Package: peacasso
PeacassoPeacasso [Beta] is a UI tool to help you generate art (and experiment) with multimodal (text, image) AI models (stable diffusion). This project is still in development (see roadmap below).Why Use Peacasso?Because you deserve a nice UI and great workflow that makes exploring stable diffusion models fun! But seriously, here are a few things that make Peacasson interesting:Easy installation. Instead of cobbling together command line scripts, Peacasso provides apip installflow and a UI that supports a set of curated default operations.UI with good defaults. The current implementation of Peacasso provides a UI for basic operations - text and image based prompting (+ inpainting), remixing generated images as prompts, model parameter selection, image download. Also covers the little things .. like light and dark mode.Python API. While the UI is the focus here, there is an underlying python api which will bake in experimentation features (e.g. saving intermediate images in the sampling loop, exploring model explanations etc. . see roadmap below).Clearly, Peacasso (UI) might not be for those interested in low level code.Requirements and InstallationStep 1:Verify Environment - Pythong 3.7+ and CUDASetup and verify that your python environment ispython 3.7or higher (preferably, use Conda). Also verify that you have CUDA installed correctly (torch.cuda.is_available()is true) and your GPU has about7GB of VRAM memory.Once requirements are met, run the following command to install the library:pipinstallpeacassoWant to stay on the bleeding edge of updates (which might be buggy)? Install directly from the repo:gitclonehttps://github.com/victordibia/peacasso.gitcdpeacasso
pipinstall-e.Don't have a GPU, you can still use the python api and UI in a colab notebook. See thiscolab notebookfor more details.Usage - UI and Python APIYou can use the library from the ui by running the following command:peacassoui--port=8080Then navigate tohttp://localhost:8080/in your browser. Note that you can also pass flags like--model(huggingface model) and--device(cuda, cpu, mps) to the command above to specify the model and device to use.You can also use the python api by running the following command.
Note that each generation is parameterized by aGeneratorConfigobject.frompeacasso.generatorimportImageGeneratorfrompeacasso.datamodelimportGeneratorConfig,ModelConfig# model configurationmodel_config:ModelConfig=ModelConfig(device="cuda:0",# device ..cpu, cuda, cuda:0model="nitrosocke/mo-di-diffusion",revision="main",# HF model branchtoken=None,# HF_TOKEN here if needed)prompt="victorian ampitheater of sand, pillars with statues on top, lamps on ground, by peter mohrbacher dan mumford craig mullins nekro, cgsociety, pixiv, volumetric light, 3 d render"prompt_config=GeneratorConfig(prompt=prompt,num_images=3,width=512,height=512,guidance_scale=7.5,num_inference_steps=20,return_intermediates=True,# return intermediate images during diffusion samplingseed=6010691039)result=gen.generate(prompt_config)result=gen.generate(prompt_config)fori,imageinenumerate(result["images"]):image.save(f"image_{i}.png")# result["intermediates"] contains the intermediate imagesVisualizing intermediate images during the diffusion loop.You can also interpolate between along several dimensions e.g.,latentsused in the diffusion process (based on seed, image, mask or combinations) andtext embeddingsused to guide the diffusion process. You can do this by modifying theapplicationfield inGeneratorConfig. See thecolab notebookfor more details.The config below shows how to interpolate between two prompts and two latents (based on provided start and end images).interpolation_config=GeneratorConfig(prompt=start_prompt,num_inference_steps=40,seed=3167288399,strength=1,application={"type":"interpolate","config":{"num_steps":60,"image":{"start":start_image,"end":end_image},"prompt":{"start":start_prompt,"end":end_prompt}}})Design PhilosophyFeatures inPeacassoare being designed based on insights from communication theory [^1] and also research on Human-AI interaction design [^2]. Learn more about the design and components in peacasso in the paper drafthere.A general vision for the Peacasso architecture is shown below (parts of this are still being implemented):Features and Road MapCommand line interfaceUI Features. Query models with multiple parametrsPrompting: Text prompting (text2img), Image based prompting (img2img), Inpainting (img2img)Latent space interpolation (seenotebook).Full Editor (for outpainting) (see tutorialnotebook)Experimentation toolsSave intermediate images in the sampling loopWeighted prompt mixingPrompt recommendationCuration/sharing experiment resultsDefined Workflows (e.g., tiles, composition etc.)Model explanationsAcknowledgementThis work builds on the stable diffusion model and code is adapted from the HuggingFaceimplementation. Please note the -CreativeML Open RAIL-Mlicense associated with the stable diffusion model.CitationIf you usepeacassofor in your research or adopt the design guidelines used to buildpeacasso, please consider citing thepaperas follows:@misc{dibia2022peacasso,title={Interaction Design for Systems that Integrate Image Generation Models: A Case Study with Peacasso},author={Victor Dibia},year={2022},publisher={GitHub},journal={GitHub repository},primaryClass={cs.CV}}References[^1]:
Richard L Daft and Robert H Lengel. 1986. Organizational information require-
ments, media richness and structural design. Management science[^2]:
Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi,
Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, et al. 2019.
Guidelines for human-AI interaction. In Proceedings of the 2019 chi conference on
human factors in computing systems.