Luma’s Uni-1 is now available in ComfyUI via Partner Nodes. Unlike most image models, Uni-1 isn’t a diffusion model — it’s a decoder-only autoregressive transformer that treats text and images as a single interleaved sequence, jointly modeling time, space, and logic in one architecture. The result: a model that reasons about your prompt before generating — decomposing instructions, resolving constraints, and planning composition like a frontier LLM.

Try it on Comfy Cloud

Built around reasoning, not just generation

Most image models go straight from prompt to pixels in a single step. Uni-1 works differently. It thinks first, then draws.

Because Uni-1 is autoregressive and treats text and images as the same kind of sequence, it can reason through your prompt before generating anything. It breaks the instruction into parts, works out the tricky bits (how many of something, what goes where, any logical conditions), and plans the composition — all before committing to pixels.

Features

• Photorealism with material accuracy

• Text rendering that’s actually readable

• Reference-guided generation with identity preservation

• Image editing and multi-turn refinement

• Multi-panel output with temporal consistency

• Web search grounding

• Multilingual prompts

Output flexibility

Uni-1 supports 9 aspect ratios ranging from 3:1 ultra-wide panoramic banners down to 1:3 ultra-tall portrait formats, with everything in between — 16:9, 1:1, 9:16, 2:3, and more.

Getting Started

1. Update ComfyUI to the latest version, or access Comfy Cloud.

2. Find the Luma UNI-1 Image node via the Node Library, or load a template from the Templates panel.

3. Drop in your prompt (and a source image if you’re editing), connect outputs, and run.

Try it on Comfy Cloud

UNI-1 joins the growing roster of image models available through ComfyUI Partner Nodes. Try it out and let us know what you build.

Bye April! We shipped a ton of new models, features and expanded our Comfy Hub.

If you blinked, here’s the catch-up.

New models

Seedance 2.0

• ByteDance’s next-gen video model, live as a partner node on day one.

• Three modes out the gate: Text-to-Video, Reference-to-Video, and First/Last-Frame to Video, pick your level of creative control.

• Upload an AI-generated image or a Real human portrait, then create videos with a consistent identity and authentic facial expressions.

Happy Horse

• A multi-talented partner node covering Text-to-Video, Image-to-Video, Reference-to-Video, and video editing.

Wan 2.7

• The latest in the Wan 2.7 multi-use case video model is available as a partner node

Ernie-Image

• Baidu’s open-source text-to-image model built on8B Diffusion Transformer (DiT).

• Fully open weights for the folks who like to tinker

Ace Step 1.5 XL

• Open-source music generation, but commercial-grade quality.

• Tuned for full-song coherence: verses, choruses, and arrangements that actually hold together.

Quiver

• Structured SVG generation as a partner node: vector output, not pixels.

• Great for icons, diagrams, and anywhere you need infinite scalability without re-rendering.

GPT Image 2

• OpenAI’s latest image model available via partner node.

• Both text-to-image and editing in a single workflow

SUPIR

• Open-source image restoration support: bring blurry, degraded, or low-res images back to life.

• Pairs nicely with anything you’d run through an upscaler

RIFE & FILM Frame Interpolation

• Open-source frame interpolation, now natively supported.

• Use it to smooth motion, hit higher frame rates, or stretch short clips into something cinematic.

SAM 3 & SAM 3.1

• Meta’s SAM 3 and SAM 3.1 models for image and video segmentation are now natively supported

Veo 3.1 Lite & Veo 3.1 Fast

• Google’s lighter and cheaper Veo variants are now partner-node supported.

Sonilo

• Video-to-music, now inside ComfyUI.

• Drop in a clip, get a soundtrack tuned to its motion and mood.

Feature releases

Parallel Job Execution through API

• You can now run multiple workflows in parallel — a serious speedup for anyone batching, testing variations, or running production pipelines.

• The single feature most likely to change how your day actually feels.

ComfyHub is gaining momentum

• Crossed nearly 500 workflows on the Hub.

• If you haven’t browsed lately, there’s a good chance someone already built the workflow you were about to make.

Excited about May!

We’re excited to share that ComfyUI now supports HappyHorse 1.0 — a cinematic video generation model from Alibaba, designed for high-quality storytelling video and production-ready creative workflows.

HappyHorse 1.0 focuses on strong aesthetics, multi-shot sequencing, and powerful edit-style workflows (generation + editing). It’s a great fit for creators building ads, e-commerce visuals, short-form content, and social marketing videos.

Try on Comfy Cloud

Highlights

• Multi-modal creation paths: Text-to-Video (T2V), Image-to-Video (I2V), and Subject-to-Video (S2V).

• Cinematic aesthetics: Wide-aperture framing, shallow depth of field, refined texture, and atmospheric mood.

• Multi-shot output: Up to 15s at 1080p, with an emphasis on multi-shot consistency across cut transitions.

• Editing workflows: V2V and SV2V for transforming footage or replacing/inserting subjects while preserving motion and composition.

Output Examples

Get Started in ComfyUI

1. Update to the latest ComfyUI or visit Comfy Cloud

2. Open Template Library → Search for HappyHorse

3. Update prompt or input image, then hit Run

Available workflows

Image to Video

Try on Comfy Cloud

Text to Video

Try on Comfy Cloud

Video Edit

Try on Comfy Cloud

Reference to Video

Try on Comfy Cloud

As always, enjoy creating!

We’re announcing a $30 million financing at a $500 million valuation, bringing our total funding to $47 million. The round was led by Craft, with participation from Pace Capital, Chemistry, TruArrow and others.

ComfyUI started as a community project: one developer, one open-source repo, and a fast-growing group of creators who wanted more control over how they created with AI. That community now spans 4 million users, 60,000+ nodes built by contributors, and 150,000+ daily downloads. This round is a reflection of what that community has built together.

