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223 lines
8.6 KiB
223 lines
8.6 KiB
# Copyright 2025 the HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from ...configuration_utils import PreTrainedConfig
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from ...video_utils import VideoMetadata
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from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel
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from ..glm4v.image_processing_glm4v import Glm4vImageProcessor
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from ..glm4v.image_processing_glm4v_fast import Glm4vImageProcessorFast
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from ..glm4v.modeling_glm4v import Glm4vForConditionalGeneration, Glm4vModel, Glm4vPreTrainedModel
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from ..glm4v.processing_glm4v import Glm4vProcessor
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from ..glm4v.video_processing_glm4v import Glm4vVideoProcessor
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class Glm46VConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Glm4vModel`]. It is used to instantiate a
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GLM-4.6V model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of
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GLM-4.1V-9B-Thinking [zai-org/GLM-4.1V-9B-Thinking](https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking).
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Glm4vTextConfig`):
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The config object or dictionary of the text backbone.
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vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Glm4vVisionConfig`):
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The config object or dictionary of the vision backbone.
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image_token_id (`int`, *optional*, defaults to 151343):
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The image token index to encode the image prompt.
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video_token_id (`int`, *optional*, defaults to 151344):
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The video token index to encode the image prompt.
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image_start_token_id (`int`, *optional*, defaults to 151339):
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The image start token index to encode the start of image.
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image_end_token_id (`int`, *optional*, defaults to 151340):
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The image end token index to encode the end of image.
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video_start_token_id (`int`, *optional*, defaults to 151361):
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The video start token index to encode the start of video.
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video_end_token_id (`int`, *optional*, defaults to 151362):
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The video end token index to encode the end of video.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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```python
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>>> from transformers import Glm46VForConditionalGeneration, Glm46VConfig
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>>> # Initializing a GLM-4.6V style configuration
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>>> configuration = Glm46VConfig()
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>>> # Initializing a model from the GLM-4.6V style configuration
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>>> model = Glm4vForConditionalGeneration(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "glm46v"
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sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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text_config=None,
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vision_config=None,
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image_token_id=151343,
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video_token_id=151344,
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image_start_token_id=151339,
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image_end_token_id=151340,
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video_start_token_id=151361,
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video_end_token_id=151362,
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tie_word_embeddings=False,
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**kwargs,
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):
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if isinstance(vision_config, dict):
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vision_config["model_type"] = vision_config.get("model_type", "glm4v_vision")
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self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
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elif vision_config is None:
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self.vision_config = CONFIG_MAPPING["glm4v_vision"]()
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if isinstance(text_config, dict):
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text_config["model_type"] = text_config.get("model_type", "glm4v_text")
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self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
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elif text_config is None:
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self.text_config = CONFIG_MAPPING["glm4v_text"]()
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self.image_token_id = image_token_id
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self.video_token_id = video_token_id
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self.video_start_token_id = video_start_token_id
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self.video_end_token_id = video_end_token_id
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self.image_start_token_id = image_start_token_id
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self.image_end_token_id = image_end_token_id
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self.tie_word_embeddings = tie_word_embeddings
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super().__init__(**kwargs)
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class Glm46VPreTrainedModel(Glm4vPreTrainedModel):
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_can_record_outputs = None
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_no_split_modules = None
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def _init_weights(self, module):
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raise AttributeError("Not needed")
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class Glm46VModel(Glm4vModel):
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_no_split_modules = None
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def __init__(self, config):
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super().__init__(config)
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self.visual = AutoModel.from_config(config.vision_config)
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self.language_model = AutoModel.from_config(config.text_config)
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class Glm46VForConditionalGeneration(Glm4vForConditionalGeneration):
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pass
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class Glm46VProcessor(Glm4vProcessor):
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def replace_frame_token_id(self, timestamp_sec):
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return f"<|begin_of_image|>{self.image_token}<|end_of_image|>{timestamp_sec:.1f} seconds"
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class Glm46VImageProcessor(Glm4vImageProcessor):
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pass
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class Glm46VImageProcessorFast(Glm4vImageProcessorFast):
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pass
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class Glm46VVideoProcessor(Glm4vVideoProcessor):
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def sample_frames(
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self,
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metadata: VideoMetadata,
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fps: int | float | None = None,
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**kwargs,
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):
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if metadata is None or getattr(metadata, "fps", None) is None:
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raise ValueError(
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"Asked to sample frames per second but no video metadata was provided which is required when sampling in Glm46V. "
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"Please pass in `VideoMetadata` object or set `do_sample_frames=False`"
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)
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total_frames = metadata.total_num_frames
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max_frame_idx = total_frames - 1
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duration = metadata.duration or round(max_frame_idx / metadata.fps) + 1
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DYNAMIC_FPS_THRES = {30: 3, 300: 1, 2400: 0.5}
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MAX_FRAME_COUNT_DYNAMIC = 640
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MAX_DURATION = 2400
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effective_duration = min(duration, MAX_DURATION)
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if effective_duration <= 30:
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target_fps = DYNAMIC_FPS_THRES[30]
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elif effective_duration <= 300:
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target_fps = DYNAMIC_FPS_THRES[300]
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else:
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target_fps = DYNAMIC_FPS_THRES[2400]
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extract_t = int(effective_duration * target_fps * self.temporal_patch_size)
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extract_t = min(extract_t, MAX_FRAME_COUNT_DYNAMIC)
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duration_per_frame = 1 / metadata.fps
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timestamps = [i * duration_per_frame for i in range(total_frames)]
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max_second = int(duration)
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if total_frames < extract_t:
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frame_indices = np.linspace(0, total_frames - 1, extract_t, dtype=int).tolist()
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else:
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frame_indices = []
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current_second = 0
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inv_fps = 1 / (self.temporal_patch_size * target_fps)
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for frame_index in range(total_frames):
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if timestamps[frame_index] >= current_second:
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current_second += inv_fps
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frame_indices.append(frame_index)
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if current_second >= max_second:
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break
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if len(frame_indices) < extract_t:
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if len(frame_indices) == 0:
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start, end = 0, max(total_frames - 1, 0)
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else:
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start, end = frame_indices[0], frame_indices[-1]
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frame_indices = np.linspace(start, end, extract_t, dtype=int).tolist()
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elif len(frame_indices) > extract_t:
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frame_indices = np.linspace(0, total_frames - 1, extract_t, dtype=int).tolist()
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seen, uniq = set(), []
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for idx in frame_indices:
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if idx not in seen:
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seen.add(idx)
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uniq.append(idx)
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if len(uniq) & 1:
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uniq.append(uniq[-1])
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return np.array(uniq)
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__all__ = [
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"Glm46VConfig",
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"Glm46VModel",
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"Glm46VPreTrainedModel",
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"Glm46VForConditionalGeneration",
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"Glm46VProcessor",
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"Glm46VImageProcessor",
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"Glm46VImageProcessorFast",
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"Glm46VVideoProcessor",
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]
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