Stable Diffusion Download Checkpoint

Hi everyone, I've been using Stable Diffusion to generate images of people and cityscapes, but I haven't been keeping up to date with the latest models and add-ons. I'm looking for recommendations on the best models and checkpoints to use with the nmkd UI of Stable Diffusion, as well as suggestions on how to structure my text inputs for optimal results.

I've tried using some of the default models such as vanilla 1.5 and f222 checkpoints in Stable Diffusion, but I'm interested in exploring other options that may be better suited to my specific use case. In particular, I'm looking to generate high-quality, photo-realistic images of people and cityscapes (preferably different checkpoints for each case)

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If you have any recommendations on specific models or checkpoints that have worked well for you in the past, or tips on how to structure text inputs to get the best results, I would greatly appreciate your insights. Thank you in advance for your help!

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.This model card gives an overview of all available model checkpoints. For more in-detail model cards, please have a look at the model repositories listed under Model Access.

For the first version 4 model checkpoints are released.Higher versions have been trained for longer and are thus usually better in terms of image generation quality then lower versions. More specifically:

Each checkpoint can be used both with Hugging Face's ? Diffusers library or the original Stable Diffusion GitHub repository. Note that you have to "click-request" them on each respective model repository.

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.For more information about how Stable Diffusion functions, please have a look at ?'s Stable Diffusion blog.

The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.

Training ProcedureStable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,

stable-diffusion-v1-2: Resumed from stable-diffusion-v1-1.515,000 steps at resolution 512x512 on "laion-improved-aesthetics" (a subset of laion2B-en,filtered to images with an original size >= 512x512, estimated aesthetics score > 5.0, and an estimated watermark probability < 0.5. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an improved aesthetics estimator).

stable-diffusion-v1-3: Resumed from stable-diffusion-v1-2 - 195,000 steps at resolution 512x512 on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve classifier-free guidance sampling.

stable-diffusion-v1-4 Resumed from stable-diffusion-v1-2 - 225,000 steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve classifier-free guidance sampling.

stable-diffusion-v1-5 Resumed from stable-diffusion-v1-2 - 595,000 steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve classifier-free guidance sampling.

Some Stable Diffusion checkpoint models consist of two sets of weights: (1) The weights after the last training step and (2) the average weights over the last few training steps, called EMA (exponential moving average).

Do you have to merge the files? (it all starts getting really big) So say I have small niche LORA file do I have to merge it with a big checkpoint file to use it? Or can I say something in a prompt? Other methods?

Then, I tried to deploy it to the cloud instance that I have reserved. Everything worked well until the model loading step and it said:

OSError: Unable to load weights from PyTorch checkpoint file at . If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True.

I see your problem is solved, but for some folks that may get this error for the same reason as me, I was loading my pytorch checkpoint with torch v1.4 while the torch version I used when pretraining my model and saving my checkpoint was v1.9 (I pretrained my models on one server and was loading them on another server.) So, double-checking the torch version might help to resolve this error in some cases.

I had the same problem while training a Roberta model. I tried to resume my training with the last checkpoint without success. When I tried to load the second to last checkpoint it worked fine, therefore, my last checkpoint was corrupted and the solution is to restore a previous checkpoint.

I am also experiencing this error. I load the tokenizer without problem from the same checkpoint as the model, but on loading the model I get raise OSError( OSError: Unable to load weights from pytorch checkpoint file . The checkpoint was created under Pytorch 1.9, my current Pytorch is 1.10. The checkpoint is on a network drive, if I try my code and checkpoint on a local drive then I have no problem, its just when operating from a network.

Because the tokenizer is constructed with no problem from this same checkpoint I was wondering if there is a difference in the handling of OS file types between the tokenizer and the model. Because there was a space in the name of one of the directories on the path I tried relocating to a place with no space in the name, and it seemed to fix the problem, but only initially - when I tried again a day later the problem reappeared.

Following in the footsteps of DALL-E 2 and Imagen, the new Deep Learning model Stable Diffusion signifies a quantum leap forward in the text-to-image domain. Released earlier this month, Stable Diffusion promises to democratize text-conditional image generation by being efficient enough to run on consumer-grade GPUs. Just this Monday, Stable Diffusion checkpoints were released for the first time, meaning that, right now, you can generate images like the ones below with just a few words and a few minutes time.

Now that we are working in the appropriate environment to use Stable Diffusion, we need to download the weights we'll need to run it. If you haven't already read and accepted the Stable Diffusion license, make sure to do so now. Several Stable Diffusion checkpoint versions have been released. Higher version numbers have been trained on more data and are, in general, better performing than lower version numbers. We will be using checkpoint v1.4. Download the weights with the following command:

It appears that the number of steps in the diffusion process does not affect results much beyond a certain threshold of about 50 timesteps. The below images were generated using the same random seed and prompt "A red sports car". It can be seen that a greater number of timesteps consistently improves the quality of the generated images, but past 50 timesteps improvements are only manifested in a slight change to the incidental environment of the object of interest. The details of the car are in fact almost fully consistent from 25 timesteps onward, and it is the environment that is improving to become more appropriate for the car in greater timesteps.

To avoid having to suppling the checkpoint with --ckpt sd-v1-4.ckpt each time you generate an image, you can create a symbolic link between the checkpoint and the default value of --ckpt. In the terminal, navigate to the stable-diffusion directory and execute the following commands:

Stable Diffusion is a powerful tool for generating images, but to unlock its full potential, you need to have the right models or checkpoints installed. In this blog, we will guide you through the process of downloading and installing models in Stable Diffusion.

In the context of Stable Diffusion, an essential aspect lies in the utilization of models, commonly referred to as checkpoints. These checkpoints hold immense significance as they serve as pre-trained neural networks, empowering the generation of visually captivating images. By employing a diverse array of models, one gains the ability to delve into a multitude of captivating themes, artistic styles, thereby expanding the creative horizons and possibilities within the realm of image generation.

What are variable autoencoders (VAEs)?VAEs are responsible for generating the final image from the model's internal latent representation. You can further enhance your stable diffusion journey by adding custom VAEs. Obtaining VAEs involves finding suitable ones either through dedicated websites or platforms like Civit AI. For example, the reV Animated model we downloaded in the beginning of this tutorial recommends using one of three VAEs for better and higher quality image outputs.

I'm new to SD, Ive installed required software and copied the model.ckpt to models\Stable-diffusion folder. theres no error in running "webui-user.bat" but Stable diffusion checkpoint takes forever to load that is why i'm unable to generate img.

It does seem rather odd as it has said your model is loaded so im confused as to why you are seeing a loading time on the checkpoint via your web UI. I can only think of a few reasons why this is happening

See if it makes any difference at all. Good luck, stable diffusion can be a bit of a pain to get running on systems, but your issues seems a little bit unique to me. I can't say I've had it not throw an error on the command line, when it has issues with the browser. 17dc91bb1f