Instructions to use dataautogpt3/Proteus-RunDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dataautogpt3/Proteus-RunDiffusion 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/Proteus-RunDiffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "score_9, Side View of a Roman Warrior pierced By a spear, cinimatic " image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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## Introducing Proteus-RunDiffusion
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In the development of Proteus-RunDiffusion, our team embarked on an exploratory project aimed at advancing the capabilities of AI in art creation. Our journey, inspired by the broad achievements of models like Pony Diffusion v6 XL CLIP, led us to experiment with the CLIP architecture in novel ways. Through a serendipitous process of trial, error, and discovery, we developed a unique approach to retraining CLIP that we hadn't initially set out to achieve. This approach inadvertently unlocked new potentials in character recognition, natural language processing, and, most notably, the versatility of artistic expression.
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## Introducing Proteus-RunDiffusion
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https://discord.gg/EDQD3Zpwvc
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In the development of Proteus-RunDiffusion, our team embarked on an exploratory project aimed at advancing the capabilities of AI in art creation. Our journey, inspired by the broad achievements of models like Pony Diffusion v6 XL CLIP, led us to experiment with the CLIP architecture in novel ways. Through a serendipitous process of trial, error, and discovery, we developed a unique approach to retraining CLIP that we hadn't initially set out to achieve. This approach inadvertently unlocked new potentials in character recognition, natural language processing, and, most notably, the versatility of artistic expression.
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