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...
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 70,7–70,8 MB
- Versión
- vRevision 94c7c27
- Actualizado
- 27 sept 2026
- Descargas
- 10
- Fuente
- Hugging Face
Descripción de la fuente
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()
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 7 sept
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Artefactos del modelo
2 artefactos
Artefactos del modelo
2 artefactosdiffusion_pytorch_model.safetensors
safetensors · 70,7 MB · SHA-256 c07728c903ed…800c · Hugging Face
Download fileExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- 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
