Create app.py

app.py

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+import torch
+import gradio as gr
+from PIL import Image
+from transformers import pipeline
+from diffusers import StableDiffusion3Pipeline
+
+model_path = "../Models/models--Salesforce--blip-image-captioning-base/snapshots/82a37760796d32b1411fe092ab5d4e227313294b"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+caption_image = pipeline("image-to-text", model=model_path, device=device)
+# caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base", device=device)
+
+def image_generation(prompt):
+    # is_cuda = False
+    pipeline = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers",
+                                                        torch_dtype=torch.float32,
+                                                        text_encoder_3=None,
+                                                        tokenizer_3=None)
+    # pipeline.enable_model_cpu_offload()
+    pipeline.to('cpu')
+
+    image = pipeline(
+        prompt=prompt,
+        negative_prompt="blurred, ugly, watermark, low resolution, blurry",
+        num_inference_steps=15,
+        height=192,
+        width=192,
+        guidance_scale=7.0
+    ).images[0]
+
+    return image
+
+def caption_my_image(pil_image):
+    semantics = caption_image(images=pil_image)[0]['generated_text']
+    image = image_generation(semantics)
+    return image
+
+
+gr.close_all()
+
+demo = gr.Interface(fn=caption_my_image,
+                    inputs=[gr.Image(label="Select Image",type="pil")],
+                    outputs=[gr.Image(label="New Generated Image using SD3", type="pil")],
+                    title="@GenAILearniverse Project 10: Generate Similar image",
+                    description="THIS APPLICATION WILL BE USED TO GENERATE SIMILAR IMAGE BASED ON IMAGE UPLOADED.")
+
+demo.launch()