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README.md

@@ -1,6 +1,6 @@
 ---
 title: Sushi Diffusion
-emoji: 🏢
+emoji: 🍣
 colorFrom: yellow
 colorTo: blue
 sdk: streamlit

app.py

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+from denoising_diffusion_pytorch import Unet, GaussianDiffusion
+import streamlit as st
+import torch
+
+
+def get_model():
+    unet = Unet(
+        dim = 64,
+        dim_mults = (1, 2, 4, 8)
+    )
+    model = GaussianDiffusion(
+        unet,
+        image_size = 64,
+        timesteps = 1000,           # number of steps
+        sampling_timesteps = 250,   # number of sampling timesteps (using ddim for faster inference [see citation for ddim paper])
+        loss_type = 'l1' ,           # L1 or L2
+        p2_loss_weight_gamma = 1.
+    )
+    model.load_state_dict(torch.load("./model-final.pt", map_location="cpu"))
+    model.eval()
+    return model
+
+def generate_image(model):
+    img = torch.randn((1,3,64,64), device="cpu")
+    for t in reversed(range(0, model.num_timesteps)):
+        img, _ = model.p_sample(img, t, None)
+    return scale_to_255(img.squeeze().numpy().transpose(1,2,0))
+
+def scale_to_255(x):
+    return ((x+1)/2*255).astype('uint8')
+
+if __name__ == "__main__":
+    st.title("Sushi Diffusion")
+    st.text('It takes about 10 mins for the generation process. Thanks for your patience!')
+    model = get_model()
+    if st.button('🍣'):
+        img = generate_image(model)
+        st.image(img, caption='This sushi does not exist.')

model-final.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4810e45bbdf1334e9cd2eeb35d34422663a2c8049cbc77a43c48be3ef5473494
+size 145163071

requirements.txt

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+denoising_diffusion_pytorch==0.27.12