Upload 3 files

app.py

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+import gradio as gr
+from model_utils import QwenImageEdit
+from PIL import Image
+
+# load model
+model = QwenImageEdit()
+
+def infer(prompt, upload_image=None, steps=30, scale=7.5):
+    if upload_image is not None:
+        img = Image.open(upload_image.name).convert("RGB")
+    else:
+        img = None
+    result = model.generate(prompt=prompt, image=img, num_inference_steps=steps, guidance_scale=scale)
+    return result
+
+title = "Qwen Image Edit — Text to Image / Image to Image"
+description = "Enter a prompt; optionally upload an image to edit."
+
+iface = gr.Interface(
+    fn=infer,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt"),
+        gr.File(label="Upload an image (optional)"),
+        gr.Slider(minimum=10, maximum=100, step=1, value=30, label="Inference steps"),
+        gr.Slider(minimum=1.0, maximum=20.0, step=0.5, value=7.5, label="Guidance scale"),
+    ],
+    outputs=gr.Image(type="pil", label="Generated Image"),
+    title=title,
+    description=description,
+)
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)

model_utils.py

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+from diffusers import StableDiffusionPipeline
+import torch
+
+class QwenImageEdit:
+    def __init__(self, model_name="Phr00t/Qwen-Image-Edit-Rapid-AIO"):
+        self.pipe = StableDiffusionPipeline.from_pretrained(
+            model_name,
+            torch_dtype=torch.float16,
+            revision="fp16",  # if applicable
+        ).to("cuda" if torch.cuda.is_available() else "cpu")
+
+    def generate(self, prompt: str, image=None, num_inference_steps=30, guidance_scale=7.5):
+        # If image is provided → image-to-image editing, otherwise vanilla text→image
+        if image is not None:
+            out = self.pipe(
+                prompt=prompt,
+                init_image=image,
+                strength=0.7,
+                num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale
+            )
+        else:
+            out = self.pipe(
+                prompt=prompt,
+                num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale
+            )
+        return out.images[0]

requirements.txt

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+gradio
+diffusers
+transformers
+torch
+accelerate
+safetensors