Table of contents
- General info
- Granular Adaptive Learning
- Trigger Words
- Setup
- Space
- CompVis
- Diffusers
- Checkpoint Merging Data Reference
- License
General info
Protogen x3.4
Protogen was warm-started with Stable Diffusion v1-5 and fine-tuned on various high quality image datasets. Version 3.4 continued training from ProtoGen v2.2 with added photorealism.
Granular Adaptive Learning
Granular adaptive learning is a machine learning technique that focuses on adjusting the learning process at a fine-grained level, rather than making global adjustments to the model. This approach allows the model to adapt to specific patterns or features in the data, rather than making assumptions based on general trends.
Granular adaptive learning can be achieved through techniques such as active learning, which allows the model to select the data it wants to learn from, or through the use of reinforcement learning, where the model receives feedback on its performance and adapts based on that feedback. It can also be achieved through techniques such as online learning where the model adjust itself as it receives more data.
Granular adaptive learning is often used in situations where the data is highly diverse or non-stationary and where the model needs to adapt quickly to changing patterns. This is often the case in dynamic environments such as robotics, financial markets, and natural language processing.
Trigger Words
modelshoot style, analog style, mdjrny-v4 style, nousr robot
Trigger words are available for the hassan1.4 and f222, might have to google them :)
Setup
To run this model, download the model.ckpt or model.safetensor and install it in your "stable-diffusion-webui\models\Stable-diffusion" directory
Space
We support a Gradio Web UI:
CompVis
CKPT
Download ProtoGen x3.4.ckpt (5.98GB)
Download ProtoGen X3.4-pruned-fp16.ckpt (1.89 GB)
Safetensors
Download ProtoGen x3.4.safetensors (5.98GB)
Download ProtoGen x3.4-pruned-fp16.safetensors (1.89GB)
🧨 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.
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")
PENDING DATA FOR MERGE, RPGv2 not accounted..
Checkpoint Merging Data Reference
License
By downloading you agree to the terms of these licenses
CreativeML Open RAIL-M
Seek Art Mega License