---
language:
- en
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- art
- artistic
- diffusers
inference: true
license: creativeml-openrail-m
---

# Protogen_x3.4

Protogen was warm-started with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and fine-tuned on various high quality image datasets.
Version 3.4 continued training from [ProtoGen v2.2](https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release) with added photorealism.

## Space

We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run dreamlike-diffusion-1.0:
[![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/darkstorm2150/Stable-Diffusion-Protogen-webui)
### CompVis

[Download ProtoGen_X3.4.ckpt) (5.98GB)](https://huggingface.co/darkstorm2150/Protogen_x3.4_Official_Release/blob/main/ProtoGen_X3.4.ckpt)

### 🧨 Diffusers

This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).

```python
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch

prompt = (
"modelshoot style, (extremely detailed CG unity 8k wallpaper), full shot body photo of the most beautiful artwork in the world,"
"english medieval witch, black silk vale, pale skin, black silk robe, black cat, necromancy magic, medieval era,"
"photorealistic painting by Ed Blinkey, Atey Ghailan, Studio Ghibli, by Jeremy Mann, Greg Manchess, Antonio Moro, trending on ArtStation,"
"trending on CGSociety, Intricate, High Detail, Sharp focus, dramatic, photorealistic painting art by midjourney and greg rutkowski"
)

model_id = "darkstorm2150/Protogen_x3.4_Official_Release"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")

image = pipe(prompt, num_inference_steps=25).images[0]

image.save("./result.jpg")
```

![img](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/protogen/rswf5qk9be9a1.jpg)