ModelListadosExterno

Sprite Flow

Flow-based generative model for unguided generation of 128x128 RGBA pixel art characters.

Fontehuggingface.co/mradovic38/sprite-flow/tree/4f7cb81039c928ff28758e467758eff7c31ba6c4

TipoOtherVersãoRevision 4f7cb81Cnota

Fonte do modelo

Tipo: Other
Tamanho do arquivo: 290 MB
Versão: vRevision 4f7cb81
Atualizado: 27 de set. de 2026
Downloads: 7
Fonte: Hugging Face

Descrição da fonte

Sprite-flow

Flow-based generative model for unguided generation of 128x128 RGBA pixel art characters.

Model Details

Model Description

Developed by: Mihailo Radović
Model type: Unconditional Image Generation
License: MIT

Model Sources

Repository: GitHub Repo
Demo: Gradio App

Uses

Direct Use

Predicts the vector field for generating 128x128 RGBA pixel art character images from Isotropic Gaussian Distribution by simulating an ODE with Linear Noise Scheduling.

Out-of-Scope Use

Could be used with Cosine or any other Noise scheduler.

How to Get Started with the Model

Step 1 - Clone the GitHub Repo

Step 2 - Initialize the model:

from models.unet import PixelArtUNet

model = PixelArtUNet(
    channels = [128, 256, 512, 1024],
    num_residual_layers = 2,
    t_embed_dim = 128,
    midcoder_dropout_p=0.2
).to(device)

Step 3: Load Model weights:

from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

repo_id = "mradovic38/sprite-flow"
filename = "model.safetensors"
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
checkpoint = load_file(file_path)
model.load_state_dict(checkpoint)
model.to(device)
model.eval()

Step 4: Initialize the probability path:

from sampling.conditional_probability_path import GaussianConditionalProbabilityPath
from sampling.noise_scheduling import LinearAlpha, LinearBeta

path = GaussianConditionalProbabilityPath(
    p_data=None,
    p_simple_shape=[4, 128, 128],
    alpha=LinearAlpha(),
    beta=LinearBeta()
).to(device)
path.eval()

Step 5: Simulate ODE:

import torch

from diff_eq.ode_sde import UnguidedVectorFieldODE
from diff_eq.simulator import EulerSimulator

num_timesteps = 200 # example number of timesteps
num_samples = 3 # example number of samples

ts = torch.linspace(0, 1, num_timesteps).view(1, -1, 1, 1, 1).expand(num_samples, -1, 1, 1, 1).to(device)
x0 = path.p_simple.sample(num_samples).to(device)  # (num_samples, 4, 128, 128)
ode = UnguidedVectorFieldODE(model)
simulator = EulerSimulator(ode)
x1 = simulator.simulate(x0, ts)  # (num_samples, 4, 128, 128)

Step 6: Turn torch tensor to PIL:

from utils.helpers import tensor_to_rgba_image, normalize_to_unit

x1 = normalize_to_unit(x1) # [-1, 1] -> [0, 1]
imgs = tensor_to_rgba_image(x1)

Fontes

1 fonte

Hugging FacePrimárioVersão 4f7cb81039c928ff28758e467758eff7c31ba6c4

huggingface.co/mradovic38/sprite-flow/tree/4f7cb81039c928ff28758e467758eff7c31ba6c4

Verificado 31 de ago.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 290 MB · SHA-256 99546ea668e8…1998 · Hugging Face

Baixar

Trechos de fonte

2 trechos

Readme31 de ago.huggingface.co/mradovic38/sprite-flow

--- tags: - model_hub_mixin - pytorch_model_hub_mixin - art license: mit pipeline_tag: unconditional-image-generation metrics: - name: FID type: image value: 80.4755 dataset: https://www.kaggle.com/datasets/ayhantasyurt/pixel-art-2dgame-charecter-sprites-idle split: test --- # Sprite-flow Flow-based generative model for unguided generation of 128x128 RGBA pixel art characters. ## Model Details ### Model Description - **Developed by:** [Mihailo Radović](https://www.linkedin.com/in/mihailo-radović-484070278/) - **Model type:** Unconditional Image Generation - **License:** MIT ### Model Sources  - **Repository:** [GitHub Repo](https://github.com/mradovic38/sprite-flow) - **Demo:** [Gradio App](https://huggingface.co/spaces/mradovic38/sprite-flow) ## Uses  ### Direct Use Predicts the vector field for generating 128x128 RGBA pixel art character images from Isotropic Gaussian Distribution by simulating an ODE with Linear Noise Scheduling. ### Out-of-Scope Use Could be used with Cosine or any other Noise scheduler. ## How to Get Started with the Model * Step 1 - **Clone the [GitHub Repo](https://github.com/mradovic38/sprite-flow)** * Step 2 - **Initialize the model**: ```py from models.unet import PixelArtUNet model = PixelArtUNet( channels = [128, 256, 512, 1024], num_residual_layers = 2, t_embed_dim = 128, midcoder_dropout_p=0.2 ).to(device) ``` * Step 3: **Load Model weights**: ```py from huggingface_hub import hf_hub_download from safetensors.torch import load_file repo_id = "mradovic38/sprite-flow" filename = "model.safetensors" file_path = hf_hub_download(repo_id=repo_id, filename=filename) checkpoint = load_file(file_path) model.load_state_dict(checkpoint) model.to(device) model.eval() ``` * Step 4: **Initialize the probability path**: ```py from sampling.conditional_probability_path import GaussianConditionalProbabilityPath from sampling.noise_scheduling import LinearAlpha, LinearBeta path = GaussianConditionalProbabilityPath( p_data=None, p_simple_shape=[4, 128, 128], alpha=LinearAlpha(), beta=LinearBeta() ).to(device) path.eval() ``` * Step 5: **Simulate ODE**: ```py import torch from diff_eq.ode_sde impo...

Note31 de ago.huggingface.co/mradovic38/sprite-flow

Source context: 7 downloads · 0 likes · Pipeline unconditional-image-generation · Repo mradovic38/sprite-flow