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Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.
| Date | Stars |
|---|---|
| 2026-07-24 | 385 |
| 2026-07-25 | 385 |
| 2026-07-28 | 385 |
| 2026-07-30 | 385 |
| 2026-08-06 | 385 |
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# CLIP-Guided-Diffusion Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab. Original colab notebooks by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings): * Original 256x256 notebook: [![Open In Colab][colab-badge]][colab-notebook1] [colab-notebook1]: <https://colab.research.google.com/drive/12a_Wrfi2_gwwAuN3VvMTwVMz9TfqctNj#scrollTo=X5gODNAMEUCR> [colab-badge]: <https://colab.research.google.com/assets/colab-badge.svg> It uses OpenAI's 256x256 unconditional ImageNet diffusion model (https://github.com/openai/guided-diffusion) * Original 512x512 notebook: [![Open In Colab][colab-badge]][colab-notebook2] [colab-notebook2]: <https://colab.research.google.com/drive/1QBsaDAZv8np29FPbvjffbE1eytoJcsgA#scrollTo=VnQjGugaDZPJ> [colab-badge]: <https://colab.research.google.com/assets/colab-badge.svg> It uses a 512x512 unconditional ImageNet diffusion model fine-tuned from OpenAI's 512x512 class-conditional ImageNet diffusion model (https://github.com/openai/guided-diffusion) Together with CLIP (https://github.com/openai/CLIP), they connect text prompts with images. Either the 256 or 512 model can be used here (by setting `--output_size` to either 256 or 512) Some example images: "A woman standing in a park": <img src="./Samples/woman_collage.jpg" width="640px"> "An alien landscape": <img src="./Samples/alien_collage.jpg" width="640px"> "A painting of a man": <img src="./Samples/man_collage.jpg" width="640px"> *images enhanced with [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) You may also be interested in [VQGAN-CLIP](https://github.com/nerdyrodent/VQGAN-CLIP) ## Environment * Ubuntu 20.04 (Windows untested but should work) * Anaconda * Nvidia RTX 3090 Typical VRAM requirments: * 256 defaults: 10 GB * 512 defaults: 18 GB ## Set up This example uses [Anaconda](https://www.anaconda.com/products/individual#Downloads) to manage virtual Python environments. Create a new virtual Python environment for CLIP-Guided-Diffusion: ```sh conda create --name cgd python=3.9 conda activate cgd ``` Download and change directory: ```sh git clone https://github.com/nerdyrodent/CLIP-Guided-Diffusion.git cd CLIP-Guided-Diffusion ``` Run the setup file: ```sh ./setup.sh ``` Or if you want to run the commands manually: ```sh # Install dependencies pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html git clone https://github.com/openai/CLIP git clone https://github.com/crowsonkb/guided-diffusion pip install -e ./CLIP pip install -e ./guided-diffusion pip install lpips matplotlib # Download the diffusion models curl -OL 'https://the-eye.eu/public/AI/models/512x512_diffusion_unconditional_ImageNet/512x512_diffusion_uncond_finetune_008100.pt' curl -OL 'https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt' ``` ## Run The simplest way to run is just to pass in your text prompt. For example: ```sh python generate_diffuse.py -p "A painting of an apple" ``` <img src="./Samples/a_painting_of_an_apple.png" width="256px"></img> ### Multiple prompts Text and image prompts can be split using the pipe symbol in order to allow multiple prompts. You can also use a colon followed by a number to set a weight for that prompt. For example: ```sh python generate_diffuse.py -p "A painting of an apple:1.5|a surreal painting of a weird apple:0.5" ``` <img src="./Samples/weird_apple.png" width="256px"></img> ### Other options There are a variety of other options to play with. Use help to display them: ```sh python generate_diffuse.py -h ``` ```sh usage: generate_diffuse.py [-h] [-p PROMPTS] [-ip IMAGE_PROMPTS] [-ii INIT_IMAGE] [-st SKIP_TIMESTEPS] [-is INIT_SCALE] [-m CLIP_MODEL] [-t TIMESTEPS] [-ds DIFFUSION_STEPS] [-se SAVE_EVERY] [-bs BATCH_SIZE] [-nb N_BATCHES] [-cuts CUTN] [-cutb CUTN_BATCHES] [-cutp CUT_POW] [-cgs CLIP_GUIDANCE_SCALE] [-tvs T
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matched fp:9e1bfd508faaafe1, topic:text-to-image, readme:diffusion model