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2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 | """MiniMax-H3 `t2va` / `fl2va`, split deployment — the denoising half."""
from __future__ import annotations
import glob
import json
import os
import re
import shutil
import struct
import tempfile
import threading
import time
import traceback
from functools import cache
# Before anything that could initialize CUDA: `import spaces` patches `torch.cuda` so the 72 GiB load can happen at
# startup rather than on GPU time.
import spaces
import gradio as gr
MODEL_REPO = os.environ.get("H3_MODEL_REPO", "MiniMaxAI/MiniMax-H3")
CONDITIONER_SPACE = os.environ.get("H3_CONDITIONER", "multimodalart/qwen3vl-conditioner")
# `pack` places the transformer at startup, `lazy` moves everything on the first GPU call, `offload` hands placement to
# `ComponentsManager.enable_auto_cpu_offload`.
PLACEMENT = os.environ.get("H3_PLACEMENT", "pack").lower()
# cuDNN's fused attention is 10-20% faster than the SDPA default on this pool and needs nothing installed.
ATTENTION = os.environ.get("H3_ATTENTION", "_native_cudnn").lower()
GPU_SIZE = os.environ.get("H3_GPU_SIZE", "xlarge")
# larryvrh distillation LoRA (diffusers conversion): A/B factors stored on CPU, merged on-demand inside @spaces.GPU.
TURBO_LORA_REPO = os.environ.get("H3_TURBO_LORA", "InstantX/MiniMax-H3-Turbo-Lora-Diffusers")
TURBO_LORA_FILE = "minimax_h3_turbo_4step_ckpt500_diffusers.safetensors"
TURBO_LORA_SCALE = 1.0 # alpha == rank
# NSFW / anatomy LoRAs (ComfyUI format, converted at load). Lazy: downloaded + converted on CPU in generate(),
# merged on GPU inside @spaces.GPU only when selected. Standard LoRA only — LoKR (.lokr_w1/.lokr_w2) is unsupported.
NSFW_LORA_CONFIGS = {
"nsfw_innie": {
"repo": "Hearmeman/minimax-h3-loras",
"file": "HMInnie_v1_e50.safetensors",
"scale": 1.0,
"label": "NSFW Innie Pussy (HMInnie V1)",
"trigger": "inniepussy",
"desc": "Renders the vulva as a neat 'innie' — labia tucked in with no protruding inner lips. Use for close-up genital shots where you want a clean, compact vulva look. Not for scenes needing prominent outer labia or detailed pussy anatomy — pair with HMPussy for that.",
},
"nsfw_penis": {
"repo": "observantdistressed/plora-h3", # CivitAI "Penis Lora - H3" v1.1, mirrored to HF
"file": "PLORA_H3_V2-step00006300.safetensors",
"scale": 0.7,
"label": "NSFW Penis (Penis Lora - H3 v1.1)",
"trigger": "PENISLORA",
"format": "kohya",
"desc": "Adds a detailed, erect penis to male subjects — the core anatomy LoRA for any scene with a visible penis. Use whenever a penis is in frame. Not for solo female scenes, and don't stack it with HMPenis v2.0 (same purpose, conflicting weights).",
},
"nsfw_dildo": {
"repo": "EllaPriest45/MinimaxH3_Actions", # CivitAI "Dildoing" model 2903074, backup repo
"file": "Dildoing - MinimaxH3 - pumping a dildo insider her vagina.safetensors",
"scale": 0.7,
"label": "NSFW Dildoing (Dildo Insertion)",
"trigger": "pumping a dildo insider her vagina",
"desc": "Makes dildo insertion read naturally — the toy slides into the vagina with proper depth and motion instead of clipping or floating. Use for any dildo penetration scene. Not for scenes without a dildo, and don't combine it with other insertion LoRAs at full strength.",
},
# Motion/action adapter — auto-loaded (not user-selectable) whenever any anatomy LoRA is on, so the
# anatomy renders sharply AND the act moves naturally. The Hearmeman README says to stack anatomy
# adapters underneath this action adapter.
"nsfw_aio": {
"repo": "Hearmeman/minimax-h3-loras",
"file": "HMNSFW-AIO-V2.5.safetensors",
"scale": 0.7,
"label": "HMNSFW AIO V2.5 (motion)",
"trigger": "hmmotion",
"desc": "Motion/action adapter that makes anatomy LoRAs move naturally during sex acts — auto-loaded whenever any NSFW LoRA is selected. Use stacked underneath anatomy LoRAs for sharp rendering plus natural motion. Not for standalone use; it's a motion enhancer, not an anatomy fix.",
},
# ── EllaPriest45/MinimaxH3_Actions catalog (t2va-compatible, standard LoRA only) ──
# 93 LoRAs, classified by header inspection: 80 ComfyUI + 13 kohya. LoKR, I2V, REF2V, R2V, FL2VA excluded.
# Triggers/scales extracted from the CivitAI filenames (best effort — refine per-LoRA as needed).
'nsfw_ahegao_face': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Ahegao Face - MinimaxH3 - She is making the ahegao face - eyes rolled back and tongue hanging out.safetensors',
"scale": 0.7,
"label": 'Ahegao Face',
"trigger": 'ahegao face',
"desc": "Makes the subject's face contort into the ahegao expression — eyes rolled back, tongue hanging out — for intense reaction shots. Use during orgasm or overstimulation scenes. Not for normal expressions or dialogue scenes; it overrides the face.",
},
'nsfw_all_tied_up': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'All Tied Up - MinimaxH3 - 0.8str.safetensors',
"scale": 0.8,
"label": 'All Tied Up',
"trigger": 'all tied up',
"desc": "Renders the subject bound and restrained, with rope or ties visible and functional. Use for bondage and restraint scenes. Not for scenes where the subject needs to move freely; it locks them into restraint poses.",
},
'nsfw_astro_nsfw': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Astro NSFW - MinimaxH3 - improves human movement and anatomy.safetensors',
"scale": 0.7,
"label": 'Astro NSFW',
"trigger": 'astro nsfw',
"format": "kohya",
"desc": "General NSFW quality enhancer that improves human movement and anatomy in explicit scenes. Use as a base layer on top of specific anatomy or act LoRAs. Not a specific act LoRA — don't expect it to produce a particular act on its own.",
},
'nsfw_auto_cunnilingus': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Auto-Cunnilingus - MinimaxH3 - She performs oral sex on herself.She licks her vulva.safetensors',
"scale": 0.7,
"label": 'Auto-Cunnilingus',
"trigger": 'She performs oral sex on herself.She licks her vulva',
"desc": "Makes the subject perform oral sex on herself — licking her own vulva. Use for self-cunnilingus scenes. Not for partnered cunnilingus; that's a different act with its own LoRAs.",
},
'nsfw_beanflk_masturbation_finger_insertion_au': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'BEANFLK - Masturbation & Finger Insertion + Audio - MinimaxH3 - BEANFLK,0.4str.safetensors',
"scale": 0.4,
"label": 'BEANFLK - Masturbation & Finger Insertion + Audio',
"trigger": 'BEANFLK',
"desc": "Masturbation with finger insertion, plus audio cues for the act. Use for solo fingering scenes. Not for scenes without finger penetration; the audio may not fit non-masturbation content.",
},
'nsfw_bunny_general_motion_continuity_repair': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'BUNNY General Motion Continuity Repair - MinimaxH3 - for dynamic scenes,bunny_crisp_motion,resmultistep+simple,euler+beta,0.9str,stage1,0.6str,stage2,0.25str.safetensors',
"scale": 0.9,
"label": 'BUNNY General Motion Continuity Repair',
"trigger": 'bunny_crisp_motion',
"desc": "Repairs motion continuity in dynamic scenes — reduces warping and keeps movement coherent across frames. Use for fast-action or high-motion shots. Not for slow, static scenes where it adds little.",
},
'nsfw_better_motion': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Better Motion - MinimaxH3 - 720x1280,15-30steps,0.2-0.4str.safetensors',
"scale": 0.3,
"label": 'Better Motion',
"trigger": 'better motion',
"desc": "General motion-quality LoRA that makes movement smoother and more natural. Use as a base enhancer for any scene. Not for specific acts; it's a motion polish, not an action generator.",
},
'nsfw_better_titfuck_v0_95': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Better Titfuck v0.95 - MinimaxH3 - res_multistep+simple,titjob,titfuck.safetensors',
"scale": 0.7,
"label": 'Better Titfuck v0.95',
"trigger": 'titjob,titfuck',
"desc": "Makes titfuck scenes read properly — breasts wrap around the penis with realistic motion. Use for titjob/titfuck scenes. Not for other sex acts; it specializes in breast-on-penis action.",
},
'nsfw_blowjob_v3_0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Blowjob v3.0 - MinimaxH3 - bl0w_j0b.safetensors',
"scale": 0.7,
"label": 'Blowjob v3.0',
"trigger": 'bl0w_j0b',
"desc": "Core blowjob LoRA — makes oral sex on a penis read naturally with proper head motion. Use for blowjob scenes. Not for scenes without a visible penis; it won't do anything useful.",
},
'nsfw_blowjob_wearing_thong': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Blowjob wearing Thong - MinimaxH3 - blowjob_wearing_thong.safetensors',
"scale": 0.7,
"label": 'Blowjob wearing Thong',
"trigger": 'blowjob_wearing_thong',
"desc": "Blowjob variant where the performer keeps a thong on. Use for thong-clad blowjob scenes. Not for nude blowjobs — the thong is part of the concept.",
},
'nsfw_bluebella_nova_lingerie': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Bluebella Nova Lingerie - MinimaxH3 - She is wearing COLOR Bluebella Nova Lingerie.safetensors',
"scale": 0.7,
"label": 'Bluebella Nova Lingerie',
"trigger": 'She is wearing COLOR Bluebella Nova Lingerie',
"format": "kohya",
"desc": "Renders the subject wearing the Bluebella Nova lingerie set — a specific lace-and-straps design. Use for scenes featuring this exact lingerie. Not for other outfits; it's tied to this one garment.",
},
'nsfw_boob_physics': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Boob Physics - MinimaxH3 - jiggle,grab,massage,bounce.safetensors',
"scale": 0.7,
"label": 'Boob Physics',
"trigger": 'jiggle,grab,massage,bounce',
"desc": "Adds natural breast motion — jiggle, bounce, and sway during movement. Use for any scene with breast movement. Not for static shots; it only shows when there's motion.",
},
'nsfw_bustywomen': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'BustyWomen - MinimaxH3 - 0.75-1str.safetensors',
"scale": 0.88,
"label": 'BustyWomen',
"trigger": 'busty women',
"desc": "Makes female subjects busty — fuller, larger breasts. Use when you want a well-endowed figure. Not for flat-chested or petite figures; it conflicts with tiny-breast LoRAs.",
},
'nsfw_button_pop': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Button pop - MinimaxH3 - 0.2-0.5str,butt0n_p0p.safetensors',
"scale": 0.35,
"label": 'Button pop',
"trigger": 'butt0n_p0p',
"desc": "Makes buttons pop off clothing under strain — a clothing-failure effect. Use for scenes where a tight top bursts open. Not for general scenes; it's a specific gag/effect.",
},
'nsfw_cameltoe_v2_0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Cameltoe v2.0 - MinimaxH3 - cameltoe.safetensors',
"scale": 0.7,
"label": 'Cameltoe v2.0',
"trigger": 'cameltoe',
"desc": "Renders a visible cameltoe through tight clothing. Use for scenes with tight bottoms. Not for nude scenes; it needs clothing to show through.",
},
'nsfw_can_t_stop_shaking': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": "Can't Stop Shaking! - MinimaxH3 - endface1,endbody1.safetensors",
"scale": 0.7,
"label": "Can't Stop Shaking!",
"trigger": 'endface1,endbody1',
"desc": "Makes the subject shake uncontrollably — body and face tremors, often during intense stimulation. Use for overstimulation and orgasm scenes. Not for calm scenes; it adds constant shaking.",
},
'nsfw_chasing_blowjob_v1_2': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Chasing blowjob v1.2 - MinimaxH3 - The girl performs a blowjob.safetensors',
"scale": 0.7,
"label": 'Chasing blowjob v1.2',
"trigger": 'The girl performs a blowjob',
"desc": "Blowjob where the performer chases the penis — active, eager head movement. Use for enthusiastic blowjob scenes. Not for slow or gentle oral; it's an energetic variant.",
},
'nsfw_combat_base_v2_0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Combat BASE v2.0 - MinimaxH3 - prfight2,prfin1,res_multistep+simple,euler+beta.safetensors',
"scale": 0.7,
"label": 'Combat BASE v2.0',
"trigger": 'prfight2,prfin1',
"desc": "Base LoRA for fight and combat scenes — wrestling, grappling, and physical struggle. Use for combat or struggle scenes. Not for sex acts; it's a fight-motion base.",
},
'nsfw_cum_swallow': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Cum Swallow - MinimaxH3 - TADAA,0.4str.safetensors',
"scale": 0.4,
"label": 'Cum Swallow',
"trigger": 'TADAA',
"desc": "Makes the subject swallow cum after a facial or oral scene — the swallowing action reads clearly. Use for cum-swallow endings. Not for scenes without a cumshot; it needs the setup.",
},
'nsfw_cumshot': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Cumshot - MinimaxH3 - 20steps,euler+beta,0.85str.safetensors',
"scale": 0.85,
"label": 'Cumshot',
"trigger": 'cumshot',
"desc": "Renders a visible cumshot — ejaculation with realistic volume and motion. Use for cumshot scenes. Not for non-ejaculation scenes; it forces the act.",
},
'nsfw_deeper_hold_it': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Deeper... Hold It. - MinimaxH3 - oralmove1,oraldetail1,0.8str.safetensors',
"scale": 0.8,
"label": 'Deeper... Hold It.',
"trigger": 'oralmove1,oraldetail1',
"desc": "Oral-sex detail LoRA — deep-throat motion and oral detail. Use for deep oral scenes. Not for shallow kissing or foreplay; it's oral-specific.",
},
'nsfw_dildo_masturbation_v2_0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Dildo Masturbation v2.0 - MinimaxH3 - She slide a dildo in and out of her pussy.safetensors',
"scale": 0.7,
"label": 'Dildo Masturbation v2.0',
"trigger": 'She slide a dildo in and out of her pussy',
"desc": "Makes solo dildo masturbation read naturally — the subject slides a dildo in and out of her pussy. Use for solo dildo scenes. Not for partnered penetration; it's solo-focused.",
},
'nsfw_disney_style_renaissance_era': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Disney Style - Renaissance Era - MinimaxH3 - In the style of Disney animation.safetensors',
"scale": 0.7,
"label": 'Disney Style - Renaissance Era',
"trigger": 'In the style of Disney animation',
"format": "kohya",
"desc": "Applies a Disney Renaissance-era animation style — the classic 90s hand-drawn look. Use for stylized or animated scenes. Not for photorealistic content; it fights realism.",
},
'nsfw_drone_shot': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Drone Shot - MinimaxH3 - dr0nesh0t.safetensors',
"scale": 0.7,
"label": 'Drone Shot',
"trigger": 'dr0nesh0t',
"desc": "Adds a drone-style aerial camera shot — high-angle and sweeping. Use for establishing or overhead shots. Not for close-up intimate scenes; it pulls the camera away.",
},
'nsfw_facial_cumshot_v0_1': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Facial CumShot v0.1 - MinimaxH3 - cmst.safetensors',
