Update app.py

app.py

@@ -1,5 +1,6 @@
 from diffusers import StableDiffusionXLPipeline, AutoencoderKL
 import torch
+import random
 #from controlnet_aux import OpenposeDetector
 #from diffusers.utils import load_image
 import gradio as gr
@@ -38,7 +39,9 @@ css = """
 }
 """
 
-def generate(prompt, neg_prompt, samp_steps, guide_scale, lora_scale, progress=gr.Progress(track_tqdm=True)):
+def generate(prompt, neg_prompt, samp_steps, guide_scale, batch_size, seed, progress=gr.Progress(track_tqdm=True)):
+    if seed < 0:
+        seed = random.randint(1,999999)
     images = pipe(
         prompt,
         negative_prompt=neg_prompt,
@@ -47,7 +50,7 @@ def generate(prompt, neg_prompt, samp_steps, guide_scale, lora_scale, progress=g
         #cross_attention_kwargs={"scale": lora_scale},
         num_images_per_prompt=lora_scale,
         width=600,
-        #generator=torch.manual_seed(97),
+        generator=torch.manual_seed(seed),
     ).images
     return [(img, f"Image {i+1}") for i, img in enumerate(images)]
         
@@ -57,13 +60,14 @@ with gr.Blocks(css=css) as demo:
         prompt = gr.Textbox(label="Prompt")
         negative_prompt = gr.Textbox(label="Negative Prompt")
         submit_btn = gr.Button("Generate", elem_classes="btn-green")
-        gallery = gr.Gallery(label="Generated images", height=800)
         with gr.Row():
             samp_steps = gr.Slider(1, 50, value=20, step=1, label="Sampling steps")
             guide_scale = gr.Slider(1, 6, value=3, step=0.5, label="Guidance scale")
-            lora_scale = gr.Slider(1, 6, value=1, step=1, label="LoRA power")
+            batch_size = gr.Slider(1, 6, value=1, step=1, label="Batch size")
+        seed = gr.Number(label="seed", value="-1", precision=0)
+        gallery = gr.Gallery(label="Generated images", height=800)
 
-    submit_btn.click(generate, [prompt, negative_prompt, samp_steps, guide_scale, lora_scale], [gallery], queue=True)
+    submit_btn.click(generate, [prompt, negative_prompt, samp_steps, guide_scale, batch_size, seed], [gallery], queue=True)
 
 demo.queue(1)
 demo.launch(debug=True)