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Consistency Distilled Diff VAE

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I'm recently trying to use this work to my current research, but have problems reproducing the code.❤️

Hey @gabgoh, Super cool that you're open-sourcing the consistency decoder of Dalle-3 with a MIT license :heart: Any chance you can also add the model definitions of the torch.jit binary?...

Can we call consistency_decoder in context of torch.enable_grad() ?

Hi @gabgoh , I attempted to reproduce the results of the code provided in the Usage section of the README, but I encountered some discrepancies compared to the expected output...

Here is my code, however, there seems no difference between gan_vae and cosistency_vae. What's wrong? `import torch` `from diffusers import StableDiffusionPipeline, ConsistencyDecoderVAE` `vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)` `pipe_gan = StableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5",...

It's not for SD1, or it should use specified libraries versions, or something else? Code: ``` latent = txt2img( prompt=prompt, width=width, height=height, num_images_per_prompt = num_image, guidance_scale = guidance, num_inference_steps =...

![image_2023-11-06_23-07-38](https://github.com/openai/consistencydecoder/assets/148798779/0391ef8b-6a6d-4034-90c9-09106dbaa9c4) Running the consistency decoder takes several seconds and most of this time is spent in a stalled state and reducing the number of diffusion steps leads to no meaningful...

Hi, Does it work for 512x512 images? Thanks!

Thanks to open your work. Btw, I ran the code: ```python from diffusers import DiffusionPipeline,StableDiffusionPipeline import torch from consistencydecoder import ConsistencyDecoder, save_image, load_image pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main", revision="fb9c5d") decoder_consistency...

Can we use this in automatic1111? or does an extension need to be created first?