Update app.py

app.py

@@ -27,7 +27,7 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024 # @spaces.GPU #[uncomment to use ZeroGPU]
 
 def infer( 
-    prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, scheduler_name="PNDM", progress=gr.Progress(track_tqdm=True),): 
+    prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, scheduler_name="PNDM", save_format="png", progress=gr.Progress(track_tqdm=True),): 
     if randomize_seed: 
         seed = random.randint(0, MAX_SEED) 
     generator = torch.Generator().manual_seed(seed) 
@@ -39,18 +39,21 @@ def infer(
     image = pipe( 
         prompt=prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, width=width, height=height, generator=generator, 
     ).images[0] 
+    # Save in selected format for download consistency
+    output_path = f"generated_image.{save_format}"
+    image.save(output_path, format=save_format.upper())
     return image, seed 
 
 examples = [ 
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", 
-    "An astronaut riding a green horse", 
-    "A delicious ceviche cheesecake slice",] 
+    "photorealistic portrait of a young woman, cinematic rim lighting, soft golden hour backlight, detailed skin pores, realistic eyelashes, 85mm lens, shallow depth of field, ultra-detailed, high dynamic range, film grain, detailed, 8k", 
+    "full body portrait of a futuristic armored soldier, worn brushed metal armor with neon blue accents, realistic cloth under-armor, weathering and scratches, volumetric rim light, cinematic pose, high detail, photoreal", 
+    "neon cyberpunk street at night, wet pavement reflecting lights, pedestrians with umbrellas, dense signage in the distance, cinematic composition, realistic depth, crisp details, atmospheric fog, long lens compression",] 
 
 css = """#col-container { margin: 0 auto; max-width: 640px;}""" 
 
 with gr.Blocks(css=css) as demo: 
     with gr.Column(elem_id="col-container"): 
-        gr.Markdown(" # Text-to-Image Gradio Template") 
+        gr.Markdown(" # DPS-Quagmaform AI txt2img") 
         with gr.Row(): 
             prompt = gr.Text( 
                 label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, 
@@ -59,7 +62,7 @@ with gr.Blocks(css=css) as demo:
         result = gr.Image(label="Result", show_label=False) 
         with gr.Accordion("Advanced Settings", open=False): 
             negative_prompt = gr.Text( 
-                label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", visible=False, 
+                label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", visible=True, 
             ) 
             seed = gr.Slider( 
                 label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, 
@@ -67,30 +70,33 @@ with gr.Blocks(css=css) as demo:
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True) 
             with gr.Row(): 
                 width = gr.Slider( 
-                    label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512, # Replace with defaults that work for your model 
+                    label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=768, # Replace with defaults that work for your model 
                 ) 
                 height = gr.Slider( 
-                    label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512, # Replace with defaults that work for your model 
+                    label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, # Replace with defaults that work for your model 
                 ) 
             with gr.Row(): 
                 guidance_scale = gr.Slider( 
-                    label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7.5, # Replace with defaults that work for your model 
+                    label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=5, # Replace with defaults that work for your model 
                 ) 
                 num_inference_steps = gr.Slider( 
-                    label="Number of inference steps", minimum=1, maximum=50, step=1, value=50, # Replace with defaults that work for your model 
-                ) 
-                scheduler = gr.Dropdown( 
-                    label="Sampler/Scheduler", 
-                    choices=list(SCHEDULERS.keys()), 
-                    value="PNDM", 
-                    info="Try Euler or DPM++ 2M Karras for better quality" 
+                    label="Number of inference steps", minimum=1, maximum=50, step=1, value=22, # Replace with defaults that work for your model 
                 ) 
+            scheduler = gr.Dropdown( 
+                label="Sampler/Scheduler", 
+                choices=list(SCHEDULERS.keys()), 
+                value="PNDM", 
+                info="Change this setting for better quality in some situations" 
+            ) 
+            save_format = gr.Dropdown( 
+                choices=["png", "jpg"], value="png", label="Select Output Format" 
+            ) 
         gr.Examples(examples=examples, inputs=[prompt]) 
         gr.on( 
             triggers=[run_button.click, prompt.submit], 
             fn=infer, 
             inputs=[ 
-                prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, scheduler, 
+                prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, scheduler, save_format, 
             ], 
             outputs=[result, seed], 
         )