"scale": 0.7,
"label": 'Facial CumShot v0.1',
"trigger": 'cmst',
"desc": "Renders a facial cumshot — cum landing on the face. Use for facial scenes. Not for internal or swallow endings; it's face-specific.",
},
'nsfw_faster_harder_shake_harder_v0_2': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Faster! Harder! Shake Harder! v0.2 - MinimaxH3 - dynv2.safetensors',
"scale": 0.7,
"label": 'Faster! Harder! Shake Harder! v0.2',
"trigger": 'dynv2',
"desc": "Makes the subject move faster and harder — intense, vigorous motion. Use for rough or fast scenes. Not for gentle or slow scenes; it escalates intensity.",
},
'nsfw_fetish_submission_chain_teddy': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Fetish Submission Chain Teddy - MinimaxH3 - She is wearing a red latex fetish submission chain teddy.Her hands are bound in front of her.safetensors',
"scale": 0.7,
"label": 'Fetish Submission Chain Teddy',
"trigger": 'She is wearing a red latex fetish submission chain teddy',
"desc": "Renders the subject in a red latex fetish submission chain teddy, with her hands bound in front. Use for latex or bondage outfit scenes. Not for casual outfits; it's a specific fetish garment.",
},
'nsfw_fingering': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Fingering - MinimaxH3 - 0.8-1str.safetensors',
"scale": 0.9,
"label": 'Fingering',
"trigger": 'fingering',
"desc": "Makes fingering scenes read naturally — fingers inside the vagina with realistic motion. Use for manual stimulation scenes. Not for scenes without hand-on-genital contact.",
},
'nsfw_framerush': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'FrameRush - MinimaxH3 - lights.safetensors',
"scale": 0.7,
"label": 'FrameRush',
"trigger": 'lights',
"desc": "Camera and lighting effect LoRA — adds a rushed, dynamic frame feel. Use for energetic camera work. Not for stable, slow shots.",
},
'nsfw_furry_enhancer_video_v1_5': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Furry Enhancer Video v1.5 - MinimaxH3 - 0.6-0.7str.safetensors',
"scale": 0.65,
"label": 'Furry Enhancer Video v1.5',
"trigger": 'furry enhancer',
"desc": "Enhances furry and anthro characters in video — fur texture and anthro anatomy. Use for furry content. Not for human subjects; it will fur-ify them.",
},
'nsfw_futanari_transformation_futanarization_v': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Futanari transformation (Futanarization) v5.1 - MinimaxH3 - futanaei transformation,futanaei transformation.safetensors',
"scale": 0.7,
"label": 'Futanari transformation (Futanarization) v5.1',
"trigger": 'futanaei transformation',
"desc": "Renders futanari transformation — a female subject gaining a penis. Use for futanari or transformation scenes. Not for standard male-female scenes; it changes anatomy.",
},
'nsfw_grindtime': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Grindtime - MinimaxH3 - grindtime,a woman performing continuous rhythmic gyrating hip motion while having sex,front-rear-side view.safetensors',
"scale": 0.7,
"label": 'Grindtime',
"trigger": 'grindtime',
"desc": "Makes the subject perform continuous rhythmic hip grinding during sex — front, rear, and side views. Use for grinding or riding scenes. Not for static poses; it's a motion LoRA.",
},
'nsfw_h3_footjob_v0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": "H3 Footjob v0 - MinimaxH3 - a photoealistic video depicting a woman sitting in front of the viewer.the camera angle is a male pov.the viewer's penis is exposed.the woman is performing a footjob.h3fj.safetensors",
"scale": 0.7,
"label": 'H3 Footjob v0',
"trigger": 'h3fj',
"desc": "POV footjob LoRA — the subject strokes a penis with her feet from a male POV. Use for footjob scenes. Not for scenes without a visible penis and feet; it's a specific POV setup.",
},
'nsfw_hmbreasts_v2_0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'HMBreasts v2.0 - MinimaxH3 - tits,seeds2+ddim_uniform,8stepsturbolora.safetensors',
"scale": 0.7,
"label": 'HMBreasts v2.0',
"trigger": 'tits',
"desc": "Hearmeman breast LoRA — renders attractive, natural breasts. Use for breast-focused scenes. Not for flat-chested figures; it conflicts with tiny-breast LoRAs.",
},
'nsfw_hmmasturbation_squirting_and_female_mast': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'HMMasturbation - Squirting and Female Masturbation - MinimaxH3 - hmmasturbation,euler+simple,12steps,shift6,turbo lora 0.5str,use hmpussy at 0.4-0.8str.safetensors',
"scale": 0.6,
"label": 'HMMasturbation - Squirting and Female Masturbation',
"trigger": 'hmmasturbation',
"desc": "Hearmeman masturbation LoRA — solo masturbation with squirting. Use for solo scenes with squirting. Not for partnered sex; it's solo-focused.",
},
'nsfw_hmpenis_v2_0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'HMPenis v2.0 - MinimaxH3 - penis,0.9str.safetensors',
"scale": 0.9,
"label": 'HMPenis v2.0',
"trigger": 'penis',
"desc": "Hearmeman penis LoRA — detailed erect penis anatomy. Use for any scene with a visible penis. Not for solo female scenes, and don't stack it with the dedicated penis LoRA (same purpose).",
},
'nsfw_hmpussy_v1_0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'HMPussy v1.0 - MinimaxH3 - pussy,0.9str.safetensors',
"scale": 0.9,
"label": 'HMPussy v1.0',
"trigger": 'pussy',
"desc": "Hearmeman pussy LoRA — detailed vulva anatomy. Use for close-up genital shots. Not for scenes where the vulva isn't visible; it only affects what's in frame.",
},
'nsfw_hmsquirt_squirting_v0_1': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'HMSquirt - Squirting v0.1 - MinimaxH3 - euler+simple,hmsquirt,12steps,6shift,turbolora 0.5str.safetensors',
"scale": 0.5,
"label": 'HMSquirt - Squirting v0.1',
"trigger": 'hmsquirt',
"desc": "Hearmeman squirting LoRA — makes squirting read clearly during stimulation. Use for squirting scenes. Not for dry scenes; it forces the effect.",
},
'nsfw_hand_in_panties_masturbation': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Hand in Panties Masturbation - MinimaxH3 - 0.7str,She has her hand in her panties,She slides her hand in her panties.She rubs her vagina.safetensors',
"scale": 0.7,
"label": 'Hand in Panties Masturbation',
"trigger": 'She has her hand in her panties,She slides her hand in her panties.She rubs her vagina',
"desc": "Makes the subject slide her hand into her panties and rub herself. Use for clothed masturbation scenes. Not for nude masturbation; the panties are part of the concept.",
},
'nsfw_hip_gyration': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Hip Gyration - MinimaxH3 - gyrating her hips,0.8str.safetensors',
"scale": 0.8,
"label": 'Hip Gyration',
"trigger": 'gyrating her hips',
"desc": "Makes the subject gyrate her hips rhythmically — circular hip motion. Use for grinding or dancing scenes. Not for static poses; it's a motion LoRA.",
},
'nsfw_hip_sway_dance': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Hip Sway Dance - MinimaxH3 - dancing with arms in front.safetensors',
"scale": 0.7,
"label": 'Hip Sway Dance',
"trigger": 'dancing with arms in front',
"desc": "Dance LoRA — the subject dances with arms in front, swaying her hips. Use for dance scenes. Not for sex acts; it's a dance-motion LoRA.",
},
'nsfw_hitachi_magic_wand_vibrator': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Hitachi Magic Wand Vibrator - MinimaxH3 - using a Hitachi Magic Wand vibrator.safetensors',
"scale": 0.7,
"label": 'Hitachi Magic Wand Vibrator',
"trigger": 'using a Hitachi Magic Wand vibrator',
"desc": "Renders the subject using a Hitachi Magic Wand vibrator — the toy and its use read clearly. Use for vibrator scenes. Not for scenes without the toy; it won't add anything.",
},
'nsfw_holi_festival_of_colors': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Holi Festival of Colors - MinimaxH3 - Festival_of_Colors,multicolored smoke,multicolored paint,multicolored powder.safetensors',
"scale": 0.7,
"label": 'Holi Festival of Colors',
"trigger": 'Festival_of_Colors',
"desc": "Holi festival effect — multicolored smoke, paint, and powder. Use for colorful festival scenes. Not for NSFW acts; it's a visual-effects LoRA.",
},
'nsfw_huge_breasts_large_breasts': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Huge Breasts - Large Breasts - MinimaxH3 - bigsloppytits,huge-massive-enormous saggy breasts-bust,nip_protruding-flat-soft,areola_large-wide-medium-small.safetensors',
"scale": 0.7,
"label": 'Huge Breasts - Large Breasts',
"trigger": 'bigsloppytits',
"desc": "Renders huge, saggy breasts — massive with large areolas. Use for extreme-breast scenes. Not for natural or petite figures; it goes big.",
},
'nsfw_icy_ass_smacking': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Icy Ass Smacking - MinimaxH3 - icyslap.safetensors',
"scale": 0.7,
"label": 'Icy Ass Smacking',
"trigger": 'icyslap',
"desc": "Makes ass-smacking read naturally — the slap and the resulting jiggle. Use for spanking scenes. Not for non-spanking scenes; it forces the action.",
},
'nsfw_icy_twerk_pro_max_v2_0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Icy Twerk Pro MAX v2.0 - MinimaxH3 - icytwerk.safetensors',
"scale": 0.7,
"label": 'Icy Twerk Pro MAX v2.0',
"trigger": 'icytwerk',
"desc": "Twerk LoRA — vigorous twerking with strong ass motion. Use for twerk scenes. Not for slow or gentle movement; it's high-energy.",
},
'nsfw_innie_pussy': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Innie Pussy - MinimaxH3.safetensors',
"scale": 0.7,
"label": 'Innie Pussy',
"trigger": 'inniepussy',
"format": "kohya",
"desc": "Innie pussy variant (kohya format) — a clean, tucked vulva look. Use for close-up genital shots wanting a neat innie. Not for scenes needing prominent labia; same caveat as the dedicated innie LoRA.",
},
'nsfw_jav_moans_audio_only': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'JAV moans audio only - MinimaxH3 - jpnMoans.safetensors',
"scale": 0.7,
"label": 'JAV moans audio only',
"trigger": 'jpnMoans',
"format": "kohya",
"desc": "Audio LoRA — adds JAV-style moans to the soundtrack. Use for scenes where you want Japanese AV-style vocalizations. Not for scenes with dialogue; it's audio-only.",
},
'nsfw_jiggle_walk': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'JIGGLE WALK - MinimaxH3 - JGLWLK.safetensors',
"scale": 0.7,
"label": 'JIGGLE WALK',
"trigger": 'JGLWLK',
"desc": "Makes the subject walk with a pronounced jiggle — breast and body bounce while walking. Use for walking scenes. Not for static or lying-down scenes; it needs locomotion.",
},
'nsfw_kaku_table_humping': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'KAKU - Table Humping - MinimaxH3 - 0.4-0.5str,KAKU,euler+simple.safetensors',
"scale": 0.45,
"label": 'KAKU - Table Humping',
"trigger": 'KAKU',
"desc": "Makes the subject hump a table or surface — grinding against furniture. Use for table-humping scenes. Not for partnered sex; it's a solo furniture act.",
},
'nsfw_lesbian_cunnilingus_v2_0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Lesbian Cunnilingus v2.0 - MinimaxH3.safetensors',
"scale": 0.7,
"label": 'Lesbian Cunnilingus v2.0',
"trigger": 'lesbian cunnilingus',
"desc": "Lesbian cunnilingus — one woman performs oral sex on another. Use for lesbian oral scenes. Not for solo or hetero scenes; it's a partnered female act.",
},
'nsfw_licking_penis_and_testicles_nsfw_v1_2': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Licking Penis and Testicles (NSFW) v1.2 - MinimaxH3 - licking penis,licking testicles,licking penis tip,Only works if penis and testicles are in view.safetensors',
"scale": 0.7,
"label": 'Licking Penis and Testicles (NSFW) v1.2',
"trigger": 'licking penis,licking testicles,licking penis tip',
"desc": "Makes the subject lick a penis and testicles — detailed oral attention. Use for oral scenes focused on the penis and testicles. Only works if the penis and testicles are in view.",
},
'nsfw_mini_dick_fix_penis_helper': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Mini Dick Fix - Penis Helper - MinimaxH3 - 3r3kt (but not really needed).safetensors',
"scale": 0.7,
"label": 'Mini Dick Fix - Penis Helper',
"trigger": '3r3kt',
"desc": "Fixes the 'mini dick' problem — makes the penis render at a realistic size instead of shrinking. Use alongside any penis LoRA. Not a standalone anatomy LoRA; it's a helper.",
},
'nsfw_missionary_sex_pov': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Missionary Sex - POV - MinimaxH3 - having missionary sex.safetensors',
"scale": 0.7,
"label": 'Missionary Sex - POV',
"trigger": 'having missionary sex',
"desc": "Missionary sex from the male POV — the act reads naturally from above. Use for missionary scenes. Not for other positions; it's position-specific.",
},
'nsfw_moawxx_female_pleasure_moans_body_writhi': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Moawxx - Female Pleasure Moans + Body Writhing v0.1 2000e - MinimaxH3 - moawxx,0.6-0.85str.safetensors',
"scale": 0.72,
"label": 'Moawxx - Female Pleasure Moans + Body Writhing v0.1 2000e',
"trigger": 'moawxx',
"desc": "Moawxx pleasure LoRA — female moans plus body writhing during stimulation. Use for pleasure and orgasm scenes. Not for calm scenes; it adds audible and visible arousal.",
},
'nsfw_moawxx_female_pleasure_moans_body_writhi_2': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Moawxx - Female Pleasure Moans + Body Writhing v0.1 2500e - MinimaxH3 - moawxx,0.6-0.85str.safetensors',
"scale": 0.72,
"label": 'Moawxx - Female Pleasure Moans + Body Writhing v0.1 2500e',
"trigger": 'moawxx',
"desc": "Moawxx pleasure LoRA — female moans plus body writhing during stimulation (2500e training variant). Use for pleasure and orgasm scenes. Not for calm scenes; it adds audible and visible arousal.",
},
'nsfw_moawxx_female_pleasure_moans_body_writhi_3': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Moawxx - Female Pleasure Moans + Body Writhing v0.1 2750e - MinimaxH3 - moawxx,0.6-0.85str.safetensors',
"scale": 0.72,
"label": 'Moawxx - Female Pleasure Moans + Body Writhing v0.1 2750e',
"trigger": 'moawxx',
"desc": "Moawxx pleasure LoRA — female moans plus body writhing during stimulation (2750e training variant). Use for pleasure and orgasm scenes. Not for calm scenes; it adds audible and visible arousal.",
},
'nsfw_mystic_xxx_v2_0': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Mystic XXX v2.0 - MinimaxH3 - 0.7str.safetensors',
"scale": 0.7,
"label": 'Mystic XXX v2.0',
"trigger": 'mystic xxx',
"desc": "General NSFW enhancer — improves overall explicitness and scene quality. Use as a base on top of specific act LoRAs. Not a specific act; it's a general boost.",
