【CHECKPOINT】Deliberate for Invoke故意调用deliberateForInvoke_v08[56D1442A0F]

名称：deliberateForInvoke_v08.safetensors
类型：【CHECKPOINT MERGE】
程序版本：SD 1.5
文件大小：2.14GB
概述：
This is a forked version of Xpuct's phenomenal "Deliberate" v1.1 model that is designed to work in InvokeAI (and possibly other tools as well). It is an incredibly flexible model, capable of producing outstanding people, landscapes, architecture and more in a wide variety of styles.
NOTE: Xpuct's latest "Deliberate v2" works perfectly in Invoke! This forked version will still work and produce gorgeous results, but given that the purpose of this fork was to allow access to Deliberate in Invoke, I consider this forked version deprecated.
This forked version works with Invoke 2.2.5 and 2.3.0. Please note: If you convert this .ckpt file to a Diffuser model for 2.3, it will will produce different results than what you see in the samples, which were created with the .ckpt version.
All sample images posted here were created using InvokeAI 2.2.5. The prompts are formatted for Invoke. See below for details. I have tested a few of these prompts in Invoke 2.3 and can verify that they still work.
I wanted to make sure that InvokeAI users could recreate the sample images shown here, so I've included the full parameter data in the Prompt field of the image info. Since Invoke does not have a separate Negative Prompt field (negative prompts are just stored in [square brackets]), the negative prompt info that you need is included in the main Prompt field here on Civit. (Remember, this model is intended for Invoke users, not Auto1111 users.) I'm adding the size, seed, variation and seed weight info to the Negative prompt field here so you can easily copy that data into Invoke.
The samples are straight out of the Text to Image mode. No Image to Image or Unified Canvas (which tends to break or lose the metadata). If you want to recreate the samples shown, copy the full text from the Prompt field and paste it into the Prompt in Invoke. Then look down at the Negative Prompt field for the image here on Civit. That contains the details you need. Set the Steps in Invoke to what you see after the (-s), the Seed to what you see after the (-S), the width and height (-W and -H), the CFG (-C), and the Sampler (-A). If Variations are used, you'll see (-v) and (-V). Enable Variations in Invoke if you see these. Set the Variation amount to what you see after the (-v) and copy the numbers after the (-V) into the Seed Weights field in Invoke. If you do this, you should be able to recreate each of the sample images shown here.
The VAE is included in the model, so Invoke users should simply need to add it and be good to go.
All images shown were generated on a 7 year old Windows workstation with 32GB of RAM and a GTX1080 graphics card.
All the credit for this goes to Xpuct for creating such an amazing model. I'm hoping Xpuct eventually revises his model so that it works with other tools besides Automatic1111. I have no interest in maintaining a fork of his project. But I see a lot of people complaining that the model doesn't work in anything other than Automatic1111, so this is my simple attempt to fix that.
这是Xpuct非凡的“Deliberate”v1.1模型的分支版本，旨在用于InvokeAI（可能还有其他工具）。这是一个令人难以置信的灵活模型，能够产生各种风格的杰出人物、景观、建筑等。
注意：Xpuct最新的“Deliberate v2”在Invoke中完美运行！这个分叉版本仍然可以工作并产生华丽的结果，但鉴于这个分叉的目的是允许在调用中访问 Deliberate，我认为这个分叉版本已被弃用。
此分叉版本适用于 Invoke 2.2.5 和 2.3.0。请注意：如果将此 .ckpt 文件转换为 2.3 的扩散器模型，它将产生与您在示例中看到的结果不同的结果，这些示例是使用 .ckpt 版本创建的。
此处发布的所有示例映像都是使用 InvokeAI 2.2.5 创建的。提示的格式为调用。 详情见下文。 我已经在 Invoke 2.3 中测试了其中一些提示，并且可以验证它们是否仍然有效。
我想确保 InvokeAI 用户可以重新创建此处显示的示例图像，因此我在图像信息的“提示”字段中包含完整的参数数据。由于 Invoke 没有单独的否定提示字段（否定提示仅存储在 [方括号] 中），因此您需要的负面提示信息包含在 Civit 的主提示字段中。 （请记住，此模型适用于调用用户，而不是 Auto1111 用户。我在此处将大小、种子、变体和种子权重信息添加到“负提示”字段中，以便您可以轻松地将该数据复制到 Invoke 中。
这些示例直接来自文本到图像模式。没有图像到图像或统一画布（这往往会破坏或丢失元数据）。如果要重新创建显示的示例，请从“提示”字段中复制全文，并将其粘贴到“调用”中的“提示”中。然后低头查看 Civit 上此处图像的负面提示字段。其中包含您需要的详细信息。将调用中的步骤设置为在 （-s） 之后看到的内容，将种子设置为在 （-S） 之后看到的内容、宽度和高度（-W 和 -H）、CFG （-C） 和采样器 （-A）。如果使用变体，您将看到 （-v） 和 （-v）。在调用中启用变体（如果看到这些）。将变异量设置为您在 （-v） 之后看到的数量，并将 （-V） 之后的数字复制到 Invoke 的种子权重字段中。如果执行此操作，您应该能够重新创建此处显示的每个示例映像。
VAE包含在模型中，因此Invoke用户只需添加它即可。
显示的所有图像都是在具有7GB RAM和GTX32显卡的1080年Windows工作站上生成的。
所有的功劳都归功于Xpuct创造了这样一个惊人的模型。我希望 Xpuct 最终修改他的模型，以便它与 Automatic1111 以外的其他工具一起使用。我对维护他的项目的一个分支没有兴趣。但是我看到很多人抱怨该模型在Automatic1111之外的任何东西上都不起作用，所以这是我解决这个问题的简单尝试。

