mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-04 08:47:08 +08:00
Fix rebase errors.
This commit is contained in:
Robin Huang
parent
100e4fce65
commit
9601dd82d9
@ -8,68 +8,6 @@ from typing import Optional
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from pydantic import BaseModel, Field, conint, confloat
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class RecraftColor:
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def __init__(self, r: int, g: int, b: int):
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self.color = [r, g, b]
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def create_api_model(self):
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return RecraftColorObject(rgb=self.color)
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class RecraftColorChain:
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def __init__(self):
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self.colors: list[RecraftColor] = []
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def get_first(self):
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if len(self.colors) > 0:
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return self.colors[0]
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return None
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def add(self, color: RecraftColor):
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self.colors.append(color)
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def create_api_model(self):
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if not self.colors:
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return None
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colors_api = [x.create_api_model() for x in self.colors]
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return colors_api
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def clone(self):
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c = RecraftColorChain()
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for color in self.colors:
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c.add(color)
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return c
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def clone_and_merge(self, other: RecraftColorChain):
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c = self.clone()
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for color in other.colors:
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c.add(color)
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return c
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class RecraftControls:
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def __init__(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None,
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artistic_level: int=None, no_text: bool=None):
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self.colors = colors
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self.background_color = background_color
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self.artistic_level = artistic_level
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self.no_text = no_text
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def create_api_model(self):
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if self.colors is None and self.background_color is None and self.artistic_level is None and self.no_text is None:
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return None
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colors_api = None
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background_color_api = None
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if self.colors:
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colors_api = self.colors.create_api_model()
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if self.background_color:
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first_background = self.background_color.get_first()
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background_color_api = first_background.create_api_model() if first_background else None
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return RecraftControlsObject(colors=colors_api, background_color=background_color_api,
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artistic_level=self.artistic_level, no_text=self.no_text)
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class RecraftColor:
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def __init__(self, r: int, g: int, b: int):
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self.color = [r, g, b]
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@ -34,99 +34,6 @@ import time
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from server import PromptServer
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def convert_mask_to_image(mask: torch.Tensor):
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"""
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Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image.
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"""
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mask = mask.unsqueeze(-1)
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mask = torch.cat([mask]*3, dim=-1)
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return mask
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def handle_bfl_synchronous_operation(
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operation: SynchronousOperation,
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timeout_bfl_calls=360,
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node_id: Union[str, None] = None,
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):
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response_api: BFLFluxProGenerateResponse = operation.execute()
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return _poll_until_generated(
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response_api.polling_url, timeout=timeout_bfl_calls, node_id=node_id
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)
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def _poll_until_generated(
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polling_url: str, timeout=360, node_id: Union[str, None] = None
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):
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# used bfl-comfy-nodes to verify code implementation:
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# https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main
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start_time = time.time()
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retries_404 = 0
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max_retries_404 = 5
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retry_404_seconds = 2
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retry_202_seconds = 2
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retry_pending_seconds = 1
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request = requests.Request(method=HttpMethod.GET, url=polling_url)
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# NOTE: should True loop be replaced with checking if workflow has been interrupted?
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while True:
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if node_id:
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time_elapsed = time.time() - start_time
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PromptServer.instance.send_progress_text(
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f"Generating ({time_elapsed:.0f}s)", node_id
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)
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response = requests.Session().send(request.prepare())
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if response.status_code == 200:
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result = response.json()
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if result["status"] == BFLStatus.ready:
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img_url = result["result"]["sample"]
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if node_id:
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PromptServer.instance.send_progress_text(
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f"Result URL: {img_url}", node_id
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)
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img_response = requests.get(img_url)
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return process_image_response(img_response)
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elif result["status"] in [
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BFLStatus.request_moderated,
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BFLStatus.content_moderated,
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]:
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status = result["status"]
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raise Exception(
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f"BFL API did not return an image due to: {status}."
