LEOSAMs HelloWorld XL – HelloWorld XL 70

Related Keywords & Tags

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
bad hand,bad anatomy,worst quality,ai generated images,low quality,average quality

Recommended Parameters

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 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:

  1. 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”.

  2. 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.

  3. 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
  4. 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!

Creator Sponsors

All sponsors are not affiliates of Diffus. Diffus provides an alternative online Stable Diffusion WebUI experience.

🖥️Welcome to try out the open-source GPT4V-Image-Captioner, developed by my friend and me. It offers a one-click installation and comes integrated with multiple features including image pre-compression, image tagging, and tag statistics. Recently, we also launched the webui plugin version of this tool, everyone is welcome to use it!

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📖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:

  1. 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’.

  2. 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.

  3. 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
  4. 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!

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Model details

Model TypeCheckpoint
Base ModelSDXL 1.0
Model Hash573f4a7d35
Model VersionHelloWorld XL 70
Trained Words
Creator
LEOSAM's Avatar
LEOSAM
ReferenceCivitai

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Kate Thompson

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