ComfyUI is already the best software for creative AI but now we can make it even better: more features, more optimizations and more stability while accelerating our already rapid pace of development.

“With this funding we can make sure open source wins.” said Yannik Marek, cofounder and the creator of ComfyUI, “We and many in the community have spent a lot of effort: countless hours, even years working directly and indirectly on ComfyUI. Without you we would not exist. We want to live in a world where the best tool is open source. Now we can finally make our dream real.”

How professional creators are using ComfyUI

ComfyUI has become the standard for creator-controlled AI workflows, spanning image, video, 3D, and audio, locally or in the cloud. Studios, agencies, and enterprise teams are using it to power production-grade creative pipelines.

Creative studios like Black Math are using ComfyUI to build modular creative pipelines connecting their motion, texture, and generative tools in ComfyUI enabling teams to move beyond one-off deliverables and instead create scalable design systems that clients can build on over time.

Agencies & Ad studios like Silverside AI use ComfyUI to power major brand campaigns, for example, developing SVEDKA’s 2026 Super Bowl commercial, the first primarily AI-generated Super Bowl ad. This enables precise creative control, consistent quality, and faster production timelines.

Technical artists use ComfyUI the way engineers use code, composing models, LoRAs, ControlNets, and community nodes into precise, reproducible workflows. A concept artist chains an image generation model to a depth estimator and an inpainting pass, so every iteration preserves geometry while exploring style. A VFX freelancer builds a texture synthesis pipeline that processes hundreds of assets overnight. When the workflow is done, it lives in a JSON file: portable, versionable, shareable.

“ComfyUI is so important to us because it allows us to know that we always play on the bleeding edge. Not only because of the technology itself, but because of the entire community shaping the product and developing extensions,” PJ Pereira, Co-founder, Silverside AI.

“Using ComfyUI really brings artist empowerment to the forefront. I want my employees to feel like they have control over the process. The more exposed they can be to how the models work, the more we feel we’re creating something that only we could make,” Jeremy Sahlman, Co-founder & CCO, Black Math.

ComfyUI has quietly become a hiring signal. Across job boards at Enterprise companies and creative studios, “ComfyUI artist” has emerged as a job title in high demand. Creative directors, technical artists, and pipeline engineers are now expected to build in ComfyUI. What began as an open-source tool built by a single developer has become infrastructure for a new creative profession.

A community that compounds

The open ecosystem is what makes ComfyUI structurally different from any closed tool. With 60,000+ community-built nodes and extensions, every new model, every new technique, every workflow shared publicly makes the platform more powerful for everyone. No product team could ship that fast. No single company could cover that much ground.

And the platform only grows stronger as more creators build on it, extend it, and push it in directions we never anticipated.

“Comfy has innovated a new and powerful ecosystem for creativity without compromising creative control. It has been amazing to watch technical artists and curious creative minds leverage Comfy to explore the full surface area of their ideas,” Scott Belsky, Founder of Behance.

What we’re building next

We’re already one of the most widely used platforms for generative AI. But we think ComfyUI can be the best creative engine for professionals. Period. Here’s where the funding goes:

• Comfy Cloud, built for teams

  • We’re scaling cloud infrastructure to bring ComfyUI to creators who don’t have access to high-end local compute, and to studios that demand security and infrastructure out of the box.

• Collaborative workflows

  • Creative work is rarely solo. We’re building the features that let teams share, version, and iterate on workflows together so the way ComfyUI works in a studio finally matches how studios actually work.

• Investing in local experience

  • The power was always there. We are continuing to invest in Comfy Local and making updates a seamless and stable experience

• Ecosystem stability

  • 60,000+ community nodes are one of ComfyUI’s greatest strengths. We’re investing in the infrastructure and standards that make them more reliable on Local and Cloud and easier to build.

• Model support, always current

  • Every major model release should work in ComfyUI on day one. We’re building the integrations and partnerships to make that a guarantee.

We are not building a walled garden. We are building open infrastructure, the kind that lasts, the kind that empowers rather than locks in.

Why join us

ComfyUI is built by a team of engineers and operators who don’t do average. We’re solving hard problems at the intersection of creative tooling, ML infrastructure, and distributed systems, problems that matter to millions of people who create things for a living.

If you want to build tools that technical artists and studios depend on every day, if you’re the kind of engineer who gets excited when a community of 4 million people finds unexpected ways to use what you’ve built, you should be here.

Thank You to our community

None of this exists without you.

The 60,000+ nodes you’ve built. The workflows you’ve shared. The bugs you’ve filed, the fixes you’ve submitted, the tutorials you’ve posted.

ComfyUI exists because of you. The funding, the valuation, the enterprise adoption, all of it traces back to a group of people who cared enough to build something together, in public, for anyone to use.

What we’re committing to: the ComfyUI will always stays open. You will always be able to run ComfyUI on your own machine, on your own terms, with full access to everything you’ve built. Open source lasts forever, and we intend to prove it.

This round gives us the resources to go further: better infrastructure, better tooling, more support for the ecosystem you’ve created. We’ll keep building in the open, keep listening, and keep getting out of the way when the community knows better than we do.

Thank you. Genuinely.

Team Comfy

We’re excited to share that you can now generate videos featuring Virtual Portraits & Real Humans with Seedance 2.0 in ComfyUI. Upload an AI-generated image or a Real human portrait, then create videos with a consistent identity and authentic facial expressions.

Try on Comfy Cloud

Virtual Portrait VS Real Human: What’s the difference?

Real Human refers to actual living people (needing verification), while Virtual Portrait is AI-generated and non-existent in reality (no verification needed).

How to use Virtual Portrait for Seedance 2.0

To use a virtual portrait with Seedance 2.0, use the Seedance 2.0 node as the regular video generation node, upload your AI-generated character asset, and then pass it to the node.

