Initial commit

app.py

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+import gradio as gr 
+from pathlib import Path
+from huggingface_hub import notebook_login
+from diffusers import StableDiffusionPipeline
+from PIL import Image
+
+
+import torch, logging
+logging.disable(logging.WARNING)
+torch.cuda.empty_cache()
+torch.manual_seed(3407)
+from torch import autocast
+from contextlib import nullcontext
+from diffusers import StableDiffusionPipeline
+
+
+
+model_id = "CompVis/stable-diffusion-v1-4"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+context = autocast if device == "cuda" else nullcontext
+
+pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16).to(device)
+
+
+def infer(prompt,samples):
+    with context(device):
+        images = pipe(samples*[prompt], guidance_scale=7.5).images
+    return images
+
+
+
+demo = gr.Blocks()
+
+with demo:
+    text = gr.Textbox(lines=7,placeholder="Enter your prompt to generate a background image... something like - Photorealistic scenery of bookshelf in a room")
+    samples = gr.Slider(label="Number of Images", minimum=1, maximum=5, value=2, step=1)
+    btn = gr.Button("Generate images",variant="primary").style(
+                                margin=False,
+                                rounded=(False, True, True, False),
+                            )
+    gallery = gr.Gallery(label="Generated images", show_label=True).style(grid=(1, 3), height="auto")
+    
+    text.submit(infer, inputs=[text, samples], outputs=gallery)
+    btn.click(infer, inputs=[text, samples], outputs=gallery, show_progress=True, status_tracker=None)
+
+demo.launch() 
+
+
+
+
+
+
+