Instructions to use dataautogpt3/ProteusV0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dataautogpt3/ProteusV0.1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dataautogpt3/ProteusV0.1", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "black fluffy gorgeous dangerous cat animal creature, large orange eyes, big fluffy ears, piercing gaze, full moon, dark ambiance, best quality, extremely detailed" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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@@ -65,7 +65,7 @@ ProteusV0.1 is currently my best model to date.
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ProteusV0.1 uses OpenDalleV1.1 as a substantial base to work from in terms of an untuned base model. It further refines said prompt adherence and stylistic capabilities to a measurable degree.
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tuned on 10k unique examples of high-quality captioned
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ProteusV0.1 has a noticeable improvement in facial features and skin detailing. while still keeping similar or slightly improved levels of surrealism, anime, and cartoonish styles.
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ProteusV0.1 uses OpenDalleV1.1 as a substantial base to work from in terms of an untuned base model. It further refines said prompt adherence and stylistic capabilities to a measurable degree.
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tuned on 10k unique examples of high-quality captioned ai generated image pairs and then further refined using 220k GPTV captioned non-copyright stock images with some anime through in here and there.
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ProteusV0.1 has a noticeable improvement in facial features and skin detailing. while still keeping similar or slightly improved levels of surrealism, anime, and cartoonish styles.
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