The ByteDance server will detect the image type and throw an error if it’s identified as protected content. If the backend identification has passed, then you can use it as a reference asset without any issue.

Try on Comfy Cloud

How to use real human assets for Seedance2.0?

The verification process is designed to prevent impersonation and unauthorized use of someone’s likeness. It’s handled by ByteDance, which complies with emerging AI transparency regulations.

First Run (Verification Required)

1. Upload a portrait image using the ByteDance Create Image/Video Asset node

2. Run the workflow—a verification link will be generated

3. Open the link on your phone or browser and complete the liveness check (less than 30s)

4. Real human verification passed. You will receive two IDs:

• Group ID — represents the verified person (save this)

• Asset ID — this specific image, ready to use in video generation (point to the same asset, can be reused as well)

Future Image with the same person (No Verification)

1. Upload a new photo or video of the same person

2. Enter the saved Group ID in the ByteDance Create Image/Video Asset node

3. The system compares facial features against the original verification

4. If it matches, the new asset is activated automatically

Getting Started

Try on Comfy Cloud

Download Workflow

1. Update ComfyUI to the latest version, or visit Comfy Cloud

2. Go to Template Library → search Seedance 2.0

3. Update the input and follow the guide in this blog

As always, enjoy creating!

What is Seedance 2.0?

Seedance 2.0 is ByteDance’s latest video generation model, built for industrial-grade controllability and native audio-video sync. With Real Human support in ComfyUI, you’re no longer limited to virtual characters—you can bring those same strengths to videos featuring real people, with identity and performance held together across a full clip.

• Real people, cinematic control: Direct camera moves, lighting, and scene changes while keeping the subject believable.

• Identity consistency: Keeps facial features and overall appearance stable across motion, camera moves, and transitions.

• Native audio-video sync: Generates video and audio together (dialogue/ambient/music) with more reliable lip-sync and expressions.

• Multi-reference directing: Mix text with up to 9 images, 3 videos, and 3 audio clips to lock in look, motion, and rhythm.

Check more details in our documentation

​

Try on Comfy Cloud

Reasoning-Driven Generation

GPT Image 2.0 is built around a thinking step. Instead of one-shot sampling, the model plans the composition, checks its work, and iterates — which is why it handles the things that have historically broken image models. Dense text, small UI elements, iconography, infographics, maps, slides, even manga panels render cleanly. For ComfyUI users, this means you can finally ask for “a poster with a seven-item bulleted list in 11pt Helvetica, centered” and get usable output instead of glyph soup.

Try on Comfy Cloud

Image Editing That Preserves What Matters

Edit-based workflows have been a weak spot across every major image model — small changes tend to ripple outward, warping faces, altering composition, or quietly rewriting details the user never asked to touch. GPT Image 2.0 handles targeted edits with structural fidelity, keeping everything outside the edit zone pixel-stable while applying the requested change cleanly at up to 2K resolution. Colorizing a black-and-white photo or shifting a scene from noon to dusk both land cleanly, without the usual collateral damage to faces, geometry, or fine detail. For retouching workflows, archival restoration, or iterative creative direction, this is a meaningful unlock.

Try on Comfy Cloud

Eight Consistent Images, One Prompt

The model can return up to eight distinct images from a single prompt while preserving character and object continuity across the series. Storyboarding, reference sheets, character turnarounds, and product variants that used to require careful seed-locking and prompt gymnastics now come out of one node. Feed the batch straight into a Save Image loop or chain it into video workflows downstream.

Getting Started

1. Update ComfyUI to the latest version v0.19.4, or visit Comfy Cloud

2. Find the node — search for OpenAI GPT Image 1.5 in the Node Library. Select the gpt-image-2 model.

GPT Image 2.0 slots naturally into hybrid pipelines: use it for the text-heavy hero frame, then hand off to your local models for upscaling, stylization, or video. That’s the point of Partner Nodes — the best model for each step, in one graph.

Let us know what you build.

Quiver brings best-in-class SVG generation to ComfyUI as a Partner Node, giving you two powerful capabilities: text-to-SVG and image-to-SVG. Whether you’re producing logos, icons, illustrations, or converting raster artwork into clean vector files, Quiver handles it natively inside your workflows.

Try on Comfy Cloud

What you can do with Quiver in ComfyUI

Quiver is for creators who want vector deliverables, not just pixels. Here are the most common ways teams use it:

• Generate logos & wordmarks: Quickly explore multiple design directions, then refine typography, spacing, and geometry using editable vector files.

• Build icon sets: Create consistent icon variations and organize them into a unified system for UI, web, or print use.

• Vectorize raster graphics: Convert generated images, sketches, or screenshots directly into editable vectors.

• Production-ready assets: Lightweight, infinitely scalable, fully editable SVG files optimized for design and development workflows.

Quiver Capability in ComfyUI

Text to SVG

Describe what you want, and get a clean vector file back. Quiver generates production-quality SVGs from natural language prompts

Need a minimal line-art logo? A flat-style icon set? An illustrated scene for a landing page? Just prompt it.

Try on Comfy Cloud

Download Workflow

Image to SVG

Drop in a raster image — a photo, a sketch, a screenshot — and Quiver traces it into a clean SVG. The results are properly vectorized paths, not just embedded bitmaps.

This is a big deal for workflows where you’re generating raster images with other ComfyUI nodes and need vector output downstream. Chain an image generation model into Quiver and go straight from prompt to scalable vector — no manual tracing, no third-party conversion tools.

Try on Comfy Cloud

Download Workflow

Getting Started

1. Update ComfyUI to the latest version, or visit Comfy Cloud.

2. Find Quiver in the Node Library by searching “Quiver,” or browse the template library.

3. Load a workflow and start generating:

As always, enjoy creating!

