Instructions to use dataautogpt3/TempestV0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dataautogpt3/TempestV0.1 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/TempestV0.1", dtype=torch.bfloat16, device_map="cuda") prompt = "Food photography style photo RAW,piece of fried grilled meat, splashes of ketchup and mustard sauce, (rosemary), spices, exceptional shallow depth-of-field capabilities, atmospheric haze blur,vivid colors,high quality textures of materials, volumetric textures, coating textures, metal textures . Appetizing, professional, culinary, high-resolution, commercial, highly detailed" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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The TempestV0.1 Initiative is a powerhouse in image generation, leveraging an unparalleled dataset of over 6 million images. The collection's vast scale, with resolutions from 1400x2100 to 4800x7200, encompasses 200GB of high-quality content.
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With a groundbreaking 3 million iterations in its training cycle, TempestV0.1 underscores the rigorous effort input by its creator. This training intensity notably eclipses that of all other contemporarie models.
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<Gallery />
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Who needs upscalers?
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The TempestV0.1 Initiative is a powerhouse in image generation, leveraging an unparalleled dataset of over 6 million images. The collection's vast scale, with resolutions from 1400x2100 to 4800x7200, encompasses 200GB of high-quality content.
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With a groundbreaking 3 million iterations in its training cycle, TempestV0.1 underscores the rigorous effort input by its creator. This training intensity notably eclipses that of all other contemporarie models.
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