pipeline = DiffusionPipeline.from_pretrained("sd-dreambooth-library/disco-diffusion-style") #你要输入的prompt,生成你想要的style prompt = "A cyberpunk-style building" image = pipeline(prompt, num_inference_steps=50, gui
883 CrossAttentionControl github.com/bloc97/Cross Unofficial implementation of "Prompt-to-Prompt Image Editing with Cross Attention Control" with Stable Diffusion youtube.com/watch? 注意到这是一个非官方的实现,但是非官方实现有colab,而且验证了能跑。我觉得这个的理论价值也很大 如果做prompt到prompt 可以...
详见:https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py 完成checkpoint模型的转化后,我们可能还需要依赖lora,做lora的转化。 代码语言:shell AI代码解释 python ~/diffusers/scripts/convert_lora_safetensor_to_diffusers.py--base_model_pathdreamlike-art/...
from diffusersimportStableDiffusionPipeline pipe=StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4",use_auth_token=True)pipe=pipe.to("cuda")prompt="a photo of an astronaut riding a horse on mars"withautocast("cuda"):image=pipe(prompt).images[0] 如果你想提前下载模型,然后进...
image_pipe.to("cuda") prompt ="a photograph of an astronaut riding a horse" pipe_out = image_pipe(prompt) image = pipe_out.images[0] # you can save the image with # image.save(f"astronaut_rides_horse.png") 我们查看下image_pipe的内容: ...
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator ... ).to_tuple()# 使用控制管道生成最终图像>>> images = pipe( ... image_embeds=image_emb, ... negative_image_embeds=zero_image_emb, ... hint=hint,
validation_steps: 每隔这么多步,validation_image 和 validation_prompt 就会跑一下,来验证训练过程。 report_to: 向哪里报告训练情况。这里我们使用 Weights and Biases 这个平台,它可以给出美观的训练报告。 push_to_hub: 将最终结果推到 Hugging Face Hub. 但是将 train_batch_size 从 4 减小到 1 可能还不...
diffusers.utils import export_to_videopipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)pipe.enable_model_cpu_offload()prompt = "Darth Vader surfing a wave"video_frames = pipe(prompt, num_frames=24).framesvideo_path = export_to_video(video_...
import torchfrom diffusers import StableDiffusion3Pipelinepipe = StableDiffusion3Pipeline.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_st...
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...