--- 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. ## Model Weights  ## Space We support a [Gradio](https://github.com/gradio-app/gradio) Web UI: [](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") ``` 