},
'nsfw_natural_sagging_breasts': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Natural Sagging Breasts - MinimaxH3 - She has naturally sagging breasts.safetensors',
"scale": 0.7,
"label": 'Natural Sagging Breasts',
"trigger": 'She has naturally sagging breasts',
"desc": "Renders naturally sagging breasts — realistic gravity and shape. Use for mature or natural figures. Not for perky or enhanced breasts; it adds sag.",
},
'nsfw_natural_saggy_boobs': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Natural Saggy Boobs - MinimaxH3 - hugesaggytits,shirt-lift titty drop,bra unhook from behind,then drop.safetensors',
"scale": 0.7,
"label": 'Natural Saggy Boobs',
"trigger": 'hugesaggytits',
"desc": "Saggy boobs with specific reveal actions — shirt-lift titty drop and bra unhook from behind. Use for breast-reveal scenes. Not for clothed scenes; it's about the reveal.",
},
'nsfw_nipple_play': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Nipple Play - MinimaxH3 - flicks her own nipples,gently pinch and slowly roll her own beautifully formed,fully erect,firm and symmetrical nipples.safetensors',
"scale": 0.7,
"label": 'Nipple Play',
"trigger": 'nipple play',
"desc": "Makes the subject play with her nipples — flicking, pinching, and rolling them. Use for nipple-play scenes. Not for scenes without breast focus; it centers on the nipples.",
},
'nsfw_pjslide_pussyjob': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'PJSLIDE - Pussyjob - MinimaxH3 - 0.5str,PJSLIDE,add HMPenis lora at 0.5str.safetensors',
"scale": 0.5,
"label": 'PJSLIDE - Pussyjob',
"trigger": 'PJSLIDE',
"desc": "Pussyjob LoRA — the penis slides along the vulva. Use for pussyjob scenes; the author recommends adding the penis LoRA at 0.5 strength alongside it. Not for penetration scenes; it's a sliding act.",
},
'nsfw_pk_parasyte_turbo_clean_up_the_models_vi': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'PK Parasyte Turbo (clean up the models visuals and audio) - MinimaxH3 - 3.5-5str,6-8steps,8-3 shift,euler+simple.safetensors',
"scale": 4.25,
"label": 'PK Parasyte Turbo (clean up the models visuals and audio)',
"trigger": 'PK Parasyte Turbo',
"desc": "Turbo LoRA that cleans up model visuals and audio — improves overall quality at low step counts. Use with turbo mode (6-8 steps, high strength 3.5-5). Not for standard 25-step generation; it's tuned for turbo.",
},
'nsfw_pov_missionary_insertion_v0_7': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'POV Missionary Insertion v0.7 - MinimaxH3.safetensors',
"scale": 0.7,
"label": 'POV Missionary Insertion v0.7',
"trigger": 'missionary insertion',
"format": "kohya",
"desc": "POV missionary insertion — the insertion moment reads clearly from the male POV. Use for missionary insertion scenes. Not for other positions; it's position-specific.",
},
'nsfw_passionate_kissing': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Passionate Kissing - MinimaxH3 - They kiss passionately,1str.safetensors',
"scale": 1.0,
"label": 'Passionate Kissing',
"trigger": 'They kiss passionately',
"desc": "Makes passionate kissing read naturally — lip contact and head motion. Use for kissing scenes. Not for oral or sex acts; it's kissing-specific.",
},
'nsfw_photorealistic_image_generator': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Photorealistic Image Generator - MinimaxH3 - ph0t0r34l.safetensors',
"scale": 0.7,
"label": 'Photorealistic Image Generator',
"trigger": 'ph0t0r34l',
"format": "kohya",
"desc": "Pushes output toward photorealism — more realistic textures and lighting. Use for photorealistic scenes. Not for stylized or animated content; it fights the style.",
},
'nsfw_pillow_humping': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Pillow Humping - MinimaxH3 - She is straddling a pillow.She is humping the pillow.safetensors',
"scale": 0.7,
"label": 'Pillow Humping',
"trigger": 'She is straddling a pillow.She is humping the pillow',
"desc": "Makes the subject straddle and hump a pillow — solo grinding on a pillow. Use for pillow-humping scenes. Not for partnered sex; it's a solo act.",
},
'nsfw_pokies': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Pokies - MinimaxH3 - covered nipples,pokies.safetensors',
"scale": 0.7,
"label": 'Pokies',
"trigger": 'covered nipples,pokies',
"format": "kohya",
"desc": "Renders visible nipple outlines through clothing — pokies. Use for clothed scenes with tight tops. Not for nude scenes; it needs clothing.",
},
'nsfw_polyhedron_perfect_eyes_perfect_skin_per': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Polyhedron Perfect Eyes - Perfect Skin - Perfect Hands T2VA - MinimaxH3 - perfe8ct,perfect hands,perfect skin,perfect eyes.safetensors',
"scale": 0.7,
"label": 'Polyhedron Perfect Eyes - Perfect Skin - Perfect Hands T2VA',
"trigger": 'perfe8ct',
"desc": "Quality enhancer for eyes, skin, and hands — fixes common rendering flaws. Use as a general quality boost for any scene. Not for specific acts; it's a detail fixer.",
},
'nsfw_realism_people': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Realism People - MinimaxH3 - r34l1sm,0.6-1str.safetensors',
"scale": 0.8,
"label": 'Realism People',
"trigger": 'r34l1sm',
"desc": "Makes people look more realistic — natural proportions and skin. Use for photorealistic human scenes. Not for stylized content; it fights the style.",
},
'nsfw_sexgod_after_midnight_sexytime_r64': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'SexGod After Midnight Sexytime R64 - MinimaxH3 - euler+beta.safetensors',
"scale": 0.7,
"label": 'SexGod After Midnight Sexytime R64',
"trigger": 'sexgod',
"format": "kohya",
"desc": "SexGod base LoRA — general NSFW scene quality. Use as a base enhancer on top of specific act LoRAs. Not a specific act; it's a general boost.",
},
'nsfw_sexgod_s_naughtytimes_v2_0_pruned_rank25': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": "SexGod's NaughtyTimes v2.0 pruned rank256 - MinimaxH3.safetensors",
"scale": 0.7,
"label": "SexGod's NaughtyTimes v2.0 pruned rank256",
"trigger": 'naughtytimes',
"format": "kohya",
"desc": "SexGod's NaughtyTimes — general NSFW scene enhancer (rank-256 pruned). Use as a base enhancer. Not act-specific; pair it with act LoRAs.",
},
'nsfw_sexgod_s_naughtytimes_v2_0_pruned_rank51': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": "SexGod's NaughtyTimes v2.0 pruned rank512 - MinimaxH3.safetensors",
"scale": 0.7,
"label": "SexGod's NaughtyTimes v2.0 pruned rank512",
"trigger": 'naughtytimes',
"format": "kohya",
"desc": "SexGod's NaughtyTimes — general NSFW scene enhancer (rank-512 pruned, slightly higher capacity). Use as a base enhancer. Pick one of the two NaughtyTimes variants; don't stack both.",
},
'nsfw_she_struts': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'She Struts - MinimaxH3 - she struts.safetensors',
"scale": 0.7,
"label": 'She Struts',
"trigger": 'she struts',
"desc": "Makes the subject strut — confident walking with attitude. Use for walk or entrance scenes. Not for static scenes; it needs locomotion.",
},
'nsfw_sideview_deepthroat_gag_audio': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'SideView Deepthroat Gag Audio - MinimaxH3.safetensors',
"scale": 0.7,
"label": 'SideView Deepthroat Gag Audio',
"trigger": 'deepthroat',
"format": "kohya",
"desc": "Side-view deepthroat with gag audio — the gag reflex reads and sounds. Use for deepthroat scenes. Not for gentle oral; it's intense.",
},
'nsfw_skin_tight_bottoms_cameltoe': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Skin Tight Bottoms (Cameltoe) - MinimaxH3 - cameltoe,0.9str.safetensors',
"scale": 0.9,
"label": 'Skin Tight Bottoms (Cameltoe)',
"trigger": 'cameltoe',
"desc": "Skin-tight bottoms with a visible cameltoe. Use for tight-clothing scenes. Not for nude scenes; it needs the clothing.",
},
'nsfw_slop_twerk_v0_2_epoch6': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": "Slop Twerk v0.2 Epoch6 - MinimaxH3 - twerks causing her ass to jiggle and shake,0.5-1str,0.5 STR if you don't want Asians in T2V.safetensors",
"scale": 0.75,
"label": 'Slop Twerk v0.2 Epoch6',
"trigger": 'twerk',
"desc": "Twerk LoRA with strong ass jiggle and shake. Use for twerk scenes. Note: use 0.5 strength if you don't want Asian subjects in text-to-video.",
},
'nsfw_slop_bounce': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Slop bounce - MinimaxH3 - breasts bounce,shake,jiggle,sway.safetensors',
"scale": 0.7,
"label": 'Slop bounce',
"trigger": 'breasts bounce',
"desc": "Breast bounce, shake, jiggle, and sway during movement. Use for any scene with breast motion. Not for static shots; it only shows with movement.",
},
'nsfw_snow_white_lingerie': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Snow White Lingerie - MinimaxH3 - She is wearing slutty snow white lingerie.safetensors',
"scale": 0.7,
"label": 'Snow White Lingerie',
"trigger": 'She is wearing slutty snow white lingerie',
"desc": "Renders the subject in slutty Snow White lingerie. Use for scenes featuring this specific outfit. Not for other outfits; it's tied to this garment.",
},
'nsfw_strapon_dildo_v0_1': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Strapon Dildo v0.1 - MinimaxH3 - She is wearing a strapon dildo.safetensors',
"scale": 0.7,
"label": 'Strapon Dildo v0.1',
"trigger": 'She is wearing a strapon dildo',
"desc": "Makes the subject wear and use a strapon dildo. Use for strapon scenes. Not for scenes without the toy; it won't add anything.",
},
'nsfw_synth_pussy_v0_5': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Synth Pussy v0.5 - MinimaxH3.safetensors',
"scale": 0.7,
"label": 'Synth Pussy v0.5',
"trigger": 'synth pussy',
"format": "kohya",
"desc": "Synth pussy — a stylized or synthetic vulva rendering. Use for stylized genital shots. Not for photorealistic anatomy; it's a stylized look.",
},
'nsfw_the_orgasm_button': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'The Orgasm button - MinimaxH3 - Remoteorgasm.safetensors',
"scale": 0.7,
"label": 'The Orgasm button',
"trigger": 'Remoteorgasm',
"desc": "Remote-control orgasm — the subject reacts to a remote trigger. Use for orgasm-control scenes. Not for natural orgasms; it's the remote-control concept.",
},
'nsfw_thumb_finger_puppets': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Thumb-Finger puppets - MinimaxH3 - A thumb person.safetensors',
"scale": 0.7,
"label": 'Thumb-Finger puppets',
"trigger": 'A thumb person',
"desc": "Thumb-finger puppets — a 'thumb person' novelty effect. Use for puppet or novelty scenes. Not for realistic content; it's a gag.",
},
'nsfw_thumbinbutt': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'ThumbInButt - MinimaxH3 - thum1n8utt,the man inserts his right thumb in her anus causing the woman to moan with pleasure.safetensors',
"scale": 0.7,
"label": 'ThumbInButt',
"trigger": 'thum1n8utt',
"desc": "Makes the man insert his thumb into the woman's anus, causing a pleasure moan. Use for anal-fingering scenes. Not for non-anal scenes; it forces the action.",
},
'nsfw_tiddies_and_realism_slider_vbeta': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Tiddies and Realism Slider vBeta - MinimaxH3 - 0.4-2str.safetensors',
"scale": 1.2,
"label": 'Tiddies and Realism Slider vBeta',
"trigger": 'tiddies and realism',
"desc": "Slider LoRA — adjust breast size and realism on a scale (0.4-2 strength; 1.2 is a good default). Use to fine-tune breast size. Not a fixed anatomy LoRA; it's a continuous slider.",
},
'nsfw_tiny_breasts_flat_chest': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Tiny Breasts - Flat Chest - MinimaxH3 - She is flat chested,she has a flat chest.safetensors',
"scale": 0.7,
"label": 'Tiny Breasts - Flat Chest',
"trigger": 'She is flat chested,she has a flat chest',
"desc": "Renders a flat chest or tiny breasts. Use for petite figures. Not for busty figures; it conflicts with breast-size LoRAs.",
},
'nsfw_tiny_panties': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Tiny Panties - MinimaxH3 - She is wearing tiny panties.safetensors',
"scale": 0.7,
"label": 'Tiny Panties',
"trigger": 'She is wearing tiny panties',
"desc": "Renders the subject in tiny panties — minimal coverage. Use for lingerie scenes. Not for nude scenes; the panties are the point.",
},
'nsfw_torpedo_tits_t2v': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Torpedo Tits T2V - MinimaxH3 - torpedo tits,a 20 year old woman,wearing nothing shows off her medium sized torpedo tit breasts.safetensors',
"scale": 0.7,
"label": 'Torpedo Tits T2V',
"trigger": 'torpedo tits',
"desc": "Torpedo-shaped breasts — a specific conical breast shape. Use for torpedo-tit scenes. Not for natural shapes; it imposes the torpedo look.",
},
'nsfw_transition_boomsplitro': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Transition BoomSplitRo - MinimaxH3 - ysp_spliroa12 a woman. hard cut with no smooth transition, jumps straight into spitroast.safetensors',
"scale": 0.7,
"label": 'Transition BoomSplitRo',
"trigger": 'ysp_spliroa12',
"format": "kohya",
"desc": "Hard-cut transition straight into a spitroast scene — no smooth transition. Use for abrupt scene changes. Not for continuous scenes; it's a cut effect.",
},
'nsfw_transition_f2v_instant_cut_to_things': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Transition F2V - Instant Cut to Things - MinimaxH3 - ysp_smshcut.safetensors',
"scale": 0.7,
"label": 'Transition F2V - Instant Cut to Things',
"trigger": 'ysp_smshcut',
"format": "kohya",
"desc": "Instant cut transition to a new scene. Use for abrupt scene changes. Not for continuous scenes; it's a cut effect.",
},
'nsfw_tribtime': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Tribtime - MinimaxH3 - tribtime.safetensors',
"scale": 0.7,
"label": 'Tribtime',
"trigger": 'tribtime',
"desc": "Tribbing — scissoring or grinding between two women. Use for tribadism scenes. Not for solo scenes; it needs two partners.",
},
'nsfw_upskirt_helper_v0_1': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Upskirt Helper v0.1 - MinimaxH3 - upsk1rt_panties,upsk1rt_no_panties.safetensors',
"scale": 0.7,
"label": 'Upskirt Helper v0.1',
"trigger": 'upsk1rt_panties,upsk1rt_no_panties',
"format": "kohya",
"desc": "Upskirt view — with or without panties. Use for upskirt shots. Not for nude scenes; it needs a skirt.",
},
'nsfw_whispering': {
"repo": "EllaPriest45/MinimaxH3_Actions",
"file": 'Whispering - MinimaxH3 - 0.7str,she whisper XYZ.safetensors',
"scale": 0.7,
"label": 'Whispering',
"trigger": 'she whisper XYZ',
"desc": "Makes the subject whisper — replace XYZ in the trigger with what she whispers. Use for whispering scenes. Not for loud or vocal scenes; it's a quiet effect.",
