Update README.md

README.md

@@ -20,9 +20,15 @@ the model achieves state of the art performance, obviously better than the origi
 and high quality dataset, besides the aesthetic score, the prompt following ability[propose by Openai in the paper(https://cdn.openai.com/papers/dall-e-3.pdf)] and the image deformity rate[the probability that the images generate abnormal human struction]
 also improves a lot. The founder of Midjourney said that: midjourney can help those who don't know drawing to draw, so it expands the boundaries of their imagination. We have the similar vision that: we hope to let those 
 person who don't know anime or cartoons to create their own characters in a simple way, to express yourself and unleash your creativity. AIGC will reshape the animation industry, **the model we released can generate anime images with 
-aesthetic score higher than almost all popular anime websites in average, so just enjoy it**. we haven't see other SDXL-Scribble model in the opensource community, probably we are the first. To generate anime images with our model, 
-you need to use an anime sdxl base model from civitai[https://civitai.com/search/models?baseModel=SDXL%201.0&sortBy=models_v8&query=anime]. 
-The showcases we list here is based on CounterfeitXL[https://huggingface.co/gsdf/CounterfeitXL/tree/main], you can use bluepencil or other model as well. The model was trained with large amount of anime images which includes 
+aesthetic score higher than almost all popular anime websites in average, so just enjoy it**. If you want to generate especially visually appealing images, you should use danbooru tags along with natural language, due to the reason that the anime images 
+is far less than the real images, you can't just use natural language input like "a girl walk in the street" as the information is limited. Instead you should describe it with more detail such as "a girl, blue shirt, white hair, black eye, smile, pink flower, cherry blossoms ..."
+In summary, you should first use tags to describle what in the image[danbooru tag] and then describe what happened in the image[natural language], the detail the better. If you don't describe it very clean, the image generated will be something totally by probability,
+anyway, it will suit the condition image you draw and the edge detection will coincide between the condition and the generated image, the model can understand your drawing from semantics to some degree, and give you a result that is not bad. To the best of our knowledge, 
+we haven't see other SDXL-Scribble model in the opensource community, probably we are the first.
+### Attention
+To generate anime images with our model, you need to choose an 
+anime sdxl base model from huggingface[https://huggingface.co/models?pipeline_tag=text-to-image&sort=trending&search=blue] or civitai[https://civitai.com/search/models?baseModel=SDXL%201.0&sortBy=models_v8&query=anime].  
+The showcases we list here is based on CounterfeitXL[https://huggingface.co/gsdf/CounterfeitXL/tree/main], different base model have different image styles and you can use bluepencil or other model as well. The model was trained with large amount of anime images which includes 
 almost all the anime images we can found in the Internet. We filtered it seriously to preserve the images that have high visual quality, comparable to nijijourney or popular anime Illustration. We trained it with controlnet-sdxl-1.0, 
 [https://arxiv.org/abs/2302.05543], the technical detail won't not be disclosed in this report.