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README.md

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----
-title: Testspace3
-emoji: 🖼
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
-short_description: testtest3
----
-
+---
+title: TestTest
+emoji: 🚀
+colorFrom: pink
+colorTo: purple
+sdk: gradio
+sdk_version: 3.47.0
+app_file: app.py
+pinned: true
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

all_models.py

@@ -0,0 +1,27 @@
+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",
+    "aleksa-codes/flux-ghibsky-illustration",
+    "dataautogpt3/FLUX-SyntheticAnime",
+    "Shakker-Labs/FLUX.1-dev-LoRA-add-details",
+    "Shakker-Labs/FLUX.1-dev-LoRA-AntiBlur",
+    "brushpenbob/flux-midjourney-anime",
+    "stabilityai/stable-diffusion-3.5-large",
+    "stabilityai/stable-diffusion-3.5-large-turbo",
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    "Yntec/epiCPhotoGasm",
+    #"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",
+    ]

app.py

@@ -1,154 +1,85 @@
-import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+from random import randint
+from all_models import models
+
+def load_fn(models):
+    global models_load
+    models_load = {}
+    
+    for model in models:
+        if model not in models_load.keys():
+            try:
+                m = gr.load(f'models/{model}')
+            except Exception as error:
+                m = gr.Interface(lambda txt: None, ['text'], ['image'])
+            models_load.update({model: m})
+
+
+load_fn(models)
+
+
+num_models = 32
+default_models = models[:num_models]
+
+
+def extend_choices(choices):
+    return choices + (num_models - len(choices)) * ['NA']
+
+
+def update_imgbox(choices):
+    choices_plus = extend_choices(choices)
+    return [gr.Image(None, label = m, visible = (m != 'NA')) for m in choices_plus]
+
+    
+def gen_fn(model_str, prompt):
+    if model_str == 'NA':
+        return None
+    noise = str(randint(0, 99999999999))
+    return models_load[model_str](f'{prompt} {noise}')
+
+
+with gr.Blocks() as demo:
+    with gr.Tab('Multiple models'): 
+        with gr.Accordion('Model selection'):
+            model_choice = gr.CheckboxGroup(models, label = f'Choose up to {num_models} different models', value = default_models, multiselect = True, max_choices = num_models, interactive = True, filterable = False)
+
+
+        txt_input = gr.Textbox(label = 'Prompt text')
+        gen_button = gr.Button('Generate')
+        stop_button = gr.Button('Stop', variant = 'secondary', interactive = False)
+        gen_button.click(lambda s: gr.update(interactive = True), None, stop_button)
+           
+        with gr.Row():
+            output = [gr.Image(label = m) for m in default_models]
+            current_models = [gr.Textbox(m, visible = False) for m in default_models]
+            
+            model_choice.change(update_imgbox, model_choice, output)
+            model_choice.change(extend_choices, model_choice, current_models)
+
+        for m, o in zip(current_models, output):
+            gen_event = gen_button.click(gen_fn, [m, txt_input], o, queue=False)
+                    
+            
+    with gr.Tab('Single model'):
+        model_choice2 = gr.Dropdown(models, label = 'Choose model', value = models[0], filterable = False)
+        txt_input2 = gr.Textbox(label = 'Prompt text')
+        
+        max_images = 16
+        num_images = gr.Slider(1, max_images, value = max_images, step = 1, label = 'Number of images')
+        
+        gen_button2 = gr.Button('Generate')
+        stop_button2 = gr.Button('Stop', variant = 'secondary', interactive = False)
+        gen_button2.click(lambda s: gr.update(interactive = True), None, stop_button2)
+        
+        with gr.Row():
+            output2 = [gr.Image(label = '') for _ in range(max_images)]
+
+        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)
+            gen_event2 = gen_button2.click(lambda i, n, m, t: gen_fn(m, t) if (i < n) else None, [img_i, num_images, model_choice2, txt_input2], o)
+            stop_button2.click(lambda s: gr.update(interactive = False), None, stop_button2, cancels = [gen_event2])
+
+    
+demo.queue(concurrency_count = 36)                        
+demo.launch()