muhammad-taqi512/VINZING-V1

#VINZING-V1 Model Card

VINZING-V1 is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation.

Model Details

Model Description

VINZING-V1 is a distilled version of VINZING, trained for real-time synthesis. image diffusion models in 1 to 4 steps at high image quality. This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps.

• Developed by: MUHAMMAD TAQI
• Funded by: MUHAMMAD TAQI
• Model type: Generative text-to-image model
• Finetuned from model: VINZING

Uses

Direct Use

The model is intended for both non-commercial and commercial usage. Possible research areas and tasks include

• Research on generative models.
• Research on real-time applications of generative models.
• Research on the impact of real-time generative models.
• Safe deployment of models which have the potential to generate harmful content.
• Probing and understanding the limitations and biases of generative models.
• Generation of artworks and use in design and other artistic processes.
• Applications in educational or creative tools.

Excluded uses are described below.

Diffusers

pip install diffusers transformers accelerate --upgrade

• Text-to-image:

SD-Turbo does not make use of guidance_scale or negative_prompt, we disable it with guidance_scale=0.0. Preferably, the model generates images of size 512x512 but higher image sizes work as well. A single step is enough to generate high quality images.

from diffusers import AutoPipelineForText2Image
import torch

pipe = AutoPipelineForText2Image.from_pretrained("muhammad-taqi512/VINZING-V1", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")

prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]

• Image-to-image:

When using SD-Turbo for image-to-image generation, make sure that num_inference_steps * strength is larger or equal to 1. The image-to-image pipeline will run for int(num_inference_steps * strength) steps, e.g. 0.5 * 2.0 = 1 step in our example below.

from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
import torch

pipe = AutoPipelineForImage2Image.from_pretrained("muhammad-taqi512/VINZING-V1", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")

init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png").resize((512, 512))
prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"

image = pipe(prompt, image=init_image, num_inference_steps=2, strength=0.5, guidance_scale=0.0).images[0]