DDPM Uwall Morefields
This model is a diffusion model for unconditional image generation of uwall velocity field in a turbulent channel flow with Retau = 1000. This model is trained using just almost 1000 fields which are very few since...
Model source
- Kind
- Other
- File size
- 70.7–70.8 MB
- Version
- vRevision 94c7c27
- Updated
- Sep 27, 2026
- Downloads
- 10
- Source
- Hugging Face
Source description
This model is a diffusion model for unconditional image generation of uwall velocity field in a turbulent channel flow with Retau = 1000. This model is trained using just almost 1000 fields which are very few since in the most of case DDPM are trained with thousands of images.
Usage
from diffusers import DDPMPipeline
import torch
import torchvision
from torchvision import transforms
import numpy as np
from matplotlib import pyplot as plt
from huggingface_hub import hf_hub_download
import joblib
pipeline = DDPMPipeline.from_pretrained('RobbyDalmonts/DDPM_uwall_morefields')
image = pipeline().images[0]
to_tensor = transforms.ToTensor()
tensor_output_normalized = to_tensor(image)
tensor_output_normalized = tensor_output_normalized *2 -1
fig,ax = plt.subplots()
ax.imshow(tensor_output_normalized[0], cmap ='coolwarm')
ax.set_title('normalized sample')
plt.show()
plt.close()
scaler_path = hf_hub_download(repo_id='RobbyDalmonts/DDPM_uwall_morefields', filename="scaler.pkl", repo_type='model')
scaler = joblib.load(scaler_path)
tensor_output = scaler.inverse_transform(tensor_output_normalized.squeeze(0).numpy().reshape(-1,1)).reshape(128,64)
fig,ax = plt.subplots()
ax.imshow(tensor_output, cmap='coolwarm')
ax.set_title('sample')
plt.show()
plt.close()
Sources
1 source
Sources
1 sourceVerified Sep 7
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Model artifacts
2 artifacts
Model artifacts
2 artifactsdiffusion_pytorch_model.safetensors
safetensors · 70.7 MB · SHA-256 c07728c903ed…800c · Hugging Face
Download fileSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class library_name: diffusers --- This model is a diffusion model for unconditional image generation of uwall velocity field in a turbulent channel flow with Retau = 1000. This model is trained using just almost 1000 fields which are very few since in the most of case DDPM are trained with thousands of images. ## Usage ```python from diffusers import DDPMPipeline import torch import torchvision from torchvision import transforms import numpy as np from matplotlib import pyplot as plt from huggingface_hub import hf_hub_download import joblib pipeline = DDPMPipeline.from_pretrained('RobbyDalmonts/DDPM_uwall_morefields') image = pipeline().images[0] to_tensor = transforms.ToTensor() tensor_output_normalized = to_tensor(image) tensor_output_normalized = tensor_output_normalized *2 -1 fig,ax = plt.subplots() ax.imshow(tensor_output_normalized[0], cmap ='coolwarm') ax.set_title('normalized sample') plt.show() plt.close() scaler_path = hf_hub_download(repo_id='RobbyDalmonts/DDPM_uwall_morefields', filename="scaler.pkl", repo_type='model') scaler = joblib.load(scaler_path) tensor_output = scaler.inverse_transform(tensor_output_normalized.squeeze(0).numpy().reshape(-1,1)).reshape(128,64) fig,ax = plt.subplots() ax.imshow(tensor_output, cmap='coolwarm') ax.set_title('sample') plt.show() plt.close() ```
Source context: 10 downloads · 0 likes · Pipeline unconditional-image-generation · Library diffusers · Repo RobbyDalmonts/DDPM_uwall_morefields
