make a/b tester

app.py

@@ -4,8 +4,8 @@
 # in the Software without restriction, including without limitation the rights
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
-
 import spaces
+
 import os
 import random
 import uuid
@@ -13,17 +13,37 @@ import gradio as gr
 import numpy as np
 from PIL import Image
 import torch
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+from diffusers import AutoencoderKL, StableDiffusionXLPipeline, UNet2DConditionModel
+from diffusers import EulerAncestralDiscreteScheduler
+from diffusers import DPMSolverMultistepScheduler
+
 from typing import Tuple
-from transformers import AutoTokenizer, AutoModelForCausalLM
+import paramiko
+import gc
+import time
+import datetime
+#from diffusers.schedulers import AysSchedules
 
-css = '''
-.gradio-container{max-width: 570px !important}
-h1{text-align:center}
-footer {
-    visibility: hidden
-}
-'''
+from gradio import themes
+from hidiffusion import apply_hidiffusion, remove_hidiffusion
+
+import gc
+
+torch.backends.cuda.matmul.allow_tf32 = False
+torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
+torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
+torch.backends.cudnn.allow_tf32 = False
+torch.backends.cudnn.deterministic = False
+#torch.backends.cudnn.benchmark = False
+torch.backends.cuda.preferred_blas_library="cublas"
+# torch.backends.cuda.preferred_linalg_library="cusolver"
+
+torch.set_float32_matmul_precision("highest")
+
+FTP_HOST = "1ink.us"
+FTP_USER = "ford442"
+FTP_PASS = "GoogleBez12!"
+FTP_DIR = "1ink.us/stable_diff/"  # Remote directory on FTP server
 
 DESCRIPTIONXX = """
     ## REALVISXL V5.0 BF16 ⚡⚡⚡⚡
@@ -41,10 +61,10 @@ MODEL_OPTIONS = {
 
 MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
 USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+ENABLE_CPU_OFFLOAD = 0
 BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))
 
-device = "cpu"
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 style_list = [
     {
@@ -72,34 +92,92 @@ style_list = [
 styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
 DEFAULT_STYLE_NAME = "Style Zero"
 STYLE_NAMES = list(styles.keys())
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+#sampling_schedule = AysSchedules["StableDiffusionXLTimesteps"]
 
 def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
     if style_name in styles:
         p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
     else:
         p, n = styles[DEFAULT_STYLE_NAME]
-
     if not negative:
         negative = ""
     return p.replace("{prompt}", positive), n + negative
 
 def load_and_prepare_model(model_id):
-    model_dtypes = {
-        "ford442/RealVisXL_V5.0_BF16": torch.bfloat16,
-    }
-
-    # Get the dtype based on the model_id
+    model_dtypes = {"ford442/RealVisXL_V5.0_BF16": torch.bfloat16,}
     dtype = model_dtypes.get(model_id, torch.bfloat16)  # Default to float32 if not found
-
-    # Load the pipeline with the determined dtype
+    #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None)
+    #vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae")
+    #vae = AutoencoderKL.from_pretrained('cross-attention/asymmetric-autoencoder-kl-x-2',use_safetensors=False)
+    #vae = AutoencoderKL.from_single_file('https://huggingface.co/ford442/sdxl-vae-bf16/mySLR/myslrVAE_v10.safetensors')
+    #vaeX = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse",use_safetensors=True)
+    #vaeX = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
+    #vaeX = AutoencoderKL.from_pretrained('ford442/RealVisXL_V5.0_FP64',subfolder='vae').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
+    #unetX = UNet2DConditionModel.from_pretrained('ford442/RealVisXL_V5.0_BF16',subfolder='unet').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
+  #  vae = AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",safety_checker=None).to(torch.bfloat16)
+    #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear", steps_offset=1,timestep_spacing="trailing"))
+    #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler', steps_offset=1,timestep_spacing="trailing")
+    sched = EulerAncestralDiscreteScheduler.from_pretrained('SG161222/RealVisXL_V5.0', subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1,use_karras_sigmas=True)
+    #sched = EulerAncestralDiscreteScheduler.from_pretrained('SG161222/RealVisXL_V5.0', subfolder='scheduler')
+    #pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0").to(torch.bfloat16)
+    #pipeX = StableDiffusionXLPipeline.from_pretrained("ford442/Juggernaut-XI-v11-fp32",use_safetensors=True)
+    
     pipe = StableDiffusionXLPipeline.from_pretrained(
-        model_id,
-        torch_dtype=torch.bfloat16,
-        use_safetensors=True,
+        #'ford442/RealVisXL_V5.0_BF16',
+    #    'ford442/Juggernaut-XI-v11-fp32',
+        'SG161222/RealVisXL_V5.0',
+        #torch_dtype=torch.bfloat16,
         add_watermarker=False,
-    ).to(device)
-    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+       # custom_pipeline="lpw_stable_diffusion_xl",
+        #use_safetensors=True,
+      #  use_auth_token=HF_TOKEN,
+     #  vae=AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",repo_type='model',safety_checker=None),
+     #   vae=AutoencoderKL.from_pretrained("stabilityai/sdxl-vae",repo_type='model',safety_checker=None, torch_dtype=torch.float32),
+       # vae=AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16",repo_type='model',safety_checker=None),
+        #vae=vae,
+        #unet=pipeX.unet,
+        #scheduler = sched,
+      #  scheduler = EulerAncestralDiscreteScheduler.from_config(pipeX.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
+        #scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset =1)
+    )
+    
+    #sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear",use_karras_sigmas=True, algorithm_type="dpmsolver++")
+    #pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
+    #pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained('SG161222/RealVisXL_V5.0', subfolder='scheduler', algorithm_type='sde-dpmsolver++')
+    #pipe.vae = vaeX
+    #pipe.unet = unetX
+    #pipe.vae.do_resize=False
+    pipe.scheduler = sched
+    #pipe.vae=vae.to(torch.bfloat16)
+    #pipe.unet=pipeX.unet
+    #pipe.scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
+
+    pipe.to(device)
+    pipe.to(torch.bfloat16)
+    
+    apply_hidiffusion(pipe)
 
