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Movie Poster Redmond LoRA for SDXL - Generate Cinematic Posters

LoRA for SDXL that creates movie poster images with title text. Trigger with 'Poster Movie.' Works on SDXL, ZImageTurbo, Qwen, and Flux.

2 sources cited Training LoRAs

On this page

  1. What This LoRA Does
  2. Trigger Words
  3. Recommended Settings
  4. Usage Examples
  5. What the Model Excels At
  6. License
  7. Common Errors and Fixes
  8. FAQ
  9. Sources

Movie Poster Redmond LoRA for SDXL

TL;DR

What This LoRA Does

Movie Poster Redmond is a Low-Rank Adaptation (LoRA) trained on Stable Diffusion XL 1.0 that specializes in producing movie poster style images. Unlike general-purpose style LoRAs, this one has a distinct capability: it renders title text directly into the composition — think "DANGERS OF NIGHT" across a documentary tiger scene, or "LOVE YOU" on a romantic heart poster.

The model was trained with GPU compute sponsored by Redmond.AI and published by ArtificialGuyBR. It's available in four variants targeting different base models:

All variants share the same training data and trigger words.

Trigger Words

Use Poster Movie. at the start or end of your prompt to activate the style. The HF model card also lists Movie Poster and MoviePosterAF as alternative triggers, but Civitai's trained words list specifies Poster Movie. as the primary.

Parameter | Value

|-----------|-------

Base Model | SDXL 1.0 (or variant) Steps | 30 Scheduler | DPMSolverMultistepScheduler LoRA Weight | ~0.8-1.0 (start at 1.0) Resolution | 1024x1536 (portrait) or 1536x1024 (landscape) The LoRA file is 81 MB (SafeTensor format).

Usage Examples

Diffusers (Python):

from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    'stabilityai/stable-diffusion-xl-base-1.0',
    torch_dtype=torch.float16
).to('cuda')

pipeline.load_lora_weights(
    'artificialguybr/movie-poster-redmond-for-sd-xl-create-movie-poster-images',
    weight_name='MoviePosterRedmond-MoviePoster-MoviePosterRedAF.safetensors'
)

image = pipeline(
    'A dev working in a computer, cartoon, with title "NO SLEEP" on it, Poster Movie.'
).images[0]

ComfyUI: Load the LoRA via the Load LoRA node, set strength to 1.0, and include Poster Movie. in your positive prompt.

Movie poster example showing a cinematic tiger scene with title text Cinematic documentary-style poster with integrated title — typical output quality

What the Model Excels At

Based on the sample images and model card examples, the LoRA handles:

The training data appears to cover a wide range of movie poster genres, giving it flexibility beyond a single aesthetic.

License

Apache License 2.0 — permits commercial use, modification, and distribution with attribution. The HF model card references a bespoke LoRA license link, but Civitai explicitly states Apache 2.0.

Common Errors and Fixes

Error | Cause | Fix | Source

|-------|-------|-----|--------

Title text not rendering | Trigger phrase missing or weight too low | Add Poster Movie. to prompt, increase LoRA weight to 1.0 | Model card Wrong aspect ratio | Default SDXL resolution | Use 1024x1536 (portrait) or 1536x1024 (landscape) | Best practice Style not applying | Wrong base model variant | Match LoRA variant to your base model (SDXL vs ZImageTurbo vs Qwen vs Flux) | Civitai model versions

FAQ

Q: Does this work with Automatic1111 / WebUI? A: Yes. Place the .safetensors file in models/Lora and add <lora:MoviePosterRedmond-MoviePoster-MoviePosterRedAF:1.0> to your prompt with Poster Movie..

Q: Can I use this commercially? A: Yes, Apache 2.0 license permits commercial use with attribution.

Q: Which base model variant should I download? A: Match the variant to your base model. If you're on standard SDXL 1.0, use the SDXL variant. If you use ZImageTurbo, Qwen Image, or Flux Klein, download the corresponding version.

Q: Why are there multiple trigger phrases listed? A: Poster Movie. is the primary trained trigger (per Civitai). Movie Poster and MoviePosterAF are alternatives listed on HF — they may work but Poster Movie. is most reliable.

Sources