Create app.py

app.py

@@ -0,0 +1,138 @@
+from email import generator
+from diffusers import DiffusionPipeline
+
+import gradio as gr
+import torch
+from PIL import Image, ImageDraw, ImageFont
+## VAE - Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
+from diffusers import  AutoencoderKL
+
+
+
+
+model = "stabilityai/stable-diffusion-xl-base-1.0"
+finetuningLayer = "bbsgp/10xFWDLora"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+torch_dtype = torch.float16 if device.type == 'cuda' else torch.float32
+
+
+
+import os
+HF_API_TOKEN = os.getenv("HF_API_TOKEN")
+
+from huggingface_hub import login
+login(token=HF_API_TOKEN)
+
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype)
+pipe = DiffusionPipeline.from_pretrained(
+    model,
+    vae=vae, 
+    torch_dtype=torch_dtype, 
+    use_safetensors=True
+)
+pipe.load_lora_weights(finetuningLayer)
+
+pipe = pipe.to(device)
+
+
+
+
+def create_error_image(message):
+    # Create a blank image with white background
+    width, height = 512, 512
+    image = Image.new('RGB', (width, height), 'white')
+    draw = ImageDraw.Draw(image)
+    
+    # Load a truetype or opentype font file
+    font = ImageFont.load_default()
+    
+    # Position and message
+
+    draw.text((127,251), message, font=font, fill="black")
+    
+    return image
+
+def inference(model,finetuningLayer, prompt, guidance, steps, seed):
+
+  
+
+  if not prompt:
+    return create_error_image("Sorry, add your text prompt and try again!!")
+  else:
+    generator = torch.Generator(device).manual_seed(seed)
+    image = pipe(
+    prompt,
+    num_inference_steps=int(steps),
+    guidance_scale=guidance,
+    generator=generator).images[0]
+
+    return image
+
+
+css = """
+  <style>
+  .finetuned-diffusion-div {
+      text-align: center;
+      max-width: 700px;
+      margin: 0 auto;
+    }
+    .finetuned-diffusion-div div {
+      display: inline-flex;
+      align-items: center;
+      gap: 0.8rem;
+      font-size: 1.75rem;
+    }
+    .finetuned-diffusion-div div h1 {
+      font-weight: 900;
+      margin-bottom: 7px;
+    }
+    .finetuned-diffusion-div p {
+      margin-bottom: 10px;
+      font-size: 94%;
+    }
+    .finetuned-diffusion-div p a {
+      text-decoration: underline;
+    }
+  </style>
+"""
+with gr.Blocks(css=css) as demo:
+    gr.HTML(
+        """
+            <div class="finetuned-diffusion-div">
+              <div>
+                <h1>Finetuned Diffusion</h1>
+              </div>
+            </div>
+        """
+    )
+    with gr.Row():
+
+        with gr.Column():
+
+            model = gr.Dropdown(label="baseModel",choices=[model], default=model) 
+            finetuningLayer=  gr.Dropdown(label="Finetuning Layer", choices=[finetuningLayer],  default=finetuningLayer)
+            prompt = gr.Textbox(label="Prompt", placeholder="photo of McDBigMac - it is unique identifier need to be used to identify burgers")
+            
+
+            with gr.Accordion("Advanced options", open=True):
+              guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
+              steps = gr.Slider(label="Steps", value=50, maximum=100, minimum=2)
+              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
+
+            run = gr.Button(value="Run")
+            gr.Markdown(f"Running on: {device}")
+        with gr.Column():
+            image_out = gr.Image()
+
+    ## Add prompt and press enter to run
+    ##prompt.submit(inference, inputs=[model, finetuningLayer,prompt, guidance, steps, seed], outputs=image_out)
+    
+    ## Click run button to run
+    run.click(inference, inputs=[model,  finetuningLayer, prompt, guidance, steps, seed], outputs=image_out)
+
+
+
+demo.queue()
+demo.launch()