},
}
# LoRAs that overlap in purpose and fight each other when stacked. The description box warns whenever 2+ from a
# group are selected. Groups may overlap (e.g. a deepthroat LoRA is both a blowjob act and an audio LoRA).
LORA_CONFLICT_GROUPS = {
"penis anatomy": ["nsfw_penis", "nsfw_hmpenis_v2_0"],
"innie pussy": ["nsfw_innie", "nsfw_innie_pussy"],
"breast size/shape": ["nsfw_bustywomen", "nsfw_tiny_breasts_flat_chest", "nsfw_huge_breasts_large_breasts",
"nsfw_torpedo_tits_t2v", "nsfw_tiddies_and_realism_slider_vbeta",
"nsfw_natural_sagging_breasts", "nsfw_natural_saggy_boobs"],
"breast motion": ["nsfw_boob_physics", "nsfw_slop_bounce", "nsfw_jiggle_walk"],
"cameltoe": ["nsfw_cameltoe_v2_0", "nsfw_skin_tight_bottoms_cameltoe"],
"twerk": ["nsfw_icy_twerk_pro_max_v2_0", "nsfw_slop_twerk_v0_2_epoch6"],
"squirting": ["nsfw_hmmasturbation_squirting_and_female_mast", "nsfw_hmsquirt_squirting_v0_1"],
"blowjob act": ["nsfw_blowjob_v3_0", "nsfw_blowjob_wearing_thong", "nsfw_chasing_blowjob_v1_2",
"nsfw_sideview_deepthroat_gag_audio", "nsfw_deeper_hold_it",
"nsfw_licking_penis_and_testicles_nsfw_v1_2"],
"cumshot": ["nsfw_cumshot", "nsfw_facial_cumshot_v0_1"],
"scene transition": ["nsfw_transition_boomsplitro", "nsfw_transition_f2v_instant_cut_to_things"],
"moans (same LoRA, different training epochs)": ["nsfw_moawxx_female_pleasure_moans_body_writhi",
"nsfw_moawxx_female_pleasure_moans_body_writhi_2",
"nsfw_moawxx_female_pleasure_moans_body_writhi_3"],
"SexGod NaughtyTimes (same LoRA, different ranks)": ["nsfw_sexgod_s_naughtytimes_v2_0_pruned_rank25",
"nsfw_sexgod_s_naughtytimes_v2_0_pruned_rank51"],
"general NSFW enhancer": ["nsfw_astro_nsfw", "nsfw_mystic_xxx_v2_0",
"nsfw_sexgod_after_midnight_sexytime_r64",
"nsfw_sexgod_s_naughtytimes_v2_0_pruned_rank25",
"nsfw_sexgod_s_naughtytimes_v2_0_pruned_rank51"],
"visual style": ["nsfw_disney_style_renaissance_era", "nsfw_photorealistic_image_generator",
"nsfw_realism_people"],
"missionary POV": ["nsfw_missionary_sex_pov", "nsfw_pov_missionary_insertion_v0_7"],
"dildo": ["nsfw_dildo", "nsfw_dildo_masturbation_v2_0", "nsfw_strapon_dildo_v0_1"],
"audio": ["nsfw_jav_moans_audio_only", "nsfw_whispering", "nsfw_sideview_deepthroat_gag_audio"],
"hip motion": ["nsfw_grindtime", "nsfw_hip_gyration"],
}
# Must stay identical to the conditioner's table: the *label* goes over the wire, so a canvas that half does not know
# is rejected there and surfaces as a failure here.
CANVASES = {
# 16:9
"960x544 · 16:9 fast": (544, 960),
"1024x576 · 16:9 fast": (576, 1024),
"1152x640 · 16:9": (640, 1152),
"1280x704 · 16:9": (704, 1280),
"1344x768 · 16:9 full": (768, 1344),
# 9:16
"544x960 · 9:16 fast": (960, 544),
"640x1152 · 9:16": (1152, 640),
"768x1344 · 9:16 full": (1344, 768),
# 1:1
"544x544 · 1:1 fast": (544, 544),
"768x768 · 1:1 full": (768, 768),
# 4:3 / 3:4
"768x576 · 4:3 fast": (576, 768),
"1024x768 · 4:3 full": (768, 1024),
"576x768 · 3:4 fast": (768, 576),
"768x1024 · 3:4 full": (1024, 768),
# 21:9
"1152x512 · 21:9 fast": (512, 1152),
"1536x672 · 21:9 full": (672, 1536),
}
DEFAULT_CANVAS = "960x544 · 16:9 fast"
FPS, FRAMES_PER_CHUNK, LATENTS_PER_CHUNK = 24, 17, 5
# It is the *snapped* frame count the ceiling has to hold for: 15 s is 360 frames, which rounds up to 362, i.e.
# 15.083 s, and is refused.
MIN_UI_DURATION, MAX_UI_DURATION = 2, 14
def snap_frames(seconds: float) -> int:
"""The frame count MiniMax-H3's video VAE can decode: the next `17 * n + 5` at 24 fps."""
frames = max(1, round(float(seconds) * FPS))
while frames % FRAMES_PER_CHUNK != LATENTS_PER_CHUNK:
frames += 1
return frames
def lower_duration_floor(seconds: float = MIN_UI_DURATION) -> None:
"""Let the pipeline generate below its 5 s floor. 56 frames (2.33 s) is fine on the released checkpoint."""
from diffusers.modular_pipelines.minimax_h3.modular_pipeline import MiniMaxH3ModularPipeline
MiniMaxH3ModularPipeline.min_duration = property(lambda self: float(seconds))
PIPE = None
MANAGER = None
LOAD_ERROR: str | None = None
LOADED_IN: float | None = None
TURBO_AVAILABLE: bool = False
_TURBO_LoRA: list | None = None # list of (module, A, B) tuples — A/B factors on CPU, tiny footprint
_TURBO_merged = False
_NSFW_LoRA: dict[str, list] = {} # key -> list of (module, A, B) tuples, A/B on CPU
_NSFW_merged: set[str] = set()
_CUSTOM_LoRA: dict[tuple[str, str], list] = {} # (repo, file) -> list of (module, A, B) tuples, A/B on CPU
_CUSTOM_merged: set[tuple[str, str]] = set()
def _convert_comfyui_h3_lora(sd):
"""Convert ComfyUI-format MiniMax-H3 LoRA keys to the module-tree keys the loader navigates.
ComfyUI keys look like `diffusion_model.blocks.N.attn.out_proj.lora_A.weight` (and
`diffusion_model.token_refiner.blocks.N.attn.out_proj.lora_A.weight` for the token refiner). The diffusers module
tree uses `transformer_blocks.N.attn.to_out.0` / `token_refiner.refiner_blocks.N.attn.to_out.0`, `attn.to_q/to_k/to_v`
(fused `qkv_proj` split 3-way), and `ff.net.0.proj` / `ff.net.2` (for `mlp.fc1` / `mlp.fc2`).
"""
out = {}
for k, v in sd.items():
k = k.removeprefix("diffusion_model.").removeprefix("transformer.")
parts = k.split(".")
# Main blocks: [blocks, N, attn|mlp, target, lora_A|B, weight]
# Token refiner: [token_refiner, blocks, N, attn|mlp, target, lora_A|B, weight]
if parts[0] == "token_refiner":
prefix = "token_refiner.refiner_blocks."
block, layer, target = parts[2], parts[3], parts[4]
suffix = ".".join(parts[5:]) # e.g. "lora_A.weight"
else:
prefix = "transformer_blocks."
block, layer, target = parts[1], parts[2], parts[3]
suffix = ".".join(parts[4:]) # e.g. "lora_A.weight"
# Some ComfyUI exports also carry kohya-style `.alpha` scalars (alpha == rank here, so a no-op).
# Skip any non-lora_A/B key so metadata never reaches the shape-sensitive qkv split below.
if suffix not in ("lora_A.weight", "lora_B.weight"):
continue
if layer == "attn":
if target == "out_proj":
out[f"{prefix}{block}.attn.to_out.0.{suffix}"] = v
elif target == "qkv_proj":
if suffix == "lora_A.weight":
# A is shared across q/k/v
for name in ("to_q", "to_k", "to_v"):
out[f"{prefix}{block}.attn.{name}.lora_A.weight"] = v
else: # lora_B.weight — split the rows 3-way into q/k/v
head_dim = v.shape[0] // 3
for i, name in enumerate(("to_q", "to_k", "to_v")):
out[f"{prefix}{block}.attn.{name}.lora_B.weight"] = v[i * head_dim:(i + 1) * head_dim]
elif layer == "mlp":
if target == "fc1":
out[f"{prefix}{block}.ff.net.0.proj.{suffix}"] = v
elif target == "fc2":
out[f"{prefix}{block}.ff.net.2.{suffix}"] = v
return out
def _convert_kohya_h3_lora(sd):
"""Convert kohya/musubi-tuner-format MiniMax-H3 LoRA keys to ComfyUI format, then to module-tree keys.
kohya keys look like `lora_unet_blocks_N_attn_out_proj.lora_down.weight` / `.lora_up.weight` / `.alpha`
(also `attn_qkv_proj`, `mlp_fc1`, `mlp_fc2`). Some exports are hybrids: the ComfyUI-style prefix
(`diffusion_model.blocks.N.attn.out_proj`) with kohya-style suffixes — the prefix is already the
ComfyUI layout, so only the suffix needs renaming. We first rename to the ComfyUI layout the loader
understands (`diffusion_model.blocks.N.attn.out_proj.lora_A/lora_B.weight`), folding the per-module
`alpha/rank` factor into the `lora_up` (B) tensor so the loader's `B @ A * scale` stays correct, then
hand off to `_convert_comfyui_h3_lora`. kohya has no `token_refiner` keys — those are simply absent.
"""
import torch
comfy = {}
alphas: dict[str, float] = {}
# First pass: collect alpha factors (alpha/rank) per module base name. `.alpha` is a 0-dim
# scalar tensor (ndim == 0), so the rank must come from the lora_down weight's shape[0].
for k, v in sd.items():
if k.endswith(".alpha"):
base = k[: -len(".alpha")]
_down = sd.get(f"{base}.lora_down.weight")
rank = _down.shape[0] if _down is not None else float(v)
alphas[base] = float(v) / float(rank)
for k, v in sd.items():
if k.endswith(".alpha"):
continue
if k.endswith(".lora_down.weight"):
base = k[: -len(".lora_down.weight")]
lora = "lora_A"
elif k.endswith(".lora_up.weight"):
base = k[: -len(".lora_up.weight")]
lora = "lora_B"
else:
continue
if base.startswith("diffusion_model.") or base.startswith("transformer."):
# Hybrid export: ComfyUI-style prefix with kohya-style suffixes
# (diffusion_model.blocks.N.attn.out_proj.lora_down.weight). The prefix is already the
# ComfyUI layout — only the suffix needs renaming. token_refiner keys, if any, flow
# through unchanged too.
comfy_key = f"{base}.{lora}.weight"
else:
# base: lora_unet_blocks_N_attn_out_proj -> diffusion_model.blocks.N.attn.out_proj
parts = base.split("_")
# parts: [lora, unet, blocks, N, attn, out, proj] (mlp_fc1 -> [.., mlp, fc1])
if len(parts) < 6:
continue # unknown key shape — skip rather than crash
block = parts[3]
layer = parts[4]
target = "_".join(parts[5:])
comfy_key = f"diffusion_model.blocks.{block}.{layer}.{target}.{lora}.weight"
if lora == "lora_B" and base in alphas:
v = v * alphas[base]
comfy[comfy_key] = v
return _convert_comfyui_h3_lora(comfy)
def _apply_turbo():
"""Merge LoRA into transformer weights using A/B factors.
