLoRAs(state, g, denoise, modelLoader, posCond);
 
-  g.addEdge(posCond, 'conditioning', denoise, 'positive_text_conditioning');
-
-  g.addEdge(denoise, 'latents', l2i, 'latents');
-
   const modelConfig = await fetchModelConfigWithTypeGuard(model.key, isNonRefinerMainModelConfig);
   assert(modelConfig.base === 'flux');
 
@@ -196,31 +199,50 @@ export const buildFLUXGraph = async (
     type: 'collect',
     id: getPrefixedId('control_net_collector'),
   });
-  const controlNetResult = await addControlNets(
+  const controlNetResult = await addControlNets({
     manager,
-    canvas.controlLayers.entities,
+    entities: canvas.controlLayers.entities,
     g,
-    canvas.bbox.rect,
-    controlNetCollector,
-    modelConfig.base
-  );
+    rect: canvas.bbox.rect,
+    collector: controlNetCollector,
+    model: modelConfig,
+  });
   if (controlNetResult.addedControlNets > 0) {
     g.addEdge(controlNetCollector, 'collection', denoise, 'control');
   } else {
     g.deleteNode(controlNetCollector.id);
   }
 
-  const ipAdapterCollector = g.addNode({
+  const ipAdapterCollect = g.addNode({
     type: 'collect',
     id: getPrefixedId('ip_adapter_collector'),
   });
-  const ipAdapterResult = addIPAdapters(canvas.referenceImages.entities, g, ipAdapterCollector, modelConfig.base);
+  const ipAdapterResult = addIPAdapters({
+    entities: canvas.referenceImages.entities,
+    g,
+    collector: ipAdapterCollect,
+    model: modelConfig,
+  });
 
-  const totalIPAdaptersAdded = ipAdapterResult.addedIPAdapters;
+  const regionsResult = await addRegions({
+    manager,
+    regions: canvas.regionalGuidance.entities,
+    g,
+    bbox: canvas.bbox.rect,
+    model: modelConfig,
+    posCond,
+    negCond: null,
+    posCondCollect,
+    negCondCollect: null,
+    ipAdapterCollect,
+  });
+
+  const totalIPAdaptersAdded =
+    ipAdapterResult.addedIPAdapters + regionsResult.reduce((acc, r) => acc + r.addedIPAdapters, 0);
   if (totalIPAdaptersAdded > 0) {
-    g.addEdge(ipAdapterCollector, 'collection', denoise, 'ip_adapter');
+    g.addEdge(ipAdapterCollect, 'collection', denoise, 'ip_adapter');
   } else {
-    g.deleteNode(ipAdapterCollector.id);
+    g.deleteNode(ipAdapterCollect.id);
   }
 
   if (state.system.shouldUseNSFWChecker) {

invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSD1Graph.ts

@@ -227,14 +227,14 @@ export const buildSD1Graph = async (
     type: 'collect',
     id: getPrefixedId('control_net_collector'),
   });
-  const controlNetResult = await addControlNets(
+  const controlNetResult = await addControlNets({
     manager,
-    canvas.controlLayers.entities,
+    entities: canvas.controlLayers.entities,
     g,
-    canvas.bbox.rect,
-    controlNetCollector,
-    modelConfig.base
-  );
+    rect: canvas.bbox.rect,
+    collector: controlNetCollector,
+    model: modelConfig,
+  });
   if (controlNetResult.addedControlNets > 0) {
     g.addEdge(controlNetCollector, 'collection', denoise, 'control');
   } else {
@@ -245,46 +245,50 @@ export const buildSD1Graph = async (
     type: 'collect',
     id: getPrefixedId('t2i_adapter_collector'),
   });
-  const t2iAdapterResult = await addT2IAdapters(
+  const t2iAdapterResult = await addT2IAdapters({
     manager,
-    canvas.controlLayers.entities,
+    entities: canvas.controlLayers.entities,
     g,
-    canvas.bbox.rect,
-    t2iAdapterCollector,
-    modelConfig.base
-  );
+    rect: canvas.bbox.rect,
+    collector: t2iAdapterCollector,
+    model: modelConfig,
+  });
   if (t2iAdapterResult.addedT2IAdapters > 0) {
     g.addEdge(t2iAdapterCollector, 'collection', denoise, 't2i_adapter');
   } else {
     g.deleteNode(t2iAdapterCollector.id);
   }
 
