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  1. README.md +199 -0
  2. config.json +14 -0
  3. configuration_vqmodel.py +24 -0
  4. model.safetensors +3 -0
  5. modeling_vqmodel.py +39 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags: []
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+ This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+ [More Information Needed]
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+ ### Training Procedure
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+ #### Preprocessing [optional]
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+ [More Information Needed]
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+ #### Training Hyperparameters
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ [More Information Needed]
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+ #### Testing Data
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+ <!-- This should link to a Dataset Card if possible. -->
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+ [More Information Needed]
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+ [More Information Needed]
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+ ### Results
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+ [More Information Needed]
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+ #### Summary
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+ ## Model Examination [optional]
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+ <!-- Relevant interpretability work for the model goes here -->
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+ [More Information Needed]
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+ ## Environmental Impact
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+ ## Technical Specifications [optional]
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+ ### Model Architecture and Objective
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+ [More Information Needed]
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+ ### Compute Infrastructure
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+ [More Information Needed]
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+ #### Hardware
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+ [More Information Needed]
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+ #### Software
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+ [More Information Needed]
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+ ## Citation [optional]
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+ **BibTeX:**
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+ [More Information Needed]
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+ **APA:**
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+ [More Information Needed]
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+ ## Glossary [optional]
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+ [More Information Needed]
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+ ## More Information [optional]
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+ [More Information Needed]
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+ ## Model Card Authors [optional]
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+ [More Information Needed]
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+ ## Model Card Contact
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+ [More Information Needed]
config.json ADDED
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+ {
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+ "architectures": [
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+ "VQModel"
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+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_vqmodel.VQModelConfig",
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+ "AutoModel": "modeling_vqmodel.VQModel"
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+ },
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+ "model_type": "vqmodel",
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+ "repo_id": "ktrk115/ldm-vq-f16",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.42.4",
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+ "yaml_path": "config.yaml"
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+ }
configuration_vqmodel.py ADDED
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+ """VQModel configuration"""
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+
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+ from transformers import PretrainedConfig
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+ from transformers.utils import logging
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+
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+ logger = logging.get_logger(__name__)
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+
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+
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+ class VQModelConfig(PretrainedConfig): # type: ignore
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+ model_type = "vqmodel"
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+
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+ def __init__(
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+ self,
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+ repo_id: str | None = None,
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+ yaml_path: str | None = None,
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+ **kwargs: dict,
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+ ) -> None:
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+ if repo_id is not None:
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+ yaml_path = "config.yaml"
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+
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+ self.repo_id = repo_id
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+ self.yaml_path = yaml_path
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+
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+ super().__init__(**kwargs)
model.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:05a2df1510c510d44af67f82028e86a105ffa98d1c531992be8b294d6a6f11a3
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+ size 278410844
modeling_vqmodel.py ADDED
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+ """PyTorch VQModel."""
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+
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+ from huggingface_hub import hf_hub_download
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+ from ldm.util import instantiate_from_config
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+ from omegaconf import OmegaConf
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+ from transformers import PreTrainedModel
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+ from transformers.utils import logging
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+
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+ from .configuration_vqmodel import VQModelConfig
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+
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+ logger = logging.get_logger(__name__)
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+
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+
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+ class VQModel(PreTrainedModel): # type: ignore
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+ config_class = VQModelConfig
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+
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+ def __init__(self, config: VQModelConfig) -> None:
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+ logger.info(f"VQModel config: {config}")
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+ super().__init__(config)
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+
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+ yaml_path = (
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+ config.yaml_path
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+ if config.repo_id is None
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+ else hf_hub_download(config.repo_id, config.yaml_path)
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+ )
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+
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+ # Load vq_cfg
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+ vq_cfg = OmegaConf.load(yaml_path)
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+ vq_cfg.model.params.lossconfig = "__is_first_stage__"
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+ self.vq_cfg = vq_cfg
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+
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+ # Initialize model
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+ self.model = instantiate_from_config(vq_cfg.model)
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+
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+ # Remove loss attribute if exists
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+ try:
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+ delattr(self.model, "loss")
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+ except AttributeError:
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+ pass