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Includes the setup for authentication, basic image generation, post-processing to check inference progress, and examples of advanced generation techniques like ControlNet with depth maps, and txt2img inferences with LCM (Latent Constraint Model) using IP Adapters. This illustrates more flexible and accurate image generation, maintaining fidelity to a given subject while controlling and conditioning the generation process. polling for inference status, retrieving results, and demonstrating the use of IP Adapter and LCM Scheduler for image manipulation. - Established API integration with `requests` - Implemented functions for checking inference progress and displaying results - Utilized IP Adapter and LCM Scheduler to showcase advanced image generation techniques - Included detailed markdown cells for documentation and user guidance Relevant API documentation links and usage examples are provided to assist users in replicating the results. This notebook serves as an educational tool for users interested in AI-powered image generation and manipulation.
Expanded the tools section in the README to include newly added Scenario API documentation links. These additions offer guidance on integrating IP Adapters and LoRA Character Bases into game development.
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Apr 17, 2024
…s-polling Jupyter notebook for image generation using Scenario API
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Includes the setup for authentication, basic image generation, post-processing to check inference progress, and examples of advanced generation techniques like ControlNet with depth maps, and txt2img inferences with LCM (Latent Constraint Model) using IP Adapters. This illustrates more flexible and accurate image generation, maintaining fidelity to a given subject while controlling and conditioning the generation process.
requestsRelevant API documentation links and usage examples are provided to assist users in replicating the results. This notebook serves as an educational tool for users interested in AI-powered image generation and manipulation.