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@@ -1,35 +1,35 @@
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README.md

@@ -1,13 +1,13 @@
 ---
-title: Sexy Reality
-emoji: ⚡
-colorFrom: indigo
-colorTo: gray
+title: Compare-6
+emoji: ✨6️⃣✨
+colorFrom: green
+colorTo: blue
 sdk: gradio
-sdk_version: 5.10.0
+sdk_version: 4.42.0
 app_file: app.py
-pinned: false
-short_description: Photorealistic Pron
+pinned: true
+short_description: Compare 6 image gen models at a time, feat. FLUX & SD 3.5
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

all_models.py

@@ -2,8 +2,8 @@ models = [
     "black-forest-labs/FLUX.1-dev",
     "black-forest-labs/FLUX.1-schnell",
     "XLabs-AI/flux-RealismLora",
-    "prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA",
-    "xey/sldr_flux_nsfw_v2-studio",
+    "enhanceaiteam/Flux-uncensored",
+    "enhanceaiteam/Flux-Uncensored-V2",
     "aleksa-codes/flux-ghibsky-illustration",
     "dataautogpt3/FLUX-SyntheticAnime",
     "Shakker-Labs/FLUX.1-dev-LoRA-add-details",
@@ -12,17 +12,10 @@ models = [
     "stabilityai/stable-diffusion-3.5-large",
     "stabilityai/stable-diffusion-3.5-large-turbo",
     "stabilityai/stable-diffusion-xl-base-1.0",
+    "SG161222/RealVisXL_V4.0",
     "Yntec/epiCPhotoGasm",
-    "Jovie/Midjourney",
+    "dataautogpt3/ProteusV0.2",
     "dataautogpt3/OpenDalleV1.1",
-    "falanaja/Amateur-Photography",
-    "lexa862/NSFWmodel",
-    "Keltezaa/ShowerGirls",
-    "pimpilikipilapi1/fltax",
-    "Jonny001/Alita-v1",
-    "Jonny001/EXD-v1",
-    "Jonny001/NSFW_master",
-    "aifeifei798/sldr_flux_nsfw_v2-studio",
-    "getad72493/innipssy",
-    "strangerzonehf/Flux-Super-Realism-LoRA",
-    ]
+    "prompthero/openjourney",
+    "nerijs/pixel-art-xl",
+]

app.py

@@ -1,204 +1,172 @@
 import gradio as gr
+from random import randint
 from all_models import models
-from _prompt import thePrompt, howManyModelsToUse
-from externalmod import gr_Interface_load, save_image, randomize_seed
+
+from externalmod import gr_Interface_load, randomize_seed
+
 import asyncio
 import os
 from threading import RLock
-from datetime import datetime
-
-preSetPrompt = thePrompt
-negPreSetPrompt = "[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry, text, fuzziness, asian, african, collage, composite, combined image"
 
+# Create a lock to ensure thread safety when accessing shared resources
 lock = RLock()
-
+# Load Hugging Face token from environment variable, if available
 HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
 
-def get_current_time():
-    now = datetime.now()
-    current_time = now.strftime("%y-%m-%d %H:%M:%S")
-    return current_time
-
+# Function to load all models specified in the 'models' list
 def load_fn(models):
     global models_load
     models_load = {}
+    
+    # Iterate through all models to load them
     for model in models:
         if model not in models_load.keys():
             try:
+                # Log model loading attempt
+                print(f"Attempting to load model: {model}")
+                # Load model interface using externalmod function
                 m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
+                print(f"Successfully loaded model: {model}")
             except Exception as error:
-                print(error)
+                # In case of an error, print it and create a placeholder interface
+                print(f"Error loading model {model}: {error}")
                 m = gr.Interface(lambda: None, ['text'], ['image'])
+            # Update the models_load dictionary with the loaded model
             models_load.update({model: m})
 
-
+# Load all models defined in the 'models' list
+print("Loading models...")
 load_fn(models)
+print("Models loaded successfully.")
 
