File size: 3,462 Bytes
3b12f1b b121468 3b12f1b b121468 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
language:
- en
tags:
- text-to-image
- stable-diffusion
pipeline_tag: text-to-image
---
# ColorfulXL-Lightning
## Model Details
ColorfulXL merged with lightning loras (2,8 steps) from bytedance, for fast inference (3-6 steps).
High range of resolutions supported (576 - 1280), 576*832 example:
Due to training LORA's on the base version of SDXL, there are problems with hands and faces:
## Usage
```python
from diffusers import DiffusionPipeline
from diffusers import EulerDiscreteScheduler
import torch
pipeline = DiffusionPipeline.from_pretrained("recoilme/ColorfulXL-Lightning", torch_dtype=torch.float16,variant="fp16", use_safetensors=True).to("cuda")
pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config, timestep_spacing="trailing")
prompt = "girl sitting on a small hill looking at night sky, fflix_dmatter, back view, distant exploding moon, nights darkness, intricate circuits and sensors, photographic realism style, detailed textures, peacefulness, mysterious."
height = 1024
width = 1024
steps = 3
scale = 0
seed = 2139965163
generator = torch.Generator(device="cpu").manual_seed(seed)
image = pipeline(
prompt = prompt,
height=height,
width=width,
guidance_scale=scale,
num_inference_steps=steps,
generator=generator,
).images[0]
image.show()
image.save("girl.png")
```
## Model Details
* **Developed by**: [AiArtLab](https://aiartlab.org/)
* **Model type**: Diffusion-based text-to-image generative model
* **Model Description**: This model is a fine-tuned model based on [colorfulxl](https://civitai.com/models/185258/colorfulxl).
* **License**: This model is not permitted to be used behind API services. Please contact vadim-kulibaba@yandex.ru for business inquires, commercial licensing, custom models, and consultation.
## Uses
### Direct Use
Research: possible research areas/tasks include:
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
Excluded uses are described below.
### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
## Contact
* For questions and comments about the model, please join [https://aiartlab.org/](https://aiartlab.org/).
* For future announcements / information about AiArtLab AI models, research, and events, please follow [Discord](https://discord.com/invite/gsvhQEfKQ5).
* For business and partnership inquiries, please contact https://t.me/recoilme
|