Protogen x5.8 (Scifi-Anime) Official Release
Research Model by darkstorm2150
Table of contents
- General info
- Granular Adaptive Learning
- Trigger Words
- Setup
- Space
- CompVis
- Diffusers
- Checkpoint Merging Data Reference
- License
General info
Protogen x5.8
Protogen was warm-started with Stable Diffusion v1-5 and is rebuilt using dreamlikePhotoRealV2.ckpt as a core, adding small amounts during merge checkpoints.
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 x5.8.ckpt (7.7GB)
Download ProtoGen X5.8-pruned-fp16.ckpt (1.72 GB)
Safetensors
Download ProtoGen x5.8.safetensors (7.7GB)
Download ProtoGen x5.8-pruned-fp16.safetensors (1.72GB)
🧨 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_v5.8_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
| Models | Protogen v2.2 (Anime) | Protogen x3.4 (Photo) | Protogen x5.3 (Photo) | Protogen x5.8 (Sci-fi/Anime) | Protogen x5.9 (Dragon) | Protogen x7.4 (Eclipse) | Protogen x8.0 (Nova) | Protogen x8.6 (Infinity) |
|---|---|---|---|---|---|---|---|---|
| seek_art_mega v1 | 52.50% | 42.76% | 42.63% | 25.21% | 14.83% | |||
| modelshoot v1 | 30.00% | 24.44% | 24.37% | 2.56% | 2.05% | 3.48% | 22.91% | 13.48% |
| elldreth v1 | 12.64% | 10.30% | 10.23% | 6.06% | 3.57% | |||
| photoreal v2 | 10.00% | 48.64% | 38.91% | 66.33% | 20.49% | 12.06% | ||
| analogdiffusion v1 | 4.75% | 4.50% | 1.75% | 1.03% | ||||
| openjourney v2 | 4.51% | 4.28% | 4.75% | 2.26% | 1.33% | |||
| hassan1.4 | 2.63% | 2.14% | 2.13% | 1.26% | 0.74% | |||
| f222 | 2.23% | 1.82% | 1.81% | 1.07% | 0.63% | |||
| hasdx | 20.00% | 16.00% | 4.07% | 5.01% | 2.95% | |||
| moistmix | 16.00% | 12.80% | 3.86% | 4.08% | 2.40% | |||
| roboDiffusion v1 | 4.29% | 12.80% | 10.24% | 3.67% | 4.41% | 2.60% | ||
| RPG v3 | 5.00% | 20.00% | 4.29% | 4.29% | 2.52% | |||
| anything&everything | 4.51% | 0.56% | 0.33% | |||||
| dreamlikediff v1 | 5.0% | 0.63% | 0.37% | |||||
| sci-fidiff v1 | 3.10% | |||||||
| synthwavepunk v2 | 3.26% | |||||||
| mashupv2 | 11.51% | |||||||
| dreamshaper 252 | 4.04% | |||||||
| comicdiff v2 | 4.25% | |||||||
| artEros | 15.00% |
License
License
This model is licesed under a modified CreativeML OpenRAIL-M license.
You are not allowed to host, finetune, or do inference with the model or its derivatives on websites/apps/etc. If you want to, please email us at [email protected]
You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Photoreal 2.0) and include the license as well as a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0)
You are free to use the outputs (images) of the model for commercial purposes in teams of 10 or less
You can't use the model to deliberately produce nor share illegal or harmful outputs or content
The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the modified CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md
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