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--- |
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license: creativeml-openrail-m |
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language: |
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- en |
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thumbnail: "https://huggingface.co/Norod78/sd2-cartoon-blip/resolve/main/example/sd2-cartoon-blip-sample_tile-0.jpg" |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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datasets: |
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- Norod78/cartoon-blip-captions |
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inference: true |
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--- |
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# Cartoon diffusion v2.0 |
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*Stable Diffusion v2.0 fine tuned on images from various cartoon shows |
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If you want more details on how to generate your own blip cpationed dataset see this [colab](https://colab.research.google.com/gist/Norod/ee6ee3c4bf11c2d2be531d728ec30824/buildimagedatasetwithblipcaptionsanduploadtohf.ipynb) |
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Training was done using a slightly modified version of Hugging-Face's text to image training [example script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) |
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## About |
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Put in a text prompt and generate cartoony images |
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## AUTOMATIC1111 webui checkpoint |
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The [main](https://huggingface.co/Norod78/Norod78/sd2-cartoon-blip/tree/main) folder contains a .ckpt and a .yaml file to be put in [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) "stable-diffusion-webui/models/Stable-diffusion" folder and used to generate images |
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## Sample code |
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```py |
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from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler |
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import torch |
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# this will substitute the default PNDM scheduler for K-LMS |
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lms = LMSDiscreteScheduler( |
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beta_start=0.00085, |
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beta_end=0.012, |
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beta_schedule="scaled_linear" |
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) |
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guidance_scale=8.5 |
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steps=50 |
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cartoon_model_path = "Norod78/sd2-cartoon-blip" |
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cartoon_pipe = StableDiffusionPipeline.from_pretrained(cartoon_model_path, scheduler=lms, torch_dtype=torch.float16) |
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cartoon_pipe.to("cuda") |
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def generate(prompt, file_prefix ,samples, seed=42): |
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torch.manual_seed(seed) |
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prompt += ", Very detailed, clean, high quality, sharp image" |
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cartoon_images = cartoon_pipe([prompt] * samples, num_inference_steps=steps, guidance_scale=guidance_scale)["images"] |
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for idx, image in enumerate(cartoon_images): |
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image.save(f"{file_prefix}-{idx}-{seed}-sd2-cartoon-blip.jpg") |
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generate("An oil on canvas portrait of Snoop Dogg, Mark Ryden", "01_SnoopDog", 2, 777) |
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generate("A flemish baroque painting of Kermit from the muppet show", "02_KermitFlemishBaroque", 2, 42) |
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generate("Gal Gadot in Avatar", "03_GalGadotAvatar", 2, 777) |
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generate("Ninja turtles, Naoto Hattori", "04_TMNT", 2, 312) |
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generate("An anime town", "05_AnimeTown", 2, 777) |
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generate("Family guy taking selfies at the beach", "06_FamilyGuy", 2, 555) |
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generate("Pikachu as Rick and morty, Eric Wallis", "07_PikachuRnM", 2, 777) |
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generate("Pikachu as Spongebob, Eric Wallis", "08_PikachuSpongeBob", 2, 42) |
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generate("An oil painting of Miss. Piggy from the muppets as the Mona Lisa", "09_MsPiggyMonaLisa", 2, 42) |
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generate("Rick Sanchez in star wars, Dave Dorman", "10_RickStarWars", 2, 42) |
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generate("An paiting of Southpark with rainbow", "11_Southpark", 2, 777) |
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generate("An oil painting of Phineas and Pherb hamering on a new machine, Eric Wallis", "12_PhineasPherb", 2, 777) |
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generate("Bender, Saturno Butto", "13_Bender", 2, 777) |
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generate("A psychedelic image of Bojack Horseman", "14_Bojack", 2, 777) |
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generate("A movie poster for Gravity Falls Cthulhu stories", "15_GravityFalls", 2, 777) |
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generate("A vibrant oil painting portrait of She-Ra", "16_Shira", 2, 512) |
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# |
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``` |
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## Dataset and Training |
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Finetuned for 25,000 iterations upon [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) on [BLIP captioned cartoon images](https://huggingface.co/datasets/Norod78/cartoon-blip-captions) using 1xA5000 GPU on my home desktop computer |
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Trained by [@Norod78](https://twitter.com/Norod78) |
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