noobaiXLNAIXL_epsilonPred10Version.safetensors

6.46 GB · SafeTensor · fp16 · full

Vyžaduje členství

<noobai-epsilonpred1-xl>

Model Introduction This image generation model, based on Laxhar/noobai-XL_v1.0, leverages full Danbooru and e621 datasets with native tags and natural language captioning. Implemented as a v-prediction model (distinct from eps-prediction), it requires specific parameter configurations - detailed in following sections. Special thanks to my teammate euge for the coding work, and we're grateful for the technical support from many helpful community members. ⚠️ IMPORTANT NOTICE ⚠️ THIS MODEL WORKS DIFFERENT FROM EPS MODELS! PLEASE READ THE GUIDE CAREFULLY! Model Details - Developed by: - Model Type: Diffusion-based text-to-image generative model - Fine-tuned from: Laxhar/noobai-XL_v1.0 - Sponsored by from: - Collaborative testing: How to Use the Model. Guidebook for NoobAI XL: ENG: CHS: Recommended LoRa List for NoobAI XL: Method I: - (If you haven't installed reForge) Install reForge by following the instructions in the repository; - Launch WebUI and use the model as usual! Method II: SAMLPLE with NODES Method III: Note that dev branch is not stable and may contain bugs. 1. (If you haven't installed WebUI) Install WebUI by following the instructions in the repository. For simp 2.Switch to dev branch: git switch dev 3. Pull latest updates: git pull 4. Launch WebUI and use the model as usual! Method IV: import torch from diffusers import StableDiffusionXLPipeline from diffusers import EulerDiscreteScheduler ckpt_path = "/path/to/model.safetensors" pipe = StableDiffusionXLPipeline.from_single_file( ckpt_path, use_safetensors=True, torch_dtype=torch.float16, ) scheduler_args = {"prediction_type": "v_prediction", "rescale_betas_zero_snr": True} pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, **scheduler_args) pipe.enable_xformers_memory_efficient_attention() pipe = "cuda") prompt = """masterpiece, best quality,artist:john_kafka,artist:nixeu,artist:quasarcake, chromatic aberration, film grain, horror \(theme\), limited palette, x-shaped pupils, high contrast, color contrast, cold colors, arlecchino \(genshin impact\), black theme, gritty, graphite \(medium\)""" negative_prompt = "nsfw, worst quality, old, early, low quality, lowres, signature, username, logo, bad hands, mutated hands, mammal, anthro, furry, ambiguous form, feral, semi-anthro" image = pipe( prompt=prompt, negative_prompt=negative_prompt, width=832, height=1216, num_inference_steps=28, guidance_scale=5, generator=torch.Generator().manual_seed(42), ).images[0] image.save("output.png") Note: Please make sure Git is installed and environment is properly configured on your machine. Recommended Settings Parameters - CFG: 4 ~ 5 - Steps: 28 ~ 35 - Sampling Method: Euler (⚠️ Other samplers will not work properly) - Resolution: Total area around 1024x1024. Best to choose from: 768x1344, 832x1216, 896x1152, 1024x1024, 1152x896, 1216x832, 1344x768 Prompts - Prompt Prefix: masterpiece, best quality, newest, absurdres, highres, safe, - Negative Prompt: nsfw, worst quality, old, early, low quality, lowres, signature, username, logo, bad hands, mutated hands, mammal, anthro, furry, ambiguous form, feral, semi-anthro Usage Guidelines Caption , , , , , , Quality Tags For quality tags, we evaluated image popularity through the following process: - Data normalization based on various sources and ratings. - Application of time-based decay coefficients according to date recency. - Ranking of images within the entire dataset based on this processing. Our ultimate goal is to ensure that quality tags effectively track user preferences in recent years. Percentile RangeQuality Tags> 95thmasterpiece> 85th, 60th, 30th, <= 60thnormal quality<= 30thworst quality Aesthetic Tags TagDescriptionvery awaTop 5% of images in terms of aesthetic score by worst aestheticAll the bottom 5% of images in terms of aesthetic score by and ...... Date Tags There are two types of date tags: year tags and period tags. For year tags, use year xxxx format, i.e., year 2021. For period tags, please refer to the following table: Year RangePeriod tag2005-2010old2011-2014early2014-2017mid2018-2020recent2021-2024newest Dataset - The latest Danbooru images up to the training date (approximately before 2024-10-23) - E621 images dataset on Hugging Face Communication - QQ Groups: - 427280545 - 677964513 - 852429527 - 914818692 - 635772191 - 870086562 - Discord: How to train a LoRA on v-pred SDXL model A tutorial is intended for LoRA trainers based on sd-scripts. article link: Utility Tool Laxhar Lab is training a dedicated ControlNet model for NoobXL, and the models are being released progressively. So far, the normal, depth, and canny have been released. Model link: Model License This model's license inherits from fair-ai-public-license-1.0-sd and adds the following terms. Any use of this model and its variants is bound by this license. I. Usage Restrictions - Prohibited use for harmful, malicious, or illegal activities, including but not limited to harassment, threats, and spreading misinformation. - Prohibited generation of unethical or offensive content. - Prohibited violation of laws and regulations in the user's jurisdiction. II. Commercial Prohibition We prohibit any form of commercialization, including but not limited to monetization or commercial use of the model, derivative models, or model-generated products. III. Open Source Community To foster a thriving open-source community,users MUST comply with the following requirements: - Open source derivative models, merged models, LoRAs, and products based on the above models. - Share work details such as synthesis formulas, prompts, and workflows. - Follow the fair-ai-public-license to ensure derivative works remain open source. IV. Disclaimer Generated models may produce unexpected or harmful outputs. Users must assume all risks and potential consequences of usage. Participants and Contributors Participants - L_A

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