>>> 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.hunyuandi
guidance_scale=cfg_scale, strength = strength, image=init_image, # target_size = (1024, 1024) ).images torch.cuda.empty_cache() cost_time=(datetime.now()-start).seconds print(f"cost time={cost_time},{datetime.now()}") ...
width, scale_factor=8):# 计算新的高度,按照比例因子缩小new_height = height // scale_factor**2# 如果高度不是比例因子的整数倍,向上取整ifheight % scale_factor**2
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...
from_pretrained("stabilityai/stable-diffusion-3.5-large-turbo", torch_dtype=torch.bfloat16).to("cuda")image = pipe( prompt="a photo of a cat holding a sign that says hello world", num_inference_steps=4, height=1024, width=1024, guidance_scale=1.0,).images[]image.sav...
其中guidance_scale参数表示图片生成过程中考虑 prompt 的权重。 创建text embedding 接下来,我们来对条件 prompt 进行 tokenize,并通过 text encoder 模型产生文本 embedding: text_input=tokenizer(prompt,padding="max_length",max_length=tokenizer.model_max_length,truncation=True,return_tensors="pt")withtorch.no...
夹pipe=DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",torch_dtype=torch.float16)pipe.to("cuda")pipe.load_lora_weights(model_path)prompt="A moose in watercolor painting style"# 生成内容image=pipe(prompt,num_inference_steps=30,guidance_scale=7.5).images[0]image.save...
(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_...
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) ...
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...