Create app_new.py

app_new.py

@@ -0,0 +1,199 @@
+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;
+}
+"""
+
+def controlnet_params(show_extra):
+    return gr.update(visible=show_extra)
+    
+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.Row():
+            controlnet_checkbox = gr.Checkbox(
+                label="ControlNet",
+            )
+            with gr.Group(visible=False) as controlnet_params:
+                control_strength = gr.Slider(
+                    label="ControlNet conditioning scale",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.01,
+                    value=1.0,  
+                )
+                control_mode = gr.Dropdown(
+                    label="ControlNet mode",
+                    choises=["edge_detection"]
+                )
+            controlnet_checkbox.change(
+                controlnet_params, 
+                inputs=controlnet_checkbox,
+                outputs=controlnet_params
+            )
+
+        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()