-num_models = howManyModelsToUse
-max_images = howManyModelsToUse
-inference_timeout = 400
-default_models = models[:num_models]
-MAX_SEED = 2**32-1
+num_models = 6
 
+# Set the default models to use for inference
+default_models = models[:num_models]
+inference_timeout = 600
+MAX_SEED = 3999999999
+# Generate a starting seed randomly between 1941 and 2024
+starting_seed = randint(1941, 2024)
+print(f"Starting seed: {starting_seed}")
 
+# Extend the choices list to ensure it contains 'num_models' elements
 def extend_choices(choices):
-    return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
-
+    print(f"Extending choices: {choices}")
+    extended = choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
+    print(f"Extended choices: {extended}")
+    return extended
 
+# Update the image boxes based on selected models
 def update_imgbox(choices):
+    print(f"Updating image boxes with choices: {choices}")
     choices_plus = extend_choices(choices[:num_models])
-    return [gr.Image(None, label=m, visible=(m!='NA')) for m in choices_plus]
-
+    imgboxes = [gr.Image(None, label=m, visible=(m != 'NA')) for m in choices_plus]
+    print(f"Updated image boxes: {imgboxes}")
+    return imgboxes
 
-def random_choices():
-    import random
-    random.seed()
-    return random.choices(models, k=num_models)
-
-
-async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout):
+# Asynchronous function to perform inference on a given model
+async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
+    from pathlib import Path
     kwargs = {}
-    if height > 0: kwargs["height"] = height
-    if width > 0: kwargs["width"] = width
-    if steps > 0: kwargs["num_inference_steps"] = steps
-    if cfg > 0: cfg = kwargs["guidance_scale"] = cfg
-
-    if seed == -1:
-        theSeed = randomize_seed()
-    else: 
-        theSeed = seed
-    kwargs["seed"] = theSeed
-        
-    task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, prompt=prompt, negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
-    await asyncio.sleep(0)
+    noise = ""
+    kwargs["seed"] = seed
+    # Create an asynchronous task to run the model inference
+    print(f"Starting inference for model: {model_str} with prompt: '{prompt}' and seed: {seed}")
+    task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
+                               prompt=f'{prompt} {noise}', **kwargs, token=HF_TOKEN))
+    await asyncio.sleep(0)  # Allow other tasks to run
     try:
+        # Wait for the task to complete within the specified timeout
         result = await asyncio.wait_for(task, timeout=timeout)
-    except asyncio.TimeoutError as e:
-        print(e)
-        print(f"infer: Task timed out: {model_str}")
-        if not task.done(): task.cancel()
+        print(f"Inference completed for model: {model_str}")
+    except (Exception, asyncio.TimeoutError) as e:
+        # Handle any exceptions or timeout errors
+        print(f"Error during inference for model {model_str}: {e}")
+        if not task.done():
+            task.cancel()
+            print(f"Task cancelled for model: {model_str}")
         result = None
-        raise Exception(f"Task timed out: {model_str}") from e
-    except Exception as e:
-        print(e)
-        print(f"infer: exception: {model_str}")
-        if not task.done(): task.cancel()
-        result = None
-        raise Exception() from e
-    if task.done() and result is not None and not isinstance(result, tuple):
+    # If the task completed successfully, save the result as an image
+    if task.done() and result is not None:
         with lock:
-            png_path =  model_str.replace("/", "_") + " - " + get_current_time() + "_" + str(theSeed) + ".png"
-            image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, theSeed)
+            png_path = "image.png"
+            result.save(png_path)
+            image = str(Path(png_path).resolve())
+            print(f"Result saved as image: {image}")
         return image
+    print(f"No result for model: {model_str}")
     return None
 