-  const ipAdapterCollector = g.addNode({
+  const ipAdapterCollect = g.addNode({
     type: 'collect',
     id: getPrefixedId('ip_adapter_collector'),
   });
-  const ipAdapterResult = addIPAdapters(canvas.referenceImages.entities, g, ipAdapterCollector, modelConfig.base);
-
-  const regionsResult = await addRegions(
-    manager,
-    canvas.regionalGuidance.entities,
+  const ipAdapterResult = addIPAdapters({
+    entities: canvas.referenceImages.entities,
     g,
-    canvas.bbox.rect,
-    modelConfig.base,
-    denoise,
+    collector: ipAdapterCollect,
+    model: modelConfig,
+  });
+
+  const regionsResult = await addRegions({
+    manager,
+    regions: canvas.regionalGuidance.entities,
+    g,
+    bbox: canvas.bbox.rect,
+    model: modelConfig,
     posCond,
     negCond,
     posCondCollect,
     negCondCollect,
-    ipAdapterCollector
-  );
+    ipAdapterCollect,
+  });
 
   const totalIPAdaptersAdded =
     ipAdapterResult.addedIPAdapters + regionsResult.reduce((acc, r) => acc + r.addedIPAdapters, 0);
   if (totalIPAdaptersAdded > 0) {
-    g.addEdge(ipAdapterCollector, 'collection', denoise, 'ip_adapter');
+    g.addEdge(ipAdapterCollect, 'collection', denoise, 'ip_adapter');
   } else {
-    g.deleteNode(ipAdapterCollector.id);
+    g.deleteNode(ipAdapterCollect.id);
   }
 
   if (state.system.shouldUseNSFWChecker) {

invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSDXLGraph.ts

@@ -232,14 +232,14 @@ export const buildSDXLGraph = async (
     type: 'collect',
     id: getPrefixedId('control_net_collector'),
   });
-  const controlNetResult = await addControlNets(
+  const controlNetResult = await addControlNets({
     manager,
-    canvas.controlLayers.entities,
+    entities: canvas.controlLayers.entities,
     g,
-    canvas.bbox.rect,
-    controlNetCollector,
-    modelConfig.base
-  );
+    rect: canvas.bbox.rect,
+    collector: controlNetCollector,
+    model: modelConfig,
+  });
   if (controlNetResult.addedControlNets > 0) {
     g.addEdge(controlNetCollector, 'collection', denoise, 'control');
   } else {
@@ -250,46 +250,50 @@ export const buildSDXLGraph = async (
     type: 'collect',
     id: getPrefixedId('t2i_adapter_collector'),
   });
-  const t2iAdapterResult = await addT2IAdapters(
+  const t2iAdapterResult = await addT2IAdapters({
     manager,
-    canvas.controlLayers.entities,
+    entities: canvas.controlLayers.entities,
     g,
-    canvas.bbox.rect,
-    t2iAdapterCollector,
-    modelConfig.base
-  );
+    rect: canvas.bbox.rect,
+    collector: t2iAdapterCollector,
+    model: modelConfig,
+  });
   if (t2iAdapterResult.addedT2IAdapters > 0) {
     g.addEdge(t2iAdapterCollector, 'collection', denoise, 't2i_adapter');
   } else {
     g.deleteNode(t2iAdapterCollector.id);
   }
 
