Update README.md

README.md

@@ -29,5 +29,23 @@ The feature alignment loss is designed in a way such that the output of `block-x
 The distillation process is performed with `512x512` Laion images recaptioned with `Qwen-VL` in the first stage for `90k steps`, 
 and `1024x1024` images generated by `Flux` using the prompts in `JourneyDB` with another `90k steps`.
 
+## Disclaimer
+
+Compared to other text-to-image models, our model was trained with limited computing resources and synthetic data with mediocre quality. 
+Thus, the generation capability of our model is still limited in certain categories.
+
+The current model is ok with generating common images such as human/animal faces, landscapes, fantasy and abstract scenes.  
+Unfortunately, it is still incompetent in many scenarios. Including but not limited to:
+* Fine-grained details, such as texts, human/animal structures,
+* Perspective and Geometric Structure
+* Dynamics and Motion
+* Commonsense knowledge, e.g., brand logo
+* Physical Plausibility
+* Cultural Diversity
+
+Since our model is trained with prompts in JourneyDB, we encourage users to apply our model with similar prompt formats (compositions of nouns and adjectives) to achieve the best quality. 
+For example: "profile of sad Socrates, full body, high detail, dramatic scene, Epic dynamic action, wide angle, cinematic, hyper-realistic, concept art, warm muted tones as painted by Bernie Wrightson, Frank Frazetta."
+
+Thank you for your attention! We will continue to improve our model and release new versions in the future.
 
 github link: https://github.com/TencentARC/flux-toolkits