Add application file

app.py

@@ -0,0 +1,185 @@
+import gradio as gr
+import numpy as np
+import random
+import os
+
+# import spaces #[uncomment to use ZeroGPU]
+from diffusers import DiffusionPipeline
+from peft import PeftModel, LoraConfig
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+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
+
+LoRA_path = 'new_model'
+
+# @spaces.GPU #[uncomment to use ZeroGPU]
+def infer(
+    model_id,
+    prompt,
+    negative_prompt,
+    seed,
+    randomize_seed,
+    width,
+    height,
+    guidance_scale,
+    num_inference_steps,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(seed)
+
+    if model_id == 'Maria_Lashina_LoRA':
+        adapter_name = 'a cartoonish mouse'
+        unet_sub_dir = os.path.join(LoRA_path, "unet")
+        text_encoder_sub_dir = os.path.join(LoRA_path, "text_encoder")
+        
+        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
+        pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
+
+        pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)
+
+        if torch_dtype == torch.float16:
+            pipe.unet.half()
+            pipe.text_encoder.half()
+
+        pipe.to(device)
+    
+    else:
+        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).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, seed
+
+
+examples = [
+    "The image of a cartoonish mouse eating from a red bowl of yellow triangle chips, her cheeks are full. The mouse is gray with big pink ears, small white eyes and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
+    "The image of a cartoonish mouse with red hearts instead of eyes meaning that the mouse is in love with something. The mouse is gray with big pink ears and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
+    "The image of a cartoonish mouse with sunglasses and smiling. The mouse is gray with big pink ears and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
+]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 640px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(" # Text-to-Image Gradio Template")
+
+        MODEL_LIST = [
+            "CompVis/stable-diffusion-v1-4",
+            "stable-diffusion-v1-5/stable-diffusion-v1-5",
+            "Maria_Lashina_LoRA"
+        ]
+        with gr.Row():
+            model_id = gr.Dropdown(
+                label="Model",
+                choices=MODEL_LIST
+            )
+
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+
+            run_button = gr.Button("Run", scale=0, variant="primary")
+
+        result = gr.Image(label="Result", show_label=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=False,
+            )
+
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+
+            with gr.Row():
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,  # Replace with defaults that work for your model
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,  # Replace with defaults that work for your model
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=7.0,  # Replace with defaults that work for your model
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=20,  # Replace with defaults that work for your model
+                )
+
+        gr.Examples(examples=examples, inputs=[prompt])
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            model_id,
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()