https://civitai.com/models/204962?modelVersionId=251594有点超现实 Sampling method: DPM++ 2M Karras...
\(word\) - 在提示词中使用字面意义上的 () 字符其中前面很长一串都是描述照片本身的质量,如masterpiece,best quality, ultra high res等等,这类可以当作基础起手式,每一张都可以套用,末尾的tag才属于对主体的描述,如extremely detailed eyes and face,beach of sea, sunset等建议对每类tag使用换行符分类整理,...
For better results, try to add “close-up” to the prompt AnRealSpiceMix https://civitai.com/models/204962?modelVersionId=251594 有点超现实 Sampling method: DPM++ 2M Karras Sampling Steps: 20-45 DreamShaper https://civitai.com/models/4384/dreamshaper 在2.5D类型中也是属于最全面和万能的模型...
Prompt: "a beautiful landscape with mountains and a river, sunset, high detail, 4k" Negative Prompt: "blurry, low quality, distorted" Sampling Method: Euler a Steps: 50 CFG Scale: 7 Seed: 123456 Width: 768 Height: 512 希望这些提示词和参数能帮助您更好地使用Stable Diffusion Webui。如果有...
Sampling method: DPM++ 2M Karras | DPM++ SDE Karras Prompt: (masterpiece, best quality, very detailed, Ultra HD: 1.2) | (((masterpiece))), (((best quality))), (((extremely detailed))), Depth of field, illustration, shiny, pastel color, 案例: (masterpiece, best quality, very de...
best quality, space-themed, cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed"-n"realistic, photo-realistic, worst quality, greyscale, bad anatomy, bad hands, error, text"--cfg-scale 5.0 --sampling-method euler -H 1024 -W 1024 --style-ratio 10 --vae...
However, this amplifies the diffi- culty of separating the effects of image and video data on the final model. To address these shortcomings, this work presents a systematic study of methods for video data cu- ration and further introduces a general three-stage training str...
If you know what you are doing, you can install Forge using same method as SD-WebUI. (Install Git, Python, Git Clone the forge repohttps://github.com/lllyasviel/stable-diffusion-webui-forge.gitand then run webui-user.bat). Or you can just use this one-click installation package (wi...
method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly met...
Relative to SDEdit-based methods, DDIM inversion-based methods [27], [29], [51], [52] have an advantage in maintaining faithfulness to the reference image due to DDIM inversion’s deterministic sampling. DiffEdit [51] automatically generates masks to mark the regions that should be edited gui...