import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("NisargUpadhyay/ImageSuperResolution-replication", dtype=torch.bfloat16, device_map="cuda")
prompt = "Turn this cat into a dog"
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
image = pipe(image=input_image, prompt=prompt).images[0]DiT4SR Replication
This repository contains the DiT4SR transformer weights exported from the local dit4sr-replication experiment at checkpoint-150000.
What This Repo Contains
This Hugging Face repo publishes only the transformer/ checkpoint used by SD3Transformer2DModel. It does not include the full Stable Diffusion 3.5 base model, tokenizers, schedulers, or the rest of the DiT4SR inference stack.
Files
transformer/contains the publishable model weights and config.source_checkpoint.jsonrecords the local source path and checkpoint name used for the upload.
Checkpoint Metadata
- Experiment:
dit4sr-replication - Source checkpoint:
checkpoint-150000 - Published artifact: transformer weights only
Loading In DiT4SR
from model_dit4sr.transformer_sd3 import SD3Transformer2DModel
model = SD3Transformer2DModel.from_pretrained("NisargUpadhyay/ImageSuperResolution-replication", subfolder="transformer")
You still need the rest of the DiT4SR codebase and the base SD3 assets described in the project README.
Related Resources
- Project repo:
NisargUpadhyayIITJ/Deep-Learning-Course-Project - Training/evaluation dataset repo:
NisargUpadhyay/ImageSuperResolution - Matching training subset in the dataset repo:
Replication/
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Model tree for NisargUpadhyay/ImageSuperResolution-replication
Base model
stabilityai/stable-diffusion-3.5-medium