guidance_scale=cfg_scale, strength = strength, image=init_image, # target_size = (1024, 1024) ).images torch.cuda.empty_cache() cost_time=(datetime.now()-start).seconds
AI代码解释 fromdiffusersimport(StableDiffusionPipeline,PNDMScheduler,LMSDiscreteScheduler,DDIMScheduler,EulerDiscreteScheduler,EulerAncestralDiscreteScheduler,DPMSolverMultistepScheduler,UniPCMultistepScheduler,)defmake_scheduler(name,config):return{"PNDM":PNDMScheduler.from_config(config),"KLMS":LMSDiscreteScheduler...
>>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0] >>> export_to_video(video, "output.mp4", fps=8) ```py """# 定义一个函数,用于计算调整大小和裁剪区域,以适应网格# 该函数类似于 diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region...
prompt = "A cyberpunk-style building" image = pipeline(prompt, num_inference_steps=50, guidance_scale=7.5).images[0] plt.imshow(image) plt.axis('off') plt.savefig("00zuxuezhixin/diffusers_practice/disco.png") plt.show() 效果如下: 1.2 Diffusers核心API 三部分 管线:从高层次设计的多种类函...
fromtqdm.autoimporttqdmscheduler.set_timesteps(num_inference_steps)fortintqdm(scheduler.timesteps):# 我们要做 classifier-guidance generation,所以先扩一下 latent,方便并行推理latent_model_input=torch.cat([latents]*2)latent_model_input=scheduler.scale_model_input(latent_model_input,timestep=t)# 预测噪...
guidance_scale: float = 4.0, num_images_per_prompt: int = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, output_type: Optional[str] = "pil", callback: Optional[Callable[[int, int, torch.Tensor], None]] = None...
(best quality),(ultra-detailed),1boy, full body, chibi, yellow, outdoors, beret",negative_prompt="(low quality:1.3), (worst quality:1.3)",width=512,height=768,guidance_scale=9,num_inference_steps=30,generator=generator).images[0]output_path=f"/tmp/out-{seed}.png"image.save(output_...
image = pipeline(text_prompt, guidance_scale=7.5).images[0] # 显示图像 image.show() 在上面的代码中,我们首先加载了训练好的ControlNet模型。然后,我们初始化了一个DiffusionPipeline生成器,该生成器将负责实际的图像生成过程。接着,我们输入了一个文本描述作为生成图像的依据,并通过pipeline方法生成了图像。最后...
pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16).to("cuda")image = pipe( prompt="a photo of a cat holding a sign that says hello world", negative_prompt="", num_inference_steps=40, height=1024, width=1024, guidance_scale=4.5,).imag...
guidance_scale=7.0, ).images[0] image 图生图 import torch from diffusers import StableDiffusion3Img2ImgPipeline from diffusers.utils import load_image pipe = StableDiffusion3Img2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16) ...