Buckets:
twanghcmut/backup-foundation-physics / third_party /diffsynth /examples /z_image /model_inference /Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py
| from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig, ControlNetInput | |
| from modelscope import dataset_snapshot_download | |
| from PIL import Image | |
| import torch | |
| pipe = ZImagePipeline.from_pretrained( | |
| torch_dtype=torch.bfloat16, | |
| device="cuda", | |
| model_configs=[ | |
| ModelConfig(model_id="PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1", origin_file_pattern="Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.safetensors"), | |
| ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors"), | |
| ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"), | |
| ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), | |
| ], | |
| tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"), | |
| ) | |
| # Control | |
| dataset_snapshot_download( | |
| dataset_id="DiffSynth-Studio/example_image_dataset", | |
| local_dir="./data/example_image_dataset", | |
| allow_file_pattern="depth/image_1.jpg" | |
| ) | |
| controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1024, 1024)) | |
| prompt = "精致肖像,水下少女,蓝裙飘逸,发丝轻扬,光影透澈,气泡环绕,面容恬静,细节精致,梦幻唯美。" | |
| image = pipe(prompt=prompt, seed=0, height=1024, width=1024, controlnet_inputs=[ControlNetInput(image=controlnet_image, scale=0.7)]) | |
| image.save("image_control.jpg") | |
| # Inpaint | |
| dataset_snapshot_download( | |
| dataset_id="DiffSynth-Studio/example_image_dataset", | |
| local_dir="./data/example_image_dataset", | |
| allow_file_pattern="inpaint/*.jpg" | |
| ) | |
| inpaint_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1024, 1024)) | |
| inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1024, 1024)) | |
| prompt = "一只戴着墨镜的猫" | |
| image = pipe(prompt=prompt, seed=0, height=1024, width=1024, controlnet_inputs=[ControlNetInput(inpaint_image=inpaint_image, inpaint_mask=inpaint_mask, scale=0.7)]) | |
| image.save("image_inpaint.jpg") | |
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