README.md

metadata

license: bigscience-bloom-rail-1.0
language:
  - en
library_name: diffusers
tags:
  - stable-diffusion
  - text-to-image

pony-diffusion-g5 - a new generation of waifus

pony-diffusion-g5 is a latent text-to-image diffusion model that has been conditioned on high quality pony images through fine-tuning.

Finetuned for MLP G5 main characters, based on AstraliteHeart/pony-diffusion

!!IMPORTANT: DUE TO LACK OF DATASETS ONLY SUNNY AND IZZY CAN GENERATE QUALITY IMAGES

!!IMPORTANT: TRY NEGATIVE PROMPT "3d, sfm"

Dataset criteria

All training images from Derpibooru using the search criteria below

• General: "g5, safe, solo, score.gte:250, -webm, -animate || g5, suggestive, solo, score.gte:250, -webm, -animate", 856 entries wo/ gifs, 12 epochs

These data are not used in this model, but will be considered to train on later

• Izzy moonbow: "izzy moonbow, safe, solo, score.gte:200, -webm, -animate || izzy moonbow, suggestive, solo, score.gte:200, -webm, -animate", 531 entries wo/ gifs, 6 epochs
• Sunny starscout: "sunny starscout, safe, solo, score.gte:200, -webm, -animate || sunny starscout, suggestive, solo, score.gte:200, -webm, -animate", 252 entries wo/ gifs, 6 epochs
• Pipp petals: "pipp petals, safe, solo, score.gte:200, -webm, -animate || pipp petals, suggestive, solo, score.gte:200, -webm, -animate", 218 entries wo/ gifs, 6 epochs
• Zipp storm: "zipp storm, safe, solo, score.gte:200, -webm, -animate || pipp petals, suggestive, solo, score.gte:200, -webm, -animate", 167 entries wo/ gifs, 6 epochs
• Hitch trailblzer: "hitch trailblazer, safe, solo, score.gte:200, -webm, -animate || hitch trailblazer, suggestive, solo, score.gte:200, -webm, -animate", 34 entries wo/ gifs (wat), 6 epochs

Why the model's quality is bad?

The amount of G5 pony images within the search criteria is little, so don't really expect the quality to be as high as the original model is

Also bcs im new to ai stuff i don't know how to train datasets correctly if u could help me great thx

Example code

from diffusers import StableDiffusionPipeline
import torch
from diffusers import DDIMScheduler

model_path = "GrieferPig/pony-diffusion-g5"  
prompt = "(((izzy moonbow))), pony, looking at you, smiling, sitting on beach, cute, portrait, intricate, digital painting, smooth, sharp, focus, depth of field"
negative= "3d sfm"
# torch.manual_seed(1145141919810)

pipe = StableDiffusionPipeline.from_pretrained(
        model_path, 
        torch_dtype=torch.float16,
        scheduler=DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=True,
        ),
#        safety_checker=None
    )

pipe = pipe.to("cuda")
images = pipe(prompt, width=512, height=512, num_inference_steps=50, num_images_per_prompt=5, negative_prompt=negative).images
for i, image in enumerate(images):
    image.save(f"test-{i}.png")

Thanks

AstraliteHeart/pony-diffusion, for providing a solid start-point to train on

This project would not have been possible without the incredible work by the CompVis Researchers.

With special thanks to Waifu-Diffusion for providing finetuning expertise and Novel AI for providing necessary compute.

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license: bigscience-bloom-rail-1.0