Create app1.py

app1.py

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+import gradio as gr
+import torch
+from diffusers import DiffusionPipeline
+from diffusers.quantizers import PipelineQuantizationConfig
+import imageio
+
+# Checkpoint ID
+ckpt_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
+
+# Configure quantization (bitsandbytes 4-bit)
+quant_config = PipelineQuantizationConfig(
+    quant_backend="bitsandbytes_4bit",
+    quant_kwargs={
+        "load_in_4bit": True,
+        "bnb_4bit_quant_type": "nf4",
+        "bnb_4bit_compute_dtype": torch.bfloat16
+    },
+    components_to_quantize=["transformer", "text_encoder"]
+)
+
+# Load pipeline with quantization
+pipe = DiffusionPipeline.from_pretrained(
+    ckpt_id,
+    quantization_config=quant_config,
+    torch_dtype=torch.bfloat16
+).to("cuda")
+
+# Optimize memory and performance
+pipe.enable_model_cpu_offload()
+torch._dynamo.config.recompile_limit = 1000
+torch._dynamo.config.capture_dynamic_output_shape_ops = True
+pipe.transformer.compile()
+
+# Duration function
+def get_duration(prompt, height, width, 
+                 negative_prompt, duration_seconds,
+                 guidance_scale, steps,
+                 seed, randomize_seed):
+    if steps > 4 and duration_seconds > 2:
+        return 90
+    elif steps > 4 or duration_seconds > 2:
+        return 75
+    else:
+        return 60
+
+# Gradio inference function (no @spaces.GPU decorator) to avoid progress ContextVar error
+def generate_video(prompt, seed, steps, duration_seconds):
+    generator = torch.manual_seed(seed) if seed else None
+    fps = 8
+    num_frames = duration_seconds * fps if duration_seconds else 16
+
+    video_frames = pipe(
+        prompt=prompt,
+        num_frames=num_frames,
+        generator=generator,
+        num_inference_steps=steps
+    ).frames[0]
+
+    out_path = "output.gif"
+    imageio.mimsave(out_path, video_frames, fps=fps)
+    return out_path
+
+# Build Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🚀 Wan2.1 T2V - Text to Video Generator (Quantized, Dynamic Duration)")
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Prompt", lines=3, value="A futuristic cityscape with flying cars and neon lights.")
+            seed_input = gr.Number(value=42, label="Seed (optional)")
+            steps_input = gr.Slider(1, 50, value=20, step=1, label="Inference Steps")
+            duration_input = gr.Slider(1, 10, value=2, step=1, label="Video Duration (seconds)")
+            run_btn = gr.Button("Generate Video")
+        with gr.Column():
+            output_video = gr.Video(label="Generated Video")
+
+    run_btn.click(fn=generate_video, inputs=[prompt_input, seed_input, steps_input, duration_input], outputs=output_video)
+
+# Launch demo
+demo.launch()