#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]