import torchfrom diffusers import StableDiffusion3Pipelinepipe = StableDiffusion3Pipeline.from_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="", ...
fromdiffusersimportStableDiffusion3Pipelinepipeline=StableDiffusion3Pipeline.from_pretrained(model_id,transformer=model_nf4,torch_dtype=torch.bfloat16)pipeline.enable_model_cpu_offload()prompt="A whimsical and creative image depicting a hybrid creature that is a mix of a waffle and a hippopotamus, baski...
Stable Diffusion 3.5 的 transformer 模型还可以使用 Stability AI 发布的原生参数文件来进行初始化 。 这里需要使用from_single_file方法: importtorch fromdiffusersimportSD3Transformer2DModel, StableDiffusion3Pipeline transformer = SD3Transformer2DModel.from_single_file( "https://huggingface.co/stabilityai/stable...
importtorchfromdiffusersimportSD3Transformer2DModel,StableDiffusion3Pipeline transformer=SD3Transformer2DModel.from_single_file("https://huggingface.co/stabilityai/stable-diffusion-3.5-large-turbo/blob/main/sd3.5_large.safetensors",torch_dtype=torch.bfloat16,)pipe=StableDiffusion3Pipeline.from_pretrained("s...
import torchfrom diffusers import StableDiffusion3Pipelinepipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", text_encoder_3=None, tokenizer_3=None, torch_dtype=torch.float16)pipe = pipe.to("cuda")prompt = "smiling cartoon dog sits at a table, ...
Intro Diffusers provides a Stable Diffusion pipeline compatible with the ONNX Runtime. This allows you to run Stable Diffusion on any hardware that supports ONNX (including CPUs), and where an accelerated version of PyTorch is not availa...
pipe=StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers",text_encoder_3=None,tokenizer_3=None,torch_dtype=torch.float16)pipe=pipe.to("cuda")prompt="smiling cartoon dog sits at a table, coffee mug on hand, as a room goes up in flames. “This is fine...
DreamBooth是存放在Hugging Face的一个文生图项目模型,我们可以用diffusers包来调用,当然也可以自己克隆项目来创建自己的后端产品,此外能够让我们对stable diffusion模型进行微调,这个过程可以引入特定的面部、物体或风格等额外的信息。目前有249个各色各样的模型来玩耍~ ...
python ./scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path xxx.ckpt --dump_path save_dir 转换完成后，可直接利用 diffusers 的 API 进行模型加载 from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained (save_dir,torch_dtype=torch.float...
给定一个mask图像和一句提示,可编辑给定图像的特定部分。使用StableDiffusionInpaintPipeline来实现,输入包含三部分:原始图像,mask图像和一个prompt, 示例代码: fromdiffusersimportStableDiffusionInpaintPipeline img_url ="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture...