Update app.py

app.py

@@ -1,35 +1,98 @@
 import gradio as gr
-import torch
+import numpy as np
+import random
 from diffusers import StableDiffusionXLPipeline
+import torch
 from huggingface_hub import hf_hub_download
 
-# Set device: use "cuda" if available; otherwise "cpu"
+# Set device: use "cuda" if available, otherwise "cpu"
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Load the SDXL base pipeline; adjust torch_dtype for CPU if necessary.
+# Change the model to SDXL 1.0 base
+model_repo_id = "stabilityai/stable-diffusion-xl-base-1.0"
+
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+# Load the SDXL 1.0 base pipeline with safetensors support.
 pipe = StableDiffusionXLPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    model_repo_id,
+    torch_dtype=torch_dtype,
     use_safetensors=True
 )
-pipe.to(device)
+pipe = pipe.to(device)
 
-# Download your LoRA weights from your Hugging Face repository.
+# Download your DreamCartoonLora weights from Hugging Face and load them into the pipeline.
 lora_path = hf_hub_download(repo_id="Leofreddare/DreamCartoonLora", filename="DreamCartoonLora.safetensors")
-print("LoRA weights downloaded to:", lora_path)
-
-# Load the LoRA weights into the pipeline.
+print("Loaded DreamCartoonLora from:", lora_path)
 pipe.load_lora_weights(lora_path)
 
-def generate(prompt):
-    with torch.no_grad():
-        image = pipe(prompt=prompt).images[0]
-    return image
-
-gr.Interface(
-    fn=generate,
-    inputs=gr.Textbox(label="Enter your prompt"),
-    outputs="image",
-    title="DreamCartoonLora Inference"
-).launch()
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+def infer(
+    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(device=device).manual_seed(seed)
+    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 = [
+    "A dreamy cartoon landscape with vivid colors",
+    "A futuristic city rendered in a cartoon style",
+    "A magical forest with a cartoon twist",
+]
+
+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 with DreamCartoonLora on SDXL 1.0")
+        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=0)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            with gr.Row():
+                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+            with gr.Row():
+                guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7.5)
+                num_inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=20)
+        gr.Examples(examples=examples, inputs=[prompt])
+    gr.on(triggers=[run_button.click, prompt.submit],
+         fn=infer,
+         inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+         outputs=[result, seed])
+
+if __name__ == "__main__":
+    demo.launch()