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