Update app.py

app.py

@@ -206,7 +206,7 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
         seed = random.randint(0, MAX_SEED)
     return seed
 
-def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
+def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp,denoise):
     filename= f'tst_A_{timestamp}.txt'
     with open(filename, "w") as f:
         f.write(f"Realvis 5.0 (Tester B) \n")
@@ -214,6 +214,7 @@ def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
         f.write(f"Prompt: {prompt} \n")
         f.write(f"Steps: {num_inference_steps} \n")
         f.write(f"Guidance Scale: {guidance_scale} \n")
+        f.write(f"Denoise Strength: {denoise} \n")
         f.write(f"SPACE SETUP: \n")
         f.write(f"Use Model Dtype: no \n")
         f.write(f"Model Scheduler: Euler_a all_custom before cuda \n")
@@ -240,6 +241,7 @@ def generate_30(
     use_resolution_binning: bool = True, 
     num_images: int = 1,  
     juggernaut: bool = True,
+    denoise: float = 0.3,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
     torch.backends.cudnn.benchmark = False
@@ -256,6 +258,7 @@ def generate_30(
         "prompt": [prompt] * num_images,
         "negative_prompt": [negative_prompt],
         "negative_prompt_2": [neg_prompt_2],
+        "strength": denoise,
         "width": width,
         "height": height,
         "guidance_scale": guidance_scale,
@@ -269,7 +272,7 @@ def generate_30(
     images = []
     pipe.scheduler.set_timesteps(num_inference_steps,device)
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
+    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp,denoise)
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
         batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
@@ -300,6 +303,7 @@ def generate_60(
     use_resolution_binning: bool = True, 
     num_images: int = 1,  
     juggernaut: bool = True,
+    denoise: float = 0.3,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
     torch.backends.cudnn.benchmark = True
@@ -316,6 +320,7 @@ def generate_60(
         "prompt": [prompt] * num_images,
         "negative_prompt": [negative_prompt],
         "negative_prompt_2": [neg_prompt_2],
+        "strength": denoise,
         "width": width,
         "height": height,
         "guidance_scale": guidance_scale,
@@ -329,7 +334,7 @@ def generate_60(
     images = []
     pipe.scheduler.set_timesteps(num_inference_steps,device)
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
+    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp,denoise)
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
         batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
@@ -360,6 +365,7 @@ def generate_90(
     use_resolution_binning: bool = True, 
     num_images: int = 1,  
     juggernaut: bool = True,
+    denoise: float = 0.3,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
     torch.backends.cudnn.benchmark = True
@@ -376,6 +382,7 @@ def generate_90(
         "prompt": [prompt] * num_images,
         "negative_prompt": [negative_prompt],
         "negative_prompt_2": [neg_prompt_2],
+        "strength": denoise,
         "width": width,
         "height": height,
         "guidance_scale": guidance_scale,
@@ -389,7 +396,7 @@ def generate_90(
     images = []
     pipe.scheduler.set_timesteps(num_inference_steps,device)
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
+    uploadNote(prompt,num_inference_steps,guidance_scale,timestamp,denoise)
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
         batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
@@ -487,6 +494,13 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             step=1,
             value=0,
         )
+        denoise = gr.Slider(
+            label="Denoising Strength",
+            minimum=0.0,
+            maximum=1.0,
+            step=0.01,
+            value=0.3,
+        )
         randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
         juggernaut = gr.Checkbox(label="Use Juggernaut VAE", value=True)
         with gr.Row():
@@ -553,6 +567,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             randomize_seed, 
             num_images,
             juggernaut,
+            denoise
         ],
         outputs=[result, seed],
     )
@@ -577,6 +592,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             randomize_seed, 
             num_images,
             juggernaut,
+            denoise
         ],
         outputs=[result, seed],
     )
@@ -601,6 +617,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             randomize_seed, 
             num_images,
             juggernaut,
+            denoise
         ],
         outputs=[result, seed],
     )