Hash	parameter
SHA256	56D1442A0FEEFD64836A19AC8E3527EC71C884FC962E246DDF67B03E42921272
CRC32	A05610FA
BLAKE3	9D292D1A90616497838FA7F1BD8BEB0177A33C7DB8E5522824F39B74A9D30DB7
AUTOV1	97E44EA7
AUTOV2	56D1442A0F

作品展示：

Generation Data：

1. a professional photo of (two women gazing seductively+ into each others eyes)+, posing in a pink pastel candy store, (intricate details, (intricate++ eyes), sexy clothing, lace+, studio lighting, (detailed skin and hair texture)+++, highly contrasted, high-quality photo shot on iPhone [nude+, low quality, low resolution, bad quality, b&w, bad anatomy, bad photo, bad art, bad proportions, (mutated hands and fingers)+++, 3d, cartoon, illustration, art, (body hair)+, (leg hair+), anime, (facial hair)+, watermark, logo, clothes writing]
   
2. Negative prompt: -s 20 -S 451685995 -W 768 -H 1024 -C 7 -A k_heun --hires NOTE: The eyes were fixed using Inpainting via the Unified Canvas in InvokeAI.
   
3. Steps: 20, Seed: 451685995, Steps: 20, Sampler: Heun, CFG scale: 7

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Generation Data：

1. vector painting of a cyberpunk girl, (Graffiti art)++, (by Carne Griffiths)++, vector art, close up, squatting, sunglasses, high heel shoes, perfect hourglass figure, latex bra, red wall background, insane details, intricate details, hyperdetailed, low contrast, soft cinematic light, dim colors, exposure blend, hdr, front
   
2. 
   
3. [deformed, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, disgusting, poorly drawn hands, missing limb, floating limbs, disconnected limbs, malformed hands, blurry, (mutated hands and fingers)++++, watermark, watermarked, oversaturated, censored, distorted hands, amputation, missing hands, obese, doubled face, double hands, asian, b&w, black and white, sepia]
   
4. Negative prompt: -s 30 -S 4141461773 -W 768 -H 1024 -C 7 -A ddim -v 0.005 -V 1934268991:0.05,4272053423:0.005
   
5. Steps: 30, Seed: 4141461773, Steps: 30, Sampler: DDIM, CFG scale: 7

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Generation Data：

1. pebble beach waterfall, extremely detailed oil painting, unreal 5 render, rhads, sargent and leyendecker, savrasov levitan polenov, bruce pennington, studio ghibli, tim hildebrandt, digital art, landscape painting, octane render, beautiful composition, trending on artstation, award winning photograph, masterpiece
   
2. Steps: 50, Seed: 2295575152, Steps: 50, Sampler: DDIM, CFG scale: 10

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Generation Data：

1. a gorgeous princess of the sea, artgrem, wlop, ultra realistic, render
   
2. Steps: 50, Seed: 3980288542, Steps: 50, Sampler: DDIM, CFG scale: 10

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Generation Data：

1. a masterpiece detailed beautiful cityscape, early hour morning, a super cute girl drinking coffee and look at her phone, studio lighting, (detailed skin and hair texture)+++, highly contrasted, high-quality
   
2. Steps: 50, Seed: 1008903990, Steps: 50, Sampler: DDIM, CFG scale: 10

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