Music generation just got a serious upgrade. ACE-Step 1.5 XL brings a 4B-parameter Diffusion Transformer decoder to the ACE-Step framework, delivering audio quality that competes with commercial music models — and it runs locally on your GPU.

It comes in 3 flavors: xl-base for maximum versatility, xl-sft for peak audio quality, and xl-turbo for speed. All are commercially licensed under the MIT license and use legally compliant training data.

Try on Comfy Cloud

Dark Synthwave (Instrumental)

Melodic Dubstep (Female Vocal)

Ambient Electronic (Female Vocal)

Key Highlights

• Commercial-Grade Quality — Evaluation metrics place output between Suno v4.5 and v5, with 4B parameters delivering richer audio than the 2B predecessors

• Ultra-Fast Generation — Under 2 seconds per full song on an A100, under 10 seconds on an RTX 3090. xl-turbo cuts inference to just 8 steps (~6x faster than base/sft)

• Flexible Duration — Generate anything from 10-second loops to full 10-minute compositions

• 1000+ Instruments and Styles — Fine-grained timbre description across a massive range of musical genres

• 50+ Language Lyrics — Prompt with lyrics for structure and style control in over 50 languages

• Commercially Licensed — MIT license, trained on licensed music, royalty-free/public domain, and synthetic MIDI-to-Audio data

Pick Your Variant

All three XL models share the same 4B-parameter architecture.

XL-Base — Most versatile, highest diversity. For maximum creative range.

Download Workflow

XL-SFT — Peak audio quality, some loss in diversity. For clean final outputs.

Try on Comfy Cloud

Download Workflow

XL-Turbo— 8 steps, ~6x faster, no CFG. For fast iteration.

Try on Comfy Cloud

Download Workflow

Getting Started

1. Download or update ComfyUI to the latest version, or visit Comfy Cloud

2. Open the Template Library and search for “ACE Step”

3. Select a workflow

4. Following the guide in the workflow to download the model

5. update prompt, then hit Run

As always, enjoy creating!

Try on Comfy Cloud

Download Workflow

Model Highlights

• Precise text rendering — dense and layout-sensitive text in English, Chinese, and more

• Strong instruction following — handles complex prompts, multi-object relations, and knowledge-intensive descriptions

• Structured visual generation — posters, manga/anime storyboards, multi-panel compositions

• Broad stylistic range — realistic photography to cinematic film-like aesthetics

• Compact and deployable — 8B parameters, runs on 24 GB VRAM

• Built-in Prompt Enhancer — 3B model that expands short inputs into richer prompts

Examples

Text Rendering & design layouts

Prompt: 
Educational comic book style infographic showing the coffee making process. The background has a light beige vintage paper texture. At the top center of the image is a bold brown title that reads 'The Coffee Making Process', with a smaller English subtitle 'How Coffee is Made' below. The main part consists of six step blocks connected by brown dotted arrows, arranged in two rows up and down, forming a Z-shaped visual guide.

The first step in the upper left corner illustrates a coffee tree full of ripe red coffee cherries, with a cut coffee cherry next to it showing the beans inside, labeled 'Step 1: Harvesting Cherries' below the block.
The second step in the upper middle shows wooden fermentation boxes filled with coffee beans, labeled 'Step 2: Pulping and Fermentation'.
The third step in the upper right depicts coffee beans being dried under the sun on bamboo mats, labeled 'Step 3: Sun Drying'.
The fourth step in the lower left shows a vintage metal roasting machine with coffee beans rolling inside and steam rising, labeled 'Step 4: Roasting'.
The fifth step in the lower middle features a stone grinder pouring out smooth coffee grounds, labeled 'Step 5: Grinding'.
The sixth step in the lower right shows a production line where coffee liquid is being poured into molds, with finished packaged coffee beside it, labeled 'Step 6: Brewing and Forming'.

The four corners of the image are decorated with hand-drawn coffee leaves and coffee beans. The overall color palette consists of warm brown, caramel, cream, deep red and olive green, with delicate lines and clear layout.

Prompt: 
Vertical rectangular language learning flashcard in traditional Japanese Ukiyo-e woodblock style. Whole card framed by dark blue traditional Seigaiha wave borders, with aged beige rough washi paper texture background.

Upper two-thirds: Ukiyo-e illustration. A graceful spotted sika deer stands calmly in the center, with delicate black outlines and traditional flat coloring. Background: classic Japanese scenery — snow-capped Mount Fuji in distance, stylized layered clouds, falling pink cherry blossom petals. A red square antique seal with the Chinese character '鹿' in seal script at the lower right. Color scheme: indigo, vermilion, moss green, beige, with woodblock color misalignment and vintage charm.

Lower one-third: centered text area. Top: small hiragana しか; large Japanese kanji 鹿; below it romaji shika; then English "Deer". Bottom: bilingual example — Japanese: 鹿が桜の花を見ている。English: The deer is looking at the cherry blossoms. All black text with slight brush calligraphy texture, matching Ukiyo-e antique style.

