Update app.py

app.py

@@ -1,219 +1,211 @@
-import gradio as gr
-from all_models import models
-from externalmod import gr_Interface_load, save_image, randomize_seed
-import asyncio
-import os
-from threading import RLock
-lock = RLock()
-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 load_fn(models):
-    global models_load
-    models_load = {}
-    for model in models:
-        if model not in models_load.keys():
-            try:
-                m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
-            except Exception as error:
-                print(error)
-                m = gr.Interface(lambda: None, ['text'], ['image'])
-            models_load.update({model: m})
-
-
-load_fn(models)
-
-
-num_models = 6
-max_images = 6
-inference_timeout = 300
-default_models = models[:num_models]
-MAX_SEED = 2**32-1
-
-
-def extend_choices(choices):
-    return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
-
-
-def update_imgbox(choices):
-    choices_plus = extend_choices(choices[:num_models])
-    return [gr.Image(None, label=m, visible=(m!='NA')) for m in choices_plus]
-
-
-def random_choices():
-    import random
-    random.seed()
-    return random.choices(models, k=num_models)
-
-
-# https://huggingface.co/docs/api-inference/detailed_parameters
-# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
-async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout):
-    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: kwargs["seed"] = randomize_seed()
-    else: kwargs["seed"] = seed
-    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)
-    try:
-        result = await asyncio.wait_for(task, timeout=timeout)
-    except asyncio.TimeoutError as e:
-        print(e)
-        print(f"Task timed out: {model_str}")
-        if not task.done(): task.cancel()
-        result = None
-        raise Exception(f"Task timed out: {model_str}") from e
-    except Exception as e:
-        print(e)
-        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):
-        with lock:
-            png_path = "image.png"
-            image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, seed)
-        return image
-    return None
-
-
-def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1):
-    try:
-        loop = asyncio.new_event_loop()
-        result = loop.run_until_complete(infer(model_str, prompt, nprompt,
-                                         height, width, steps, cfg, seed, inference_timeout))
-    except (Exception, asyncio.CancelledError) as e:
-        print(e)
-        print(f"Task aborted: {model_str}")
-        result = None
-        raise gr.Error(f"Task aborted: {model_str}, Error: {e}")
-    finally:
-        loop.close()
-    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
-
-
-CSS="""
-.gradio-container { max-width: 1200px; margin: 0 auto; !important; }
-.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
-.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
-.guide { text-align: center; !important; }
-"""
-
-
-with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=CSS) as demo:
-    gr.HTML(
-    """
-        <div>
-        <p> <center>For simultaneous generations without hidden queue check out <a href="https://huggingface.co/spaces/Yntec/ToyWorld">Toy World</a>! For more options like single model x6 check out <a href="https://huggingface.co/spaces/John6666/Diffusion80XX4sg">Diffusion80XX4sg</a> by John6666!</center>
-        </p></div>
-    """
-)  
-    with gr.Tab('Huggingface Diffusion'):
-        with gr.Column(scale=2):
-            with gr.Group():
-                txt_input = gr.Textbox(label='Your prompt:', lines=4)
-                neg_input = gr.Textbox(label='Negative prompt:', 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 in up to 3 minutes total', variant='primary', scale=3)
-                random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1)
-                #stop_button = gr.Button('Stop', variant='stop', interactive=False, scale=1)
-                #gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
-            gr.Markdown("Scroll down to see more images and select models.", elem_classes="guide")
-
-        with gr.Column(scale=1):
-            with gr.Group():
-                with gr.Row():
-                    output = [gr.Image(label=m, show_download_button=True, elem_classes="output",
-                              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, elem_classes="gallery",
-                                interactive=False, show_share_button=True, 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) # Be sure to delete ", queue=False" when activating the stop button
-            o.change(add_gallery, [o, m, gallery], [gallery])
-            #stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
-
-        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:', lines=4)
-                neg_input2 = gr.Textbox(label='Negative prompt:', 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('Generate', variant='primary', scale=2)
-                #stop_button2 = gr.Button('Stop', variant='stop', interactive=False, scale=1)
-                #gen_button2.click(lambda: gr.update(interactive=True), None, stop_button2)
-
-        with gr.Column(scale=1):
-            with gr.Group():
-                with gr.Row():
-                    output2 = [gr.Image(label='', show_download_button=True, elem_classes="output",
-                               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, elem_classes="gallery",
-                                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)  # Be sure to delete ", queue=False" when activating the stop button
-            o.change(add_gallery, [o, model_choice2, gallery2], [gallery2])
-            #stop_button2.click(lambda: gr.update(interactive=False), None, stop_button2, cancels=[gen_event2])
-
-    gr.Markdown("Based on the [TestGen](https://huggingface.co/spaces/derwahnsinn/TestGen) Space by derwahnsinn, the [SpacIO](https://huggingface.co/spaces/RdnUser77/SpacIO_v1) Space by RdnUser77 and Omnibus's Maximum Multiplier!")
