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app.py

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+import gradio as gr
+from PIL import Image
+import diffusers
+from diffusers.models import AutoencoderKL
+
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
+
+
+def read_content(file_path: str) -> str:
+    """read the content of target file
+    """
+    with open(file_path, 'r', encoding='utf-8') as f:
+        content = f.read()
+
+    return content
+
+
+def predict(prompt, negative_prompt, guidance_scale, num_inference_steps,model, scheduler, lora, lora_weight):
+    pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", vae=vae).to("cuda")
+    pipeline.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
+    if model == "Realistic_V5.1":
+        pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", vae=vae).to("cuda")
+    if model == "Realistic_V5.0":
+        pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE", vae=vae).to("cuda")
+    if model == "EpicRealism":
+        pipeline = diffusers.DiffusionPipeline.from_pretrained("emilianJR/epiCRealism", vae=vae).to("cuda")
+        pipeline.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
+    
+    scheduler_class_name = scheduler.split("-")[0]
+    add_kwargs = {}
+    if len(scheduler.split("-")) > 1:
+        add_kwargs["use_karras_sigmas"] = True
+    if len(scheduler.split("-")) > 2:
+        add_kwargs["algorithm_type"] = "sde-dpmsolver++"
+    scheduler = getattr(diffusers, scheduler_class_name)
+    
+    pipeline.scheduler = scheduler.from_pretrained("emilianJR/epiCRealism", subfolder="scheduler", **add_kwargs)
+    
+    
+    if lora == "nayanthara":
+        lora = "profaker/Naya_lora"
+    if lora == "saipallavi":
+        lora = "profaker/saipallavi_lora"
+    if lora == "shobita":
+        lora = "profaker/Shobita_lora"
+    if lora == "surya":
+        lora = "profaker/Surya_lora"
+    if lora == "vijay":
+        lora = "profaker/Vijay_lora"
+    if lora == "None":
+        images = pipeline(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=int(num_inference_steps),
+        guidance_scale=guidance_scale,
+        clip_skip=1
+        ).images[0]
+        print("Prompt", prompt)
+        print("Negative", negative_prompt)
+        print("Steps", num_inference_steps)
+        print("Scale", guidance_scale)
+        print("Scheduler", scheduler)
+        return images
+        
+    pipeline.load_lora_weights(lora)
+    
+    images = pipeline(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=int(num_inference_steps),
+        guidance_scale=guidance_scale,
+        cross_attention_kwargs={"scale": lora_weight}
+    ).images[0]
+    
+    print("Prompt", prompt)
+    print("Negative", negative_prompt)
+    print("Steps", num_inference_steps)
+    print("Scale", guidance_scale)
+    print("Scheduler", scheduler)
+
+    return images
+
+
+css = '''
+.gradio-container{max-width: 1100px !important}
+#image_upload{min-height:400px}
+#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
+#mask_radio .gr-form{background:transparent; border: none}
+#word_mask{margin-top: .75em !important}
+#word_mask textarea:disabled{opacity: 0.3}
+.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
+.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
+.dark .footer {border-color: #303030}
+.dark .footer>p {background: #0b0f19}
+.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
+#image_upload .touch-none{display: flex}
+@keyframes spin {
+    from {
+        transform: rotate(0deg);
+    }
+    to {
+        transform: rotate(360deg);
+    }
+}
+
+#prompt-container{margin-top:-18px;}
+#prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0}
+'''
+
+image_blocks = gr.Blocks(css=css, elem_id="total-container")
+with image_blocks as demo:
+    gr.HTML(read_content("header.html"))
+    with gr.Row():
+        with gr.Column():
+            with gr.Row(elem_id="prompt-container", equal_height=True):
+                with gr.Row():
+                    prompt = gr.Textbox(placeholder="Your prompt", show_label=False, elem_id="prompt", lines=5)
+
+            with gr.Accordion(label="Advanced Settings", open=False):
+                with gr.Row(equal_height=True):
+                    guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
+                    steps = gr.Number(value=40, minimum=0, maximum=100, step=1, label="steps")
+                with gr.Row(equal_height=True):
+                    negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt",
+                                                 info="what you don't want to see in the image")
+                with gr.Row(equal_height=True):
+                    models = ['Realistic_V6.0','Realistic_V5.1','Realistic_V5.0','EpicRealism']
+                    model = gr.Dropdown(label="Models",choices=models,value="Realistic_V6.0")
+                with gr.Row(equal_height=True):
+                    schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler",
+                                  "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras",
+                                  "DPMSolverMultistepScheduler-Karras-SDE"]
+                    scheduler = gr.Dropdown(label="Schedulers", choices=schedulers,
+                                            value="DPMSolverMultistepScheduler-Karras")
+                with gr.Row(equal_height=True):
+                    loras = ['None','add_detail','nayanthara','shobita','surya','vijay','saipallavi']
+                    lora = gr.Dropdown(label='Lora', choices=loras, value="None")
+                    lora_weight = gr.Number(value=0.5, minimum=0, maximum=1, step=0.01, label="Lora Weights")
+            with gr.Row(equal_height=True):
+                btn = gr.Button("Generate", elem_id="run_button")
+
+        with gr.Column():
+            image_out = gr.Image(label="Output", elem_id="output-img", height=1024, width=512)
+    btn.click(fn=predict, inputs=[prompt, negative_prompt, guidance_scale, steps, model,scheduler, lora, lora_weight],
+              outputs=[image_out], api_name='run')
+    prompt.submit(fn=predict, inputs=[prompt, negative_prompt, guidance_scale, steps, model,scheduler, lora, lora_weight],
+                  outputs=[image_out])
+
+image_blocks.queue(max_size=25, api_open=True).launch(show_api=True)

header.html

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+<div style="text-align: center; max-width: 650px; margin: 0 auto;">
+  <div style="
+        display: inline-flex;
+        gap: 0.8rem;
+        font-size: 1.75rem;
+        justify-content: center;
+        margin-bottom: 10px;
+      ">
+    <h1 style="font-weight: 900; align-items: center; margin-bottom: 7px; margin-top: 20px;">
+      <b>Profaker⛥</b>
+    </h1>
+  </div>
+</div>

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cu118
+torch
+git+https://github.com/huggingface/diffusers.git
+transformers
+accelerate
+gradio==3.50.0
+ftfy
+numpy
+matplotlib
+uuid
+opencv-python
+safetensors