Recommended Prompts
conceptual art featuring a human hand wrapped in red and beige ribbons, isolated against a plain, light background, realistic style, minimalist color scheme, smooth textures, elongated and surreal aesthetic
film grain texture
analog photography aesthetic
Recommended Negative Prompts
bad hand,bad anatomy,worst quality,ai generated images,low quality,average quality,jpeg artifacts,blurry,poorly drawn,ugly
low quality,jpeg artifacts,blurry,poorly drawn,ugly,worst quality
| samplers | DPM++ 2M Karras, Eular a | |
| steps | 25+ | |
| cfg | 10 | |
| resolution | 1024×1024 | |
Recommended Hires (High-Resolution) Parameters
| upscaler | ESRGAN 4x, 8x_NMKD-Faces_160000_G | |
| upscale | 1.5x | |
| steps | 8 steps | |
| denoising strength | 0.3 | |
Tips
- Use ADetailer to correct distant faces.
- Use simple natural language prompts for better AI realistic photos.
- High-quality portraits can be improved with ADetailer and 1.5x Hires fix at 0.3 intensity.
Version Highlights
HelloWorld 6.0 Update – April 20, 2024
Thank you for your patience. I have been job hunting recently, which caused some delays in the HelloWorld updates. Here are the main updates in version 6.0:
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HelloWorld 6.0 is an iterative improvement based on version 5.0. Based on my own testing, the realism effect is not significantly different from version 5.0. The main advantage of version 6.0 lies in its broader coverage of concepts in the training set. According to feedback, enhancements have been made in various themes including surrealism, boudoir, group photos, masks, origami, 3D renders, cars, dragons, and maternity photography. Some examples are provided in the illustrations.
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HelloWorld 6.0 intentionally includes some low-quality images in the training to enhance the model’s response to negative prompts. It is recommended to use the following terms in negative prompts: “low quality, jpeg artifacts, blurry, poorly drawn, ugly, worst quality”.
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The main body of the HelloWorld 6.0 training set employs GPT4v tagging. For images that GPT4v cannot tag, cogVQA guided by blip2-opt-6.7b is used for tagging. The tagging language style of these multimodal models differs significantly from the traditional WD1.4 tagger. To facilitate more accurate triggering of different concepts in the training set, I have compiled the top 250 high-frequency tagging words from the HelloWorld 6.0 training set. You can view these high-frequency words in this document.
Finally, although SD3 is about to be released, I will still update to HelloWorld XL 7.0, hoping to achieve greater enhancements in version 7.0!
Creator Sponsors
📖HelloWorld 7.0 Update – June 13, 2024
One-sentence update summary: HelloWorld 7.0 is an iteratively optimized version, with the best body performance in the entire series, and further enhanced concept scope and detail richness.
Update details:
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By adding negative training images, strengthening pose training, and optimizing the clip model, the accuracy of the model’s limbs and hands has been improved compared to previous versions. The recommended negative prompt words are: ‘bad hand, bad anatomy, worst quality, ai generated images, low quality, average quality’.
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Extracted the fine-tuned LoRA from the official SPO model and incorporated it into HelloWorld 7.0. SPO is a further improvement of the DPO method. The SPO base model is used for better performance than the DPO XL base model and the original SDXL base model. The SPO LoRA can enhance image details & contrast and beautify images. Thanks to the technical team behind SPO.
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Continued to expand the concept scope of the training set, but optimized and streamlined the training set (large training set fine-tuning is too expensive, and H800 is difficult to rent recently, can’t afford the local training time). The current total training set is 20,821 images. The training set resolution distribution is as follows, and it is recommended to use several resolutions with a larger number of images for output:
(832, 1248) - Count: 7128 (896, 1152) - Count: 6250 (1248, 832) - Count: 2402 (1024, 1024) - Count: 1639 (1360, 768) - Count: 928 (1152, 896) - Count: 870 (768, 1360) - Count: 432 (960, 1088) - Count: 506 (992, 1056) - Count: 162 (1088, 960) - Count: 140 (704, 1472) - Count: 120 (1056, 992) - Count: 122 (1472, 704) - Count: 115 (1632, 640) - Count: 75 (640, 1632) - Count: 12 -
Used GPT4O to re-label all datasets. This time, a structured labeling method was used, with the specific structure being: ‘one-sentence summary description + multiple image element tags + inspired by XXX + aesthetic quality description words’, where the aesthetic quality description words are divided into five levels: worst quality, low quality, average quality, best quality, and masterpiece. A typical labeling example is as follows:
conceptual art featuring a human hand wrapped in red and beige ribbons, isolated against a plain, light background, realistic style, minimalist color scheme, smooth textures, elongated and surreal aesthetic, inspired by salvador dalí's surrealist works, masterpiece
The ‘High-Frequency Tagging Word List’ and the ‘High-Frequency Art Style List’ involved in the Inspired by XXX for the HelloWorld 7.0 version will only be provided to commercial licensing users. Partners who have purchased Helloworld XL series model authorization in the past, please contact me if there are any omissions to get it for free.
Players can refer to the High-Frequency Tagging Word List of HelloWorld 6.0. In addition, I have also provided 150+ high-quality HelloWorld 7.0 example images in the gallery, which can be used as a reference for everyone’s output. Model making is not easy, thank you players for your understanding and tolerance!