Prompt: Split-screen conceptual poster with vertical split composition, left half representing the present and future featuring a solid stone analog clock dial with bright red hands and red scale markings, set against a deeply cracked, dry, parched earth texture in muted cool gray tones, embodying slow, static structural decay and temporal erosion, right half representing the past that is violently disintegrating, dissolving and exploding into chaotic dust, debris, flying stone shards and swirling cosmic nebula energy with a deep blood red and black color palette, the circular clock frame itself is half solid weathered stone and half crumbling into particles merging the two worlds, with the red clock hands fully positioned on the left present/future side of the dial, embodying the concept of time, past vs future, slow decay vs violent collapse, hyper-realistic 3D render with cinematic dramatic lighting, high contrast, sharp details on the left side, motion blur and dynamic particle effects on the right side, photorealistic textures of cracked earth and floating dust, atmospheric haze, futuristic graphic design with minimalist red vertical UI elements and custom technical text overlays in the corners, bold red stylized title text "ETERNAL DAWN" in the top right corner, small red technical metadata text "SPECDARY BOTHUNG" directly below the main title in the top right, dense lines of small red technical data blocks including "TIMESTAMP: 00:00:00", "COLLAPSE RATE: 99.8%", "TEMPORAL ANOMALY DETECTED", "PAST DIMENSION: DECAYING" in the bottom right corner with a red horizontal accent bar and footer text "FOR DECAY OF THE EONS" below, small red header text "TEMPORAL SHIFT PROTOCOL" with vertical red accent line running down the left edge in the top left, vertical red accent line with small red technical text annotations including "PRESENT: STABLE", "FUTURE: UNFOLDING" along it in the bottom left, red text "WARNING: TEMPORAL DISINTEGRATION" curving around the center clock dial perimeter, small red footer text "PAST FADES, FUTURE RISES" spanning the split at the bottom center, shot in the style of a high-end sci-fi movie poster, 8K ultra-high resolution, photorealistic, cinematic composition, dramatic depth, moody and intense atmosphere, detailed particle simulation, photorealistic material rendering.

Cinematic & stylized aesthetics

Prompt: First-person vertical real-world urban night photo. Narrow, wet, busy city street with strong depth; towering buildings on both sides with fire escapes, pipes, AC units, warm window lights. Red vertical “HOTEL” neon on left, bright blue “BAR” sign and yellow “OPEN 24 HOURS” box on right. Distant huge digital billboard glowing cyan “NEON CITY”. Rough wet asphalt reflects neon lights; vintage yellow taxi with “TAXI” sign drives down center, red taillights on. Two pedestrians in dark coats with black umbrellas walk away on right sidewalk. Giant needle-like steel TV tower peeks through building gaps at end, red aircraft warning light glowing. Cinematic volumetric light, misty air, cool cyan-blue vs warm orange-red contrast, immersive and realistic.

Prompt: Full-body editorial fashion shot, elegant European woman aged 28-30 with soft natural blonde hair styled in a loose low bun, wearing a tailored soft cream ribbed knit long-sleeve maxi dress with a fitted waist and flowing floor-length skirt, minimalist nude leather mules and delicate gold jewelry, standing leaning against an ornate carved stone balustrade on a gravel terrace in the garden of a grand Lake Como villa, tall iconic Italian cypress trees framing the background, calm still Lake Como water stretching out to hazy misty Alpine mountains in the distance, overcast soft diffused natural light, muted warm earthy color palette, quiet luxury aesthetic, hyper-realistic editorial fashion photography, natural skin texture with subtle pores, soft bokeh foreground foliage, shallow depth of field, shot on a medium format camera with an 85mm f/1.4 lens, sharp focus on the subject, fine film grain, muted contrast, timeless sophisticated mood, shot by Lachlan Bailey in the style of Vogue editorial, 8K ultra-high resolution, photorealistic, cinematic composition following the rule of thirds, golden hour overcast lighting, atmospheric haze, refined Italian villa garden details.

A charming, whimsical 3D illustration featuring a fluffy, plush-textured white duck sitting comfortably in a fuzzy coral-pink armchair, holding a bright red mug of steaming black coffee against a solid warm coral-red background; defined by its tactile, huggable fabric-like texture across all elements, a bold, minimalist warm color palette of creamy white, vibrant orange, and rich reds, gentle anthropomorphism that blends cuteness with relatable cozy relaxation, soft diffused lighting, and a subtle film grain that adds a nostalgic, handcrafted feel, creating a playful yet serene mood perfect for themes of calm downtime and morning routines.

Multi-panel compositions

Educational comic book infographic. Five vertical panels side by side, earthy color palette, hand-drawn illustration style. Top title: "NORTH AMERICAN NATIVE SPECIES". Style: comic line art, watercolor fills, white panel backgrounds, bold headers.
Panel 1: Gray squirrel holding an acorn. Header: "EASTERN GRAY SQUIRREL" Fact: "Buries acorns to help forests grow." Arrow → tail: "Bushy tail"

Panel 2: Robin with orange-red breast on a branch. Header: "AMERICAN ROBIN" Fact: "A sign of spring returning." Arrow → breast: "Orange-red breast"

Panel 3: Red maple tree with autumn leaves. Header: "RED MAPLE TREE" Fact: "Shelters wildlife year-round." Arrow → leaves: "Red in autumn"

Panel 4: Deer with white tail raised. Header: "WHITE-TAILED DEER" Fact: "White tail signals danger." Arrow → tail: "Alarm signal"

Panel 5: Monarch butterfly on a flower. Header: "MONARCH BUTTERFLY" Fact: "Migrates 3,000 miles each year." Arrow → wings: "Warning colors"

A 6-panel comic page, 2 columns × 3 rows, black border between each panel.

Panel 1 (top-left): wide shot — a young woman in a red coat stands at a rainy train station. Caption box: "She had waited three years for this moment."
Panel 2 (top-right): close-up on her face — anxious eyes, rain on her cheek. No text.
Panel 3 (mid-left): medium shot — a train arrives, doors slide open, steam rising. Sound effect text: "WHOOOOSH"
Panel 4 (mid-right): her point-of-view — a man in a gray jacket steps out, back to camera.
Panel 5 (bottom-left): extreme close-up — her hand trembling as she reaches forward.
Panel 6 (bottom-right): wide shot — they face each other under one umbrella. Caption box: "Some arrivals change everything."

Ink and watercolor style, cool blue-gray palette, expressive line art. Same character design consistent across all 6 panels.

