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...
Source du modèle
- Type
- Other
- Taille du fichier
- 70,7–70,8 MB
- Version
- vRevision 94c7c27
- Mis à jour
- 27 sept. 2026
- Téléchargements
- 10
- Source
- Hugging Face
Description de la source
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 sourceVérifié 7 sept.
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Artefacts du modèle
2 artefacts
Artefacts du modèle
2 artefactsdiffusion_pytorch_model.safetensors
safetensors · 70,7 MB · SHA-256 c07728c903ed…800c · Hugging Face
Download fileExtraits de sources
2 extraits
Extraits de sources
2 extraits--- 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
