Create app.py

app.py

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+import torch
+import gradio as gr
+from diffusers import QwenImagePipeline, QwenImageTransformer2DModel
+from transformers import Qwen2_5_VLForConditionalGeneration
+
+# Set device and dtype
+torch_dtype = torch.bfloat16
+
+# Load models
+transformer = QwenImageTransformer2DModel.from_pretrained(
+    "OzzyGT/Qwen-Image-2512-bnb-4bit-transformer",
+    torch_dtype=torch_dtype,
+    device_map="cpu"  # offloading to CPU
+)
+text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "OzzyGT/Qwen-Image-2512-bnb-4bit-text-encoder",
+    torch_dtype=torch_dtype,
+    device_map="cpu"
+)
+
+pipe = QwenImagePipeline.from_pretrained(
+    "Qwen/Qwen-Image-2512",
+    transformer=transformer,
+    text_encoder=text_encoder,
+    torch_dtype=torch_dtype
+)
+
+pipe.enable_model_cpu_offload()
+
+# Define the Gradio function
+def generate_image(prompt, negative_prompt, width=1664, height=928, seed=42):
+    generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        num_inference_steps=28,
+        true_cfg_scale=4.0,
+        generator=generator,
+    ).images[0]
+    return image
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Qwen Image Generation")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Prompt", lines=8, placeholder="Enter your prompt here...")
+            negative_prompt_input = gr.Textbox(label="Negative Prompt", lines=4, placeholder="Enter negative prompt...")
+            width_input = gr.Slider(256, 2048, value=1664, step=64, label="Width")
+            height_input = gr.Slider(256, 2048, value=928, step=64, label="Height")
+            seed_input = gr.Number(value=42, label="Random Seed")
+            generate_btn = gr.Button("Generate Image")
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image")
+    
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt_input, negative_prompt_input, width_input, height_input, seed_input],
+        outputs=output_image
+    )
+
+demo.launch()