Update space with app.py

app.py

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+# import os
+# import sys
+
+# import torch
+# import torchvision.utils as vutils
+
+# from models.clip_encoder import CLIPModel
+# from models.denoising_network import DenoisingNetwork
+# from utils.sde_functions import SDE, add_noise  # Import SDE class
+
+# sys.path.append(os.path.abspath(os.path.join(os.path.dirname(_file_), '..')))
+
+
+# def sample(text, checkpoint_path='checkpoints/model_epoch_50.pth', output_path='generated_images/sample.png', num_steps=1000, guidance_scale=3.5):
+#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
+#     model = DenoisingNetwork().to(device)
+#     model.load_state_dict(torch.load(checkpoint_path))
+#     model.eval()
+
+#     clip_model = CLIPModel()
+#     text_embedding = clip_model.encode_text(text).to(device)
+
+#     # Initialize SDE instance
+#     sde = SDE(noise_type="additive", noise_scale=0.1, guidance_scale=guidance_scale)
+
+#     with torch.no_grad():
+#         # Start with random noise
+#         x = torch.randn(1, 3, 32, 32).to(device)
+#         for t in reversed(range(1, num_steps + 1)):
+#             time_factor = t / num_steps
+#             x = add_noise(x, time_factor)  # Adding noise following the new schedule
+#             x = sde.reverse_sde(x, time_factor, model)  # Call reverse_sde as a method of the SDE instance
+
+#     vutils.save_image(x, output_path, normalize=True)
+#     print(f"Generated image saved at {output_path}")
+
+# if _name_ == "_main_":
+#     sample("A cat riding a skateboard")
+# scripts/sample.py
+import torch
+from diffusers import StableDiffusionPipeline
+
+
+def generate_images(text_prompts, output_dir='generated_images/'):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model_id = "CompVis/stable-diffusion-v1-4"
+    pipe = StableDiffusionPipeline.from_pretrained(model_id).to(device)
+
+    for i, prompt in enumerate(text_prompts):
+        with torch.no_grad():
+            image = pipe(prompt).images[0]
+            image.save(f"{output_dir}/sample_{i}.png")
+            print(f"Generated image {i+1} saved at {output_dir}/sample_{i}.png")
+
+if __name__ == "__main__":
+    prompts = [
+        "A cat riding a skateboard",
+        "A futuristic city skyline at sunset",
+        "A serene mountain landscape in spring"
+    ]
+    generate_images(prompts)