Instructions to use Nihirc/Prompt2MedImage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Nihirc/Prompt2MedImage with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Nihirc/Prompt2MedImage", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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license:
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# Prompt2MedImage - Diffusion for Medical Images
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"Radiology Objects in COntext (ROCO): A Multimodal Image Dataset".
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MICCAI Workshop on Large-scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS) 2018, September 16, 2018, Granada, Spain. Lecture Notes on Computer Science (LNCS), vol. 11043, pp. 180-189, Springer Cham, 2018.
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doi: 10.1007/978-3-030-01364-6_20
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license: wtfpl
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datasets:
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- MedIR/roco
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language:
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- en
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pipeline_tag: text-to-image
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# Prompt2MedImage - Diffusion for Medical Images
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"Radiology Objects in COntext (ROCO): A Multimodal Image Dataset".
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MICCAI Workshop on Large-scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS) 2018, September 16, 2018, Granada, Spain. Lecture Notes on Computer Science (LNCS), vol. 11043, pp. 180-189, Springer Cham, 2018.
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doi: 10.1007/978-3-030-01364-6_20
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```
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