Get Started in ComfyUI

1. Update ComfyUI to v0.19.1 or visit Comfy Cloud

2. Go to Template → Search for ERNIE-Image

3. Select the ERNIE-Image workflow

4. Download the missing models and update the prompt, then hit Run

Available Models

🤗ERNIE-Image - Main SFT model — stronger quality and instruction fidelity, ~50 inference steps

Try on Comfy Cloud

Download Workflow

🤗 ERNIE-Image-Turbo - Turbo model optimized with DMD and RL — faster generation in just 8 steps

Try on Comfy Cloud

Download Workflow

For ComfyUI support, please visit https://huggingface.co/Comfy-Org/ERNIE-Image

As always, enjoy creating!

​

We’re excited to announce that Sonilo is now available in ComfyUI via Partner Nodes. Sonilo is an AI-powered video-to-music service that generates soundtracks synchronized to your footage—analyzing visuals, pacing, and emotional cues to produce music locked to your timeline, frame by frame.

You can now drive music generation directly from your ComfyUI node graphs, alongside your existing workflows.

Try on Comfy Cloud

Sonilo Highlights:

• Length, Pace & Emotion Alignment — Music is generated to match the exact duration, pacing, and emotional arc of your video, delivering soundtracks that feel intentionally composed for the content.

• Instant Generation — Generate full-length, high-quality music in seconds, enabling faster iteration and a seamless creative workflow.

• Commercial-Ready Usage — All generated music includes commercial use rights, making it ready for use across professional, brand, and production environments without additional licensing complexity.

Who is Sonilo for?

Sonilo is built for creative professionals who need music that works as hard as the rest of their production. Whether you’re turning around dozens of assets a week or crafting a single high-stakes deliverable, Sonilo removes the bottleneck of sourcing and timing music manually — so you can stay in your creative flow from first cut to final export.

Use cases:

• Advertising & creative studios — core product videos, brand spots, and social ads with music that matches the pacing of each cut, without waiting on a composer or digging through stock libraries.

• Video production & post houses — automate soundtrack generation across large volumes of footage, keeping timelines tight and revision cycles short.

• VFX & motion graphics artists — generate music that evolve with your visuals, frame by frame, for title sequences, reels, and motion pieces.

• Content creators & agencies — produce platform-ready videos with synchronized music across multiple formats and aspect ratios without re-editing audio for every version.

• Developers & pipeline engineers — integrate music generation directly into automated workflows via ComfyUI, removing manual handoffs between creative and technical teams.

Examples

Video to Music Examples

Try on Comfy Cloud

Text to Audio Examples

Prompt: Dreamy ambient electronic music, gentle rhythm, suitable for short videos

Prompt: Futuristic electronic instrumental, sci-fi sense, ethereal and deep

Prompt: Lazy jazz instrumental, saxophone, relaxing café atmosphere

Try on Comfy Cloud

Get Started in ComfyUI

1. Update ComfyUI to the latest or go to Comfy Cloud

2. Open the Workflow Library → Browse Templates and search for “Sonilo” to find a ready-to-use workflow.

3. Connect your video source and update the prompt, then run.

As always, enjoy creating!

Seedance 2.0 broke the internet before most people could even access it. Video generations from the model have been circulating online, nearly indistinguishable from real camera work. Today, the model is now available inside ComfyUI. Seedance 2.0 turns text, images, video, and audio into high-quality video with synced audio, consistent characters, and cinematic camera control.

Try Seedance 2.0 on Comfy Cloud

Today we are rolling out:

• Seedance 2.0 Text to Video

• Seedance 2.0 Reference to Video

• Seedance 2.0 First–Last-Frame to Video

Note: Realism character reference is on the way. We’re opening early access soon. Please fill out the survey below to sign up and get notified when it launches.

Sign Up for Real Human Reference

Model Strengths

Seedance 2.0 has a lot going for it. The model covers everything from text generation and built-in professional camera movements to multi-shot narration and native synchronized audio. The official documentation breaks its core capabilities into three areas, each highlighting a different part of the production pipeline. All video examples below were generated by and sourced from the official Seedance 2.0 documentation.

Starting with what’s arguably the model’s biggest draw:

Multimodal Reference-Based Generation with Fine-Grained Feature Preservation

Add up to 9 reference images, 3 reference videos, and 3 reference audio files, and the model pulls from all of them to create a single coherent output. Object details, textures, visual styles, timbres, and character features carry through the entire generation.

Video References. Upload a clip and the model will replicate its camera movements, action choreography, editing rhythm, and visual effects.

Audio References. The model syncs visuals to music, dialogue, or sound effects at a phoneme level, with support for multiple languages and dialects.

Image References. Lock in character identity, product appearance, and stylistic consistency. Faces, clothing, and material textures stay stable across the full video.

Again: Realism character reference is on the way. We’re opening early access soon. Please fill out the survey below to sign up and get notified when it launches.

Sign Up for Real Human Reference

Precise, Targeted Video Editing

Edit existing video content directly without regenerating from scratch.

Subject Replacement. Swap characters in an existing clip while keeping the original motion, camera work, and composition intact.

Object-Level Editing. Add, remove, or change specific elements in a scene. The rest of the video stays untouched.

Inpainting. Reconstruct regions of existing video with context-aware generation that maintains temporal coherence and visual consistency.

Seamless Video Extension with Temporal Coherence

Turn short clips into longer sequences with natural continuity.

Video Extension. Extend any clip forward in time. Character appearance, lighting, and motion blend smoothly with the existing footage.

Preceding Scene Generation. Generate footage that leads into an existing clip, effectively extending video backward to build narrative context.

Interpolation Completion. Bridge gaps between two separate clips with generated intermediate footage that respects the visual and temporal logic of both endpoints.

Get Started

Try Seedance 2.0 on Comfy Cloud

• Update ComfyUI to the latest version, or access Comfy Cloud.

• Find the Seedance 2.0 node in the Node Library, or load the Seedance 2.0 template from Templates.

• Drop the node on your canvas and start creating.

