trained model import using gradio

app.py

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+import gradio as gr
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
+import torch
+from PIL import Image
+
+def text_to_image(prompt, negative_prompt, guidance_scale, num_inference_steps):
+    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
+    image = pipe(prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
+    return image
+
+def image_to_image(prompt, negative_prompt, init_image, strength, guidance_scale, num_inference_steps):
+    pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
+    init_image = init_image.convert("RGB").resize((512, 512))
+    image = pipe(prompt, negative_prompt=negative_prompt, init_image=init_image, strength=strength, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
+    return image
+
+with gr.Blocks(theme='Respair/Shiki@1.2.2') as demo:
+    gr.Markdown("# Stable Diffusion: Text-to-Image and Image-to-Image")
+
+    with gr.Tab("Text-to-Image"):
+        with gr.Row():
+            text_prompt = gr.Textbox(label="Prompt", placeholder="Enter your text here...")
+            text_negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter what to avoid...")
+        with gr.Row():
+            guidance_scale = gr.Slider(1, 20, value=7.5, step=0.1, label="Guidance Scale")
+            num_inference_steps = gr.Slider(10, 100, value=50, step=1, label="Inference Steps")
+        with gr.Row():
+            generate_btn = gr.Button("Generate", elem_classes=["primary-button"])
+        with gr.Row():
+            text_output = gr.Image(label="Generated Image")
+        
+        generate_btn.click(text_to_image, inputs=[text_prompt, text_negative_prompt, guidance_scale, num_inference_steps], outputs=text_output)
+
+    with gr.Tab("Image-to-Image"):
+        with gr.Row():
+            init_image = gr.Image(source="upload", tool="editor", type="pil", label="Initial Image")
+        with gr.Row():
+            img_prompt = gr.Textbox(label="Prompt", placeholder="Describe modifications...")
+            img_negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter what to avoid...")
+        with gr.Row():
+            strength = gr.Slider(0.1, 1.0, value=0.75, step=0.05, label="Strength")
+            img_guidance_scale = gr.Slider(1, 20, value=7.5, step=0.1, label="Guidance Scale")
+            img_num_inference_steps = gr.Slider(10, 100, value=50, step=1, label="Inference Steps")
+        with gr.Row():
+            img_generate_btn = gr.Button("Generate", elem_classes=["primary-button"])
+        with gr.Row():
+            img_output = gr.Image(label="Modified Image")
+
+        img_generate_btn.click(image_to_image, inputs=[img_prompt, img_negative_prompt, init_image, strength, img_guidance_scale, img_num_inference_steps], outputs=img_output)
+
+demo.launch(share=True)

requirements.txt

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+torch==2.0.1
+diffusers==0.18.2
+gradio==3.35.2
+Pillow==9.5.0