+    #pipe.unet.set_default_attn_processor()
+    #pipe.vae.set_default_attn_processor()
+    
+    print(f'Pipeline: ')
+    print(f'_optional_components: {pipe._optional_components}')
+    print(f'watermark: {pipe.watermark}')
+    print(f'image_processor: {pipe.image_processor}')
+    print(f'feature_extractor: {pipe.feature_extractor}')
+    print(f'init noise scale: {pipe.scheduler.init_noise_sigma}')
+    #print(f'UNET: {pipe.unet}')
+    pipe.watermark=None
+    pipe.safety_checker=None  
+    #pipe.to(torch.device("cuda:0"))
+    #pipe.vae.to(torch.bfloat16)
+    #pipe.to(device, torch.bfloat16)
+    #del pipeX
+    #sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", algorithm_type="dpmsolver++")
+    #sched = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, beta_schedule="linear", algorithm_type="dpmsolver++")
+    #sched = DDIMScheduler.from_config(pipe.scheduler.config)
     return pipe
 
 # Preload and compile both models
@@ -107,9 +185,24 @@ models = {key: load_and_prepare_model(value) for key, value in MODEL_OPTIONS.ite
 
 MAX_SEED = np.iinfo(np.int32).max
 
+neg_prompt_2 = " 'non-photorealistic':1.5, 'unrealistic skin','unattractive face':1.3, 'low quality':1.1, ('dull color scheme', 'dull colors', 'digital noise':1.2),'amateurish', 'poorly drawn face':1.3, 'poorly drawn', 'distorted face', 'low resolution', 'simplistic' "
+
+def upload_to_ftp(filename):
+    try:
+        transport = paramiko.Transport((FTP_HOST, 22))
+        destination_path=FTP_DIR+filename
+        transport.connect(username = FTP_USER, password = FTP_PASS)
+        sftp = paramiko.SFTPClient.from_transport(transport)
+        sftp.put(filename, destination_path)
+        sftp.close()
+        transport.close()
+        print(f"Uploaded {filename} to FTP server")
+    except Exception as e:
+        print(f"FTP upload error: {e}")
+        
 def save_image(img):
     unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
+    img.save(unique_name,optimize=False,compress_level=0)
     return unique_name
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
@@ -117,54 +210,211 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
         seed = random.randint(0, MAX_SEED)
     return seed
 
