Delete app.py

app.py

@@ -1,175 +0,0 @@
-import gradio as gr
-import numpy as np
-import random
-import os
-import torch
-from diffusers import StableDiffusionPipeline
-from peft import PeftModel, LoraConfig
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_id_default = "stable-diffusion-v1-5/stable-diffusion-v1-5"
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    width=512,
-    height=512,
-    model_id=model_id_default,
-    seed=42,
-    guidance_scale=7.0,
-    lora_scale=1.0,
-    num_inference_steps=20,
-    progress=gr.Progress(track_tqdm=True),
-):  
-    generator = torch.Generator(device).manual_seed(seed)
-
-    ckpt_dir='./model_output'
-    unet_sub_dir = os.path.join(ckpt_dir, "unet")
-    text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
-
-    if model_id is None:
-        raise ValueError("Please specify the base model name or path")
-
-    pipe = StableDiffusionPipeline.from_pretrained(model_id, 
-                                                   torch_dtype=torch_dtype, 
-                                                   safety_checker=None).to(device)
-    pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir)
-    pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir)
-
-    pipe.unet.load_state_dict({k: lora_scale*v for k, v in pipe.unet.state_dict().items()})
-    pipe.text_encoder.load_state_dict({k: lora_scale*v for k, v in pipe.text_encoder.state_dict().items()})
-    
-    if torch_dtype in (torch.float16, torch.bfloat16):
-        pipe.unet.half()
-        pipe.text_encoder.half()
-
-    pipe.to(device)
-    
-    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
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css, fill_height=True) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image demo")
-
-        with gr.Row():
-            model_id = gr.Textbox(
-                label="Model ID",
-                max_lines=1,
-                placeholder="Enter model id",
-                value=model_id_default,
-            )
-
-        prompt = gr.Textbox(
-            label="Prompt",
-            max_lines=1,
-            placeholder="Enter your prompt",
-        )
-        
-        negative_prompt = gr.Textbox(
-            label="Negative prompt",
-            max_lines=1,
-            placeholder="Enter your negative prompt",
-        )
-        
-        with gr.Row():
-            seed = gr.Number(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=42,
-            )
-            
-            guidance_scale = gr.Slider(
-                label="Guidance scale",
-                minimum=0.0,
-                maximum=30.0,
-                step=0.1,
-                value=7.0,  # Replace with defaults that work for your model
-            )
-        with gr.Row():
-            lora_scale = gr.Slider(
-                label="LoRA scale",
-                minimum=0.0,
-                maximum=1.0,
-                step=0.01,
-                value=1.0,
-            )
-
-            num_inference_steps = gr.Slider(
-                label="Number of inference steps",
-                minimum=1,
-                maximum=100,
-                step=1,
-                value=20,  # Replace with defaults that work for your model
-            )
-
-        with gr.Accordion("Optional Settings", open=False):
-            
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,  # Replace with defaults that work for your model
-                )
-        
-        run_button = gr.Button("Run", scale=0, variant="primary")
-        result = gr.Image(label="Result", show_label=False)
-            
-    gr.on(
-        triggers=[run_button.click],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            width,
-            height,
-            model_id,
-            seed,
-            guidance_scale,      
-            lora_scale,
-            num_inference_steps
-            
-        ],
-        outputs=[result],
-    )
-
-if __name__ == "__main__":
-    demo.launch()