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+ # Compiled Object files
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README.md CHANGED
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1
- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license: apache-2.0
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+ tags:
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+ - text-to-image
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+ - image-generation
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+ - baai-nova
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+ ---
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+
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+ # NOVA (d48w768-sdxl1024) Model Card
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+
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+ ## Model Details
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+ - **Developed by:** BAAI
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+ - **Model type:** Masked Autoregressive Text-to-Image Generation Model
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+ - **Model size:** 363M
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+ - **Model precision:** torch.float16 (FP16)
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+ - **Model resolution:** 1024x1024
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+ - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Masked Autoregressive (MAR)](https://arxiv.org/abs/2406.11838) diffusion model that uses a pretrained text encoder ([Phi-2](https://huggingface.co/microsoft/phi-2)) and one VAE image tokenizer ([SDXL-VAE](https://huggingface.co/stabilityai/sdxl-vae)).
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+ - **Model License:** [Apache 2.0 License](LICENSE)
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+ - **Resources for more information:** [GitHub Repository](https://github.com/baaivision/NOVA).
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+
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+ ## Examples
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+
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+ Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run NOVA in a simple and efficient manner.
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+
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+ ```bash
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+ pip install diffusers transformers accelerate
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+ pip install git+ssh://[email protected]/baaivision/NOVA.git
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+ ```
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+
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+ Running the pipeline:
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+
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+ ```python
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+ import torch
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+ from diffnext.pipelines import NOVAPipeline
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+
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+ model_id = "BAAI/nova-d48w768-sdxl1024"
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+ model_args = {"torch_dtype": torch.float16, "trust_remote_code": True}
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+ pipe = NOVAPipeline.from_pretrained(model_id, **model_args)
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+ pipe = pipe.to("cuda")
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+
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+ prompt = "a shiba inu wearing a beret and black turtleneck."
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+ image = pipe(prompt).images[0]
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+
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+ image.save("shiba_inu.jpg")
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+ ```
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+
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+ # Uses
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+
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+ ## Direct Use
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+ The model is intended for research purposes only. Possible research areas and tasks include
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+
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+ - Research on generative models.
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+ - Applications in educational or creative tools.
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+ - Generation of artworks and use in design and other artistic processes.
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+ - Probing and understanding the limitations and biases of generative models.
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+ - Safe deployment of models which have the potential to generate harmful content.
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+
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+ Excluded uses are described below.
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+
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+ #### Out-of-Scope Use
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+ The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
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+
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+ #### Misuse and Malicious Use
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+ Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
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+
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+ - Mis- and disinformation.
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+ - Representations of egregious violence and gore.
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+ - Impersonating individuals without their consent.
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+ - Sexual content without consent of the people who might see it.
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+ - Sharing of copyrighted or licensed material in violation of its terms of use.
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+ - Intentionally promoting or propagating discriminatory content or harmful stereotypes.
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+ - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
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+ - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
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+
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+ ## Limitations and Bias
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+
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+ ### Limitations
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+
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+ - The autoencoding part of the model is lossy.
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+ - The model cannot render complex legible text.
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+ - The model does not achieve perfect photorealism.
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+ - The fingers, .etc in general may not be generated properly.
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+ - The model was trained on a subset of the web datasets [LAION-5B](https://laion.ai/blog/laion-5b/) and [COYO-700M](https://github.com/kakaobrain/coyo-dataset), which contains adult, violent and sexual content.
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+
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+ ### Bias
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+ While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
model_index.json ADDED
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+ {
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+ "_class_name": "NOVAPipeline",
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+ "tokenizer": [
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+ "transformers",
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+ "CodeGenTokenizerFast"
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+ ],
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+ "scheduler": [
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+ "diffusers",
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+ "FlowMatchEulerDiscreteScheduler"
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+ ],
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+ "vae": [
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+ "__vae__",
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+ "AutoencoderKL"
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+ ],
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+ "text_encoder": [
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+ "__text_encoder__",
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+ "PhiEncoderModel"
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+ ],
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+ "transformer": [
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+ "__transformer__",
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+ "NOVATransformer3DModel"
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+ ]
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+ }
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+ {
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+ "_class_name": "FlowMatchEulerDiscreteScheduler",
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+ "num_train_timesteps": 1000,
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+ "shift": 1.0
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+ }
text_encoder/__text_encoder__.py ADDED
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1
+ # Copyright (c) 2024-present, BAAI. All Rights Reserved.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
4
+ # you may not use this file except in compliance with the License.
5
+ # You may obtain a copy of the License at
6
+ #
7
+ # http://www.apache.org/licenses/LICENSE-2.0
8
+ #
9
+ # Unless required by applicable law or agreed to in writing, software
10
+ # distributed under the License is distributed on an "AS IS" BASIS,
11
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ # See the License for the specific language governing permissions and
13
+ # limitations under the License.
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+ ##############################################################################
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+ """Text encoder."""
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+
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+ from diffnext.models.text_encoders.phi import PhiEncoderModel # noqa
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+ "bos_token_id": 50256,
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+ "eos_token_id": 50256,
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+ "pad_token_id": 50256,
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+ "hidden_act": "gelu_pytorch_tanh",
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+ "hidden_size": 2560,
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+ "intermediate_size": 10240,
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 2048,
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+ "model_type": "phi",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 32,
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+ "partial_rotary_factor": 0.4,
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+ "rope_scaling": null,
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+ "rope_theta": 10000.0,
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+ "torch_dtype": "float16",
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+ "use_cache": true,
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+ "vocab_size": 51200
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+ }
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+ # Copyright (c) 2024-present, BAAI. 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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+ # you may not use this file except in compliance with the License.
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