File size: 2,446 Bytes
6ce3611 b40b2e6 27fa72e b40b2e6 f9ecab4 b40b2e6 27fa72e b40b2e6 616aa52 b40b2e6 |
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 |
---
license: creativeml-openrail-m
---
**Archer Diffusion**
This is the fine-tuned Stable Diffusion model trained on screenshots from the TV-show Archer.
Use the tokens **_archer style_** in your prompts for the effect.
If you enjoy this model, please check out my other models on [Huggingface](https://huggingface.co/nitrosocke)
### 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
#!pip install diffusers transformers scipy torch
from diffusers import StableDiffusionPipeline
import torch
model_id = "nitrosocke/archer-diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a magical princess with golden hair, archer style"
image = pipe(prompt).images[0]
image.save("./magical_princess.png")
```
**Portraits rendered with the model:**
**Celebrities rendered with the model:**
**Landscapes rendered with the model:**
**Animals rendered with the model:**
**Sample images used for training:**
#### Prompt and settings for portraits:
**archer style beautiful portrait of ariana grande**
_Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 946707299, Size: 512x704_
#### Prompt and settings for landscapes:
**archer style suburban street night blue indoor lighting Negative prompt: grey cars**
_Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 2915669764, Size: 1024x576_
This model was trained using the diffusers based dreambooth training and prior-preservation loss in 4.000 steps and using the _train-text-encoder_ feature. |