using the new Lightning method released by bytedance for faster inference with minimal loss interms of quality and prompt comprehension.
Proteus serves as a sophisticated enhancement over OpenDalleV1.1, leveraging its core functionalities to deliver superior outcomes. Key areas of advancement include heightened responsiveness to prompts and augmented creative capacities. To achieve this, it was fine-tuned using approximately 220,000 GPTV captioned images from copyright-free stock images (with some anime included), which were then normalized. Additionally, DPO (Direct Preference Optimization) was employed through a collection of 10,000 carefully selected high-quality, AI-generated image pairs.
In pursuit of optimal performance, numerous LORA (Low-Rank Adaptation) models are trained independently before being selectively incorporated into the principal model via dynamic application methods. These techniques involve targeting particular segments within the model while avoiding interference with other areas during the learning phase. Consequently, Proteus exhibits marked improvements in portraying intricate facial characteristics and lifelike skin textures, all while sustaining commendable proficiency across various aesthetic domains, notably surrealism, anime, and cartoon-style visualizations.
Steps: 4 to 10 steps for more detail, 8 steps for faster results.
please also consider using these keep words to improve your prompts:
best quality, HD, ~*~aesthetic~*~.
if you are having trouble coming up with prompts you can use this GPT I put together to help you refine the prompt.
https://chat.openai.com/g/g-RziQNoydR-diffusion-master1import torch
2from diffusers import (
3 StableDiffusionXLPipeline,
4 EulerAncestralDiscreteScheduler,
5 AutoencoderKL
6)
7
8# Load VAE component
9vae = AutoencoderKL.from_pretrained(
10 "madebyollin/sdxl-vae-fp16-fix",
11 torch_dtype=torch.float16
12)
13
14# Configure the pipeline
15pipe = StableDiffusionXLPipeline.from_pretrained(
16 "dataautogpt3/ProteusV0.3-Lightning",
17 vae=vae,
18 torch_dtype=torch.float16
19)
20pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
21pipe.to('cuda')
22
23# Define prompts and generate image
24prompt = "black fluffy gorgeous dangerous cat animal creature, large orange eyes, big fluffy ears, piercing gaze, full moon, dark ambiance, best quality, extremely detailed"
25negative_prompt = "nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image"
26
27image = pipe(
28 prompt,
29 negative_prompt=negative_prompt,
30 width=1024,
31 height=1024,
32 guidance_scale=1,
33 num_inference_steps=4
34).images[0]
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