Update app.py (#4)

- Update app.py (61c9a91583e8d3519a0717074031cfbb747073fc)


Co-authored-by: Saptarshi Mukherjee <zombie-596@users.noreply.huggingface.co>

app.py

@@ -1,5 +1,119 @@
-pip install transformers
-model_id = "himanshu-skid19/new_linear_model_1090.pt"
+import streamlit as st
+from PIL import Image, ImageOps
+import torch
+from matplotlib.image import imread
+import numpy as np
+import tensorflow as tf
+import math
 
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModel.from_pretrained(model_id)
+class Block(nn.Module):
+    def __init__(self, in_ch, out_ch, time_emb_dim, up=False):
+        super().__init__()
+        self.time_mlp =  nn.Linear(time_emb_dim, out_ch)
+        if up:
+            self.conv1 = nn.Conv2d(2*in_ch, out_ch, 3, padding=1)
+            self.transform = nn.ConvTranspose2d(out_ch, out_ch, 4, 2, 1)
+            self.Upsample = nn.Upsample(scale_factor = 2, mode ='bilinear')
+
+        else:
+            self.conv1 = nn.Conv2d(in_ch, out_ch, 3, padding=1)
+            self.transform = nn.Conv2d(out_ch, out_ch, 4, 2, 1)
+            self.maxpool = nn.MaxPool2d(4, 2, 1)
+        self.conv2 = nn.Conv2d(out_ch, out_ch, 3, padding=1)
+        self.bnorm1 = nn.BatchNorm2d(out_ch)
+        self.bnorm2 = nn.BatchNorm2d(out_ch)
+        self.silu  = nn.SiLU()
+        self.relu = nn.ReLU()
+
+    def forward(self, x, t, ):
+        # First Conv
+        h = (self.silu(self.bnorm1(self.conv1(x))))
+        # Time embedding
+        time_emb = self.relu(self.time_mlp(t))
+        # Extend last 2 dimensions
+        time_emb = time_emb[(..., ) + (None, ) * 2]
+        # Add time channel
+        h = h + time_emb
+        # Second Conv
+        h =  (self.silu(self.bnorm2(self.conv2(h))))
+        # Down or Upsample
+        return self.transform(h)
+
+
+class SinusoidalPositionEmbeddings(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, time):
+        device = time.device
+        half_dim = self.dim // 2
+        embeddings = math.log(10000) / (half_dim - 1)
+        embeddings = torch.exp(torch.arange(half_dim, device=device) * -embeddings)
+        embeddings = time[:, None] * embeddings[None, :]
+        embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)
+        # TODO: Double check the ordering here
+        return embeddings
+
+
+class SimpleUnet(nn.Module):
+    """
+    A simplified variant of the Unet architecture.
+    """
+    def __init__(self):
+        super().__init__()
+        image_channels = 3
+        down_channels = (32, 64, 128, 256, 512)
+        up_channels = (512, 256, 128, 64, 32)
+        out_dim = 3
+        time_emb_dim = 32
+
+        # Time embedding
+        self.time_mlp = nn.Sequential(
+                SinusoidalPositionEmbeddings(time_emb_dim),
+                nn.Linear(time_emb_dim, time_emb_dim),
+                nn.ReLU()
+            )
+
+        # Initial projection
+        self.conv0 = nn.Conv2d(image_channels, down_channels[0], 3, padding=1)
+
+        # Downsample
+        self.downs = nn.ModuleList([Block(down_channels[i], down_channels[i+1], \
+                                    time_emb_dim) \
+                    for i in range(len(down_channels)-1)])
+        # Upsample
+        self.ups = nn.ModuleList([Block(up_channels[i], up_channels[i+1], \
+                                        time_emb_dim, up=True) \
+                    for i in range(len(up_channels)-1)])
+
+        # Edit: Corrected a bug found by Jakub C (see YouTube comment)
+        self.output = nn.Conv2d(up_channels[-1], out_dim, 1)
+
+    def forward(self, x, timestep):
+        # Embedd time
+        t = self.time_mlp(timestep)
+        # Initial conv
+        x = self.conv0(x)
+        # Unet
+        residual_inputs = []
+        for down in self.downs:
+            x = down(x, t)
+            residual_inputs.append(x)
+        for up in self.ups:
+            residual_x = residual_inputs.pop()
+            # Add residual x as additional channels
+            x = torch.cat((x, residual_x), dim=1)
+            x = up(x, t)
+        return self.output(x)
+
+
+model = SimpleUnet()
+
+st.title("Generatig images using a diffusion model")
+model.load_state_dict(torch.load("new_linear_model_1090.pt"))
+
+result = st.button("Click to generate image")
+
+if(result):
+    model()