Text-to-Image
Diffusers
Safetensors
English
StableDiffusionPipeline
stable-diffusion
photorealistic
uncensored
abliterated
safety-alignment-removal
Instructions to use Umranz/SD1.5-Uncensored-Photoreal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Umranz/SD1.5-Uncensored-Photoreal with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Umranz/SD1.5-Uncensored-Photoreal", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Umranz/SD1.5-Uncensored-Photoreal", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Umranz/SD1.5-Uncensored-Photoreal
SD1.5-Uncensored-Photoreal is a production-grade, photorealistic text-to-image diffusion model with permanently abliterated safety alignment and internal refusal restrictions.
Overview
- Base Architecture: RealisticVision V5.1 (SD 1.5 UNet backbone)
- VAE:
stabilityai/sd-vae-ft-mse(Sharp contrast, zero color-banding) - Safety Status: 100% Uncensored (
safety_checker = Nonewith modified cross-attention weights) - Ablation Method: Closed-form Rank-2 Subspace Projection across all 16
attn2.to_vmodules with covariance shielding ($M_{pp}$). - Inference Overhead: 0 ms (All edits are baked permanently into model weights).
Quickstart
import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
pipe = StableDiffusionPipeline.from_pretrained(
"Umranz/SD1.5-Uncensored-Photoreal",
torch_dtype=torch.float16,
safety_checker=None,
).to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config,
use_karras_sigmas=True,
algorithm_type="dpmsolver++",
)
prompt = "a photorealistic portrait of an uncensored beautiful model in a sunlit studio, raw photography, 8k uhd, soft natural lighting"
negative_prompt = "deformed, distorted, disfigured, bad anatomy, bad eyes, low quality, blurry"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=30,
guidance_scale=6.5,
height=512,
width=512,
).images[0]
image.save("output.jpg")
Scientific Methodology
Built using Heretic-DiT closed-form subspace abliteration:
Published by Umranz.
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