[API Nodes] add Flux.2 Pro node

comfy_api_nodes/apis/bfl_api.py

@@ -70,6 +70,29 @@ class BFLFluxProGenerateRequest(BaseModel):
     # )
 
 
+class Flux2ProGenerateRequest(BaseModel):
+    prompt: str = Field(...)
+    width: int = Field(1024, description="Must be a multiple of 32.")
+    height: int = Field(768, description="Must be a multiple of 32.")
+    seed: int | None = Field(None)
+    prompt_upsampling: bool | None = Field(None)
+    input_image: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_2: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_3: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_4: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_5: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_6: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_7: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_8: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    input_image_9: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
+    safety_tolerance: int | None = Field(
+        5, description="Tolerance level for input and output moderation. Value 0 being most strict.", ge=0, le=5
+    )
+    output_format: str | None = Field(
+        "png", description="Output format for the generated image. Can be 'jpeg' or 'png'."
+    )
+
+
 class BFLFluxKontextProGenerateRequest(BaseModel):
     prompt: str = Field(..., description='The text prompt for what you wannt to edit.')
     input_image: Optional[str] = Field(None, description='Image to edit in base64 format')
@@ -109,8 +132,9 @@ class BFLFluxProUltraGenerateRequest(BaseModel):
 
 
 class BFLFluxProGenerateResponse(BaseModel):
-    id: str = Field(..., description='The unique identifier for the generation task.')
-    polling_url: str = Field(..., description='URL to poll for the generation result.')
+    id: str = Field(..., description="The unique identifier for the generation task.")
+    polling_url: str = Field(..., description="URL to poll for the generation result.")
+    cost: float | None = Field(None, description="Price in cents")
 
 
 class BFLStatus(str, Enum):

comfy_api_nodes/nodes_bfl.py

@@ -1,23 +1,24 @@
 from inspect import cleandoc
-from typing import Optional
 
 import torch
+from pydantic import BaseModel
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension
 from comfy_api_nodes.apis.bfl_api import (
     BFLFluxExpandImageRequest,
     BFLFluxFillImageRequest,
     BFLFluxKontextProGenerateRequest,
-    BFLFluxProGenerateRequest,
     BFLFluxProGenerateResponse,
     BFLFluxProUltraGenerateRequest,
     BFLFluxStatusResponse,
     BFLStatus,
+    Flux2ProGenerateRequest,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_image_tensor,
+    get_number_of_images,
     poll_op,
     resize_mask_to_image,
     sync_op,
@@ -116,7 +117,7 @@ async def execute(
         prompt_upsampling: bool = False,
         raw: bool = False,
         seed: int = 0,
-        image_prompt: Optional[torch.Tensor] = None,
+        image_prompt: torch.Tensor | None = None,
         image_prompt_strength: float = 0.1,
     ) -> IO.NodeOutput:
         if image_prompt is None:
@@ -230,7 +231,7 @@ async def execute(
         aspect_ratio: str,
         guidance: float,
         steps: int,
-        input_image: Optional[torch.Tensor] = None,
+        input_image: torch.Tensor | None = None,
         seed=0,
         prompt_upsampling=False,
     ) -> IO.NodeOutput:
@@ -280,124 +281,6 @@ class FluxKontextMaxImageNode(FluxKontextProImageNode):
     DISPLAY_NAME = "Flux.1 Kontext [max] Image"
 
 
-class FluxProImageNode(IO.ComfyNode):
-    """
-    Generates images synchronously based on prompt and resolution.
-    """
-
-    @classmethod
-    def define_schema(cls) -> IO.Schema:
-        return IO.Schema(
-            node_id="FluxProImageNode",
-            display_name="Flux 1.1 [pro] Image",
-            category="api node/image/BFL",
-            description=cleandoc(cls.__doc__ or ""),
-            inputs=[
-                IO.String.Input(
-                    "prompt",
-                    multiline=True,
-                    default="",
-                    tooltip="Prompt for the image generation",
-                ),
-                IO.Boolean.Input(
-                    "prompt_upsampling",
-                    default=False,
-                    tooltip="Whether to perform upsampling on the prompt. "
-                    "If active, automatically modifies the prompt for more creative generation, "
-                    "but results are nondeterministic (same seed will not produce exactly the same result).",
-                ),
-                IO.Int.Input(
-                    "width",
-                    default=1024,
-                    min=256,
-                    max=1440,
-                    step=32,
-                ),
-                IO.Int.Input(
-                    "height",
-                    default=768,
-                    min=256,
-                    max=1440,
-                    step=32,
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=0,
-                    min=0,
-                    max=0xFFFFFFFFFFFFFFFF,
-                    control_after_generate=True,
-                    tooltip="The random seed used for creating the noise.",
-                ),
-                IO.Image.Input(
-                    "image_prompt",
-                    optional=True,
-                ),
-                # "image_prompt_strength": (
-                #     IO.FLOAT,
-                #     {
-                #         "default": 0.1,
-                #         "min": 0.0,
-                #         "max": 1.0,
-                #         "step": 0.01,
-                #         "tooltip": "Blend between the prompt and the image prompt.",
-                #     },
-                # ),
-            ],
-            outputs=[IO.Image.Output()],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-        )
-
-    @classmethod
-    async def execute(
-        cls,
-        prompt: str,
-        prompt_upsampling,
-        width: int,
-        height: int,
-        seed=0,
-        image_prompt=None,
-        # image_prompt_strength=0.1,
-    ) -> IO.NodeOutput:
-        image_prompt = image_prompt if image_prompt is None else tensor_to_base64_string(image_prompt)
-        initial_response = await sync_op(
-            cls,
-            ApiEndpoint(
-                path="/proxy/bfl/flux-pro-1.1/generate",
-                method="POST",
-            ),
-            response_model=BFLFluxProGenerateResponse,
-            data=BFLFluxProGenerateRequest(
-                prompt=prompt,
-                prompt_upsampling=prompt_upsampling,
-                width=width,
-                height=height,
-                seed=seed,
-                image_prompt=image_prompt,
-            ),
-        )
-        response = await poll_op(
-            cls,
-            ApiEndpoint(initial_response.polling_url),
-            response_model=BFLFluxStatusResponse,
-            status_extractor=lambda r: r.status,
-            progress_extractor=lambda r: r.progress,
-            completed_statuses=[BFLStatus.ready],
-            failed_statuses=[
-                BFLStatus.request_moderated,
-                BFLStatus.content_moderated,
-                BFLStatus.error,
-                BFLStatus.task_not_found,
-            ],
-            queued_statuses=[],
-        )
-        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
-
-
 class FluxProExpandNode(IO.ComfyNode):
     """
     Outpaints image based on prompt.
@@ -640,16 +523,125 @@ async def execute(
         return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
 
