Callable,Dict,List,Optional,Union# 导入类型注解importPIL.Image# 导入用于图像处理的PIL库importtorch# 导入PyTorch库fromtransformersimportCLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWith
fill the masked part of an image given the image, the mask and a text prompt inpaint Text-Guided Depth-to-Image Translation adapt parts of an image guided by a text prompt while preserving structure via depth estimation from diffusers import DiffusionPipeline 代码语言:javascript 代码运行次数:0 ...
)# 获取标记器的最大长度self.tokenizer_max_length = self.tokenizer.model_max_length# 获取变换器的默认样本大小self.default_sample_size = self.transformer.config.sample_size# 从 diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds 复制的方法...
Diffusers Image Fill a Pinokio install script:pinokio.computer 🚀 3 Apps in 1! Three diffusers apps combining the speed of SDXL lightning (RealVisXL 5) with the precision of ControlNetPlus Promax Based on a Proof of Concept by OzzyGT:https://huggingface.co/blog/OzzyGT/diffusers-image-fil...
import uuid from PIL import Image import numpy as np import cv2 def generate_and_extract_image(user_input, output_path): # 生成图像 image = pipe( user_input, width=512, height=512, guidance_scale=7.0, num_inference_steps=50, negative_prompt="(worst quality:2),(low quality:2),(normal...
bad-artist-anime,bad_prompt_version2,negative_hand-neg,", ).images[0] # 保存图像 image.save(output_filename) return output_filename if __name__ == "__main__": input_profile = "周老六, 男生,戴眼镜" output_path = generate_image_from_profile(input_profile) print(f"Image saved to {...
with torch.autocast("cuda"): output = pipeline(prompt=prompt, image=image, mask_image=mask, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).images[0]. 这里使用了torch.autocast来利用GPU的加速功能(前提是你有可用的GPU)。 2. 保存生成结果。 生成的扩展图像可以通过PIL库进行...
image_output = None return prompt, negative_prompt,image_input,image_output with gr.Blocks(title="Stable Diffusion",theme=gr.themes.Default(primary_hue=gr.themes.colors.blue))as demo: with gr.Tab("Text-to-Image"): ...
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX. - diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py at main · huggingface/diffusers
Next, you can create an “extended image” to match the shape of an outpainted one. Together with the mask created, you converted an outpainting problem into an inpainting problem in which the mask is along the border. You can simply fill the pixels outside the original border with gray....