-@spaces.GPU(duration=40, enable_queue=True)
-def generate(
+@spaces.GPU(duration=30)
+def generate_30(
     model_choice: str,
     prompt: str,
     negative_prompt: str = "",
     use_negative_prompt: bool = False,
-    style_selection: str = DEFAULT_STYLE_NAME,
+    style_selection: str = "",
     seed: int = 1,
     width: int = 768,
     height: int = 768,
     guidance_scale: float = 4,
-    num_inference_steps: int = 150,
+    num_inference_steps: int = 125,
     randomize_seed: bool = False,
     use_resolution_binning: bool = True, 
     num_images: int = 1,  
-    progress=gr.Progress(track_tqdm=True),
+    progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
+    torch.cuda.empty_cache()
+    gc.collect()
     global models
     pipe = models[model_choice]
-    
     seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator(device=device).manual_seed(seed)
-
-    prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
-
+    generator = torch.Generator(device='cuda').manual_seed(seed)
+    #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
     options = {
         "prompt": [prompt] * num_images,
-        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
+        "negative_prompt": [negative_prompt],
+        "negative_prompt_2": [neg_prompt_2],
         "width": width,
         "height": height,
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
         "generator": generator,
+     #   "timesteps": sampling_schedule,
         "output_type": "pil",
     }
-
     if use_resolution_binning:
         options["use_resolution_binning"] = True
+    images = []
+    pipe.scheduler.set_timesteps(num_inference_steps,device)
+      # write note txt
+    filename= f'tst_{seed}.txt'
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    with open(filename, "w") as f:
+        f.write(f"Realvis 5.0: {seed} png\n")
+        f.write(f"Date/time: {timestamp} \n")
+        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"SPACE SETUP: \n")
+        f.write(f"Use Model Dtype: no \n")
+        f.write(f"Model Scheduler: Euler_a custom before cuda \n")
+        f.write(f"Model VAE: FP64 \n")
+        f.write(f"Model UNET: FP64 \n")
+    upload_to_ftp(filename) 
+    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_{seed}.png"
+    images[0].save(sd_image_path,optimize=False,compress_level=0)
+    upload_to_ftp(sd_image_path)    
+    image_paths = [save_image(img) for img in images]
+    torch.cuda.empty_cache()
+    gc.collect()
+    return image_paths, seed
 
+@spaces.GPU(duration=60)
+def generate_60(
+    model_choice: str,
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    style_selection: str = "",
+    seed: int = 1,
+    width: int = 768,
+    height: int = 768,
+    guidance_scale: float = 4,
+    num_inference_steps: int = 250,
+    randomize_seed: bool = False,
+    use_resolution_binning: bool = True, 
+    num_images: int = 1,
+    progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
+):
+    torch.cuda.empty_cache()
+    gc.collect()
+    global models
+    pipe = models[model_choice]
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator(device='cuda').manual_seed(seed)
+    #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
+    options = {
+        "prompt": [prompt] * num_images,
+        "negative_prompt": [negative_prompt],
+        "negative_prompt_2": [neg_prompt_2],
+        "width": width,
+        "height": height,
+        "guidance_scale": guidance_scale,
+        "num_inference_steps": num_inference_steps,
+        "generator": generator,
+     #   "timesteps": sampling_schedule,
+        "output_type": "pil",
+    }
+    if use_resolution_binning:
+        options["use_resolution_binning"] = True
     images = []
+    pipe.scheduler.set_timesteps(num_inference_steps,device)
+      # write note txt
+    filename= f'tst_{seed}.txt'
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    with open(filename, "w") as f:
+        f.write(f"Realvis 5.0: {seed} png\n")
+        f.write(f"Date/time: {timestamp} \n")
+        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"SPACE SETUP: \n")
+        f.write(f"Use Model Dtype: no \n")
+        f.write(f"Model Scheduler: Euler_a custom before cuda \n")
+        f.write(f"Model VAE: FP64 \n")
+        f.write(f"Model UNET: FP64 \n")
+    upload_to_ftp(filename) 
     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_{seed}.png"
+    images[0].save(sd_image_path,optimize=False,compress_level=0)
+    upload_to_ftp(sd_image_path)    
+    image_paths = [save_image(img) for img in images]
+    torch.cuda.empty_cache()
+    gc.collect()
+    return image_paths, seed
 
+@spaces.GPU(duration=90)
+def generate_90(
+    model_choice: str,
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    style_selection: str = "",
+    seed: int = 1,
+    width: int = 768,
+    height: int = 768,
+    guidance_scale: float = 4,
+    num_inference_steps: int = 250,
+    randomize_seed: bool = False,
+    use_resolution_binning: bool = True, 
+    num_images: int = 1,  
+    progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
+):
+    torch.cuda.empty_cache()
+    gc.collect()
+    global models
+    pipe = models[model_choice]
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator(device='cuda').manual_seed(seed)
+    #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
+    options = {
+        "prompt": [prompt] * num_images,
+        "negative_prompt": [negative_prompt],
+        "negative_prompt_2": [neg_prompt_2],
+        "width": width,
+        "height": height,
+        "guidance_scale": guidance_scale,
+        "num_inference_steps": num_inference_steps,
+        "generator": generator,
+     #   "timesteps": sampling_schedule,
+        "output_type": "pil",
+    }
+    if use_resolution_binning:
+        options["use_resolution_binning"] = True
+    images = []
+    pipe.scheduler.set_timesteps(num_inference_steps,device)
+      # write note txt
+    filename= f'tst_{seed}.txt'
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    with open(filename, "w") as f:
+        f.write(f"Realvis 5.0: {seed} png\n")
+        f.write(f"Date/time: {timestamp} \n")
+        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"SPACE SETUP: \n")
+        f.write(f"Use Model Dtype: no \n")
+        f.write(f"Model Scheduler: Euler_a custom before cuda \n")
+        f.write(f"Model VAE: FP64 \n")
+        f.write(f"Model UNET: FP64 \n")
+    upload_to_ftp(filename) 
+    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_{seed}.png"
+    images[0].save(sd_image_path,optimize=False,compress_level=0)
+    upload_to_ftp(sd_image_path)    
     image_paths = [save_image(img) for img in images]
+    torch.cuda.empty_cache()
+    gc.collect()
     return image_paths, seed
 