-def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1):
+# Function to generate an image based on the given model, prompt, and seed
+def gen_fnseed(model_str, prompt, seed=1):
+    if model_str == 'NA':
+        print(f"Model is 'NA', skipping generation.")
+        return None
     try:
+        # Create a new event loop to run the asynchronous inference function
+        print(f"Generating image for model: {model_str} with prompt: '{prompt}' and seed: {seed}")
         loop = asyncio.new_event_loop()
-        result = loop.run_until_complete(infer(model_str, prompt, nprompt,
-                                         height, width, steps, cfg, seed, inference_timeout))
+        result = loop.run_until_complete(infer(model_str, prompt, seed, inference_timeout))
     except (Exception, asyncio.CancelledError) as e:
-        print(e)
-        print(f"gen_fn: Task aborted: {model_str}")
+        # Handle any exceptions or cancelled tasks
+        print(f"Error during generation for model {model_str}: {e}")
         result = None
-        raise gr.Error(f"Task aborted: {model_str}, Error: {e}")
     finally:
+        # Close the event loop
         loop.close()
+        print(f"Event loop closed for model: {model_str}")
     return result
 
-
-def add_gallery(image, model_str, gallery):
-    if gallery is None: gallery = []
-    with lock:
-        if image is not None: gallery.insert(0, (image, model_str))
-    return gallery
-
-with gr.Blocks(fill_width=True) as demo:
-    with gr.Tab(str(num_models) + ' Models'):
-        with gr.Column(scale=2):
-            with gr.Group():
-                txt_input = gr.Textbox(label='Your prompt:', value=preSetPrompt, lines=3, autofocus=1)
-                neg_input = gr.Textbox(label='Negative prompt:', value=negPreSetPrompt, lines=1)
-                with gr.Accordion("Advanced", open=False, visible=True):
-                    with gr.Row():
-                        width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
-                        height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
-                    with gr.Row():
-                        steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
-                        cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
-                        seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
-                        seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
-                        seed_rand.click(randomize_seed, None, [seed], queue=False)
-            with gr.Row():
-                gen_button = gr.Button(f'Generate up to {int(num_models)} images', variant='primary', scale=3, elem_classes=["butt"])
-                random_button = gr.Button(f'Randomize Models', variant='secondary', scale=1)
-
-        with gr.Column(scale=1):
-            with gr.Group():
-                with gr.Row():
-                    output = [gr.Image(label=m, show_download_button=True, elem_classes=["image-monitor"],
-                              interactive=False, width=112, height=112, show_share_button=False, format="png",
-                              visible=True) for m in default_models]
-                    current_models = [gr.Textbox(m, visible=False) for m in default_models]
-
-        with gr.Column(scale=2):
-            gallery = gr.Gallery(label="Output", show_download_button=True,
-                                interactive=False, show_share_button=False, container=True, format="png",
-                                preview=True, object_fit="cover", columns=2, rows=2) 
-
-        for m, o in zip(current_models, output):
-            gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fn,
-                              inputs=[m, txt_input, neg_input, height, width, steps, cfg, seed], outputs=[o],
-                              concurrency_limit=None, queue=False)
-            o.change(add_gallery, [o, m, gallery], [gallery])
-
-        with gr.Column(scale=4):
-            with gr.Accordion('Model selection'):
-                model_choice = gr.CheckboxGroup(models, label = f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
-                model_choice.change(update_imgbox, model_choice, output)
-                model_choice.change(extend_choices, model_choice, current_models)
-                random_button.click(random_choices, None, model_choice)
-
-    with gr.Tab('Single model'):
-        with gr.Column(scale=2):
-            model_choice2 = gr.Dropdown(models, label='Choose model', value=models[0])
-            with gr.Group():
-                txt_input2 = gr.Textbox(label='Your prompt:', value = preSetPrompt, lines=3, autofocus=1)
-                neg_input2 = gr.Textbox(label='Negative prompt:', value=negPreSetPrompt, lines=1)
-                with gr.Accordion("Advanced", open=False, visible=True):
-                    with gr.Row():
-                        width2 = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
-                        height2 = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
-                    with gr.Row():
-                        steps2 = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
-                        cfg2 = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
