big-asp-v2
fancyfeast基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
입력 텍스트→ 출력 이미지
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗DISCOVER & COMPARE
AI 모델 탐색 8
bigasp-v1
fancyfeast基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
입력 미확인→ 출력 미확인
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗bigaspv2-5
fancyfeastDrag this image into ComfyUI to load the example workflow. I've done limited testing so far on running the model, so take that workflow and the following advice with a grain of salt. bigASP V2.5 is an intermediary experiment I wanted to run while I prepared for V3. Like V2, it is a large scale finetune from SDXL, and follows most of V2's approach except for these bizarre tweaks: 1. Dataset expanded from 6.7M to 13M images by including additional sources as well as, significantly, anime. Why? Because I wanted to see if the model could dual purpose for both photoreal gens and anime gens. Also I wanted to see if photoreal gens could benefit from the more varied concepts present in anime/furry/etc images. 2. Training length expanded from 40M samples to 150M samples. One of my theories for the tempermental behavior of V2 is the scale of the training. Models like Pony v6 had a much larger amount of training. 150M samples brings V2.5 within the ballpark of that class of model. 3. Using JoyCaption Beta One instead of the Pre-Alphas used in V2. Beta One has more modes, and I tweaked its language and verbage quite a bit, so hopefully V2.5 is less sensitive to prompts. 4. Swapping SDXL's training objective over to Rectified Flow Matching like more modern models (i.e. Flux, Chroma, etc). This was done for two reasons. One, Flow Matching makes higher quality generations. And two, it allowed me to ditch SDXL's broken noise schedule. That latter bit greatly enhances the model's ability to control the overall structure of generations, resulting in less mangled mess generations and extra limbs. It also allows V2.5 to generate more dynamic range from very dark images to very bright images.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 이미지
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗bigaspv26-training
fancyfeast基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 이미지
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
기본 정보 등록 · 상세 정보 보완 예정
입력 이미지→ 출력 미확인
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗joytag
fancyfeast基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
입력 이미지→ 출력 미확인
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗llama-joycaption-alpha-two-hf-llava
fancyfeastJoyCaption is an image captioning Visual Language Model (VLM) being built from the ground up as a free, open, and uncensored model for the community to use in training Diffusion models. Key Features: - **Free and Open**: It will be released for free, open weights, no restrictions, and just like bigASP, will come with training scripts and lots of juicy details on how it gets built. - **Uncensored**: Equal coverage of SFW and NSFW concepts. No "cylindrical shaped object with a white substance coming out on it" here. - **Diversity**: All are welcome here. Do you like digital art? Photoreal? Anime? Furry? JoyCaption is for everyone. Pains are being taken to ensure broad coverage of image styles, content, ethnicity, gender, orientation, etc. - **Minimal Filtering**: JoyCaption is trained on large swathes of images so that it can understand almost all aspects of our world. almost. Illegal content will never be tolerated in JoyCaption's training. Automated descriptive captions enable the training and finetuning of diffusion models on a wider range of images, since trainers are no longer required to either find images with already associated text or write the descriptions themselves. They also improve the quality of generations produced by Text-to-Image models trained on them (ref: DALL-E 3 paper). But to-date, the community has been stuck with ChatGPT, which is expensive and heavily censored; or alternative models, like CogVLM, which are weaker than ChatGPT and have abysmal performance outside of the SFW domain.
번역 대기 중 · 원문 표시
입력 미확인→ 출력 미확인
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗llama-joycaption-beta-one-hf-llava
fancyfeastJoyCaption is an image captioning Visual Language Model (VLM) built from the ground up as a free, open, and uncensored model for the community to use in training Diffusion models. Key Features: - **Free and Open**: Always released for free, open weights, no restrictions, and just like bigASP, will come with training scripts and lots of juicy details on how it gets built. - **Uncensored**: Equal coverage of SFW and NSFW concepts. No "cylindrical shaped object with a white substance coming out on it" here. - **Diversity**: All are welcome here. Do you like digital art? Photoreal? Anime? Furry? JoyCaption is for everyone. Pains are being taken to ensure broad coverage of image styles, content, ethnicity, gender, orientation, etc. - **Minimal Filtering**: JoyCaption is trained on large swathes of images so that it can understand almost all aspects of our world. almost. Illegal content will never be tolerated in JoyCaption's training. Automated descriptive captions enable the training and finetuning of diffusion models on a wider range of images, since trainers are no longer required to either find images with already associated text or write the descriptions themselves. They also improve the quality of generations produced by Text-to-Image models trained on them (ref: DALL-E 3 paper). But to-date, the community has been stuck with ChatGPT, which is expensive and heavily censored; or alternative models, like CogVLM, which are weaker than ChatGPT and have abysmal performance outside of the SFW domain.
번역 대기 중 · 원문 표시
입력 텍스트 · 이미지→ 출력 텍스트
요금 미확인멀티모달 및 채널 요금 별도 확인
자세히 보기 ↗일치하는 모델 없음
제공사, 분류, 입출력 조건을 조정하세요. 새 모델이 계속 등록됩니다.
COST EXPLORER
좋은 모델, 명확한 비용.
월간 토큰 사용량으로 예상 API 비용을 비교하세요.
1 M = 100만 토큰 · USD
동일 채널의 기본 텍스트 토큰 요금 비교
동일 채널의 기본 텍스트 토큰 요금 비교
수집된 백만 토큰당 USD 요금입니다. 캐시 할인, 구간 요금, 추가 추론 출력, 세금은 제외됩니다. 최신 요금은 출처에서 확인하세요.
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