The precomputed delta (B @ A) is ~57 MB per module × 363 = ~20 GB — far more GPU memory
than is free after the transformer is unpacked (~14 GB on an 80 GB card). Instead we store
the LoRA factors A (~2.6 MB) and B (~2.6 MB) per module — total ~1.2 GB — and compute
the delta on-the-fly: mod.weight += B @ A * scale. Peak extra GPU memory is one temporary
delta tensor (~57 MB), freed each iteration.
A/B factors stay CPU-resident in `_TURBO_LoRA`; local CUDA copies are made per merge so the
store is never mutated and no CUDA memory is held between requests.
"""
global _TURBO_merged, _TURBO_LoRA
if _TURBO_merged or _TURBO_LoRA is None:
return
_t0 = time.time()
for mod, A, B in _TURBO_LoRA:
# delta = B @ A is temporary (~57 MB), freed next iteration — never stored simultaneously.
mod.weight.data += (B.to("cuda", non_blocking=True) @ A.to("cuda", non_blocking=True)) * TURBO_LORA_SCALE
_elapsed = time.time() - _t0
_TURBO_merged = True
_log(f"turbo LoRA merged ({len(_TURBO_LoRA)} modules in {_elapsed:.2f}s)")
def _remove_turbo():
"""Restore base weights by undoing the LoRA merge. Idempotent: only acts when `_TURBO_merged`,
and rolls back in reverse order so a partial failure leaves the fewest modules touched."""
global _TURBO_merged
if not _TURBO_merged or _TURBO_LoRA is None:
return
_t0 = time.time()
_done = 0
try:
for mod, A, B in reversed(_TURBO_LoRA):
mod.weight.data -= (B.to("cuda", non_blocking=True) @ A.to("cuda", non_blocking=True)) * TURBO_LORA_SCALE
_done += 1
except Exception as _e:
_log(f"turbo LoRA partial unmerge ({_done}/{len(_TURBO_LoRA)}): {_e}", "ERROR")
raise
_elapsed = time.time() - _t0
_TURBO_merged = False
import torch as _torch
_torch.cuda.empty_cache()
_log(f"turbo LoRA removed ({len(_TURBO_LoRA)} modules in {_elapsed:.2f}s)")
# ── Debug logging ──────────────────────────────────────────────────────────────
def _log(msg, level="INFO"):
"""Timestamped line to the Space logs (stdout). Levels: DEBUG / INFO / WARN / ERROR."""
print(f"[gen][{time.strftime('%H:%M:%S')}][{level}] {msg}", flush=True)
# ── Memory / OOM diagnostics ───────────────────────────────────────────────────
# The Space has a history of OOM crashes. A hard kill leaves no traceback, so the phase
# logs + bucket dumps below are the only evidence. `_log_mem` snapshots GPU + CPU at phase
# boundaries; `_debug_dump` persists diagnostics to the bucket under /data/debug;
# `_oom_dump` captures a CUDA OOM (traceback + torch memory summary) before re-raising.
# `H3_MEM_SAMPLE_INTERVAL` (s, 0 = off) drives the in-generation sampler thread.
_DEBUG_DIR = os.environ.get("H3_DEBUG_DIR", "/data/debug")
_MEM_SAMPLE_INTERVAL = float(os.environ.get("H3_MEM_SAMPLE_INTERVAL", "5"))
def _mem_info():
"""Compact GPU + CPU memory snapshot for the logs. Never raises."""
_parts = []
try:
import torch
if torch.cuda.is_available():
_free, _total = torch.cuda.mem_get_info()
_parts.append(
f"gpu_alloc={torch.cuda.memory_allocated() / 2**30:.2f}G "
f"gpu_resv={torch.cuda.memory_reserved() / 2**30:.2f}G "
f"gpu_max={torch.cuda.max_memory_allocated() / 2**30:.2f}G "
f"gpu_free={_free / 2**30:.2f}G/{_total / 2**30:.2f}G"
)
except Exception:
pass
try:
import psutil
_parts.append(f"rss={psutil.Process().memory_info().rss / 2**30:.2f}G")
except Exception:
try:
with open("/proc/self/status") as _f:
for _line in _f:
if _line.startswith("VmRSS:"):
_parts.append(f"rss={int(_line.split()[1]) / 2**20:.2f}G")
break
except Exception:
pass
return " ".join(_parts) or "mem=n/a"
def _log_mem(tag):
"""Log a memory snapshot under the greppable `MEM` level."""
_log(f"{tag}: {_mem_info()}", "MEM")
def _debug_dump(tag, text):
"""Persist a diagnostic to the bucket debug folder (/data/debug). Never raises."""
try:
os.makedirs(_DEBUG_DIR, exist_ok=True)
_path = os.path.join(_DEBUG_DIR, f"{tag}_{time.strftime('%Y%m%d_%H%M%S')}.txt")
_body = (
f"=== {tag} @ {time.strftime('%Y-%m-%d %H:%M:%S')} ===\n"
f"{_mem_info()}\n"
f"{text}\n"
)
_tmp = _path + ".tmp"
with open(_tmp, "w", encoding="utf-8") as _f:
_f.write(_body)
os.replace(_tmp, _path)
try:
shutil.copyfile(_path, os.path.join(_DEBUG_DIR, f"{tag}_latest.txt"))
except OSError:
pass
_log(f"debug dump written: {_path}")
except Exception as _e:
_log(f"debug dump failed ({_e}); see logs", "WARN")
def _oom_dump(tag, exc, extra=""):
"""Capture a CUDA OOM: exception + context + torch memory summary, saved to the bucket."""
_text = [f"exception: {type(exc).__name__}: {exc}"]
if extra:
_text.append(extra)
_text.append(traceback.format_exc())
try:
import torch
if torch.cuda.is_available():
_text.append(torch.cuda.memory_summary())
except Exception:
pass
_debug_dump(f"oom_{tag}", "\n".join(_text))
_log(f"OOM captured ({tag}): {exc}", "ERROR")
# ── Bucket-backed LoRA cache ───────────────────────────────────────────────────
# Every downloaded LoRA is copied into the mounted bucket (/data) so a Space restart
# loads it from the bucket instead of re-downloading it. Falls back to the HF cache
# whenever the bucket isn't mounted or a write fails. `H3_LORA_CACHE_DIR` overrides
# the mount path (used by tests).
_LORA_CACHE_DIR = os.environ.get("H3_LORA_CACHE_DIR", "/data/loras")
def _lora_cache_path(repo, file):
"""Bucket path for a (repo, file) LoRA, or None if the bucket isn't usable."""
try:
os.makedirs(_LORA_CACHE_DIR, exist_ok=True)
_probe = os.path.join(_LORA_CACHE_DIR, ".probe")
with open(_probe, "w") as _f:
_f.write("ok")
os.remove(_probe)
except Exception:
return None
_key = re.sub(r"[^A-Za-z0-9._-]", "_", f"{repo.replace('/', '__')}__{file}")
return os.path.join(_LORA_CACHE_DIR, _key)
def _fetch_lora_file(repo, file):
"""Local path to a LoRA .safetensors, preferring the bucket cache over a fresh download."""
from huggingface_hub import hf_hub_download
_cached = _lora_cache_path(repo, file)
if _cached and os.path.exists(_cached):
try:
with open(_cached, "rb") as _f:
_n = struct.unpack("<Q", _f.read(8))[0]
json.loads(_f.read(_n))
_log(f"LoRA cache hit: {repo}/{file} ({os.path.getsize(_cached) / 1e6:.0f} MB)")
return _cached
except Exception:
_log(f"LoRA cache corrupt, re-downloading: {repo}/{file}", "WARN")
try:
os.remove(_cached)
except OSError:
pass
_t0 = time.time()
_path = hf_hub_download(repo, file)
_log(f"LoRA downloaded: {repo}/{file} ({os.path.getsize(_path) / 1e6:.0f} MB in {time.time() - _t0:.0f}s)")
if _cached:
try:
_tmp = _cached + ".tmp"
with open(_path, "rb") as _src, open(_tmp, "wb") as _dst:
shutil.copyfileobj(_src, _dst, 1024 * 1024)
os.replace(_tmp, _cached)
_log(f"LoRA cached to bucket: {repo}/{file}")
except Exception as _e:
_log(f"LoRA bucket cache write failed ({_e}); using HF cache", "WARN")
return _path
def _ensure_nsfw_lora_loaded(key):
"""Download + convert + store a lazy NSFW LoRA's A/B factors on CPU. No-op if already loaded."""
if key in _NSFW_LoRA:
return
cfg = NSFW_LORA_CONFIGS[key]
import torch
from safetensors.torch import load_file as _load_lora
_t0 = time.time()
_log(f"loading NSFW LoRA {key} from {cfg['repo']} ...")
_path = _fetch_lora_file(cfg["repo"], cfg["file"])
_raw = _load_lora(_path)
if cfg.get("format") == "kohya":
_sd = _convert_kohya_h3_lora(_raw)
else:
_sd = _convert_comfyui_h3_lora(_raw)
_pairs: dict[str, dict[str, torch.Tensor]] = {}
for _k, _v in _sd.items():
if ".lora_A." in _k:
_pairs.setdefault(_k.split(".lora_A.")[0], {})["A"] = _v
elif ".lora_B." in _k:
_pairs.setdefault(_k.split(".lora_B.")[0], {})["B"] = _v
_loaded = []
for _base, _pair in _pairs.items():
if "A" not in _pair or "B" not in _pair:
continue
_parts = _base.split(".")
_obj = PIPE.transformer
for _p in _parts:
_obj = _obj[int(_p)] if _p.isdigit() else getattr(_obj, _p)
_loaded.append((_obj, _pair["A"].to(_obj.weight.dtype), _pair["B"].to(_obj.weight.dtype)))
del _sd, _pairs
import gc as _gc; _gc.collect()
_NSFW_LoRA[key] = _loaded
_log(f"NSFW LoRA {key} parsed ({len(_loaded)} modules in {time.time() - _t0:.1f}s)")
def _ensure_custom_lora_loaded(repo, file):
"""Download + convert + store a user-supplied Hugging Face LoRA's A/B factors on CPU. No-op if already loaded."""
key = (repo, file)
if key in _CUSTOM_LoRA:
return
import torch
from safetensors.torch import load_file as _load_lora
_t0 = time.time()
_log(f"loading custom LoRA {repo}/{file} ...")
try:
_path = _fetch_lora_file(repo, file)
_raw = _load_lora(_path)
except Exception as _e:
raise gr.Error(f"Couldn't fetch custom LoRA `{repo}/{file}`: {_e}")
_has_comfy = any(".lora_A." in _k for _k in _raw)
_has_kohya = any(".lora_down.weight" in _k or _k.startswith("lora_unet_") for _k in _raw)
if _has_comfy:
_sd = _convert_comfyui_h3_lora(_raw)
elif _has_kohya:
_sd = _convert_kohya_h3_lora(_raw)
else:
raise gr.Error(f"`{repo}/{file}` doesn't look like a MiniMax-H3 LoRA (no lora_A/lora_down keys found).")
_pairs: dict[str, dict[str, torch.Tensor]] = {}
for _k, _v in _sd.items():
if ".lora_A." in _k:
_pairs.setdefault(_k.split(".lora_A.")[0], {})["A"] = _v
elif ".lora_B." in _k:
_pairs.setdefault(_k.split(".lora_B.")[0], {})["B"] = _v
_loaded = []
for _base, _pair in _pairs.items():
if "A" not in _pair or "B" not in _pair:
continue
_parts = _base.split(".")
_obj = PIPE.transformer
for _p in _parts:
_obj = _obj[int(_p)] if _p.isdigit() else getattr(_obj, _p)
_loaded.append((_obj, _pair["A"].to(_obj.weight.dtype), _pair["B"].to(_obj.weight.dtype)))
if not _loaded:
raise gr.Error(f"`{repo}/{file}` converted to no usable modules — is it a MiniMax-H3 LoRA?")
del _sd, _pairs, _raw
import gc as _gc; _gc.collect()
_CUSTOM_LoRA[key] = _loaded
_log(f"custom LoRA {repo}/{file} parsed ({len(_loaded)} modules in {time.time() - _t0:.1f}s)")
def _parse_hf_link(url):
"""Parse a Hugging Face repo or file link into (repo, file). `file` is None for a bare repo link."""
url = (url or "").strip().split("?")[0].split("#")[0]
if not url:
raise gr.Error("Paste a Hugging Face link first.")
if not url.startswith("http"):
parts = [p for p in url.split("/") if p]
if len(parts) >= 2:
return parts[0] + "/" + parts[1], "/".join(parts[2:]) or None
raise gr.Error(f"Couldn't parse `{url}` as a Hugging Face repo link.")
if "huggingface.co" not in url:
raise gr.Error("Only Hugging Face links are supported (huggingface.co).")
parts = [p for p in url.split("huggingface.co", 1)[1].strip("/").split("/") if p]
if parts and parts[0] in ("spaces", "models", "datasets"):
parts = parts[1:] # https://huggingface.co/spaces/Owner/Repo → Owner/Repo
if len(parts) < 2:
raise gr.Error(f"Couldn't parse `{url}` as a Hugging Face repo link.")
repo = parts[0] + "/" + parts[1]
if len(parts) == 2:
return repo, None
if parts[2] in ("resolve", "tree", "blob"):
if len(parts) == 4 and parts[2] in ("tree", "blob"):
return repo, None # tree/main or blob/main with no subpath → treat as a repo link
if len(parts) < 5:
raise gr.Error(f"Couldn't parse a filename from `{url}`.")
if parts[3] != "main":
raise gr.Error("Only `main`-branch links are supported — use a `resolve/main/...` link.")
