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README.md
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---
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language:
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- en
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license: other
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license_name: pixelscribe-non-commercial-license
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license_link: LICENSE.md
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tags:
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- text-to-image
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- image-generation
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- Diffusers
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- PixelScribe
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base_model:
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- sbapan41/PixelScribe
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new_version: sbapan41/PixelScribe
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pipeline_tag: text-to-image
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library_name: diffusers
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---
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`PixelScribe` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions.
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# Key Features
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1. Cutting-edge output quality, second only to our state-of-the-art model `PixelScribe`.
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2. Competitive prompt following, matching the performance of closed source alternatives .
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3. Trained using guidance distillation, making `PixelScribe` more efficient.
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4. Open weights to drive new scientific research, and empower artists to develop innovative workflows.
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5. Generated outputs can be used for personal, scientific, and commercial purposes as described
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# Usage
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We provide a reference implementation of `PixelScribe`, as well as sampling code, in a dedicated [github repository](https://github.com/MotoBwi/PixelScribe.git).
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Developers and creatives looking to build on top of `PixelScribe` are encouraged to use this as a starting point.
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## Diffusers
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To use `PixelScribe` with the 🧨 diffusers python library, first install or upgrade diffusers
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```shell
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pip install -U diffusers
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```
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Then you can use `FluxPipeline` to run the model
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```python
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import torch
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from diffusers import FluxPipeline
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pipe = FluxPipeline.from_pretrained("sbapan41/PixelScribe", torch_dtype=torch.bfloat16)
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pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
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prompt = "A cat holding a sign that says hello world"
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image = pipe(
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prompt,
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height=1024,
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width=1024,
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guidance_scale=3.5,
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num_inference_steps=50,
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max_sequence_length=512,
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generator=torch.Generator("cpu").manual_seed(0)
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).images[0]
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image.save("PixelScribe.png")
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```
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---
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# Limitations
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- This model is not intended or able to provide factual information.
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- As a statistical model this checkpoint might amplify existing societal biases.
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- The model may fail to generate output that matches the prompts.
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- Prompt following is heavily influenced by the prompting-style.
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# Out-of-Scope Use
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The model and its derivatives may not be used
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- In any way that violates any applicable national, federal, state, local or international law or regulation.
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- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; including but not limited to the solicitation, creation, acquisition, or dissemination of child exploitative content.
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- To generate or disseminate verifiably false information and/or content with the purpose of harming others.
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- To generate or disseminate personal identifiable information that can be used to harm an individual.
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- To harass, abuse, threaten, stalk, or bully individuals or groups of individuals.
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- To create non-consensual nudity or illegal pornographic content.
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- For fully automated decision making that adversely impacts an individual's legal rights or otherwise creates or modifies a binding, enforceable obligation.
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- Generating or facilitating large-scale disinformation campaigns.
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# License
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This model falls under the [`PixelScribe` Non-Commercial License].
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