 def load_predefined_images1():
@@ -181,23 +431,21 @@ def load_predefined_images1():
     ]
     return predefined_images1
 
+css = '''
+#col-container {
+    margin: 0 auto;
+    max-width: 640px;
+}
+h1{text-align:center}
+footer {
+    visibility: hidden
+}
+body {
+  background-color: green;
+}
+'''
 
-# def load_predefined_images():
-#     predefined_images = [
-#         "assets2/11.png",
-#         "assets2/22.png",
-#         "assets2/33.png",
-#         "assets2/44.png",
-#         "assets2/55.png",
-#         "assets2/66.png",
-#         "assets2/77.png",
-#         "assets2/88.png",
-#         "assets2/99.png",
-#     ]
-#     return predefined_image
-
-
-with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
     gr.Markdown(DESCRIPTIONXX)
     with gr.Row():
         prompt = gr.Text(
@@ -207,7 +455,9 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
             placeholder="Enter your prompt",
             container=False,
         )
-        run_button = gr.Button("Run", scale=0)
+        run_button_30 = gr.Button("Run 30 Seconds", scale=0)
+        run_button_60 = gr.Button("Run 60 Seconds", scale=0)
+        run_button_90 = gr.Button("Run 90 Seconds", scale=0)
     result = gr.Gallery(label="Result", columns=1, show_label=False) 
 
     with gr.Row():
@@ -217,7 +467,6 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
             value="REALVISXL V5.0 BF16"
         )
 
-    with gr.Accordion("Advanced options", open=False, visible=True):
         style_selection = gr.Radio(
             show_label=True,
             container=True,
@@ -241,7 +490,7 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
                     max_lines=5,
                     lines=4,
                     placeholder="Enter a negative prompt",
-                    value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
+                    value="('deformed', 'distorted', 'disfigured':1.3),'not photorealistic':1.5, 'poorly drawn', 'bad anatomy', 'wrong anatomy', 'extra limb', 'missing limb', 'floating limbs', 'poorly drawn hands', 'poorly drawn feet', 'poorly drawn face':1.3, 'out of frame', 'extra limbs', 'bad anatomy', 'bad art', 'beginner',  'distorted face','amateur'",
                     visible=True,
                 )
         seed = gr.Slider(
@@ -271,9 +520,9 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
             guidance_scale = gr.Slider(
                 label="Guidance Scale",
                 minimum=0.1,
-                maximum=6,
+                maximum=30,
                 step=0.1,
-                value=4.0,
+                value=4,
             )
             num_inference_steps = gr.Slider(
                 label="Number of inference steps",
@@ -298,12 +547,56 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
     
     gr.on(
         triggers=[
-            prompt.submit,
-            negative_prompt.submit,
-            run_button.click,
+            run_button_30.click,
+        ],
+      #  api_name="generate",  # Add this line
+        fn=generate_30,
+        inputs=[
+            model_choice,
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            style_selection,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            randomize_seed, 
+            num_images, 
+        ],
+        outputs=[result, seed],
+    )
+    
+    gr.on(
+        triggers=[
+            run_button_60.click,
+        ],
+      #  api_name="generate",  # Add this line
+        fn=generate_60,
+        inputs=[
+            model_choice,
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            style_selection,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            randomize_seed, 
+            num_images, 
+        ],
+        outputs=[result, seed],
+    )
+    
+    gr.on(
+        triggers=[
+            run_button_90.click,
         ],
-        api_name="generate",  # Add this line
-        fn=generate,
+      #  api_name="generate",  # Add this line
+        fn=generate_90,
         inputs=[
             model_choice,
             prompt,
@@ -368,7 +661,6 @@ if __name__ == "__main__":
         outputs=gr.Textbox(label="Text Generated"),
         title=title,
         description=description,
-        theme="huggingface"
     )
 
     combined_interface = gr.TabbedInterface([demo_interface, text_gen_interface], ["Image Generation", "Text Generation"])