Instructions to use sabman/map-diffuser-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sabman/map-diffuser-v3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sabman/map-diffuser-v3", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| library_name: diffusers | |
| pipeline_tag: text-to-image | |
| tags: | |
| - jax-diffusers-event | |
| - stable-diffusion | |
| license: creativeml-openrail-m | |
| datasets: | |
| - sabman/maps-stablediffusion | |
| language: | |
| - en | |
| # 🗺️ map diffuser with text to image 🗺️ | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| ## Model Details | |
| ### Model Description | |
| <!-- Provide a longer summary of what this model is. --> | |
| - **Developed by:** Decision Labs | |
| - **Model type:** Diffusion Model | |
| - **License:** CC BY-NC 4.0 | |
| - **Finetuned from model text2image:** https://huggingface.co/docs/diffusers/en/training/text2image | |
| ### Model Sources [optional] | |
| <!-- Provide the basic links for the model. --> | |
| - **Repository:** https://huggingface.co/sabman/map-diffuser-v3/ | |
| - **Paper:** Coming Soon | |
| - **Demo:** https://huggingface.co/spaces/sabman/map-diffuser | |
| ## Uses | |
| Generates images from a given text prompt. The prompts are in the format: | |
| {style} map of {city} with {features} or | |
| satellite image of {city} with {features} or | |
| satellite image with {features} or | |
| satellite image of {city} with {features} and no {features} and so on… | |
| So for example: | |
| “Satellite image of amsterdam with industrial area and highways” | |
| “Watercolor style map of Amsterdam with residential area and highways” | |
| “Toner style map of Amsterdam with residential area and highways” | |
| “Satellite image with forests and residential, no water” | |
| ### Direct Use | |
| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> | |
| [More Information Needed] | |
| ### Downstream Use [optional] | |
| <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> | |
| [More Information Needed] | |
| ### Out-of-Scope Use | |
| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> | |
| [More Information Needed] | |
| ## Bias, Risks, and Limitations | |
| <!-- This section is meant to convey both technical and sociotechnical limitations. --> | |
| [More Information Needed] | |
| ### Recommendations | |
| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> | |
| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. | |
| ## How to Get Started with the Model | |
| Use the code below to get started with the model. | |
| [More Information Needed] | |
| ## Training Details | |
| ### Training Data | |
| <!-- This should link to a Data 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. --> | |
| [More Information Needed] | |
| ### Training Procedure | |
| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> | |
| #### Preprocessing [optional] | |
| [More Information Needed] | |
| #### Training Hyperparameters | |
| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> | |
| #### Speeds, Sizes, Times [optional] | |
| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> | |
| [More Information Needed] | |
| ## Evaluation | |
| <!-- This section describes the evaluation protocols and provides the results. --> | |
| ### Testing Data, Factors & Metrics | |
| #### Testing Data | |
| <!-- This should link to a Data Card if possible. --> | |
| [More Information Needed] | |
| #### Factors | |
| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> | |
| [More Information Needed] | |
| #### Metrics | |
| <!-- These are the evaluation metrics being used, ideally with a description of why. --> | |
| [More Information Needed] | |
| ### Results | |
| [More Information Needed] | |
| #### Summary | |
| ## Model Examination [optional] | |
| <!-- Relevant interpretability work for the model goes here --> | |
| [More Information Needed] | |
| ## Environmental Impact | |
| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> | |
| 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). | |
| - **Hardware Type:** [More Information Needed] | |
| - **Hours used:** [More Information Needed] | |
| - **Cloud Provider:** [More Information Needed] | |
| - **Compute Region:** [More Information Needed] | |
| - **Carbon Emitted:** [More Information Needed] | |
| ## Technical Specifications [optional] | |
| ### Model Architecture and Objective | |
| [More Information Needed] | |
| ### Compute Infrastructure | |
| [More Information Needed] | |
| #### Hardware | |
| [More Information Needed] | |
| #### Software | |
| [More Information Needed] | |
| **BibTeX:** | |
| [More Information Needed] | |
| **APA:** | |
| [More Information Needed] | |
| ## Model Card Contact | |
| team@decision-labs.com | |