-
-#demo.queue(default_concurrency_limit=200, max_size=200)
-demo.launch(show_api=False, max_threads=400)
-# https://github.com/gradio-app/gradio/issues/6339
+import gradio as gr
+from all_models import models
+from externalmod import gr_Interface_load, save_image, randomize_seed
+import asyncio
+import os
+from threading import RLock
+lock = RLock()
+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 load_fn(models):
+    global models_load
+    models_load = {}
+    for model in models:
+        if model not in models_load.keys():
+            try:
+                m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
+            except Exception as error:
+                print(error)
+                m = gr.Interface(lambda: None, ['text'], ['image'])
+            models_load.update({model: m})
+
+
+load_fn(models)
+
+
+num_models = 6
+max_images = 6
+inference_timeout = 300
+default_models = models[:num_models]
+MAX_SEED = 2**32-1
+
+
+def extend_choices(choices):
+    return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
+
+
+def update_imgbox(choices):
+    choices_plus = extend_choices(choices[:num_models])
+    return [gr.Image(None, label=m, visible=(m!='NA')) for m in choices_plus]
+
+
+def random_choices():
+    import random
+    random.seed()
+    return random.choices(models, k=num_models)
+
+
+# https://huggingface.co/docs/api-inference/detailed_parameters
+# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
+async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout):
+    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: kwargs["seed"] = randomize_seed()
+    else: kwargs["seed"] = seed
+    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)
+    try:
+        result = await asyncio.wait_for(task, timeout=timeout)
+    except asyncio.TimeoutError as e:
+        print(e)
+        print(f"Task timed out: {model_str}")
+        if not task.done(): task.cancel()
+        result = None
+        raise Exception(f"Task timed out: {model_str}") from e
+    except Exception as e:
+        print(e)
+        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):
+        with lock:
+            png_path = "image.png"
+            image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, seed)
+        return image
+    return None
+
+
+def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1):
+    try:
+        loop = asyncio.new_event_loop()
+        result = loop.run_until_complete(infer(model_str, prompt, nprompt,
+                                         height, width, steps, cfg, seed, inference_timeout))
+    except (Exception, asyncio.CancelledError) as e:
+        print(e)
+        print(f"Task aborted: {model_str}")
+        result = None
+        raise gr.Error(f"Task aborted: {model_str}, Error: {e}")
+    finally:
+        loop.close()
+    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
+
+
+CSS="""
+.gradio-container { max-width: 1200px; margin: 0 auto; !important; }
+.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
+.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
+.guide { text-align: center; !important; }
+"""
+
+
+with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=CSS) as demo:
+    gr.HTML()  
+    with gr.Tab('Huggingface Diffusion'):
+        with gr.Column(scale=2):
+            with gr.Group():
+                txt_input = gr.Textbox(label='Your prompt:', lines=4)
+                neg_input = gr.Textbox(label='Negative prompt:', 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 in up to 3 minutes total', variant='primary', scale=3)
+                random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1)
+                #stop_button = gr.Button('Stop', variant='stop', interactive=False, scale=1)
+                #gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
+            gr.Markdown("Scroll down to see more images and select models.", elem_classes="guide")
+
+        with gr.Column(scale=1):
+            with gr.Group():
+                with gr.Row():
+                    output = [gr.Image(label=m, show_download_button=True, elem_classes="output",
+                              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, elem_classes="gallery",
+                                interactive=False, show_share_button=True, 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) # Be sure to delete ", queue=False" when activating the stop button
+            o.change(add_gallery, [o, m, gallery], [gallery])
+            #stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
+
+        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:', lines=4)
+                neg_input2 = gr.Textbox(label='Negative prompt:', 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('Generate', variant='primary', scale=2)
+                #stop_button2 = gr.Button('Stop', variant='stop', interactive=False, scale=1)
+                #gen_button2.click(lambda: gr.update(interactive=True), None, stop_button2)
+
+        with gr.Column(scale=1):
+            with gr.Group():
+                with gr.Row():
+                    output2 = [gr.Image(label='', show_download_button=True, elem_classes="output",
+                               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, elem_classes="gallery",
+                                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)  # Be sure to delete ", queue=False" when activating the stop button
+            o.change(add_gallery, [o, model_choice2, gallery2], [gallery2])
+            #stop_button2.click(lambda: gr.update(interactive=False), None, stop_button2, cancels=[gen_event2])
+
+#demo.queue(default_concurrency_limit=200, max_size=200)
+demo.launch(show_api=False, max_threads=400)
+# https://github.com/gradio-app/gradio/issues/6339