Text-to-Image
Diffusers
Safetensors
PyTorch
StableDiffusionPipeline
stable-diffusion
type_of_thingtype_of_thing
medical-imaging
diffusion-models-class
dreambooth
WBC_WBC_unclear
Instructions to use zz123ff/WBC-WBC_WBC_unclear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zz123ff/WBC-WBC_WBC_unclear with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zz123ff/WBC-WBC_WBC_unclear", dtype=torch.bfloat16, device_map="cuda") prompt = "A microscopy image of WBC showing cellular morphology" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
DreamBooth model for the WBC concept trained by zz123ff on the zz123ff/dreambooth-WBC_unclearV1-images dataset.
This is a Stable Diffusion model fine-tuned on the WBC concept with DreamBooth.
It can be used by modifying the instance_prompt: a WBC WBC_WBC_unclear
注:只是个测试(单类WBC)
Description
该模型是基于Stable Diffusion v1-4使用DreamBooth方法微调的医学显微镜图像生成模型, 专用于生成不同病理状态下的白细胞显微图像。训练数据包含多种白细胞, 分辨率512x512,适用于医学教育可视化场景。
Usage
from diffusers import StableDiffusionPipeline
# 基础用法
pipeline = StableDiffusionPipeline.from_pretrained('zz123ff/WBC-WBC_WBC_unclear')
image = pipeline("a WBC WBC_WBC_unclear under microscopy").images[0]
image.save("output.png")
# 高级用法
import torch
pipeline = StableDiffusionPipeline.from_pretrained(
"zz123ff/WBC-WBC_WBC_unclear",
torch_dtype=torch.float16
).to("cuda")
image = pipeline(
"High-resolution a WBC WBC_WBC_unclear with pathological features",
num_inference_steps=50
).images[0]
image.save("detailed_output.png")
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Model tree for zz123ff/WBC-WBC_WBC_unclear
Base model
CompVis/stable-diffusion-v1-4