import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("SushantGautam/kandi2-decoder-medical-model", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Finetuning - SushantGautam/kandi2-decoder-medical-model
This pipeline was finetuned from kandinsky-community/kandinsky-2-2-decoder on the waitwhoami/vqa_caption.dataset-full dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['The colonoscopy image contains a single, moderate-sized polyp that has not been removed, appearing in red and pink tones in the center and lower areas.']:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("SushantGautam/kandi2-decoder-medical-model", torch_dtype=torch.float16)
prompt = "The colonoscopy image contains a single, moderate-sized polyp that has not been removed, appearing in red and pink tones in the center and lower areas."
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 30
- Learning rate: 1e-05
- Batch size: 32
- Gradient accumulation steps: 1
- Image resolution: 768
- Mixed-precision: None
More information on all the CLI arguments and the environment are available on your wandb run page.
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