’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 StableDiffusionControlNetInpai
ntPipeline, ControlNetModel
from PIL import Image
import cv2
import numpy as np
Load ControlNet (pose-aware)
controlnet = ControlNetModel.from_pretraine
d(
"lllyasviel/control_v11p_sd15_
openpose",
torch_dtype=torch.float16
)
pipe = StableDiffusionControlNetInpai
ntPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-
5",
controlnet=controlnet,
torch_dtype=torch.float16
).to("cuda")
Input image
person = Image.open("person.jpg").conve
rt("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_promp
t,
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