-  const ipAdapterCollector = g.addNode({
+  const ipAdapterCollect = g.addNode({
     type: 'collect',
     id: getPrefixedId('ip_adapter_collector'),
   });
-  const ipAdapterResult = addIPAdapters(canvas.referenceImages.entities, g, ipAdapterCollector, modelConfig.base);
-
-  const regionsResult = await addRegions(
-    manager,
-    canvas.regionalGuidance.entities,
+  const ipAdapterResult = addIPAdapters({
+    entities: canvas.referenceImages.entities,
     g,
-    canvas.bbox.rect,
-    modelConfig.base,
-    denoise,
+    collector: ipAdapterCollect,
+    model: modelConfig,
+  });
+
+  const regionsResult = await addRegions({
+    manager,
+    regions: canvas.regionalGuidance.entities,
+    g,
+    bbox: canvas.bbox.rect,
+    model: modelConfig,
     posCond,
     negCond,
     posCondCollect,
     negCondCollect,
-    ipAdapterCollector
-  );
+    ipAdapterCollect,
+  });
 
   const totalIPAdaptersAdded =
     ipAdapterResult.addedIPAdapters + regionsResult.reduce((acc, r) => acc + r.addedIPAdapters, 0);
   if (totalIPAdaptersAdded > 0) {
-    g.addEdge(ipAdapterCollector, 'collection', denoise, 'ip_adapter');
+    g.addEdge(ipAdapterCollect, 'collection', denoise, 'ip_adapter');
   } else {
-    g.deleteNode(ipAdapterCollector.id);
+    g.deleteNode(ipAdapterCollect.id);
   }
 
   if (state.system.shouldUseNSFWChecker) {

invokeai/frontend/web/src/features/queue/store/readiness.ts

@@ -4,6 +4,13 @@ import type { ParamsState } from 'features/controlLayers/store/paramsSlice';
 import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import type { CanvasState } from 'features/controlLayers/store/types';
+import {
+  getControlLayerWarnings,
+  getGlobalReferenceImageWarnings,
+  getInpaintMaskWarnings,
+  getRasterLayerWarnings,
+  getRegionalGuidanceWarnings,
+} from 'features/controlLayers/store/validators';
 import type { DynamicPromptsState } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { selectDynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { getShouldProcessPrompt } from 'features/dynamicPrompts/util/getShouldProcessPrompt';
@@ -278,17 +285,10 @@ const getReasonsWhyCannotEnqueueCanvasTab = (arg: {
       const layerNumber = i + 1;
       const layerType = i18n.t(LAYER_TYPE_TO_TKEY['control_layer']);
       const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
-      const problems: string[] = [];
-      // Must have model
-      if (!controlLayer.controlAdapter.model) {
-        problems.push(i18n.t('parameters.invoke.layer.controlAdapterNoModelSelected'));
-      }
-      // Model base must match
-      if (controlLayer.controlAdapter.model?.base !== model?.base) {
-        problems.push(i18n.t('parameters.invoke.layer.controlAdapterIncompatibleBaseModel'));
-      }
+      const problems = getControlLayerWarnings(controlLayer, model);
+
       if (problems.length) {
-        const content = upperFirst(problems.join(', '));
+        const content = upperFirst(problems.map((p) => i18n.t(p)).join(', '));
         reasons.push({ prefix, content });
       }
     });
@@ -300,23 +300,10 @@ const getReasonsWhyCannotEnqueueCanvasTab = (arg: {
       const layerNumber = i + 1;
       const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
       const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
-      const problems: string[] = [];
-
-      // Must have model
-      if (!entity.ipAdapter.model) {
-        problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoModelSelected'));
-      }
-      // Model base must match
-      if (entity.ipAdapter.model?.base !== model?.base) {
-        problems.push(i18n.t('parameters.invoke.layer.ipAdapterIncompatibleBaseModel'));
-      }
-      // Must have an image
-      if (!entity.ipAdapter.image) {
-        problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoImageSelected'));
-      }
+      const problems = getGlobalReferenceImageWarnings(entity, model);
 
