Instructions to use dataautogpt3/PrometheusV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dataautogpt3/PrometheusV1 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/PrometheusV1", 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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# About PrometheusV1
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PrometheusV1 represents a significant effort to make advanced text-to-image generation more accessible to the open-source community. Built upon the Playground v2.5 architecture, it has undergone a full rank finetune using an extensive dataset of over 400,000 images from the Proteus collection.
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A key aspect of its development was the removal of custom sampling methods through brute force techniques at scale, allowing the model to work more seamlessly with standard open-source tools and pipelines. Additionally,
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This approach aims to balance the model's performance capabilities with wider compatibility and ease of use. Users can expect outputs that reflect the model's intensive training on the large Proteus dataset while benefiting from improved interoperability with common open-source frameworks and existing SDXL ecosystem.
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# Training Details
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# About PrometheusV1
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PrometheusV1 represents a significant effort to make advanced text-to-image generation more accessible to the open-source community. Built upon the Playground v2.5 architecture, it has undergone a full rank finetune using an extensive dataset of over 400,000 images from the Proteus collection.
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A key aspect of its development was the removal of custom sampling methods through brute force techniques at scale, allowing the model to work more seamlessly with standard open-source tools and pipelines. Additionally, PrometheusV1 has been made backwards compatible with most SDXL LoRAs and tools.
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This approach aims to balance the model's performance capabilities with wider compatibility and ease of use. Users can expect outputs that reflect the model's intensive training on the large Proteus dataset while benefiting from improved interoperability with common open-source frameworks and existing SDXL ecosystem.
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# Training Details
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