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)
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elif result["status"] == BFLStatus.error:
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raise Exception(f"BFL API encountered an error: {result}.")
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elif result["status"] == BFLStatus.pending:
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time.sleep(retry_pending_seconds)
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continue
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elif response.status_code == 404:
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if retries_404 < max_retries_404:
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retries_404 += 1
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time.sleep(retry_404_seconds)
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continue
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raise Exception(
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f"BFL API could not find task after {max_retries_404} tries."
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)
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elif response.status_code == 202:
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time.sleep(retry_202_seconds)
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elif time.time() - start_time > timeout:
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raise Exception(
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f"BFL API experienced a timeout; could not return request under {timeout} seconds."
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)
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else:
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raise Exception(f"BFL API encountered an error: {response.json()}")
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def convert_image_to_base64(image: torch.Tensor):
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scaled_image = downscale_image_tensor(image, total_pixels=2048 * 2048)
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# remove batch dimension if present
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if len(scaled_image.shape) > 3:
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scaled_image = scaled_image[0]
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image_np = (scaled_image.numpy() * 255).astype(np.uint8)
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img = Image.fromarray(image_np)
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img_byte_arr = io.BytesIO()
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img.save(img_byte_arr, format="PNG")
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return base64.b64encode(img_byte_arr.getvalue()).decode()
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def convert_mask_to_image(mask: torch.Tensor):
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"""
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Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image.
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@ -787,6 +787,7 @@ class IdeogramV3(ComfyNodeABC):
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display_image_urls_on_node(image_urls, unique_id)
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return (download_and_process_images(image_urls),)
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NODE_CLASS_MAPPINGS = {
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"IdeogramV1": IdeogramV1,
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"IdeogramV2": IdeogramV2,
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@ -102,23 +102,6 @@ AVERAGE_DURATION_VIDEO_EXTEND = 320
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R = TypeVar("R")
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MAX_PROMPT_LENGTH_T2V = 2500
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MAX_PROMPT_LENGTH_I2V = 500
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MAX_PROMPT_LENGTH_IMAGE_GEN = 500
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MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200
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MAX_PROMPT_LENGTH_LIP_SYNC = 120
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# TODO: adjust based on tests
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AVERAGE_DURATION_T2V = 319 # 319,
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AVERAGE_DURATION_I2V = 164 # 164,
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AVERAGE_DURATION_LIP_SYNC = 120
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AVERAGE_DURATION_VIRTUAL_TRY_ON = 19 # 19,
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AVERAGE_DURATION_IMAGE_GEN = 32
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AVERAGE_DURATION_VIDEO_EFFECTS = 320
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AVERAGE_DURATION_VIDEO_EXTEND = 320
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R = TypeVar("R")
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class KlingApiError(Exception):
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"""Base exception for Kling API errors."""
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@ -557,9 +540,6 @@ class KlingCameraControlT2VNode(KlingTextToVideoNode):
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def INPUT_TYPES(s):
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return {
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"required": {
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"start_frame": model_field_to_node_input(
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IO.IMAGE, KlingImage2VideoRequest, "image"
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),
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"prompt": model_field_to_node_input(
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IO.STRING, KlingText2VideoRequest, "prompt", multiline=True
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),
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@ -786,13 +766,6 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
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"negative_prompt",
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multiline=True,
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),
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"model_name": model_field_to_node_input(
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IO.COMBO,
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KlingImage2VideoRequest,
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"model_name",
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enum_type=ModelName,
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default="kling-v2-master",
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),
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"cfg_scale": model_field_to_node_input(
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IO.FLOAT,
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KlingImage2VideoRequest,
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@ -828,7 +801,6 @@ class KlingCameraControlI2VNode(KlingImage2VideoNode):
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start_frame: torch.Tensor,
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prompt: str,
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negative_prompt: str,
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model_name: str,
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cfg_scale: float,
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aspect_ratio: str,
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camera_control: KlingCameraControl,
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