Generation Details take 2

app.py

@@ -1,5 +1,4 @@
 import os
-import spaces
 import torch
 from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 import gradio as gr
@@ -26,7 +25,7 @@ def generate_image(prompt, negative_prompt, use_defaults, resolution, guidance_s
         seed = random.randint(0, 99999999)
     if use_defaults:
         prompt = f"{prompt}, masterpiece, best quality"
-        negative_prompt = f"lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, {negative_prompt}"
+        negative_prompt = f"nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, {negative_prompt}"
     generator = torch.manual_seed(seed)
     
     def callback(step, timestep, latents):
@@ -48,15 +47,39 @@ def generate_image(prompt, negative_prompt, use_defaults, resolution, guidance_s
 
     torch.cuda.empty_cache()
 
-    return image, seed
+    return image, seed, prompt, negative_prompt, guidance_scale, num_inference_steps, resolution
 
 # Define Gradio interface
 def interface_fn(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
-    image, seed = generate_image(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress)
-    return image, seed, gr.update(value=seed)
+    image, seed, prompt, negative_prompt, guidance_scale, num_inference_steps, resolution = generate_image(
+        prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress
+    )
+    
+    generation_details = (
+        f"{prompt}\n"
+        f"Negative Prompt: {negative_prompt}\n"
+        f"Steps: {num_inference_steps}\n"
+        f"Sampler: Euler a\n"
+        f"CFG scale: {guidance_scale}\n"
+        f"Seed: {seed}\n"
+        f"Size: {resolution}"
+    )
+    
+    return image, seed, gr.update(value=seed), gr.update(visible=True), generation_details
 
 def reset_inputs():
-    return gr.update(value=''), gr.update(value='realistic, 3d,'), gr.update(value=True), gr.update(value='832x1216'), gr.update(value=7), gr.update(value=28), gr.update(value=0), gr.update(value=True)
+    return (
+        gr.update(value=''), 
+        gr.update(value=''), 
+        gr.update(value=True), 
+        gr.update(value='832x1216'), 
+        gr.update(value=7), 
+        gr.update(value=28), 
+        gr.update(value=0), 
+        gr.update(value=True),
+        gr.update(visible=False),
+        ""
+    )
 
 with gr.Blocks(title="UrangDiffusion 1.0 Demo", theme="NoCrypt/miku@1.2.1") as demo:
     gr.HTML(
@@ -66,7 +89,7 @@ with gr.Blocks(title="UrangDiffusion 1.0 Demo", theme="NoCrypt/miku@1.2.1") as d
     with gr.Row():
         with gr.Column():
             prompt_input = gr.Textbox(lines=2, placeholder="Enter prompt here", label="Prompt")
-            negative_prompt_input = gr.Textbox(lines=2, placeholder="Enter negative prompt here", label="Negative Prompt", value="realistic, 3d,")
+            negative_prompt_input = gr.Textbox(lines=2, placeholder="Enter negative prompt here", label="Negative Prompt")
             use_defaults_input = gr.Checkbox(label="Use Default Quality Tags and Negative Prompt", value=True)
             resolution_input = gr.Radio(
                 choices=[
@@ -85,6 +108,7 @@ with gr.Blocks(title="UrangDiffusion 1.0 Demo", theme="NoCrypt/miku@1.2.1") as d
 
         with gr.Column():
             output_image = gr.Image(type="pil", label="Generated Image")
+            generation_details_output = gr.Markdown("", visible=False)
             gr.Markdown(
                 """
                 ### Recommended prompt formatting:
@@ -103,15 +127,15 @@ with gr.Blocks(title="UrangDiffusion 1.0 Demo", theme="NoCrypt/miku@1.2.1") as d
         inputs=[
             prompt_input, negative_prompt_input, use_defaults_input, resolution_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input
         ],
-        outputs=[output_image, seed_input]
+        outputs=[output_image, seed_input, gr.update(value=seed), generation_details_output]
     )
     
     reset_button.click(
         reset_inputs,
         inputs=[],
         outputs=[
-            prompt_input, negative_prompt_input, use_defaults_input, resolution_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input
+            prompt_input, negative_prompt_input, use_defaults_input, resolution_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input, generation_details_output, gr.update(value="")
         ]
     )
 
-demo.queue(max_size=20).launch(share=False)
+demo.queue(max_size=20).launch(share=False)