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。
入力 テキスト→ 出力 画像
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗DISCOVER & COMPARE
AIモデルを探す 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。
入力 未確認→ 出力 未確認
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗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.
翻訳待ち・原文を表示
入力 テキスト→ 出力 画像
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗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。
基本情報を収録・詳細は順次追加
入力 テキスト→ 出力 画像
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗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。
基本情報を収録・詳細は順次追加
入力 画像→ 出力 未確認
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗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。
入力 画像→ 出力 未確認
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗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.
翻訳待ち・原文を表示
入力 未確認→ 出力 未確認
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗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.
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗該当するモデルがありません
提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
COST EXPLORER
モデル選びに、費用の視点も。
月間トークン数を調整してAPI費用を比較。
1 M = 100万トークン・米ドル
同一チャネルの基本テキスト料金を比較
同一チャネルの基本テキスト料金を比較
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
MODEL HANDBOOK
モデルを理解して選ぶ
API料金はどう比較しますか？+
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
オープンウェイトは無料で商用利用できますか？+
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
ランキング1位が最適ですか？+
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。