Create app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+import requests
+import io
+from PIL import Image
+import json
+import os
+import shutil
+import logging
+import math 
+from tqdm import tqdm
+import time
+from diffusers import DiffusionPipeline
+
+def run_lora(base_model, lora, prompt, neg_prompt, progress=gr.Progress(track_tqdm=True)):
+    print(f"Inside run_lora, base_model: {base_model}, lora: {lora.name}, prompt: {prompt}, neg_prompt: {neg_prompt}")
+
+    base_repo = None
+    if  base_model == "v1-5":
+        base_repo = "runwayml/stable-diffusion-v1-5"
+    elif base_model == "v2-1":
+        base_repo = "stabilityai/stable-diffusion-2-1"
+    elif base_model == "v2":
+        base_repo = "stabilityai/stable-diffusion-2"
+
+    print(f"base_repo: {base_repo}")
+
+    pipeline = DiffusionPipeline.from_pretrained(base_repo)
+    pipeline.load_lora_weights(lora.name)
+    print(pipeline) 
+
+    image = pipeline(prompt, negative_prompt = neg_prompt).images[0]
+    return image
+            
+demo = gr.Interface(
+    run_lora,
+    [
+        gr.Dropdown(
+            ["v1-5", "v2", "v2-1"], label="Base Model", info="Stable Diffusion Base Model."
+        ),
+        gr.File(file_count="single", file_types=[".safetensors"]),
+        gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="Type a prompt after selecting a LoRA"),
+        gr.Textbox(label="Negative Prompt", show_label=False, lines=1, max_lines=1, placeholder="Type negative prompt here.")
+    ],
+    "image",
+)
+
+demo.launch()