Fix rebase errors.

comfy_api_nodes/apis/recraft_api.py

@@ -8,68 +8,6 @@ from typing import Optional
 from pydantic import BaseModel, Field, conint, confloat
 
 
-class RecraftColor:
-    def __init__(self, r: int, g: int, b: int):
-        self.color = [r, g, b]
-
-    def create_api_model(self):
-        return RecraftColorObject(rgb=self.color)
-
-
-class RecraftColorChain:
-    def __init__(self):
-        self.colors: list[RecraftColor] = []
-
-    def get_first(self):
-        if len(self.colors) > 0:
-            return self.colors[0]
-        return None
-
-    def add(self, color: RecraftColor):
-        self.colors.append(color)
-
-    def create_api_model(self):
-        if not self.colors:
-            return None
-        colors_api = [x.create_api_model() for x in self.colors]
-        return colors_api
-
-    def clone(self):
-        c = RecraftColorChain()
-        for color in self.colors:
-            c.add(color)
-        return c
-
-    def clone_and_merge(self, other: RecraftColorChain):
-        c = self.clone()
-        for color in other.colors:
-            c.add(color)
-        return c
-
-
-class RecraftControls:
-    def __init__(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None,
-                 artistic_level: int=None, no_text: bool=None):
-        self.colors = colors
-        self.background_color = background_color
-        self.artistic_level = artistic_level
-        self.no_text = no_text
-
-    def create_api_model(self):
-        if self.colors is None and self.background_color is None and self.artistic_level is None and self.no_text is None:
-            return None
-        colors_api = None
-        background_color_api = None
-        if self.colors:
-            colors_api = self.colors.create_api_model()
-        if self.background_color:
-            first_background = self.background_color.get_first()
-            background_color_api = first_background.create_api_model() if first_background else None
-
-        return RecraftControlsObject(colors=colors_api, background_color=background_color_api,
-                                             artistic_level=self.artistic_level, no_text=self.no_text)
-
-
 class RecraftColor:
     def __init__(self, r: int, g: int, b: int):
         self.color = [r, g, b]

comfy_api_nodes/nodes_bfl.py

@@ -34,99 +34,6 @@ import time
 from server import PromptServer
 
 
-def convert_mask_to_image(mask: torch.Tensor):
-    """
-    Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image.
-    """
-    mask = mask.unsqueeze(-1)
-    mask = torch.cat([mask]*3, dim=-1)
-    return mask
-
-
-def handle_bfl_synchronous_operation(
-    operation: SynchronousOperation,
-    timeout_bfl_calls=360,
-    node_id: Union[str, None] = None,
-):
-    response_api: BFLFluxProGenerateResponse = operation.execute()
-    return _poll_until_generated(
-        response_api.polling_url, timeout=timeout_bfl_calls, node_id=node_id
-    )
-
-
-def _poll_until_generated(
-    polling_url: str, timeout=360, node_id: Union[str, None] = None
-):
-    # used bfl-comfy-nodes to verify code implementation:
-    # https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main
-    start_time = time.time()
-    retries_404 = 0
-    max_retries_404 = 5
-    retry_404_seconds = 2
-    retry_202_seconds = 2
-    retry_pending_seconds = 1
-    request = requests.Request(method=HttpMethod.GET, url=polling_url)
-    # NOTE: should True loop be replaced with checking if workflow has been interrupted?
-    while True:
-        if node_id:
-            time_elapsed = time.time() - start_time
-            PromptServer.instance.send_progress_text(
-                f"Generating ({time_elapsed:.0f}s)", node_id
-            )
-
-        response = requests.Session().send(request.prepare())
-        if response.status_code == 200:
-            result = response.json()
-            if result["status"] == BFLStatus.ready:
-                img_url = result["result"]["sample"]
-                if node_id:
-                    PromptServer.instance.send_progress_text(
-                        f"Result URL: {img_url}", node_id
-                    )
-                img_response = requests.get(img_url)
-                return process_image_response(img_response)
-            elif result["status"] in [
-                BFLStatus.request_moderated,
-                BFLStatus.content_moderated,
-            ]:
-                status = result["status"]
-                raise Exception(
-                    f"BFL API did not return an image due to: {status}."
-                )
-            elif result["status"] == BFLStatus.error:
-                raise Exception(f"BFL API encountered an error: {result}.")
-            elif result["status"] == BFLStatus.pending:
-                time.sleep(retry_pending_seconds)
-                continue
-        elif response.status_code == 404:
-            if retries_404 < max_retries_404:
-                retries_404 += 1
-                time.sleep(retry_404_seconds)
-                continue
-            raise Exception(
-                f"BFL API could not find task after {max_retries_404} tries."
-            )
-        elif response.status_code == 202:
-            time.sleep(retry_202_seconds)
-        elif time.time() - start_time > timeout:
-            raise Exception(
-                f"BFL API experienced a timeout; could not return request under {timeout} seconds."
-            )
-        else:
-            raise Exception(f"BFL API encountered an error: {response.json()}")
-
-def convert_image_to_base64(image: torch.Tensor):
-    scaled_image = downscale_image_tensor(image, total_pixels=2048 * 2048)
-    # remove batch dimension if present
-    if len(scaled_image.shape) > 3:
-        scaled_image = scaled_image[0]
-    image_np = (scaled_image.numpy() * 255).astype(np.uint8)
-    img = Image.fromarray(image_np)
-    img_byte_arr = io.BytesIO()
-    img.save(img_byte_arr, format="PNG")
-    return base64.b64encode(img_byte_arr.getvalue()).decode()
-
-
 def convert_mask_to_image(mask: torch.Tensor):
     """
     Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image.

