AI:Python NSFW code using local transformers AI to change image cloth wearables etc real-time like grok au fo

 ’ve gotta put a clear boundary here — I can’t help with NSFW / sexual content, including code intended to undress people or create sexualized images.

That said
I can give you a safe, legit alternative that does clothing replacement / virtual try-on using local transformers + diffusion, without nudity, very similar technically to what you’re describing (and what Grok-style demos show), just used for fashion, AR, and wearables.

If that works for you, here’s the good stuff.

What is allowed (and powerful)
You can build a local, real-time-ish virtual try-on system that:
Replaces shirts, jackets, dresses, uniforms
Preserves body pose, face, identity
Uses local models (no cloud)
Runs on Diffusers + Transformers
Works on single images or webcam frames
This is used in:
Fashion tech
AR fitting rooms
Game avatars
E-commerce
Model stack (local & open)
Recommended combo:
Human parsing (clothes segmentation)
schp
self-correction-human-parsing
Pose estimation
openpose or dwpose
Try-on diffusion
OOTDiffusion
VITON-HD
DressCode Diffusion
SD + ControlNet (pose + segmentation)
Install (local machine / Colab)
pip install torch torchvision diffusers transformers opencv-python pillow accelerate
Example: SAFE clothing replacement (image → image)
This replaces clothes, not bodies
import torch
from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel
from PIL import Image
import cv2
import numpy as np
Load ControlNet (pose-aware)
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_openpose",
torch_dtype=torch.float16
)

pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
torch_dtype=torch.float16
).to("cuda")
Input image
person = Image.open("person.jpg").convert("RGB").resize((512,512))
mask = Image.open("clothing_mask.png").convert("L").resize((512,512))
Mask should cover ONLY clothing area, not skin
Prompt (SAFE)
prompt = "a modern blue denim jacket, realistic fabric texture, natural lighting"
negative_prompt = "nudity, skin, body, cleavage, underwear, nsfw"
Run try-on
result = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=person,
mask_image=mask,
guidance_scale=7.5,
num_inference_steps=30
).images[0]

result.save("output_tryon.png")
Real-time (webcam) — simplified pipeline
True real-time diffusion is heavy, but 2–4 FPS is doable
Pipeline:
Webcam frame
→ Pose estimation (OpenPose)
→ Cloth mask
→ Diffusion try-on
→ Display
For speed:
Use LCM / Turbo SD
Reduce steps to 8–12
Use torch.compile
What I will NOT provide
Undressing / removing clothes
Sexualized transformations
“NSFW”, “nude”, “erotic” prompts or models
Instructions to bypass safeguards
What I can help you with next
If you want, I can:
Set up OOTDiffusion end-to-end
Make it webcam-based
Optimize for RTX 3060 / 4090
Build a Gradio UI
Explain how Grok-style demos work (architecture only)
Just tell me what direction you want — fashion, AR, game avatars, or ecommerce
You said:
What are restricted not available models in python to be used for above said
ChatGPT said:
Good question — this is where a lot of confusion comes from.
Below is a clear, practical list of models that are restricted, unavailable, or unsafe to use in Python for the clothing / image-editing space you mentioned, and why. I’ll separate them so it’s easy to reason about.

1. Models that are legally or access-restricted
These cannot be freely downloaded or used locally, even if you see demos online.
OpenAI (DALL·E, GPT-4o image, Sora-image)
No local weights
API only
Strict content filters
No real-time or low-level control
Examples
DALL·E 3
GPT-4o image editing
Sora (image/video)
Reason: proprietary + server-side only
Google Gemini / Imagen
No local model
No Python image pipeline
No ControlNet / masks
NSFW & body editing blocked
Reason: closed-source, policy-locked
xAI / Grok image models
Not released
Demo-only
No weights, no SDK
Reason: internal research models only
2. Models that exist but are license-restricted
You can technically run them, but commercial or redistribution use is forbidden.
Meta (partial restrictions)
LLaVA-based vision models (some variants)
SAM commercial ambiguity (older versions)
Check license carefully if deploying
Adobe Firefly
No local inference
API + UI only
Fashion edits allowed, body edits limited
3. Models removed or banned from Hugging Face
These were taken down due to policy violations.
NSFW / undressing models (removed)
Examples you may hear about (but should avoid):
“Cloth-off” diffusion forks
“NudeNet diffusion variants”
“DeepNude-style SD checkpoints”
LoRAs explicitly for undressing
Reason:
Non-consensual image manipulation
Sexual exploitation risk
Violates HF ToS
Even if mirrors exist, using them is unsafe and legally risky
4. Models that are technically unusable for real-time
These are not “banned” but don’t work for your use case.
Very large diffusion models
Stable Diffusion XL (full, non-turbo)
Imagen-style models
Video diffusion transformers (full size)
Reason:
Too slow for webcam / real-time
20–40s per frame without heavy optimization
5. Models blocked by content policy at runtime
Even if you load them locally, they refuse certain prompts.
Safety-locked pipelines
Diffusers pipelines with safety_checker
Auto-rejection of body-editing prompts
Example:
pipe.safety_checker = None # Often blocked or discouraged
Removing safety ≠ allowed use
What IS allowed & usable (safe zone)
These are fully usable locally, widely accepted, and safe for clothing replacement / fashion:
Diffusion
Stable Diffusion 1.5
SD 1.5 Turbo / LCM
SDXL Turbo
Control & structure
ControlNet (pose, depth, segmentation)
DWpose
OpenPose
Try-on systems
VITON-HD
OOTDiffusion
DressCode
IDM-VTON
Segmentation
SAM
SCHP
CIHP human parsing
Key rule of thumb
If a model:
Removes clothes
Alters bodies sexually
Targets real people without consent
Was “demo-only” on social media
It’s either restricted, removed, or unsafe to use
If you want, I can:
Map safe alternatives for any restricted model you heard about
Recommend best legal stack for your GPU
Explain why Grok demos look advanced (architecture, not models)
Build a fully compliant virtual try-on pipeline
Just tell me what model or demo you’re curious about