awesome-controllable-diffusion
github.com/atfortes/awesome-controllable-diffusion ↗Papers and resources on Controllable Generation using Diffusion Models, including ControlNet, DreamBooth, IP-Adapter.
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21 hours ago
Last Refreshed
20242023
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Installation instructions →What's inside
2023
- Adding Conditional Control to Text-to-Image Diffusion Models.
- Awesome-Controllable-T2I-Diffusion-Models
- Awesome-LLM-Reasoning
- BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing.
- Controlling Text-to-Image Diffusion by Orthogonal Finetuning.
- DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation.
2024
- Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation.
- Compositional Text-to-Image Generation with Dense Blob Representations.
- Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models.
- Context Diffusion: In-Context Aware Image Generation.
- Continuous Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions.
- ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback.
Showing a sample of 73 resources. View the full list on GitHub →