Enjoy creating!

We’re excited to announce that Wan2.7 — Alibaba’s latest video generation model — is now available in ComfyUI via Partner Nodes!

Wan2.7 is a comprehensive upgrade over version 2.6, with significant improvements across image quality, audio, motion dynamics, stylization, and consistency. It also expands the creative workflow toolkit considerably — supporting up to 5 real-person image inputs, a vocal timbre reference, and 3×3 grid-based image generation.

This release brings a fully upgraded multimodal video pipeline directly into your node graph, supporting text, image, audio, and video inputs across four task types:

• Image-to-Video: first-frame, first+last-frame, and audio-driven generation

• Text-to-Video: pure text prompts with optional audio input and multi-shot narration

• Video Continuation: extend an existing clip with new content guided by a text prompt

• Reference-to-Video: reference both a subject’s visual appearance and vocal timbre; supports up to 5 real-person inputs and multi-character interactions

• Video Edit: edit or replicate videos via text prompts, reference image, or style transfer

Examples

Image to Video

The flock of flamingos in yellow sunglasses stands in the bright daylight as in the image. The camera frame slowly tilts to a Dutch angle. Funky bass groove begins. One flamingo — slightly taller than the others — notices the tilt, looks directly at the lens, then deliberately tilts its own head to match. Flamingo: "Better. Now we're both weird."

Starting from the front view, the camera slowly orbits behind the TV-headed figure, settling over its shoulder and looking out across the dim, blurred office into the darkness beyond. A deep ambient bass tone hums. The figure doesn’t move, but the screen’s glow casts a faint rectangle of light forward into the dark room. TV Head (quietly): “Something’s out there.” The screen flickers once. “It’s been watching us back.”

Download Wan2.7 Image to Video Workflow

Text to Video

Warm amber candlelight, low angle wide shot. A vast library where bookshelves extend infinitely upward into darkness, books drifting open in mid-air, pages glowing softly. A young scholar walks the central aisle, candlelight in hand. A deep disembodied voice resonates from above: "Have you found what you came for?"

Rain-streaked glass, night, interior car — dashboard light only. Two people sit in a parked car outside a lit house, neither moving. Woman (staring ahead): "I don't know how to go in there and pretend everything's fine." 

Download Wan2.7 Text to Video Workflow

Reference to Video

Wan2.7 Reference to Video Workflow

Video Edit

before:

after:

Download Wan2.7 Video Edit Workflow

Get Started in ComfyUI

1. Update ComfyUI to the 0.18.5, (ComfyUI Desktop and Comfy Cloud will be ready soon.)

2. Go to Template Library → Video and search for Wan2.7

3. Choose the workflow that matches your task and run!

As always, enjoy creating!

​

Topaz Starlight Precise 2.5 is now available as a Partner Node in ComfyUI. This latest update to the Starlight Precise upscaling model delivers noticeably sharper results with fewer artifacts — and it’s available at the same cost as the existing SLP-2 model.

If you’ve been using Starlight Precise for video upscaling, this is a direct upgrade. Select the new model from the dropdown and get better results immediately.

Try on Comfy Cloud

What’s Improved in v2.5

• Sharper output — Fine details are better preserved through the upscaling process, especially noticeable in 720p to 4K workflows.

• Fewer artifacts — Cleaner results with reduced hallucination compared to SLP-2.

• Convincing creative detail — The model dreams in realistic detail when upscaling, filling in information that makes the output look native rather than stretched.

• Same cost — Starlight Precise 2.5 is available at the same per-frame pricing as SLP-2. No price increase for better quality.

• Drop-in replacement — Select Starlight Precise 2.5 from the model dropdown in the existing Topaz upscaling node. No workflow changes needed.

720p to 4K: Before & After

Getting Started

1. Update ComfyUI to the latest version, or access Comfy Cloud.

2. Find the Topaz Video Enchance node in the Node Library, or load the Topaz Starlight Precise 2.5 template from Templates.

3. Select Starlight Precise 2.5 from the model dropdown and run your workflow.

Try on Comfy Cloud

Download Workflow

Already using Starlight Precise for your upscaling pipelines? Swap to v2.5 in the model dropdown — same cost, better results.

ComfyUI has since the beginning always been the most efficient way of running diffusion models and we just made it significantly better. Our goal is to make even the largest open models more accessible to everyone.

Available in ComfyUI stable since 3 weeks ago for Nvidia hardware on Windows and Linux (WSL support is currently not planned), this update is designed to drastically reduce system RAM usage while accelerating overall workflow execution.

Dynamic VRAM fundamentally changes how ComfyUI handles model weights, making the experience much smoother for users on memory-constrained hardware. Key improvements include:

• Lower System RAM Usage: A noticeable reduction in the amount of traditional RAM required to run complex workflows.

• Elimination of OOM Errors: Out-Of-Memory crashes caused by insufficient weight offloading should be fully resolved.

• Faster Loading Times: Initial model loads and LoRA applications are significantly faster in some cases.

• Paging Prevention: You can now run models that exceed your physical RAM capacity without relying on your operating system’s slow page file.

• Increased VRAM Utilization: You may notice your GPU’s VRAM usage is higher than before. This is completely normal and indicates the system is utilizing your fastest available memory much more effectively.

• Simplified Development: The previous memory system depended on trying to predict the amount of memory models would take before inferencing them and trying to keep enough memory free so that the operations could complete without OOM. With dynamic vram we no longer need to do any of this.

A Note on Windows Task Manager: If you check Task Manager, it may not immediately reflect a drop in system RAM usage. If you have plenty of available memory, ComfyUI will smartly keep weights cached in your RAM to maintain high speeds. However, unlike previous iterations, these cached weights will never be pushed to your page file. The moment another application needs that memory, ComfyUI will instantly unload the weights to make room.

