Create app.py

app.py

@@ -0,0 +1,49 @@
+import torch
+from PIL import Image
+import gradio as gr
+from huggingface_hub import hf_hub_download
+
+from src_inference.pipeline import FluxPipeline
+from src_inference.lora_helper import set_single_lora, clear_cache
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print("Running on:", device)
+
+# Download and load model
+base_path = hf_hub_download(repo_id="showlab/OmniConsistency", filename="OmniConsistency.safetensors", local_dir="./Model")
+
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev", 
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
+).to(device)
+
+set_single_lora(pipe.transformer, base_path, lora_weights=[1], cond_size=512)
+
+def generate_manga(input_image, prompt):
+    spatial_image = [input_image.convert("RGB")]
+    image = pipe(
+        prompt,
+        height=1024,
+        width=1024,
+        guidance_scale=3.5,
+        num_inference_steps=25,
+        max_sequence_length=512,
+        spatial_images=spatial_image,
+        subject_images=[],
+        cond_size=512,
+    ).images[0]
+
+    clear_cache(pipe.transformer)
+    return image
+
+demo = gr.Interface(
+    fn=generate_manga,
+    inputs=[
+        gr.Image(type="pil", label="Input Character"),
+        gr.Textbox(label="Scene Prompt")
+    ],
+    outputs=gr.Image(label="Generated Manga Frame"),
+    title="OmniConsistency Manga Generator"
+)
+
+demo.launch()