add app.py

app.py

@@ -0,0 +1,38 @@
+from diffusers import StableDiffusionPipeline
+import torch
+import requests
+from PIL import Image
+from io import BytesIO
+from diffusers import StableDiffusionImg2ImgPipeline
+
+device = "cpu"
+#model_path = "weights"
+#model_id_or_path = "runwayml/stable-diffusion-v1-5"
+
+model_id = "rikdas/weights"
+pipe = StableDiffusionPipeline.from_pretrained(model_id).to(device)
+
+pipe2 = StableDiffusionImg2ImgPipeline.from_pretrained(model_id).to(device)
+
+import gradio as gr
+
+def generate_txt2img(prompt):
+    return pipe(prompt, num_inference_steps=25, guidance_scale=7.5).images[0]
+def generate_img2img(img, prompt):
+    image = Image.fromarray(img)
+    return pipe2(prompt=prompt, image=image, strength=0.75, guidance_scale=7.5).images[0]
+
+with gr.Blocks() as demo:
+    with gr.Tab("Text2Image"):
+        inp_txt = gr.Text(showlabel=False, placeholder="Enter your prompt here...")
+        btn = gr.Button("Generate")
+        out_img = gr.Image()
+        btn.click(fn=generate_txt2img, inputs=[inp_txt], outputs=[out_img])
+    with gr.Tab("Image2Image"):
+        inp_img = gr.Image()
+        inp_txt2 = gr.Text(showlabel=False,placeholder="Enter your prompt here...")
+        btn2 = gr.Button("Generate")
+        out_img2 = gr.Image()
+        btn2.click(fn=generate_img2img, inputs=[inp_img, inp_txt2], outputs=[out_img2])
+        
+demo.launch(debug=True)

requirements.txt

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+torch
+accelerate
+diffusers[torch]
+transformers