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--- |
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language: |
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- en |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- art |
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- artistic |
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- diffusers |
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inference: true |
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license: creativeml-openrail-m |
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--- |
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# Protogen_x2.2 by [darkstorm2150](https://instagram.com/officialvictorespinoza) |
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Protogen was warm-started with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and fine-tuned on a large amount |
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of data from large datasets new and trending on civitai.com. |
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You can enforce camera capture by using the prompt with "modelshoot style". |
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It should also be very "dreambooth-able", being able to generate high fidelity faces with a little amount of steps (see [dreambooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)). |
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## Space |
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We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run dreamlike-diffusion-1.0: |
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[](https://huggingface.co/spaces/darkstorm2150/Stable-Diffusion-Protogen-webui) |
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### CompVis |
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[Download Protogen_V2.2.ckpt) (5.98GB)](https://huggingface.co/darkstorm2150/Protogen_v2.2_Official_Release/blob/main/Protogen_V2.2.ckpt) |
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### 🧨 Diffusers |
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This model can be used just like any other Stable Diffusion model. For more information, |
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please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). |
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```python |
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from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler |
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import torch |
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prompt = ( |
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"modelshoot style, (extremely detailed CG unity 8k wallpaper), full shot body photo of the most beautiful artwork in the world, " |
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"english medieval witch, black silk vale, pale skin, black silk robe, black cat, necromancy magic, medieval era, " |
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"photorealistic painting by Ed Blinkey, Atey Ghailan, Studio Ghibli, by Jeremy Mann, Greg Manchess, Antonio Moro, trending on ArtStation, " |
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"trending on CGSociety, Intricate, High Detail, Sharp focus, dramatic, photorealistic painting art by midjourney and greg rutkowski" |
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) |
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model_id = "darkstorm2150/Protogen_v2.2_Official_Release" |
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) |
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) |
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pipe = pipe.to("cuda") |
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image = pipe(prompt, num_inference_steps=25).images[0] |
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image.save("./result.jpg") |
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``` |
