r/StableDiffusionInfo

I'm really tired of commercial platforms blocking inputs due to overly sensitive or restrictive criteria.

The catch is that my PC doesn't have the specs or GPU power to run tools like Stable Diffusion, Flux, or LLMs locally via Automatic1111 or Ollama (as much as I'd love to).

Could you recommend the best cloud platforms, WebUIs, or affordable services where I can access and experiment with these open-source models without heavy censorship? I'm open to Google Colab notebooks, Hugging Face spaces, or any other web-based alternatives you guys use.

Thanks in advance for any tips!

TLDR:
Faster LTX 2.3 generations on RTX 3060 with Sage Attention + transition support + audio fixes Updated my LTX 2.3 workflow for faster generations + cleaner setup

Hey everyone, I updated my personal LTX 2.3 workflow and wanted to share it.

I’m trying to keep things practical with useful features while avoiding turning it into one of those workflows that becomes impossible to run

This update includes:
• Sage Attention support for noticeably faster generations
• First frame / last frame transitions
• Audio fix from the previous video
• GGUF workflow running on my RTX 3060

I’m getting pretty solid speed improvements while still keeping the workflow lightweight enough for more people to actually use.

TLDR:
Faster LTX 2.3 generations on RTX 3060 with Sage Attention + transition support + audio fixes

Links:

Sage Attention:
https://github.com/DazzleML/comfyui-t...

Repo V3:
https://huggingface.co/The-frizzy1/LT...

CivitAI:
https://civitai.com/models/2339823/lt...

Previous Video:
https://www.youtube.com/watch?v=LNs2l...

If anyone needs help setting it up or troubleshooting anything, I’ll be active in the YouTube comments 👍 ok

Hey pro-level folks — VFX / pipeline people specifically.

I'm building a workflow that mimics a real multi-camera film shoot of a car driving through a real intersection and turning from one street onto another. The goal isn't "AI-looking video" — it's spatially coherent, location-accurate footage from multiple angles, the same way you'd cover a car scene with:

• A ground-level tracking shot following the car
• A side-angle static from the corner
• A high oblique (simulated drone/crane)
• A cut to the opposite corner as the car exits frame

All of these need to feel like they were shot at the same real intersection. Not inspired by it — actually it.

Tool stack I'm working with:

• FLUX 2 Pro/Max — first frame / keyframe / environment image generation (HEX-locked to real location palette)
• Luma Uni-1 — Kontext-style image editing + realistic image anchoring from photo references (Create → Modify chain)
• Seedance 2.0 — final video generation with Video1 / Image1 reference system
• Claude — prompt engineering for all three models (structured JSON DNA-lock format for FLUX, role-labeled refs for Uni-1, time-segmented prompts for Seedance)
• NukeX — compositing
• Baselight — color grading

No characters. Pure location + one vehicle.

The core pipeline question

Think of it like real car shoot coverage:

Unit 1: Tracking shot — camera car follows the vehicle around the turn
Unit 2: Static corner — camera planted at the exit of the turn
Unit 3: Aerial — 45° oblique drone above the intersection
Unit 4: Opposite POV — looking back at where the car came from

Each of these is a separate Seedance generation. Each needs to feel like the same intersection in the same light at the same moment.

My current theory for location anchoring:

Real street photos (4-8 angles) + drone stills
        ↓
Uni-1 [Create] — generate photoreal environment keyframe per camera angle
        (using street photos as ENVIRONMENT refs, drone stills for aerial angles)
        ↓
Uni-1 [Modify] — lock car into each keyframe at correct position/scale for that angle
        ↓
FLUX 2 Pro — alternative path: HEX-locked environment keyframe per angle
        (5+ color zones locked to real location, ARRI Alexa 65 / 2.39:1 camera DNA)
        ↓
Seedance 2.0 — per-angle clip generation
        image_1 = Uni-1 or FLUX keyframe of that angle
        video_1 = face-blurred reference video from real location (same angle)
         as the first frame, u/Video1's camera movement and environment as reference
        ↓
NukeX — spatial comp, plate alignment, vehicle contact shadows
        ↓
Baselight — grade match to real location reference

Specific questions

1. Uni-1 vs FLUX for environment keyframes — which locks location better?

Uni-1's Create mode with IMAGE1 (ENVIRONMENT) + IMAGE2 (COMPOSITION) roles seems stronger for photorealism when working from real photo refs. FLUX 2 Pro gives me more predictable HEX palette control but hallucinates architecture more freely.

Anyone tested both as image_1 anchors going into Seedance? Which holds location geometry better through the video generation?

2. Reference injection cadence in Seedance — how often to re-anchor?

For a 4-angle sequence on the same intersection:

• Does each Seedance clip need its own angle-specific keyframe as image_1?
• Or can you use one establishing shot as a global environment anchor and trust Seedance to infer the correct geometry for other angles?

My assumption: you need a unique keyframe per angle — same intersection, camera repositioned, same lighting and palette. Is that correct?

3. Seedance slot logic for a pure vehicle shot (no characters)

Without characters there are no asset IDs needed. Current slot assignment I'm testing:

image_1 = Uni-1/FLUX keyframe — this camera angle
image_2 = car reference (specific model, color, exact spec)
image_3 = lighting/time-of-day reference (golden hour, shadow direction)
image_4 = empty
video_1 = real location reference clip, this angle (face-blurred)
video_2 = car motion reference (matching speed/direction)
asset_1–3 = empty

Does it make sense to use image_2 and image_3 as additional location refs from adjacent angles to give Seedance spatial context for the turn geometry? Or does that confuse the model?

4. The turn itself — spatial continuity across the cut

This is the hardest part. Unit 1 sees the car approach the corner. Unit 2 (static at exit) sees it complete the turn and accelerate away. These are two separate Seedance generations that need to feel spatially connected.

Options I'm testing:

• Last frame of Unit 1 → image_1 of Unit 2 (temporal handoff)
• Shared overhead aerial keyframe as a spatial map referenced in both clips
• Generate the aerial first, use a frame extract from it as the COMPOSITION reference in Uni-1 when building ground-level keyframes

Has anyone found a reliable method for spatial continuity across angle cuts that wasn't stitched together in comp?

5. Claude in the loop — structured prompt generation

Using Claude with custom skill files to generate:

• FLUX 2 Pro JSON blocks with per-zone HEX color assignment for environment keyframes
• Uni-1 multi-role prompts (ENVIRONMENT + COMPOSITION + LIGHTING per angle)
• Seedance time-segmented prompts with correct @ syntax per clip

The idea is to generate all prompts for all 4 angles in one structured Claude session, with shared location DNA (palette, light direction, time of day) locked across the entire batch. Anyone running a similar prompt-generation layer upstream of their ComfyUI workflow?

What I'd love to hear:

• Your asset injection strategy for multi-angle single-scene coverage
• Whether Uni-1's ENVIRONMENT role actually holds real architecture accurately enough to anchor Seedance
• Any ComfyUI graph patterns for this kind of angle-set batch generation
• How you handle the turn geometry problem in comp vs. at the generation stage

This is production-pipeline territory, not "make a cool AI video" territory. Looking for people who've actually pushed multi-angle location lock past the single-shot level. 👇