return repo, "/".join(parts[4:])
return repo, "/".join(parts[2:])
def _resolve_repo_file(repo):
"""Pick the single .safetensors file of a repo link; error if there isn't exactly one."""
from huggingface_hub import list_repo_files
try:
files = [f for f in list_repo_files(repo) if f.endswith(".safetensors")]
except Exception as _e:
raise gr.Error(f"Couldn't list files in `{repo}`: {_e}")
if len(files) == 1:
return files[0]
if not files:
raise gr.Error(f"No .safetensors files found in `{repo}`.")
raise gr.Error(f"`{repo}` has multiple .safetensors files — paste a direct file link instead: "
+ ", ".join(files[:5]) + ("…" if len(files) > 5 else ""))
def _download_custom_lora(url, scale=1.0, trigger=""):
"""Download + verify a user-supplied Hugging Face LoRA and report status. Loads it for the next Generate."""
try:
repo, file = _parse_hf_link(url)
if file is None:
file = _resolve_repo_file(repo)
_ensure_custom_lora_loaded(repo, file)
except gr.Error as _e:
return gr.update(value=f"**❌ {_e}**", visible=True)
except Exception as _e:
return gr.update(value=f"**❌ Unexpected error loading the LoRA: {_e}**", visible=True)
n = len(_CUSTOM_LoRA[(repo, file)])
note = f" (scale {scale:g}" + (f", trigger `{trigger}`)" if trigger else ")")
return gr.update(
value=f"**✅ Loaded `{repo}/{file}`** — {n} modules, will apply on Generate{note}.",
visible=True,
)
def _apply_lora(key, scale, store, merged):
"""Merge a loaded LoRA into transformer weights on GPU. A/B factors stay CPU-resident in
`store`; local CUDA copies are made per merge so the store is never mutated."""
if key in merged or key not in store:
return
_t0 = time.time()
for _mod, _A, _B in store[key]:
_mod.weight.data += (_B.to("cuda", non_blocking=True) @ _A.to("cuda", non_blocking=True)) * scale
merged.add(key)
_log(f"LoRA {key} merged ({len(store[key])} modules in {time.time() - _t0:.2f}s)")
def _remove_lora(key, scale, store, merged):
"""Undo a LoRA merge. Idempotent: only acts when `key` is in `merged`, and rolls back in
reverse order so a partial failure leaves the fewest modules touched."""
if key not in merged or key not in store:
return
_t0 = time.time()
_done = 0
try:
for _mod, _A, _B in reversed(store[key]):
_mod.weight.data -= (_B.to("cuda", non_blocking=True) @ _A.to("cuda", non_blocking=True)) * scale
_done += 1
except Exception as _e:
_log(f"LoRA {key} partial unmerge ({_done}/{len(store[key])}): {_e}", "ERROR")
raise
merged.discard(key)
import torch as _torch
_torch.cuda.empty_cache()
_log(f"LoRA {key} removed ({len(store[key])} modules in {time.time() - _t0:.2f}s)")
def _apply_nsfw(key):
_apply_lora(key, NSFW_LORA_CONFIGS[key]["scale"], _NSFW_LoRA, _NSFW_merged)
def _remove_nsfw(key):
_remove_lora(key, NSFW_LORA_CONFIGS[key]["scale"], _NSFW_LoRA, _NSFW_merged)
def _apply_custom(key, scale):
_apply_lora(key, scale, _CUSTOM_LoRA, _CUSTOM_merged)
def _remove_custom(key, scale):
_remove_lora(key, scale, _CUSTOM_LoRA, _CUSTOM_merged)
def status() -> str:
if LOAD_ERROR:
return LOAD_ERROR
if PIPE is None:
return f"Loading `{MODEL_REPO}` (transformer + VAEs, 77.3 GB). Watch the Space logs."
import h3_aoti
return (
f"Ready · transformer + VAEs **bfloat16, unquantized** · placement `{PLACEMENT}` · attention `{ATTENTION}` · "
f"{h3_aoti.status()} · loaded in {LOADED_IN:.0f}s · conditioner `{CONDITIONER_SPACE}`"
)
def load_models() -> str | None:
"""Load the denoising half at startup.
`MiniMaxH3GeneratorBlocks` declares `transformer`, `vae`, `audio_vae`, the two schedulers and `video_processor`,
so `load_components` fetches exactly those subfolders — `text_encoder/` and `transformer_ref/` are never touched.
Both autoencoders carry `_keep_in_fp32_modules` over every module and stay float32: a bfloat16 audio VAE decodes
the soundtrack roughly 20 dB too quiet.
"""
global PIPE, MANAGER, LOAD_ERROR, LOADED_IN
if PIPE is not None or LOAD_ERROR is not None:
return LOAD_ERROR
started = time.time()
try:
_log_mem("startup begin")
import torch
from diffusers import ComponentsManager
from h3_split_blocks import MiniMaxH3GeneratorBlocks
lower_duration_floor()
manager = ComponentsManager()
blocks = MiniMaxH3GeneratorBlocks()
_log(f"loading {[c.name for c in blocks.expected_components]} from {MODEL_REPO} ...")
pipe = blocks.init_pipeline(MODEL_REPO, components_manager=manager, collection="h3")
pipe.load_components(dtype=torch.bfloat16)
pipe.transformer.set_attention_backend(ATTENTION)
_log(f"components loaded in {time.time() - started:.0f}s")
_log_mem("startup components loaded")
if TURBO_LORA_REPO:
from safetensors.torch import load_file as _load_lora
_t0 = time.time()
_log(f"loading turbo LoRA from {TURBO_LORA_REPO} ...")
_lora_path = _fetch_lora_file(TURBO_LORA_REPO, TURBO_LORA_FILE)
_lora_sd = _load_lora(_lora_path)
# Group LoRA A/B pairs by target module name.
_lora_pairs: dict[str, dict[str, torch.Tensor]] = {}
for _k, _v in _lora_sd.items():
if ".lora_A." in _k:
_base = _k.split(".lora_A.")[0]
_lora_pairs.setdefault(_base, {})["A"] = _v
elif ".lora_B." in _k:
_base = _k.split(".lora_B.")[0]
_lora_pairs.setdefault(_base, {})["B"] = _v
# Store LoRA A/B factors on CPU (~1.2 GB total). The full delta (B @ A) would be
# ~20 GB and overflow GPU memory when moved to CUDA inside @spaces.GPU (~14 GB free).
# Instead _apply_turbo computes the delta on-the-fly: one temporary ~57 MB tensor
# per module, freed each iteration.
global TURBO_AVAILABLE, _TURBO_LoRA
_TURBO_LoRA = []
for _base_name, _pair in _lora_pairs.items():
if "A" not in _pair or "B" not in _pair:
continue
_base_name = _base_name.removeprefix("transformer.")
_parts = _base_name.split(".")
_obj = pipe.transformer
for _p in _parts:
_obj = _obj[int(_p)] if _p.isdigit() else getattr(_obj, _p)
_A = _pair["A"].to(_obj.weight.dtype) # (rank, in_features)
_B = _pair["B"].to(_obj.weight.dtype) # (out_features, rank)
_TURBO_LoRA.append((_obj, _A, _B))
del _lora_sd, _lora_pairs
import gc as _gc; _gc.collect()
TURBO_AVAILABLE = True
_log(f"turbo LoRA parsed ({len(_TURBO_LoRA)} modules in {time.time() - _t0:.0f}s, merge pending)")
if _TURBO_LoRA:
_m, _A, _B = _TURBO_LoRA[0]
_log(f" sample: module={type(_m).__name__}, "
f"A.shape={_A.shape}, B.shape={_B.shape}, "
f"A.device={_A.device}, dtype={_A.dtype}")
_log_mem("startup turbo lora parsed")
# Still startup, still free: an AoTI package carries no weights and opens its archive lazily inside the GPU
# worker. Off unless `H3_AOTI=1`.
import h3_aoti
h3_aoti.maybe_load(pipe.transformer)
if PLACEMENT == "pack":
# Scoped to the transformer. `spaces` packs every startup-resident CUDA tensor into a second on-disk copy,
# and packing all 77.3 GB busts the 150 GB storage quota; the 61.7 GB transformer alone fits. The ~10 GB of
# fp32 VAEs move on the first GPU call instead.
pipe.transformer.to("cuda")
import gc as _gc
_gc.collect()
torch.cuda.empty_cache()
_log_mem("startup transformer packed")
if PLACEMENT == "offload":
manager.enable_auto_cpu_offload(device="cuda")
_arm_decode_hooks(pipe)
PIPE, MANAGER = pipe, manager
LOADED_IN = time.time() - started
_log(f"ready in {LOADED_IN:.0f}s")
_log_mem("startup ready")
_dump_text = [f"placement={PLACEMENT}, attention={ATTENTION}, model={MODEL_REPO}"]
try:
import torch as _torch
if _torch.cuda.is_available():
_dump_text.append(_torch.cuda.memory_summary())
except Exception:
pass
_debug_dump("startup", "\n".join(_dump_text))
if _lora_cache_path("probe", "probe.safetensors"):
_log(f"LoRA bucket cache ready: {_LORA_CACHE_DIR}")
else:
_log(f"LoRA bucket cache unavailable ({_LORA_CACHE_DIR} not writable); using HF cache only", "WARN")
except Exception as error:
traceback.print_exc()
LOAD_ERROR = f"**Loading `{MODEL_REPO}` failed** after {time.time() - started:.0f}s: `{type(error).__name__}: {error}`"
return LOAD_ERROR
def _arm_decode_hooks(pipe):
"""Make the offload hooks fire for the two VAEs.
`enable_auto_cpu_offload` wraps `forward`, and the decode blocks call `vae.decode(...)` directly, so the hook
never runs and the VAE is still on the host when the latents arrive on the card.
"""
for name in ("vae", "audio_vae"):
module = getattr(pipe, name)
inner = module.decode
def armed(*args, _module=module, _decode=inner, **kwargs):
hook = getattr(_module, "_hf_hook", None)
if hook is not None:
hook.pre_forward(_module)
return _decode(*args, **kwargs)
module.decode = armed
@cache
def conditioner():
"""The other half, over the gradio API. `gradio_client` attaches the caller's own ZeroGPU token per call, so the
conditioner's booking is billed to whoever asked for the video."""
from gradio_client import Client
return Client(CONDITIONER_SPACE)
def encode_remote(prompt, image_path, last_image_path, canvas, num_frames, rewrite_prompt=False):
"""`/encode` on the conditioner Space: a safetensors file holding `prompt_embeds` + `text_token_tags`, with the
resolved `height` / `width` / `num_frames` in its metadata, plus the plan. `canvas` is the label.
The cached `Client` is dropped and rebuilt on a failed call (the conditioner may have restarted
or the connection dropped); one retry before giving up."""
from gradio_client import handle_file
from safetensors import safe_open
_t0 = time.time()
_log(f"encode_remote: conditioning on {CONDITIONER_SPACE} (canvas={canvas}, {num_frames}f, "
f"rewrite={rewrite_prompt}, image={bool(image_path)}, last_image={bool(last_image_path)}) ...")
for _attempt in (1, 2):
try:
path, plan = conditioner().predict(
prompt=prompt,
image_path=handle_file(image_path) if image_path else None,
last_image_path=handle_file(last_image_path) if last_image_path else None,
canvas=canvas,
num_frames=num_frames,
rewrite_prompt=bool(rewrite_prompt),
api_name="/encode",
)
break
except Exception:
conditioner.cache_clear()
if _attempt == 2:
raise
_log("encode_remote: conditioner call failed; reconnecting ...", "WARN")
time.sleep(2)
with safe_open(path, framework="pt") as handle:
metadata = handle.metadata()
_log(f"encode_remote: done in {time.time() - _t0:.0f}s "
f"({plan.get('num_text_tokens')} tokens, {metadata.get('height')}x{metadata.get('width')}, "
f"{metadata.get('num_frames')}f)")
_log_mem("encode_remote done")
_embeds = handle.get_tensor("prompt_embeds")
_tags = handle.get_tensor("text_token_tags")
try:
os.remove(path)
except OSError:
pass
return _embeds, _tags, metadata, plan
# Seconds of GPU one request needs, from the packed video rows it is about to denoise: linear in the rows for the
# matmuls, quadratic for the attention, against the AoTI block package this Space runs.
_DUR_B, _DUR_C = 1.1745e-4, 3.8396e-9
# The two resident decoders and the mux, which scale with the output rather than with the step count.
_DECODE_BASE, _DECODE_PER_DEFAULT_CANVAS, _DEFAULT_CANVAS_PIXELS = 15, 15, 960 * 544 * 124
# `pack` mode: only the ~10 GB of VAEs move on a cold worker.
_PLACEMENT_ALLOWANCE, _PAD = 12, 10
def get_duration(prompt_embeds, text_token_tags, image, last_image, height, width, num_frames, steps, seed, *a, **k):
height, width, num_frames, steps = int(height), int(width), int(num_frames), int(steps)
latent_frames = (num_frames - LATENTS_PER_CHUNK) // FRAMES_PER_CHUNK * LATENTS_PER_CHUNK + 2
patches = (height // 32) * (width // 32)
rows = latent_frames * patches + (int(image is not None) + int(last_image is not None)) * patches
denoise = steps * (_DUR_B * rows + _DUR_C * rows**2)
decode = _DECODE_BASE + _DECODE_PER_DEFAULT_CANVAS * (height * width * num_frames) / _DEFAULT_CANVAS_PIXELS
_dur = max(60, int(denoise + decode) + _PLACEMENT_ALLOWANCE + _PAD)
_log(f"get_duration: {width}x{height}, {num_frames}f, {steps} steps, rows={rows} -> {_dur}s "
f"(denoise {denoise:.0f}s + decode {decode:.0f}s + {_PLACEMENT_ALLOWANCE + _PAD}s allowance)")
return _dur
@spaces.GPU(duration=get_duration, size=GPU_SIZE)
def _generate(prompt_embeds, text_token_tags, image, last_image, height, width, num_frames, steps, seed, turbo=False, nsfw_keys=(), custom_loras=()):
"""The only thing on GPU time: the packed-sequence denoise loop and the two decoders.
Only the three generated outputs come back — a `@spaces.GPU` return crosses a process boundary by pickling, and
the full `PipelineState` still holds the packed latents, the rotary grid and the row indices on the card.
