Text-to-Image
Diffusers
Safetensors
English
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
art
artistic
protogen
Instructions to use darkstorm2150/Protogen_Nova_Official_Release with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use darkstorm2150/Protogen_Nova_Official_Release with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("darkstorm2150/Protogen_Nova_Official_Release", dtype=torch.bfloat16, device_map="cuda") prompt = "modelshoot style, (extremely detailed CG unity 8k wallpaper), full shot body photo of the most beautiful artwork in the world, english medieval witch, black silk vale, pale skin, black silk robe, black cat, necromancy magic, medieval era, photorealistic painting by Ed Blinkey, Atey Ghailan, Studio Ghibli, by Jeremy Mann, Greg Manchess, Antonio Moro, trending on ArtStation, trending on CGSociety, Intricate, High Detail, Sharp focus, dramatic, photorealistic painting art by midjourney and greg rutkowski" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Commit ·
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Parent(s): e5df751
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README.md
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The Protogen Nova is a checkpoint model that merges all the previous models into one
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This merger includes
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* Protogen v2.2
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* Protogen x3.4
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* ProtoGen x5.3
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* ProtoGen x5.8
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* ProtoGen x5.9
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* ProtoGen x7.4
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* ProtoGen x7.9
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As part the of the checkpoint merging, Granular Adaptive Learning is a technique where traininig data is lessen selectively from 30% to 0.05%, and as the training is eventually saturated, the process reduces loss and introduces elements from various checkpoints
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The Protogen Nova is a checkpoint model that merges all the previous models into one
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This merger includes
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* Protogen v2.2 (Anime)
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* Protogen x3.4 (Photorealism)
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* ProtoGen x5.3 (Photorealism)
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* ProtoGen x5.8 Rebuilt (Scifi+Anime)
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* ProtoGen x5.9 (Dragon)
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* ProtoGen x7.4 (Eclipse)
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As part the of the checkpoint merging, Granular Adaptive Learning is a technique where traininig data is lessen selectively from 30% to 0.05%, and as the training is eventually saturated, the process reduces loss and introduces elements from various checkpoints
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