[Doc] Add Z-Image Base support and fix Z-Image Turbo parameters #1229
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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python3 | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
| # SPDX-FileCopyrightText: Copyright contributors to the vLLM project | ||
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| """ | ||
| Z-Image Base vs Turbo Comparison Examples | ||
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| This script demonstrates the differences between Z-Image Base and Z-Image Turbo models. | ||
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| Key Differences: | ||
| - Z-Image Base: Foundation model with full CFG support, fine-tunable, 28-50 steps | ||
| - Z-Image Turbo: Distilled model optimized for speed, 8 steps, guidance_scale must be 0.0 | ||
| """ | ||
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| from vllm_omni.entrypoints.omni import Omni | ||
| from vllm_omni.inputs.data import OmniDiffusionSamplingParams | ||
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| def z_image_base_example(): | ||
| """ | ||
| Z-Image Base - High Quality Generation | ||
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| Features: | ||
| - Full CFG support (guidance_scale 3.0-5.0) | ||
| - Negative prompts work | ||
| - Fine-tunable | ||
| - 28-50 inference steps (default 50) | ||
| - Scheduler shift: 6.0 | ||
| """ | ||
| print("\n=== Z-Image Base (High Quality) ===") | ||
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| omni_base = Omni(model="Tongyi-MAI/Z-Image") | ||
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| outputs_base = omni_base.generate( | ||
| { | ||
| "prompt": "a majestic mountain landscape at sunset, detailed, photorealistic", | ||
| "negative_prompt": "blurry, low quality, distorted, oversaturated", | ||
| }, | ||
| OmniDiffusionSamplingParams( | ||
| height=1280, | ||
| width=720, | ||
| num_inference_steps=50, | ||
| guidance_scale=4.0, | ||
| seed=42, | ||
|
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There was a problem hiding this comment. great, refactored it to correctly match being packed in a prompt dict. |
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| ), | ||
| ) | ||
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| images = outputs_base[0].request_output[0].images | ||
| images[0].save("z_image_base_output.png") | ||
| print("Saved to: z_image_base_output.png") | ||
| print(f"Generated {len(images)} image(s) with 50 steps and CFG=4.0") | ||
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| def z_image_turbo_example(): | ||
| """ | ||
| Z-Image Turbo - Fast Inference | ||
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| Features: | ||
| - Optimized for speed | ||
| - guidance_scale MUST be 0.0 (no CFG) | ||
| - Negative prompts not supported | ||
| - 8 inference steps | ||
| - Scheduler shift: 3.0 | ||
| """ | ||
| print("\n=== Z-Image Turbo (Fast) ===") | ||
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| omni_turbo = Omni(model="Tongyi-MAI/Z-Image-Turbo") | ||
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| outputs_turbo = omni_turbo.generate( | ||
| "a majestic mountain landscape at sunset, detailed, photorealistic", | ||
| OmniDiffusionSamplingParams( | ||
| height=1024, | ||
| width=1024, | ||
| num_inference_steps=8, | ||
| guidance_scale=0.0, # MUST be 0.0 for Turbo! | ||
| seed=42, | ||
| ), | ||
| ) | ||
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| images = outputs_turbo[0].request_output[0].images | ||
| images[0].save("z_image_turbo_output.png") | ||
| print("Saved to: z_image_turbo_output.png") | ||
| print(f"Generated {len(images)} image(s) with 8 steps (no CFG)") | ||
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| def batch_inference_example(): | ||
| """ | ||
| Batch inference with Z-Image Base | ||
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| Note: Batch processing depends on max_batch_size in stage configs. | ||
| By default, diffusion models process one prompt at a time. | ||
| """ | ||
| print("\n=== Batch Inference Example ===") | ||
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| omni = Omni(model="Tongyi-MAI/Z-Image") | ||
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| prompts = [ | ||
| {"prompt": "a cup of coffee on a wooden table", "negative_prompt": "blurry, low quality"}, | ||
| {"prompt": "a cat sleeping on a cozy blanket", "negative_prompt": "blurry, low quality"}, | ||
| {"prompt": "a futuristic city skyline at night", "negative_prompt": "blurry, low quality"}, | ||
| ] | ||
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| # Note: These will be processed sequentially unless max_batch_size > 1 | ||
| outputs = omni.generate( | ||
| prompts, | ||
| OmniDiffusionSamplingParams( | ||
| height=1024, | ||
| width=1024, | ||
| num_inference_steps=40, | ||
| guidance_scale=4.0, | ||
| seed=42, | ||
| ), | ||
| ) | ||
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| for i, output in enumerate(outputs): | ||
| image = output.request_output[0].images[0] | ||
| image.save(f"batch_output_{i}.png") | ||
| print(f"Saved to: batch_output_{i}.png") | ||
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| def recommended_settings(): | ||
| """ | ||
| Print recommended settings for both models | ||
| """ | ||
| print("\n=== Recommended Settings ===\n") | ||
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| print("Z-Image Base (Tongyi-MAI/Z-Image):") | ||
| print(" - num_inference_steps: 28-50 (default: 50)") | ||
| print(" - guidance_scale: 3.0-5.0 (default: 4.0)") | ||
| print(" - negative_prompt: Supported and recommended") | ||
| print(" - resolution: 1280x720 or 720x1280") | ||
| print(" - cfg_normalization: False (default)") | ||
| print(" - Use when: Quality is priority, fine-tuning needed") | ||
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| print("\nZ-Image Turbo (Tongyi-MAI/Z-Image-Turbo):") | ||
| print(" - num_inference_steps: 8") | ||
| print(" - guidance_scale: 0.0 (REQUIRED)") | ||
| print(" - negative_prompt: Not supported") | ||
| print(" - resolution: 1024x1024") | ||
| print(" - Use when: Speed is priority, quick iterations") | ||
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| if __name__ == "__main__": | ||
| import argparse | ||
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| parser = argparse.ArgumentParser(description="Z-Image Base vs Turbo comparison examples") | ||
| parser.add_argument( | ||
| "--example", | ||
| choices=["base", "turbo", "batch", "all"], | ||
| default="all", | ||
| help="Which example to run (default: all)", | ||
| ) | ||
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| args = parser.parse_args() | ||
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| recommended_settings() | ||
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| if args.example in ("base", "all"): | ||
| z_image_base_example() | ||
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| if args.example in ("turbo", "all"): | ||
| z_image_turbo_example() | ||
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| if args.example in ("batch", "all"): | ||
| batch_inference_example() | ||
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| print("\nDone!") | ||
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I don't think we need to modify this file.