-                        seed2 = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
-                        seed_rand2 = gr.Button("Randomize Seed", size="sm", variant="secondary")
-                        seed_rand2.click(randomize_seed, None, [seed2], queue=False)
-            num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')
-            with gr.Row():
-                gen_button2 = gr.Button('Let the machine halucinate', variant='primary', scale=2, elem_classes=["butt"])
-
-        with gr.Column(scale=1):
-            with gr.Group():
-                with gr.Row():
-                    output2 = [gr.Image(label='', show_download_button=True,
-                               interactive=False, width=112, height=112, visible=True, format="png",
-                               show_share_button=False, show_label=False) for _ in range(max_images)]
-
-        with gr.Column(scale=2):
-            gallery2 = gr.Gallery(label="Output", show_download_button=True,
-                                interactive=False, show_share_button=True, container=True, format="png",
-                                preview=True, object_fit="cover", columns=2, rows=2) 
-
-        for i, o in enumerate(output2):
-            img_i = gr.Number(i, visible=False)
-            num_images.change(lambda i, n: gr.update(visible = (i < n)), [img_i, num_images], o, queue=False)
-            gen_event2 = gr.on(triggers=[gen_button2.click, txt_input2.submit],
-                               fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5: gen_fn(m, t1, t2, n1, n2, n3, n4, n5) if (i < n) else None,
-                               inputs=[img_i, num_images, model_choice2, txt_input2, neg_input2,
-                                       height2, width2, steps2, cfg2, seed2], outputs=[o],
-                                       concurrency_limit=None, queue=False)
-            o.change(add_gallery, [o, model_choice2, gallery2], [gallery2])
-
+# Create the Gradio Blocks interface with a custom theme
+print("Creating Gradio interface...")
+with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
+    gr.HTML("<center><h1>Compare-6</h1></center>")
+    with gr.Tab('Compare-6'):
+        # Text input for user prompt
+        txt_input = gr.Textbox(label='Your prompt:', lines=4)
+        # Button to generate images
+        gen_button = gr.Button('Generate up to 6 images in up to 3 minutes total')
+        with gr.Row():
+            # Slider to select a seed for reproducibility
+            seed = gr.Slider(label="Use a seed to replicate the same image later (maximum 3999999999)", minimum=0, maximum=MAX_SEED, step=1, value=starting_seed, scale=3)
+            # Button to randomize the seed
+            seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary", scale=1)    
+        # Set up click event to randomize the seed
+        seed_rand.click(randomize_seed, None, [seed], queue=False)
+        print("Seed randomization button set up.")
+        # Button click to start generation
+        gen_button.click(lambda s: gr.update(interactive=True), None)
+        print("Generation button set up.")
+
+        with gr.Row():
+            # Create image output components for each model
+            output = [gr.Image(label=m, min_width=480) for m in default_models]
+            # Create hidden textboxes to store the current models
+            current_models = [gr.Textbox(m, visible=False) for m in default_models]
+            
+            # Set up generation events for each model and output image
+            for m, o in zip(current_models, output):
+                print(f"Setting up generation event for model: {m.value}")
+                gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fnseed,
+                                  inputs=[m, txt_input, seed], outputs=[o], concurrency_limit=None, queue=False)
+                # The commented stop button could be used to cancel the generation event
+                #stop_button.click(lambda s: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
+        # Accordion to allow model selection
+        with gr.Accordion('Model selection'):
+            # Checkbox group to select up to 'num_models' different models
+            model_choice = gr.CheckboxGroup(models, label=f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
+            # Update image boxes and current models based on model selection
+            model_choice.change(update_imgbox, model_choice, output)
+            model_choice.change(extend_choices, model_choice, current_models)
+            print("Model selection setup complete.")
+        with gr.Row():
+            # Placeholder HTML to add additional UI elements if needed
+            gr.HTML(
+)
+
+# Queue settings for handling multiple concurrent requests
+print("Setting up queue...")
+demo.queue(default_concurrency_limit=200, max_size=200)
+print("Launching Gradio interface...")
 demo.launch(show_api=False, max_threads=400)
+print("Gradio interface launched successfully.")

externalmod.py

@@ -606,7 +606,7 @@ def save_image(image, savefile, modelname, prompt, nprompt, height=0, width=0, s
 
 def randomize_seed():
     from random import seed, randint
-    MAX_SEED = 2**32-1
+    MAX_SEED = 3999999999
     seed()
     rseed = randint(0, MAX_SEED)
     return rseed