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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The thorough redesign and complete retraining of CLIP has resulted in an innovative phenomenon termed "style unlocking". This enhancement enables SDXL-based models to transcend stylistic constraints. For instance, applying it to a model previously confined to anime generation now permits the creation of high-fidelity photorealistic images, among other capabilities. applying this to a anime only model allows it to generate high fiedelity photorealism and so on.
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## Optimal Creative Settings
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CLIP Settings:
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The thorough redesign and complete retraining of CLIP has resulted in an innovative phenomenon termed "style unlocking". This enhancement enables SDXL-based models to transcend stylistic constraints. For instance, applying it to a model previously confined to anime generation now permits the creation of high-fidelity photorealistic images, among other capabilities. applying this to a anime only model allows it to generate high fiedelity photorealism and so on.
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The comprehensive rework of the latent representation has successfully addressed CFG scaling issues, allowing the model to effectively handle CFG ranges from 3 to 50. This eliminates the occurrence of total image failures, which were common at CFG levels above 10, without the need for additional steps.
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## Optimal Creative Settings
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CLIP Settings:
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