"""
import torch
_t0 = time.time()
_log(f"_generate ENTER: turbo={turbo}, TURBO_AVAILABLE={TURBO_AVAILABLE}, "
f"_TURBO_merged={_TURBO_merged}, steps={steps}, {width}x{height}, {num_frames}f, "
f"nsfw={tuple(nsfw_keys)}, custom={[(r, f) for r, f, _s in custom_loras]}")
_log_mem("_generate ENTER")
# Intra-phase memory sampler: a hard OOM kill leaves no traceback, so the last sampler
# lines before the crash are the only evidence of where memory grew. Queries only.
_stop_sampler = threading.Event()
def _sampler():
while not _stop_sampler.wait(_MEM_SAMPLE_INTERVAL):
_log_mem("sampler")
if _MEM_SAMPLE_INTERVAL > 0:
threading.Thread(target=_sampler, daemon=True).start()
try:
if PLACEMENT == "lazy":
PIPE.to("cuda")
elif PLACEMENT == "pack":
PIPE.vae.to("cuda")
PIPE.audio_vae.to("cuda")
_log_mem("_generate VAEs on cuda")
# Merge LoRA inside @spaces.GPU where the real weights are live on CUDA (in pack mode,
# ZeroGPU unpacks the real transformer weights before calling this function).
if turbo and TURBO_AVAILABLE:
_log("_generate: calling _apply_turbo ...")
_apply_turbo()
_log("_generate: _apply_turbo done")
for _key in nsfw_keys:
_apply_nsfw(_key)
for _repo, _file, _scale in custom_loras:
_apply_custom((_repo, _file), _scale)
_log_mem("_generate LoRAs merged")
_log(f"_generate: starting PIPE (steps={steps}) ...")
_log_mem("_generate before PIPE")
state = PIPE(
prompt_embeds=prompt_embeds.to("cuda"),
text_token_tags=text_token_tags,
image=image,
last_image=last_image,
height=height,
width=width,
num_frames=num_frames,
num_inference_steps=int(steps),
generator=torch.Generator("cpu").manual_seed(int(seed) if int(seed) >= 0 else torch.randint(0, 2**32, (1,)).item()),
)
_log(f"_generate: PIPE done in {time.time() - _t0:.0f}s")
_log_mem("_generate after PIPE")
# Pull the outputs out of the state before the finally block drops it: the state still holds
# the packed latents, rotary grid and row indices on the card.
_videos = state.get("videos")[0]
_audio = state.get("audio")[0].cpu()
_sampling_rate = state.get("sampling_rate")
except torch.cuda.OutOfMemoryError as _e:
_oom_dump("generate", _e, f"request: {width}x{height}, {num_frames}f, steps={steps}, "
f"turbo={turbo}, nsfw={tuple(nsfw_keys)}, "
f"custom={[(r, f) for r, f, _s in custom_loras]}")
raise
finally:
_stop_sampler.set()
# Each unmerge is attempted independently so one failure can't leave the others merged.
for _repo, _file, _scale in custom_loras:
try:
_remove_custom((_repo, _file), _scale)
except Exception as _e:
_log(f"custom LoRA unmerge failed for {_repo}/{_file}: {_e}", "ERROR")
for _key in nsfw_keys:
try:
_remove_nsfw(_key)
except Exception as _e:
_log(f"NSFW LoRA unmerge failed for {_key}: {_e}", "ERROR")
if turbo and TURBO_AVAILABLE:
try:
_remove_turbo()
except Exception as _e:
_log(f"turbo LoRA unmerge failed: {_e}", "ERROR")
# Aggressive worker-side release: drop the packed state and hand every cached CUDA block back
# so the worker's GPU + RSS footprint is flat between requests.
try:
del state
except Exception:
pass
import gc as _gc
_gc.collect()
torch.cuda.empty_cache()
_log_mem("_generate EXIT")
_log(f"_generate EXIT: {(time.time() - _t0):.0f}s total (denoise + decode)")
return _videos, _audio, _sampling_rate
def _generate_impl(prompt, image_path=None, last_image_path=None, canvas=DEFAULT_CANVAS, duration=5, steps=25, seed=-1, upsample=False, turbo=False, nsfw_lora_keys=(), custom_lora_url="", custom_lora_scale=1.0, custom_lora_trigger="", progress=gr.Progress(track_tqdm=True)):
"""The request pipeline: LoRA prep → remote conditioning → @spaces.GPU denoise → mux."""
if LOAD_ERROR:
raise gr.Error(LOAD_ERROR)
if PIPE is None:
raise gr.Error("The denoiser is still loading.")
if not prompt or not prompt.strip():
raise gr.Error("MiniMax-H3 always takes a prompt, keyframes or not.")
from PIL import Image, ImageOps
from diffusers.utils import encode_video
num_frames = snap_frames(duration)
if turbo and TURBO_AVAILABLE:
steps = 7 # 7 sigma points → 6 transformer evaluations (sweet spot for larryvrh v4-step600-EMA)
# LoRAs the user selected via the catalog dropdown; the AIO motion adapter is auto-loaded whenever any is on.
nsfw_keys = tuple(k for k in (nsfw_lora_keys or ()) if k in NSFW_LORA_CONFIGS)
# User-supplied LoRA from the custom URL box: parse the link and require it was downloaded & verified.
custom = []
if custom_lora_url and str(custom_lora_url).strip():
_repo, _file = _parse_hf_link(custom_lora_url)
if _file is None:
_file = _resolve_repo_file(_repo)
if (_repo, _file) not in _CUSTOM_LoRA:
raise gr.Error(f"`{_repo}/{_file}` isn't downloaded yet — press 'Download & Verify' first.")
try:
_scale = float(custom_lora_scale) if custom_lora_scale not in (None, "") else 1.0
except (TypeError, ValueError):
raise gr.Error("Custom LoRA scale must be a number.")
if not 0.01 <= _scale <= 4.0:
raise gr.Error("Custom LoRA scale must be between 0.01 and 4.")
_trigger = str(custom_lora_trigger or "").strip()
custom = [(_repo, _file, _scale, _trigger)]
if nsfw_keys or custom:
nsfw_keys += ("nsfw_aio",)
for _key in nsfw_keys:
_ensure_nsfw_lora_loaded(_key)
for _key in nsfw_keys:
# The trigger token must be in the prompt text for the conditioner to encode the LoRA's concept.
prompt = f"{prompt}, {NSFW_LORA_CONFIGS[_key]['trigger']}"
for _repo, _file, _scale, _trigger in custom:
if _trigger:
prompt = f"{prompt}, {_trigger}"
progress(0.0, desc=f"Upsampling the prompt on {CONDITIONER_SPACE} ..." if upsample else f"Conditioning on {CONDITIONER_SPACE} ...")
conditioned = time.time()
prompt_embeds, text_token_tags, metadata, plan = encode_remote(
prompt, image_path, last_image_path, canvas, num_frames, rewrite_prompt=upsample
)
condition_seconds = time.time() - conditioned
_log_mem("after conditioning")
height, width, num_frames = (int(metadata[key]) for key in ("height", "width", "num_frames"))
refined = plan.get("refined_prompt") or ""
def keyframe(path):
# The conditioning latents encoded here have to be of the image the conditioner looked at, which it prepares
# exactly this way.
return ImageOps.exif_transpose(Image.open(path)).convert("RGB") if path else None
progress(0.1, desc=f"Denoising {steps} steps at {width}x{height}, {num_frames} frames ...")
started = time.time()
frames, audio, sampling_rate = _generate(
prompt_embeds,
text_token_tags,
keyframe(image_path),
keyframe(last_image_path),
height,
width,
num_frames,
steps,
seed,
turbo,
nsfw_keys=nsfw_keys,
custom_loras=[(r, f, s) for r, f, s, _t in custom],
)
generate_seconds = time.time() - started
directory = os.path.join(tempfile.gettempdir(), "h3-outputs")
os.makedirs(directory, exist_ok=True)
# Rotate: keep only the newest _MAX_OUTPUTS MP4s so /tmp never fills up.
_MAX_OUTPUTS = 25
for _f in sorted(glob.glob(os.path.join(directory, "*.mp4")), key=os.path.getmtime)[:-_MAX_OUTPUTS]:
try:
os.remove(_f)
except OSError:
pass
path = os.path.join(directory, f"h3-{int(time.time() * 1000)}.mp4")
encode_video(frames, fps=FPS, output_path=path, audio=audio, audio_sample_rate=sampling_rate)
_log_mem("after mux")
# The frames list and audio tensor are the last big main-process objects; drop them now.
del frames, audio
import gc as _gc
_gc.collect()
lora_note = " · LoRA: " + ", ".join(NSFW_LORA_CONFIGS[k]["trigger"] for k in nsfw_keys) if nsfw_keys else ""
lora_label_text = "**LoRAs applied:** " + ", ".join(NSFW_LORA_CONFIGS[k]["label"] for k in nsfw_keys) if nsfw_keys else ""
if custom:
_custom_note = ", ".join(f"{r}/{f} (×{s:g})" for r, f, s, _t in custom)
lora_note = (lora_note + " · custom: " + _custom_note) if lora_note else " · custom: " + _custom_note
lora_label_text = (lora_label_text + " · " + _custom_note) if lora_label_text else "**LoRAs applied:** " + _custom_note
report = (
f"`{width}x{height}`, {num_frames} frames ({num_frames / FPS:.3f} s), {int(steps)} steps · "
f"conditioner {condition_seconds:.0f}s ({plan['num_text_tokens']} tokens"
f"{', upsampled' if refined else ''}) · "
f"denoise + decode {generate_seconds:.0f}s ({generate_seconds / int(steps):.1f} s/step) · seed {int(seed)}"
f"{lora_note}"
)
_log(report)
return path, report, refined, gr.update(visible=bool(refined)), gr.DownloadButton(value=path, visible=True), gr.update(value=lora_label_text, visible=bool(nsfw_keys) or bool(custom))
def generate(prompt, image_path=None, last_image_path=None, canvas=DEFAULT_CANVAS, duration=5, steps=25, seed=-1, upsample=False, turbo=False, nsfw_lora_keys=(), custom_lora_url="", custom_lora_scale=1.0, custom_lora_trigger="", progress=gr.Progress(track_tqdm=True)):
"""One request. `upsample` is last among the pre-existing positional args, so a positional API client that
predates it is unaffected; the custom-LoRA args come after it.
Logs the full request to the Space logs, then runs the pipeline. Any failure is logged with a full
traceback before it reaches the UI.
"""
_log(f"generate ENTER: prompt={prompt!r:.120}, canvas={canvas}, duration={duration}s -> {snap_frames(duration)}f, "
f"steps={steps}, seed={seed}, upsample={upsample}, turbo={turbo}, "
f"nsfw={tuple(nsfw_lora_keys or ())}, custom_url={custom_lora_url!r}, "
f"custom_scale={custom_lora_scale}, custom_trigger={custom_lora_trigger!r}")
_t_req = time.time()
try:
return _generate_impl(
prompt, image_path, last_image_path, canvas, duration, steps, seed, upsample, turbo,
nsfw_lora_keys, custom_lora_url, custom_lora_scale, custom_lora_trigger, progress,
)
except gr.Error as _e:
_log(f"generate rejected: {_e}", "WARN")
raise
except Exception as _e:
_log(f"generate FAILED after {time.time() - _t_req:.0f}s:\n{traceback.format_exc()}", "ERROR")
try:
import torch as _torch
if isinstance(_e, _torch.cuda.OutOfMemoryError):
_oom_dump("request", _e)
except Exception:
pass
raise
def export_last_frame(video_path):
"""Extract the last frame from a generated video and return it as an image file."""
if not video_path:
raise gr.Error("No video to extract from.")
import subprocess, tempfile
out = os.path.join(tempfile.gettempdir(), f"h3-lastframe-{int(time.time() * 1000)}.png")
result = subprocess.run(
["ffmpeg", "-sseof", "-0.1", "-i", video_path, "-frames:v", "1", "-y", out],
capture_output=True, text=True,
)
if result.returncode != 0:
raise gr.Error(f"ffmpeg failed: {result.stderr[-300:]}")
return out
def _fit_keyframe(image_path, current_canvas):
"""Cover-crop an uploaded keyframe to the closest supported aspect ratio and select that ratio's smallest
(fastest) canvas, unless the user already picked a matching ratio."""
if not image_path:
return gr.update(), gr.update()
from PIL import Image as _Image
img = _Image.open(image_path)
aspect = img.width / img.height
fastest = {}
for label, (h, w) in CANVASES.items():
r = w / h
if r not in fastest or w * h < fastest[r][1][0] * fastest[r][1][1]:
fastest[r] = (label, (h, w))
ratio = min(fastest, key=lambda r: abs(r - aspect))
label, (h, w) = fastest[ratio]
cur_h, cur_w = CANVASES[current_canvas]
if abs(cur_w / cur_h - aspect) <= abs(ratio - aspect):
label = current_canvas
h, w = cur_h, cur_w
target = w / h
if abs(img.width / img.height - target) <= 1e-3:
return gr.update(value=image_path), gr.update(value=label)
if img.width / img.height > target:
new_w = int(img.height * target)
left = (img.width - new_w) // 2
img = img.crop((left, 0, left + new_w, img.height))
else:
new_h = int(img.width / target)
top = (img.height - new_h) // 2
img = img.crop((0, top, img.width, top + new_h))
img.save(image_path)
return gr.update(value=image_path), gr.update(value=label)
load_models()
INTRO = """# minimax h3 turbo + lora's
<div align="center">
<a href="https://huggingface.co/MiniMaxAI/MiniMax-H3" target="_blank" rel="noopener"><strong>[ model ]</strong></a>
<a href="https://www.minimax.io/blog/minimax-h3" target="_blank" rel="noopener"><strong>[ blog ]</strong></a>
<a href="https://huggingface.co/spaces/multimodalart/minimax-h3-reference" target="_blank" rel="noopener"><strong>[ reference to video ]</strong></a>
</div>
**MiniMax-H3** is a 33B parameter state of the art video generation model that produces video and a
fully synchronized soundtrack (ambience, foley, speech).
