Update app.py

app.py

@@ -274,12 +274,9 @@ def generate_30(
     num_inference_steps: int = 125,
     randomize_seed: bool = False,
     use_resolution_binning: bool = True, 
-    num_images: int = 1, 
     denoise: float = 0.3,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
-    
 ):
-    print(f'debug: num_images: {num_images} denoise: {denoise}')
     torch.backends.cudnn.benchmark = False
     torch.cuda.empty_cache()
     gc.collect()
@@ -289,7 +286,7 @@ def generate_30(
     generator = torch.Generator(device='cuda').manual_seed(seed)
     #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
     options = {
-        "prompt": [prompt] * num_images,
+        "prompt": [prompt],
         "negative_prompt": [negative_prompt],
         "negative_prompt_2": [neg_prompt_2],
         "strength": denoise,
@@ -307,25 +304,21 @@ def generate_30(
     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,denoise)
-    for i in range(0, num_images, BATCH_SIZE):
-        batch_options = options.copy()
-        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
-        if "negative_prompt" in batch_options:
-            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
-        images.extend(pipe(**batch_options).images)
-    sd_image_path = f"rv50_A_{timestamp}.png"
-    images[0].save(sd_image_path,optimize=False,compress_level=0)
+    batch_options = options.copy()
+    rv_image = images.extend(pipe(**batch_options).images[0])
+    sd_image_path = f"rv50_A_{seed}.png"
+    rv_image.save(sd_image_path,optimize=False,compress_level=0)
     upload_to_ftp(sd_image_path)
-    image_paths = [save_image(img) for img in images]
+    image_paths = save_image(rv_image)
     torch.cuda.empty_cache()
     gc.collect()
     torch.set_float32_matmul_precision("medium")
     with torch.no_grad():
-        upscale = upscaler(images[0], tiling=True, tile_width=256, tile_height=256)
+        upscale = upscaler(rv_image, tiling=True, tile_width=256, tile_height=256)
     downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
     downscale_path = f"rv50_upscale_{timestamp}.png"
     downscale1.save(downscale_path,optimize=False,compress_level=0)
-    upload_to_ftp(downscale_path)  
+    upload_to_ftp(downscale_path) 
     image_paths = [save_image(downscale1)]
     return image_paths, seed
 
@@ -343,7 +336,6 @@ def generate_60(
     num_inference_steps: int = 250,
     randomize_seed: bool = False,
     use_resolution_binning: bool = True, 
-    num_images: int = 1, 
     denoise: float = 0.3,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
@@ -356,7 +348,7 @@ def generate_60(
     generator = torch.Generator(device='cuda').manual_seed(seed)
     #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
     options = {
-        "prompt": [prompt] * num_images,
+        "prompt": [prompt],
         "negative_prompt": [negative_prompt],
         "negative_prompt_2": [neg_prompt_2],
         "strength": denoise,
@@ -374,25 +366,21 @@ def generate_60(
     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,denoise)
-    for i in range(0, num_images, BATCH_SIZE):
-        batch_options = options.copy()
-        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
-        if "negative_prompt" in batch_options:
-            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
-        images.extend(pipe(**batch_options).images)
-    sd_image_path = f"rv50_A_{timestamp}.png"
-    images[0].save(sd_image_path,optimize=False,compress_level=0)
+    batch_options = options.copy()
+    rv_image = images.extend(pipe(**batch_options).images[0])
+    sd_image_path = f"rv50_A_{seed}.png"
+    rv_image.save(sd_image_path,optimize=False,compress_level=0)
     upload_to_ftp(sd_image_path)
-    image_paths = [save_image(img) for img in images]
+    image_paths = save_image(rv_image)
     torch.cuda.empty_cache()
     gc.collect()
     torch.set_float32_matmul_precision("medium")
     with torch.no_grad():
-        upscale = upscaler(images[0], tiling=True, tile_width=256, tile_height=256)
+        upscale = upscaler(rv_image, tiling=True, tile_width=256, tile_height=256)
     downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
     downscale_path = f"rv50_upscale_{timestamp}.png"
     downscale1.save(downscale_path,optimize=False,compress_level=0)
-    upload_to_ftp(downscale_path)   
+    upload_to_ftp(downscale_path) 
     image_paths = [save_image(downscale1)]
     return image_paths, seed
 
@@ -410,7 +398,6 @@ def generate_90(
     num_inference_steps: int = 250,
     randomize_seed: bool = False,
     use_resolution_binning: bool = True, 
-    num_images: int = 1, 
     denoise: float = 0.3,
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
@@ -423,7 +410,7 @@ def generate_90(
     generator = torch.Generator(device='cuda').manual_seed(seed)
     #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
     options = {
-        "prompt": [prompt] * num_images,
+        "prompt": [prompt],
         "negative_prompt": [negative_prompt],
         "negative_prompt_2": [neg_prompt_2],
         "strength": denoise,
@@ -441,21 +428,17 @@ def generate_90(
     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,denoise)
-    for i in range(0, num_images, BATCH_SIZE):
-        batch_options = options.copy()
-        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
-        if "negative_prompt" in batch_options:
-            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
-        images.extend(pipe(**batch_options).images)
+    batch_options = options.copy()
+    rv_image = images.extend(pipe(**batch_options).images[0])
     sd_image_path = f"rv50_A_{seed}.png"
-    images[0].save(sd_image_path,optimize=False,compress_level=0)
+    rv_image.save(sd_image_path,optimize=False,compress_level=0)
     upload_to_ftp(sd_image_path)
-    image_paths = [save_image(img) for img in images]
+    image_paths = save_image(rv_image)
     torch.cuda.empty_cache()
     gc.collect()
     torch.set_float32_matmul_precision("medium")
     with torch.no_grad():
-        upscale = upscaler(images[0], tiling=True, tile_width=256, tile_height=256)
+        upscale = upscaler(rv_image, tiling=True, tile_width=256, tile_height=256)
     downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
     downscale_path = f"rv50_upscale_{timestamp}.png"
     downscale1.save(downscale_path,optimize=False,compress_level=0)
@@ -521,13 +504,6 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             value=DEFAULT_STYLE_NAME,
             label="Quality Style",
         )
-        num_images = gr.Slider(
-            label="Number of Images",
-            minimum=1,
-            maximum=5,
-            step=1,
-            value=1,
-        )
         with gr.Row():
             with gr.Column(scale=1):
                 use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
@@ -616,7 +592,6 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             guidance_scale,
             num_inference_steps,
             randomize_seed, 
-            num_images,
             denoise
         ],
         outputs=[result, seed],
@@ -640,7 +615,6 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             guidance_scale,
             num_inference_steps,
             randomize_seed, 
-            num_images,
             denoise
         ],
         outputs=[result, seed],
@@ -664,7 +638,6 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             guidance_scale,
             num_inference_steps,
             randomize_seed, 
-            num_images,
             denoise
         ],
         outputs=[result, seed],