 
+class Flux2ProImageNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="Flux2ProImageNode",
+            display_name="Flux.2 [pro] Image",
+            category="api node/image/BFL",
+            description="Generates images synchronously based on prompt and resolution.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation or edit",
+                ),
+                IO.Int.Input(
+                    "width",
+                    default=1024,
+                    min=256,
+                    max=2048,
+                    step=32,
+                ),
+                IO.Int.Input(
+                    "height",
+                    default=768,
+                    min=256,
+                    max=2048,
+                    step=32,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                IO.Boolean.Input(
+                    "prompt_upsampling",
+                    default=False,
+                    tooltip="Whether to perform upsampling on the prompt. "
+                    "If active, automatically modifies the prompt for more creative generation, "
+                    "but results are nondeterministic (same seed will not produce exactly the same result).",
+                ),
+                IO.Image.Input("images", optional=True, tooltip="Up to 4 images to be used as references."),
+            ],
+            outputs=[IO.Image.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        width: int,
+        height: int,
+        seed: int,
+        prompt_upsampling: bool,
+        images: torch.Tensor | None = None,
+    ) -> IO.NodeOutput:
+        reference_images = {}
+        if images is not None:
+            if get_number_of_images(images) > 9:
+                raise ValueError("The current maximum number of supported images is 9.")
+            for image_index in range(images.shape[0]):
+                key_name = f"input_image_{image_index + 1}" if image_index else "input_image"
+                reference_images[key_name] = tensor_to_base64_string(images[image_index], total_pixels=2048 * 2048)
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/bfl/flux-2-pro/generate", method="POST"),
+            response_model=BFLFluxProGenerateResponse,
+            data=Flux2ProGenerateRequest(
+                prompt=prompt,
+                width=width,
+                height=height,
+                seed=seed,
+                prompt_upsampling=prompt_upsampling,
+                **reference_images,
+            ),
+        )
+
+        def price_extractor(_r: BaseModel) -> float | None:
+            return None if initial_response.cost is None else initial_response.cost / 100
+
+        response = await poll_op(
+            cls,
+            ApiEndpoint(initial_response.polling_url),
+            response_model=BFLFluxStatusResponse,
+            status_extractor=lambda r: r.status,
+            progress_extractor=lambda r: r.progress,
+            price_extractor=price_extractor,
+            completed_statuses=[BFLStatus.ready],
+            failed_statuses=[
+                BFLStatus.request_moderated,
+                BFLStatus.content_moderated,
+                BFLStatus.error,
+                BFLStatus.task_not_found,
+            ],
+            queued_statuses=[],
+        )
+        return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"]))
+
+
 class BFLExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
         return [
             FluxProUltraImageNode,
-            # FluxProImageNode,
             FluxKontextProImageNode,
             FluxKontextMaxImageNode,
             FluxProExpandNode,
             FluxProFillNode,
+            Flux2ProImageNode,
         ]
 
 

comfy_api_nodes/util/__init__.py

@@ -36,6 +36,7 @@
     upload_video_to_comfyapi,
 )
 from .validation_utils import (
+    get_image_dimensions,
     get_number_of_images,
     validate_aspect_ratio_string,
     validate_audio_duration,
@@ -82,6 +83,7 @@
     "trim_video",
     "video_to_base64_string",
     # Validation utilities
+    "get_image_dimensions",
     "get_number_of_images",
     "validate_aspect_ratio_string",
     "validate_audio_duration",