refiner model的参数量为2.3B,略小于base model。 另外refiner model的text encoder只使用了OpenCLIP ViT-bigG,也是提取倒数第二层特征以及pooled text embed。与base model一样,refiner model也使用了size and crop conditioning,除此之外还增加了图像的艺术评分aesthetic-score作为条件,处理方式和之前一样。refiner mo...
One of the most significant differences between SDXL and the rest of AI image generation models (except for Deep Floyd), is that it can generate legible text. It wasn’t possible in the previous SD or any other models. TSDXL与其他AI图像生成模型(Deep Floyd除外)最明显的区别之一是,它可以生...
原风格提示词: "professional 3d model <prompt> . octane render, highly detailed, volumetric, dramatic lighting"替换后：“professional 3d model A young and beautiful girl is in the park, smiling, brown hair, a pink top, flowers, green leaves, bright sunshine . octane render, highly detailed, ...
super-resolution text-to-image inpainting dit distillation diffusion-models sdxl Updated Jan 18, 2025 Python TheMistoAI / MistoLine Star 507 Code Issues Pull requests Discussions A Versatile and Robust SDXL-ControlNet Model for Adaptable Line Art Conditioning art stable-diffusion controlnet sd...
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Steps: 36, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 1930821284, Size: 512x512, Model hash: 31e35c80fc, Model: sd_xl_base_1.0, VAE hash: 63aeecb90f, VAE: sdxl_vae.safetensors, Lora hashes: "AP-xl: f5f7e8a091b0", Refiner: sd_xl_refiner_1.0_0.9vae [8d0ce6c016], ...
首先我们要把huggingface 下载的原始模型转化为OpenVINO IR,以便后续的NNCF 工具链进行量化工作。转换完成后你将得到对应的text_encode、unet、vae 模型。 from pathlib import Path model_dir = Path("./sdxl_vino_model") sdxl_model_id = "stabilityai/sdxl-turbo" ...
0.2对Stable Diffusion原先的U-Net(XL Base U-Net 一共14个模块),VAE,CLIP Text Encoder三大件都做了改进。可以明显减少显存占用和计算量 0.3增加一个单独的基于Latent(潜在)的Refiner(炼制)模型,来提升图像的精细化程度。【新增:对Base模型生成的图像Latent特征进行精细化,其本质上是在做图生图的工作。】 ...
[prompt, prompt_2] for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): text_inputs = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pd") text_input_ids = text_inputs['input_ids'] untruncated_...
pipe = AutoPipelineForImage2Image.from_pretrained("/本地路径/sdxl-turbo", torch_dtype=torch.float16, variant="fp16") pipe.to("cuda") init_image = load_image("/本地路径/用于图生图的原始图片").resize((913, 512)) prompt = "Miniature model, axis shifting, reality, clarity, details, pa...