initial commit.

app.py

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+import gradio as gr
+import torch
+from torch import nn
+import torchvision
+from diffusers import UNet2DModel, UNet2DConditionModel, DDPMScheduler, DDPMPipeline, DDIMScheduler
+from fastprogress.fastprogress import progress_bar
+
+labels_map = {
+    0: "T-Shirt",
+    1: "Trouser",
+    2: "Pullover",
+    3: "Dress",
+    4: "Coat",
+    5: "Sandal",
+    6: "Shirt",
+    7: "Sneaker",
+    8: "Bag",
+    9: "Ankle Boot",
+}
+
+l2i = {l:i for i,l in labels_map.items()}
+
+def label2idx(l):
+    return l2i[l]
+    
+
+unet = torch.load("unconditional01.pt", map_location=torch.device('cpu')).to("cpu")
+Emb = torch.load("unconditional_emb_01.pt", map_location=torch.device('cpu')).to("cpu")
+unet.eval()
+
+sched = DDIMScheduler(beta_end=0.01)
+sched.set_timesteps(20)
+
+@torch.no_grad
+def diff_sample(model, sz, sched, hidden, **kwargs):
+    x_t = torch.randn(sz)
+    preds = []
+    for t in progress_bar(sched.timesteps):
+        with torch.no_grad(): noise = model(x_t, t, hidden).sample
+        x_t = sched.step(noise, t, x_t, **kwargs).prev_sample
+        preds.append(x_t.float().cpu())
+    return preds
+
+
+@torch.no_grad()  
+def generate(classChoice):
+    sz = (1,1,32,32)
+    print(classChoice)
+    hidden = Emb(torch.tensor([label2idx(classChoice)]*1)[:,None]).detach().to("cpu")
+    preds = diff_sample(unet, sz, sched, hidden, eta=1.)
+
+    return((preds[-1][0] + 0.5).squeeze().clamp(-1,1).detach().numpy())
+    
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 align="center">Conditional Diffusion with DDIM</h1>""")
+    gr.HTML("""<h1 align="center">trained with FashionMNIST</h1>""")
+    session_data = gr.State([])
+
+    classChoice = gr.Radio(list(labels_map.values()), value="T-Shirt", label="Select the type of image to generate", info="")
+    sampling_button = gr.Button("Conditional image generation")
+    final_image = gr.Image(height=250,width=200) 
+
+  
+
+    sampling_button.click(
+        generate,
+        [classChoice],
+        [final_image],
+    )
+
+demo.queue().launch(share=False, inbrowser=True)

requirements.txt

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+gradio
+torchvision
+diffusers
+fastprogress

unconditional01.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fd4dfcc2296cd3f28ed358bceecb1e53ebc541162ce4c88fb510735f229e32e4
+size 15277734

unconditional_emb_01.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:51a515855b4c2e1490e9f9be7c89e23f61da5ffdd496ad23e2a99e633aa02578
+size 53277