added more args and optim

check_app.py

@@ -116,7 +116,7 @@ MODEL_CONFIGS = {
     }
 }
 
-def generate_image_with_progress(model_name,pipe, prompt, num_steps, guidance_scale=None, seed=None, progress=gr.Progress(track_tqdm=True)):
+def generate_image_with_progress(model_name,pipe, prompt, num_steps, guidance_scale=None, seed=None,negative_prompt,  randomize_seed, width, height, guidance_scale, num_inference_steps,  progress=gr.Progress(track_tqdm=True)):
     generator = None
     if seed is not None:
         generator = torch.Generator("cuda").manual_seed(seed)
@@ -136,42 +136,29 @@ def generate_image_with_progress(model_name,pipe, prompt, num_steps, guidance_sc
     has_guidance_scale = "guidance_scale" in pipe_signature.parameters
     has_callback_on_step_end = "callback_on_step_end" in pipe_signature.parameters
     
-    if has_guidance_scale and has_callback_on_step_end:
-        print("has callback_on_step_end and has guidance_scale")
-        image = pipe(
-            prompt,
-            num_inference_steps=num_steps,
-            generator=generator,
-            guidance_scale=guidance_scale,
-            callback_on_step_end=callback,
-        ).images[0]
-    elif not has_callback_on_step_end and has_guidance_scale:
-        print("NO callback_on_step_end and has guidance_scale")
-        image = pipe(
-            prompt,
-            num_inference_steps=num_steps,
-            guidance_scale=guidance_scale,
-            generator=generator,
-            callback=callback,
-            callback_steps=1,
-        ).images[0]
-    elif has_callback_on_step_end and not has_guidance_scale:
-        print("has callback_on_step_end and NO guidance_scale")
-        image = pipe(
-            prompt,
-            num_inference_steps=num_steps,
-            generator=generator,
-            callback_on_step_end=callback,
-        ).images[0]
-    elif not has_callback_on_step_end and not has_guidance_scale:
-        print("NO callback_on_step_end and NO guidance_scale")
-        image = pipe(
-            prompt,
-            num_inference_steps=num_steps,
-            generator=generator,
-            callback=callback,
-            callback_steps=1,
-        ).images[0]
+    # Define common arguments
+    common_args = {
+        "prompt": prompt,
+        "num_inference_steps": num_steps,
+        "negative_prompt": negative_prompt,
+        "width": width,
+        "height": height,
+        "generator": generator,
+    }
+    
+    if has_guidance_scale:
+        common_args["guidance_scale"] = guidance_scale
+    
+    if has_callback_on_step_end:
+        print("has callback_on_step_end and", "has guidance_scale" if has_guidance_scale else "NO guidance_scale")
+        common_args["callback_on_step_end"] = callback
+    else:
+        print("NO callback_on_step_end and", "has guidance_scale" if has_guidance_scale else "NO guidance_scale")
+        common_args["callback"] = callback
+        common_args["callback_steps"] = 1
+    
+    # Generate image
+    image = pipe(**common_args).images[0]
 
     return image
 
@@ -179,9 +166,6 @@ def generate_image_with_progress(model_name,pipe, prompt, num_steps, guidance_sc
 def create_pipeline_logic(prompt_text, model_name, negative_prompt="",  seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=4.5, num_inference_steps=40,):
     print(f"starting {model_name}")
     progress = gr.Progress(track_tqdm=True)
-    num_steps = 30
-    guidance_scale = 7.5  # Example guidance scale, can be adjusted per model
-    seed = 42
     config = MODEL_CONFIGS[model_name]
     pipe_class = config["pipeline_class"]
     pipe = None
@@ -201,7 +185,7 @@ def create_pipeline_logic(prompt_text, model_name, negative_prompt="",  seed=42,
         pipe = b_pipe
         
     image = generate_image_with_progress(
-        model_name,pipe, prompt_text, num_steps=num_steps, guidance_scale=guidance_scale, seed=seed, progress=progress
+        model_name,pipe, prompt_text, num_steps=num_inference_steps, guidance_scale=guidance_scale, seed=seed,negative_prompt,  randomize_seed, width, height, guidance_scale, num_inference_steps, progress=progress
     )
     return f"Seed: {seed}", image