File size: 1,848 Bytes
3d09cb5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | from diffusers import DDPMPipeline
from diffusers.utils import make_image_grid
import torch
from PIL import Image
import os
from dataclasses import dataclass
@dataclass
class TrainingConfig:
image_size = 256 # the generated image resolution
mixed_precision = "fp16" # `no` for float32, `fp16` for automatic mixed precision
output_dir = "skin_lesion_cancer_diffusion_256_V2" # the model name locally and on the HF Hub
saved_image_dir="diffusion_generated_image_256"
seed = 0
config = TrainingConfig()
# Define the path to the saved model
saved_model_path = config.output_dir
# torch.set_default_dtype(torch.float16)
# Load the model from the saved folder
# pipeline = DDPMPipeline.from_pretrained(saved_model_path,torch_dtype=torch.float16)
pipeline = DDPMPipeline.from_pretrained(saved_model_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipeline.to(device)
# Set the seed for reproducibility
generator = torch.manual_seed(config.seed)
# Generate images
def generate_images():
images = pipeline(
batch_size=1,
generator=generator
).images
return images[0]
# Save the images
def save_images(image, num):
os.makedirs(config.saved_image_dir, exist_ok=True)
image.save(os.path.join(config.saved_image_dir, f"{num}.png"))
for x in range(1000):
image=generate_images()
save_images(image,x)
# Save the generated images
# save_images(generated_images, config.saved_image_dir)
# import gradio as gr
# with gr.Blocks() as demo:
# gr.Markdown("Start typing below and then click **Run** to see the output.")
# with gr.Row():
# img=gr.Image(type="pil",height=256,width=256)
# with gr.Row():
# btn = gr.Button("Generate image")
# btn.click(fn=generate_images, inputs=None, outputs=img)
# demo.launch(share=True) |