       if (problems.length) {
-        const content = upperFirst(problems.join(', '));
+        const content = upperFirst(problems.map((p) => i18n.t(p)).join(', '));
         reasons.push({ prefix, content });
       }
     });
@@ -328,32 +315,10 @@ const getReasonsWhyCannotEnqueueCanvasTab = (arg: {
       const layerNumber = i + 1;
       const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
       const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
-      const problems: string[] = [];
-      // Must have a region
-      if (entity.objects.length === 0) {
-        problems.push(i18n.t('parameters.invoke.layer.rgNoRegion'));
-      }
-      // Must have at least 1 prompt or IP Adapter
-      if (entity.positivePrompt === null && entity.negativePrompt === null && entity.referenceImages.length === 0) {
-        problems.push(i18n.t('parameters.invoke.layer.rgNoPromptsOrIPAdapters'));
-      }
-      entity.referenceImages.forEach(({ ipAdapter }) => {
-        // Must have model
-        if (!ipAdapter.model) {
-          problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoModelSelected'));
-        }
-        // Model base must match
-        if (ipAdapter.model?.base !== model?.base) {
-          problems.push(i18n.t('parameters.invoke.layer.ipAdapterIncompatibleBaseModel'));
-        }
-        // Must have an image
-        if (!ipAdapter.image) {
-          problems.push(i18n.t('parameters.invoke.layer.ipAdapterNoImageSelected'));
-        }
-      });
+      const problems = getRegionalGuidanceWarnings(entity, model);
 
       if (problems.length) {
-        const content = upperFirst(problems.join(', '));
+        const content = upperFirst(problems.map((p) => i18n.t(p)).join(', '));
         reasons.push({ prefix, content });
       }
     });
@@ -365,10 +330,25 @@ const getReasonsWhyCannotEnqueueCanvasTab = (arg: {
       const layerNumber = i + 1;
       const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
       const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
-      const problems: string[] = [];
+      const problems = getRasterLayerWarnings(entity, model);
 
       if (problems.length) {
-        const content = upperFirst(problems.join(', '));
+        const content = upperFirst(problems.map((p) => i18n.t(p)).join(', '));
+        reasons.push({ prefix, content });
+      }
+    });
+
+  canvas.inpaintMasks.entities
+    .filter((entity) => entity.isEnabled)
+    .forEach((entity, i) => {
+      const layerLiteral = i18n.t('controlLayers.layer_one');
+      const layerNumber = i + 1;
+      const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
+      const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
+      const problems = getInpaintMaskWarnings(entity, model);
+
+      if (problems.length) {
+        const content = upperFirst(problems.map((p) => i18n.t(p)).join(', '));
         reasons.push({ prefix, content });
       }
     });

invokeai/frontend/web/src/services/api/schema.ts

@@ -6564,6 +6564,11 @@ export type components = {
              * @description The name of conditioning tensor
              */
             conditioning_name: string;
+            /**
+             * @description The mask associated with this conditioning tensor. Excluded regions should be set to False, included regions should be set to True.
+             * @default null
+             */
+            mask?: components["schemas"]["TensorField"] | null;
         };
         /**
          * FluxConditioningOutput
@@ -6771,15 +6776,17 @@ export type components = {
              */
             transformer?: components["schemas"]["TransformerField"];
             /**
+             * Positive Text Conditioning
              * @description Positive conditioning tensor
              * @default null
              */
-            positive_text_conditioning?: components["schemas"]["FluxConditioningField"];
+            positive_text_conditioning?: components["schemas"]["FluxConditioningField"] | components["schemas"]["FluxConditioningField"][];
             /**
+             * Negative Text Conditioning
              * @description Negative conditioning tensor. Can be None if cfg_scale is 1.0.
              * @default null
              */
-            negative_text_conditioning?: components["schemas"]["FluxConditioningField"] | null;
+            negative_text_conditioning?: components["schemas"]["FluxConditioningField"] | components["schemas"]["FluxConditioningField"][] | null;
             /**
              * CFG Scale
              * @description Classifier-Free Guidance scale
@@ -7133,6 +7140,11 @@ export type components = {
              * @default null
              */
             prompt?: string;
+            /**
+             * @description A mask defining the region that this conditioning prompt applies to.
+             * @default null
+             */
+            mask?: components["schemas"]["TensorField"] | null;
             /**
              * type
              * @default flux_text_encoder

pyproject.toml

@@ -56,7 +56,7 @@ dependencies = [
   "torchmetrics",
   "torchsde",
   "torchvision",
-  "transformers==4.41.1",
+  "transformers==4.46.3",
 
   # Core application dependencies, pinned for reproducible builds.
   "fastapi-events==0.11.1",