Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import random
+import spaces
+import torch
+from diffusers import QwenImagePipeline
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+pipe = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=dtype).to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1536
+
+@spaces.GPU()
+def infer(prompt, negative_prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, true_cfg_scale=4.0, distilled_cfg_scale=1.0, progress=gr.Progress(track_tqdm=True)):
+    """
+    Generates an image based on a user's prompt using the Qwen-Image pipeline.
+
+    This function takes textual prompts and various generation parameters,
+    handles seed randomization, and runs the diffusion model to produce an image.
+
+    Args:
+        prompt (str): The positive text prompt to guide image generation.
+        negative_prompt (str): The negative text prompt to guide the model
+            on what to avoid in the generated image.
+        seed (int, optional): The seed for the random number generator to ensure
+            reproducible results. Defaults to 42.
+        randomize_seed (bool, optional): If True, a random seed is generated,
+            overriding the `seed` parameter. Defaults to False.
+        width (int, optional): The width of the generated image in pixels.
+            Defaults to 1024.
+        height (int, optional): The height of the generated image in pixels.
+            Defaults to 1024.
+        num_inference_steps (int, optional): The number of denoising steps.
+            More steps can lead to higher quality but take longer. Defaults to 4.
+        true_cfg_scale (float, optional): The Classifier-Free Guidance scale.
+            Controls how strictly the model follows the prompt. Defaults to 4.0.
+        progress (gr.Progress, optional): A Gradio Progress object to track
+            the inference progress in the UI.
+
+    Returns:
+        tuple: A tuple containing:
+            - PIL.Image.Image: The generated image.
+            - int: The seed used for the generation, which is useful for
+              reproducibility, especially when `randomize_seed` is True.
+    """
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    generator = torch.Generator().manual_seed(seed)
+    
+    image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            width=width,
+            height=height,
+            num_inference_steps=num_inference_steps, 
+            generator=generator,
+            true_cfg_scale=true_cfg_scale,
+            guidance_scale=distilled_cfg_scale
+    ).images[0] 
+    
+    return image, seed
+ 
+examples = [
+    "a tiny dragon hatching from a crystal egg on Mars",
+    "a red panda holding a sign that says 'I love bamboo'",
+    "a photo of a capybara riding a tricycle in Paris. It is wearing a beret and a striped shirt.",
+    "an anime illustration of a delicious ramen bowl",
+    "A logo for a bookstore called 'The Whispering Page'. The logo should feature an open book with a tree growing out of it.",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 580px;
+}
+"""
+
+# Build the Gradio UI.
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        # Title and description for the demo.
+        gr.Markdown(f"""# Qwen-Image Text-to-Image
+Gradio demo for [Qwen-Image](https://huggingface.co/Qwen/Qwen-Image), a powerful text-to-image model from the Qwen (通义千问) team at Alibaba.
+        """)
+        
+        with gr.Row():
+            # Main prompt input.
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            # The "Run" button.
+            run_button = gr.Button("Run", scale=0)
+            
+        # Negative prompt input.
+        negative_prompt = gr.Text(
+            label="Negative Prompt",
+            max_lines=1,
+            placeholder="Enter a negative prompt",
+            value="text, watermark, copyright, blurry, low resolution",
+        )
+        
+        # Display area for the generated image.
+        result = gr.Image(label="Result", show_label=False)
+        
+        # Accordion for advanced settings.
+        with gr.Accordion("Advanced Settings", open=False):
+            
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+            
+            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():
+                num_inference_steps = gr.Slider(
+                    label="Inference Steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=4,
+                )
+                true_cfg_scale = gr.Slider(
+                    label="CFG Scale",
+                    info="Controls how much the model follows the prompt. Higher values mean stricter adherence.",
+                    minimum=1.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=4.0
+                )
+                distilled_cfg_scale = gr.Slider(
+                    label="Distilled Guidance",
+                    minimum=0.0,
+                    maximum=20.0,
+                    step=0.1,
+                    value=1.0
+                )
+        
+        gr.Examples(
+            examples=examples,
+            fn=infer,
+            inputs=[prompt, negative_prompt],
+            outputs=[result, seed],
+            cache_examples="lazy"
+        )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit, negative_prompt.submit],
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, num_inference_steps, true_cfg_scale, distilled_cfg_scale],
+        outputs=[result, seed]
+    )
+
+demo.launch(mcp_server=True)