**Turbo mode** (optional) applies the [larryvrh distillation LoRA](https://huggingface.co/InstantX/MiniMax-H3-Turbo-Lora-Diffusers)
at generation time, reducing inference from 25 steps to 7 for ~4× faster generation with minimal quality loss.
"""
CSS = """
.main.fillable { max-width: 1250px !important; }
/* ── Root gradient background ── */
.dark .gradio-container {
background: linear-gradient(135deg, #0a1628 0%, #0d2137 25%, #0f2b3a 50%, #0e3330 75%, #0a2a1f 100%) !important;
color: #e8f0f8 !important;
min-height: 100vh;
/* ── Override Gradio's CSS variables for dark theme ── */
--background-fill-primary: rgba(10, 22, 40, 0.6) !important;
--background-fill-secondary: rgba(10, 22, 40, 0.4) !important;
--block-background-fill: rgba(10, 22, 40, 0.5) !important;
--block-border-color: rgba(79, 195, 247, 0.15) !important;
--block-label-text-color: #a8c4d8 !important;
--block-title-text-color: #a8c4d8 !important;
--background-text-color: #e0edf5 !important;
--body-text-color: #e0edf5 !important;
--body-text-color-subdued: #8aa8bc !important;
--border-color-primary: rgba(79, 195, 247, 0.15) !important;
--color-accent: #26a69a !important;
--color-accent-soft: rgba(38, 166, 154, 0.15) !important;
--button-primary-background-fill: linear-gradient(135deg, #0288d1 0%, #00897b 50%, #2e7d32 100%) !important;
--button-primary-text-color: #ffffff !important;
--button-secondary-background-fill: rgba(255, 255, 255, 0.06) !important;
--button-secondary-border-color: rgba(255, 255, 255, 0.1) !important;
--button-secondary-text-color: #b0c8dc !important;
--input-background-fill: rgba(10, 22, 40, 0.6) !important;
--input-background-fill-focus: rgba(10, 22, 40, 0.7) !important;
--input-border-color: rgba(79, 195, 247, 0.15) !important;
--input-border-color-focus: rgba(79, 195, 247, 0.45) !important;
--slider-color: #26a69a !important;
--accordion-border-color: rgba(255, 255, 255, 0.06) !important;
}
/* ── Intro markdown ── */
.gradio-container .prose h1,
.gradio-container .prose h2 {
background: linear-gradient(135deg, #4fc3f7, #26a69a) !important;
-webkit-background-clip: text !important;
-webkit-text-fill-color: transparent !important;
background-clip: text !important;
}
.gradio-container .prose {
color: #c8dde8 !important;
}
.gradio-container .prose a {
color: #4dd0e1 !important;
}
/* ── Glass card panels (no backdrop-filter on .block — it traps fixed-position dropdown popups) ── */
.gradio-container .block {
background: rgba(255, 255, 255, 0.04) !important;
border: 1px solid rgba(255, 255, 255, 0.08) !important;
border-radius: 14px !important;
box-shadow: 0 4px 30px rgba(0, 0, 0, 0.25), inset 0 1px 0 rgba(255,255,255,0.05) !important;
}
/* ── Text / Number inputs (Gradio 6: label.container > .input-container > textarea/input) ── */
.gradio-container label.container textarea,
.gradio-container label.container input[type="text"],
.gradio-container input[type="number"] {
background: rgba(10, 22, 40, 0.6) !important;
border: 1px solid rgba(79, 195, 247, 0.15) !important;
color: #e0edf5 !important;
border-radius: 10px !important;
}
.gradio-container label.container textarea:focus,
.gradio-container label.container input[type="text"]:focus,
.gradio-container input[type="number"]:focus {
border-color: rgba(79, 195, 247, 0.45) !important;
box-shadow: 0 0 0 2px rgba(79, 195, 247, 0.12) !important;
}
/* ── Labels (Gradio 6: BlockTitle renders <span data-testid="block-info">) ── */
.gradio-container [data-testid="block-info"],
.gradio-container label {
color: #a8c4d8 !important;
font-weight: 500 !important;
}
/* ── Slider track ── */
.gradio-container input[type="range"] {
accent-color: #26a69a !important;
}
/* ── Checkbox (Gradio 6: <label class="checkbox-container"> → <input> + <span class="label-text">) ── */
.gradio-container .checkbox-container {
color: #c0d4e4 !important;
}
.gradio-container .checkbox-container .label-text {
color: #c0d4e4 !important;
}
/* ── Dropdown (Gradio 6: div.container > div.wrap > div.wrap-inner > input[role="combobox"]) ── */
.gradio-container .wrap {
background: rgba(10, 22, 40, 0.6) !important;
border: 1px solid rgba(79, 195, 247, 0.15) !important;
border-radius: 10px !important;
}
.gradio-container .wrap:focus-within {
border-color: rgba(79, 195, 247, 0.45) !important;
box-shadow: 0 0 0 2px rgba(79, 195, 247, 0.12) !important;
}
.gradio-container .wrap-inner input[role="combobox"] {
color: #e0edf5 !important;
background: transparent !important;
}
/* Dropdown popup options (Gradio 6: <ul class="options"> with position:fixed) */
.gradio-container ul.options {
background: rgba(10, 22, 40, 0.95) !important;
border: 1px solid rgba(79, 195, 247, 0.25) !important;
border-radius: 10px !important;
box-shadow: 0 8px 32px rgba(0, 0, 0, 0.5) !important;
z-index: 1000 !important;
}
.gradio-container ul.options li.item {
color: #e0edf5 !important;
}
.gradio-container ul.options li.item:hover,
.gradio-container ul.options li.item.active {
background: rgba(79, 195, 247, 0.15) !important;
}
/* ── Image upload area ── */
.gradio-container .image-container,
.gradio-container .upload-container {
background: rgba(10, 22, 40, 0.35) !important;
border: 2px dashed rgba(79, 195, 247, 0.2) !important;
border-radius: 12px !important;
}
.gradio-container .image-container:hover,
.gradio-container .upload-container:hover {
border-color: rgba(38, 166, 154, 0.5) !important;
}
/* ── Video output ── */
.gradio-container video {
border-radius: 12px !important;
border: 1px solid rgba(255,255,255,0.06) !important;
}
/* ── Generate button — hero gradient ── */
.gradio-container .lg.primary,
.gradio-container button.primary,
.gradio-container button.lg.primary {
background: linear-gradient(135deg, #0288d1 0%, #00897b 50%, #2e7d32 100%) !important;
color: #ffffff !important;
border: none !important;
border-radius: 12px !important;
font-weight: 700 !important;
letter-spacing: 0.03em !important;
text-transform: uppercase !important;
box-shadow: 0 4px 20px rgba(0, 137, 123, 0.35), 0 0 40px rgba(2, 136, 209, 0.15) !important;
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
position: relative;
overflow: hidden;
}
.gradio-container .lg.primary::before {
content: "";
position: absolute;
inset: 0;
background: linear-gradient(135deg, rgba(255,255,255,0.15) 0%, transparent 50%, rgba(255,255,255,0.05) 100%);
pointer-events: none;
}
.gradio-container .lg.primary:hover,
.gradio-container button.primary:hover {
box-shadow: 0 6px 28px rgba(0, 137, 123, 0.5), 0 0 60px rgba(2, 136, 209, 0.25) !important;
transform: translateY(-1px) !important;
}
/* ── Secondary buttons ── */
.gradio-container button.secondary,
.gradio-container button.lg.secondary {
background: rgba(255, 255, 255, 0.06) !important;
border: 1px solid rgba(255, 255, 255, 0.1) !important;
color: #b0c8dc !important;
}
.gradio-container button.secondary:hover {
background: rgba(255, 255, 255, 0.1) !important;
border-color: rgba(79, 195, 247, 0.3) !important;
}
/* ── Accordion (Gradio 6: Index.svelte adds "gr-accordion" to elem_classes; header is <button class="label-wrap">) ── */
.gradio-container .gr-accordion {
background: rgba(255, 255, 255, 0.03) !important;
border: 1px solid rgba(255, 255, 255, 0.06) !important;
border-radius: 12px !important;
}
.gradio-container .gr-accordion .label-wrap {
color: #a8c4d8 !important;
font-weight: 600 !important;
}
.gradio-container .gr-accordion .label-wrap:hover {
color: #4fc3f7 !important;
}
.gradio-container .gr-accordion [data-testid="accordion-content"] {
padding-top: var(--size-2, 8px) !important;
}
/* ── Examples ── */
.gradio-container .examples {
background: rgba(10, 22, 40, 0.3) !important;
border: 1px solid rgba(255, 255, 255, 0.06) !important;
border-radius: 12px !important;
}
/* ── Markdown output ── */
.gradio-container .prose {
color: #b8ccda !important;
}
.gradio-container .prose code {
background: rgba(79, 195, 247, 0.1) !important;
color: #4dd0e1 !important;
border-radius: 4px !important;
padding: 2px 6px !important;
}
/* ── Scrollbar ── */
.gradio-container ::-webkit-scrollbar {
width: 6px !important;
}
.gradio-container ::-webkit-scrollbar-track {
background: rgba(0,0,0,0.2) !important;
}
.gradio-container ::-webkit-scrollbar-thumb {
background: rgba(79, 195, 247, 0.3) !important;
border-radius: 3px !important;
}
"""
def _lora_descriptions(keys):
"""Markdown of each selected LoRA's label + description, plus conflict warnings; hidden when nothing is selected."""
keys = tuple(k for k in (keys or ()) if k in NSFW_LORA_CONFIGS)
if not keys:
return gr.update(visible=False)
blocks = [f"**{NSFW_LORA_CONFIGS[k]['label']}** — {NSFW_LORA_CONFIGS[k]['desc']}" for k in keys]
conflicts = []
for group, members in LORA_CONFLICT_GROUPS.items():
hit = [k for k in keys if k in members]
if len(hit) > 1:
conflicts.append(f"**{group}**: " + " + ".join(NSFW_LORA_CONFIGS[k]['label'] for k in hit))
if conflicts:
blocks.append("**⚠️ Conflicts:** " + " ".join(conflicts)
+ " — these overlap in purpose and may fight each other; consider picking one from each group.")
return gr.update(value="\n\n".join(blocks), visible=True)
with gr.Blocks(title="minimax h3 turbo + lora's") as demo:
gr.Markdown(INTRO)
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Prompt",
lines=3,
value="A red fox trotting through a snowy pine forest at dawn, snow crunching underfoot",
)
with gr.Row():
upsample = gr.Checkbox(label="Upsample prompt", value=False)
turbo = gr.Checkbox(label="Turbo (7-step LoRA)", value=False,
visible=TURBO_AVAILABLE,
info="Apply larryvrh distillation LoRA, reduce steps from 25 to 7")
with gr.Accordion("NSFW LoRAs", open=False):
nsfw_lora_keys = gr.Dropdown(
label="Select LoRAs to apply",
choices=[(cfg["label"], key) for key, cfg in NSFW_LORA_CONFIGS.items() if key != "nsfw_aio"],
value=[],
multiselect=True,
filterable=True,
info="Searchable — type to filter. Selected LoRAs show as removable tags; their trigger tokens are auto-appended to the prompt. The HMNSFW AIO motion adapter auto-loads whenever any LoRA is selected.",
)
lora_desc = gr.Markdown(visible=False)
with gr.Accordion("Custom LoRA (Hugging Face)", open=False):
gr.Markdown("Paste a Hugging Face link — a repo page or a direct `.safetensors` file link — then press "
"**Download & Verify**. The verified LoRA is applied on Generate. "
"ComfyUI and kohya formats are auto-detected.")
custom_lora_url = gr.Textbox(
label="HF repo or .safetensors link",
placeholder="https://huggingface.co/Owner/Repo or https://huggingface.co/Owner/Repo/resolve/main/file.safetensors",
)
with gr.Row():
custom_lora_scale = gr.Number(label="Scale", value=1.0, minimum=0.01, maximum=4.0, step=0.1)
custom_lora_trigger = gr.Textbox(label="Trigger (optional)",
placeholder="Auto-appended to the prompt if given")
custom_lora_btn = gr.Button("Download & Verify", size="sm")
custom_lora_status = gr.Markdown(visible=False)
with gr.Row():
image = gr.Image(label="First frame (optional)", type="filepath")
last_image = gr.Image(label="Last frame (optional)", type="filepath")
run = gr.Button("Generate", variant="primary")
with gr.Accordion("Advanced options", open=False):
canvas = gr.Dropdown(label="Canvas", choices=list(CANVASES), value=DEFAULT_CANVAS)
duration = gr.Slider(label="Duration (s)", minimum=MIN_UI_DURATION, maximum=MAX_UI_DURATION, step=1, value=5)
steps = gr.Slider(label="Steps", minimum=7, maximum=40, step=1, value=25)
seed = gr.Number(label="Seed (−1 = random)", value=-1, precision=0)
with gr.Column():
video = gr.Video(label="Video + soundtrack")
with gr.Row():
last_frame_btn = gr.Button("Export Last Frame", size="sm")
download_btn = gr.DownloadButton("Download Video", size="sm", visible=False)
last_frame_output = gr.Image(label="Last frame")
lora_label = gr.Markdown(visible=False)
report = gr.Markdown(visible=False)
# An output, so it can be revealed only for a request that asked for a rewrite.
with gr.Accordion("Upsampled prompt", open=False, visible=False) as upsampled_panel:
upsampled = gr.Textbox(show_label=False, lines=8, interactive=False)
image.upload(_fit_keyframe, [image, canvas], [image, canvas])
run.click(
generate,
[prompt, image, last_image, canvas, duration, steps, seed, upsample, turbo, nsfw_lora_keys,
custom_lora_url, custom_lora_scale, custom_lora_trigger],
[video, report, upsampled, upsampled_panel, download_btn, lora_label],
api_name="generate",
)
custom_lora_btn.click(_download_custom_lora,
[custom_lora_url, custom_lora_scale, custom_lora_trigger],
[custom_lora_status])
nsfw_lora_keys.change(_lora_descriptions, [nsfw_lora_keys], [lora_desc])
last_frame_btn.click(
export_last_frame,
[video],
[last_frame_output],
)
download_btn.click(
lambda: gr.Info("Downloading video..."),
[],
[],
)
if __name__ == "__main__":
demo.launch(show_error=True, theme=gr.themes.Base(), css=CSS)
|