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import gradio as gr
import torch
import torch.nn as nn
from transformers import XLNetTokenizer, XLNetModel
import numpy as np
class TextEncoder(nn.Module):
def __init__(self):
super().__init__()
self.transformer = XLNetModel.from_pretrained("xlnet-base-cased")
def forward(self, input_ids, token_type_ids, attention_mask):
hidden = self.transformer(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask).last_hidden_state
context = hidden.mean(dim=1)
context = context.view(*context.shape, 1, 1)
return context
class Generator(nn.Module):
def __init__(self, nz=100, ngf=64, nt=768, nc=3):
super().__init__()
self.layer1 = nn.Sequential(
nn.ConvTranspose2d(nz+nt, ngf*8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf*8)
)
self.layer2 = nn.Sequential(
nn.Conv2d(ngf*8, ngf*2, 1, 1),
nn.Dropout2d(inplace=True),
nn.BatchNorm2d(ngf*2),
nn.ReLU(True)
)
self.layer3 = nn.Sequential(
nn.Conv2d(ngf*2, ngf*2, 3, 1, 1),
nn.Dropout2d(inplace=True),
nn.BatchNorm2d(ngf*2),
nn.ReLU(True)
)
self.layer4 = nn.Sequential(
nn.Conv2d(ngf*2, ngf*8, 3, 1, 1),
nn.Dropout2d(inplace=True),
nn.BatchNorm2d(ngf*8),
nn.ReLU(True)
)
self.layer5 = nn.Sequential(
nn.ConvTranspose2d(ngf*8, ngf*4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf*4),
nn.ReLU(True)
)
self.layer6 = nn.Sequential(
nn.Conv2d(ngf*4, ngf, 1, 1),
nn.Dropout2d(inplace=True),
nn.BatchNorm2d(ngf),
nn.ReLU(True)
)
self.layer7 = nn.Sequential(
nn.Conv2d(ngf, ngf, 3, 1, 1),
nn.Dropout2d(inplace=True),
nn.BatchNorm2d(ngf),
nn.ReLU(True)
)
self.layer8 = nn.Sequential(
nn.Conv2d(ngf, ngf*4, 3, 1, 1),
nn.Dropout2d(inplace=True),
nn.BatchNorm2d(ngf*4),
nn.ReLU(True)
)
self.layer9 = nn.Sequential(
nn.ConvTranspose2d(ngf*4, ngf*2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf*2),
nn.ReLU(True)
)
self.layer10 = nn.Sequential(
nn.ConvTranspose2d(ngf*2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True)
)
self.layer11 = nn.Sequential(
nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, noise, encoded_text):
x = torch.cat([noise, encoded_text], dim=1)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
x = self.layer6(x)
x = self.layer7(x)
x = self.layer8(x)
x = self.layer9(x)
x = self.layer10(x)
x = self.layer11(x)
return x
# Load the model and tokenizer
model_path = "checkpoint.pth" # Adjust as necessary
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
text_encoder = TextEncoder()
model = Generator()
model_state_dict = torch.load(model_path, map_location="cpu")
generator = model_state_dict['models']['generator']
model.load_state_dict(generator)
text_encoder.to("cpu")
model.to("cpu")
model.eval()
def generate_image(enc_text):
noise = torch.randn((1, 100, 1, 1), device="cpu")
with torch.no_grad():
generated_image = model(noise, enc_text).detach().squeeze().cpu()
gen_image_np = generated_image.numpy()
gen_image_np = np.transpose(gen_image_np, (1, 2, 0)) # Change from CHW to HWC
gen_image_np = (gen_image_np - gen_image_np.min()) / (gen_image_np.max() - gen_image_np.min()) # Normalize to [0, 1]
gen_image_np = (gen_image_np * 255).astype(np.uint8)
return gen_image_np
def encode_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
encoded_text = text_encoder(**inputs)
return encoded_text
def on_generate_button_click(text_input):
if text_input:
encoded_text = encode_text(text_input)
generated_image = generate_image(encoded_text)
return generated_image
return None
# Create the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("## Flower Image Generator")
text_input = gr.Textbox(label="Enter a flower-related description", value="A beautiful red rose")
generate_button = gr.Button("Generate Image")
output_image = gr.Image(type="numpy") # Ensure output type is correct
generate_button.click(on_generate_button_click, inputs=text_input, outputs=output_image)
# Launch the Gradio app
demo.launch()
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