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Ddpm Bedroom Finetuned Texture Dtd 1ep Bs4

Bedroom model fine-tuned using dtd textures. Investigating pink problem

TipoOtherVersiónRevision a441f0bCcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
434 MB
Versión
vRevision a441f0b
Actualizado
27 sept 2026
Descargas
5
Fuente
Hugging Face

Descripción de la fuente

Description

Bedroom model fine-tuned using dtd textures. Investigating pink problem

Usage

from diffusers import DDPMPipeline
# Load the pretrained pipeline
pipeline_name = "ttextture/ddpm-bedroom-finetuned-texture-dtd"
image_pipe = DDPMPipeline.from_pretrained(pipeline_name).to(device)

# Sample some images with a DDIM Scheduler over 40 steps
scheduler = DDIMScheduler.from_pretrained(pipeline_name)
scheduler.set_timesteps(num_inference_steps=40)

image = pipeline().images[0]
image

Fuentes

1 fuente
Hugging FacePrincipalVersión a441f0b7e5fd587ffe116b4690aa29de16ef4091
huggingface.co/ttextture/ddpm-bedroom-finetuned-texture-dtd-1ep-bs4/tree/a441f0b7e5fd587ffe116b4690aa29de16ef4091

Verificado 7 sept

Artefactos del modelo

1 artefacto

diffusion_pytorch_model.safetensors

safetensors · 434 MB · SHA-256 9ecf3b501fec…c68f · Hugging Face

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Extractos de fuentes

2 extractos

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Description Bedroom model fine-tuned using dtd textures. Investigating pink problem ## Usage ```python from diffusers import DDPMPipeline # Load the pretrained pipeline pipeline_name = "ttextture/ddpm-bedroom-finetuned-texture-dtd" image_pipe = DDPMPipeline.from_pretrained(pipeline_name).to(device) # Sample some images with a DDIM Scheduler over 40 steps scheduler = DDIMScheduler.from_pretrained(pipeline_name) scheduler.set_timesteps(num_inference_steps=40) image = pipeline().images[0] image ```

Source context: 5 downloads · 0 likes · Pipeline unconditional-image-generation · Library diffusers · Repo ttextture/ddpm-bedroom-finetuned-texture-dtd-1ep-bs4