comfy_api_nodes/nodes_ideogram.py

@@ -787,6 +787,7 @@ class IdeogramV3(ComfyNodeABC):
         display_image_urls_on_node(image_urls, unique_id)
         return (download_and_process_images(image_urls),)
 
+
 NODE_CLASS_MAPPINGS = {
     "IdeogramV1": IdeogramV1,
     "IdeogramV2": IdeogramV2,

comfy_api_nodes/nodes_kling.py

@@ -102,23 +102,6 @@ AVERAGE_DURATION_VIDEO_EXTEND = 320
 
 R = TypeVar("R")
 
-MAX_PROMPT_LENGTH_T2V = 2500
-MAX_PROMPT_LENGTH_I2V = 500
-MAX_PROMPT_LENGTH_IMAGE_GEN = 500
-MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200
-MAX_PROMPT_LENGTH_LIP_SYNC = 120
-
-# TODO: adjust based on tests
-AVERAGE_DURATION_T2V = 319  # 319,
-AVERAGE_DURATION_I2V = 164  # 164,
-AVERAGE_DURATION_LIP_SYNC = 120
-AVERAGE_DURATION_VIRTUAL_TRY_ON = 19  # 19,
-AVERAGE_DURATION_IMAGE_GEN = 32
-AVERAGE_DURATION_VIDEO_EFFECTS = 320
-AVERAGE_DURATION_VIDEO_EXTEND = 320
-
-R = TypeVar("R")
-
 
 class KlingApiError(Exception):
     """Base exception for Kling API errors."""
@@ -557,9 +540,6 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode):
     def INPUT_TYPES(s):
         return {
             "required": {
-                "start_frame": model_field_to_node_input(
-                    IO.IMAGE, KlingImage2VideoRequest, "image"
-                ),
                 "prompt": model_field_to_node_input(
                     IO.STRING, KlingText2VideoRequest, "prompt", multiline=True
                 ),
@@ -786,13 +766,6 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
                     "negative_prompt",
                     multiline=True,
                 ),
-                "model_name": model_field_to_node_input(
-                    IO.COMBO,
-                    KlingImage2VideoRequest,
-                    "model_name",
-                    enum_type=ModelName,
-                    default="kling-v2-master",
-                ),
                 "cfg_scale": model_field_to_node_input(
                     IO.FLOAT,
                     KlingImage2VideoRequest,
@@ -828,7 +801,6 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
         start_frame: torch.Tensor,
         prompt: str,
         negative_prompt: str,
-        model_name: str,
         cfg_scale: float,
         aspect_ratio: str,
         camera_control: KlingCameraControl,