Performance Benchmarks

ComfyUI was already the most memory-efficient way to run these models on consumer hardware, but the new optimization yields substantial speedup metrics, here are some quick benchmarks we did:

Video Workloads (WAN2.2 (2x14B fp16 and fp8 models), 320x320x81f): Tested on Windows, RTX 5060, 32GB and 64GB RAM

Note that the total diffusion model size is 2x28GB for the fp16 weights so 56GB total.

Flux 2 Dev, default workflow, bf16 text encoder and diffusion model: Tested on Linux, Blackwell 6000 Pro

Deep Dive: The Mechanics of the AI Model Dynamic Offloader (aimdo)

Dynamic VRAM isn’t just a tweak to existing settings; it is a custom PyTorch VRAM allocator specifically designed to handle on-demand offloading of model weights when the primary PyTorch allocator comes under pressure.

Here is exactly how it manages your memory pipeline:

1. The Virtual Base Address Register (VBAR) When you load a model, the application creates a VBAR for it. The brilliant part here is that creating a VBAR costs absolutely zero physical VRAM; it only consumes GPU virtual address space (which is essentially free and unlimited). ComfyUI then allocates the tensors for the model weights inside this VBAR. Initially, these tensors are completely un-allocated. If the system tried to touch them normally at this stage, it would trigger a segfault.

2. The fault() API (Just-in-Time Allocation) Instead of loading everything upfront, the application “faults in” the tensors using a custom fault() API at the precise millisecond the tensor is needed for a calculation. This is the exact moment physical VRAM is actually consumed.

3. Success vs. Pressure Scenarios When a layer requests a weight via fault(), two things can happen depending on your available memory:

• If successful (sufficient VRAM): The system has allocated VRAM for this weight and ComfyUI will populate this allocated VRAM with the weight data the first time. On subsequent successful faults (e.g. on the next step of sampler), the weight can just be used immediately. This means the weight stays in VRAM for speed, but can be instantly freed later if the system comes under memory pressure. These frees can be efficiently detected with the fault() API on any step if they happen in the middle of sampling.

• If unsuccessful (insufficient VRAM / offloaded weight): ComfyUI doesn’t crash with an OOM. Instead, it allocates a temporary, regular GPU tensor, copies the required weight data over just for that specific layer, and uses it to execute the layers operation. The temporary regular tensor is then freed or reused for other offloaded layers after the layer executes.

4. Priorities and the “Watermark” System To prevent the engine from violently thrashing—where it constantly tries and fails to fault in every single weight on every single iteration—the allocator uses a strict hierarchy and watermark system.

• The most recently loaded VBARs (your current active model) are given the highest priority.

• If a high-priority weight requires space, it will forcefully evict lower-priority weights.

• When a weight gets evicted from a VBAR, the system sets a watermark at that weight’s level. Any weights in that same VBAR above the watermark will automatically fail the fault() API moving forward. This allows the application to smoothly check for space without wasting compute cycles constantly attempting to load weights into a full GPU.

Because of this architecture, there is no need to manually manage VRAM quotas or limits anymore. The allocator continuously polls and automatically balances the pinned and unpinned tensors natively.

New Ram Behavior

ComfyUI now has its own safetensors loader which uses a more efficient file opening mode to avoid committed memory allocations. Files are open and mapped to uncommitted file-backed memory and instead of being deep copied into the pytorch model, the weights are assigned by pointer to uncommitted memory. This is why the model loader nodes now execute almost instantly in Dynamic VRAM mode. Because the memory is in an uncommitted state the operating system is free to reclaim that memory at any time to keep your system stable. Windows users will often observe high RAM usage - because we keep what we can, but as soon as Windows needs RAM for anything its able to just take it back from ComfyUI. When comfy needs those model weights, the OS will re-read them from disk and bring them back to RAM automatically. NOTE: In Linux system monitors, this looks like very low RAM usage with the rest of RAM dedicated to disk cache as Linux doesn’t count uncommitted RAM as usage in System Monitor - it counts it as “cache”.

ComfyUI now no longer unloads models from VRAM back to RAM at all and instead, the above uncommitted memory allocations are held for the lifetime of the model (including across workflow runs). This saves PCIe and DDR bus traffic but also avoids the previously very common case of RAM exhaustion when unloading models in multi-model workflows. For many users this lead to use of pagefile to hold these unloaded models. This doesn’t happen anymore, instead the VRAM is just freed, and the model instantly restored to the “uncommitted” load state describe above.

Next Steps in Development

We are continuously working to improve this system. Our immediate roadmap includes:

• Addressing any reported performance bugs or regressions.

• Implementing AMD and other hardware support.

• Further reducing the overall RAM footprint by freeing intermediate values between nodes in a smart way, making them smaller (--fp16-intermediates (still experimental)) and other more advanced tricks.

• Faster disk loading. If your NVMe SSD is fast enough we may be able to optimize things to eventually achieve full disk offloading without any slowdowns depending on the model and your hardware configuration.

If you encounter any issues related to dynamic vram, please open an issue on GitHub with a detailed report (including your full logs, the workflow, your hardware, and your operating system) so we can fix it. For performance troubleshooting, please ensure you are comparing the total workflow execution time and not just the iterations per second (it/s).

Video Extend on ComfyCloud

Reference-to-Video on ComfyCloud

The speed of these models is very impressive and allows for quick generation and iteration. Using Grok Video Extend at 10 seconds, the generation takes ~60 seconds. With Reference-to-Video, 10 second generation times at 720p take ~100 seconds. Check out the examples below to see both of the new features in action!

Get Started

1. Update ComfyUI to the latest version or visit Comfy Cloud

2. Find the Grok Reference-to-Video or Grok Video Extend nodes in the Node Library or Templates in the left sidebar.

3. Drop a node on your canvas and start creating.

Try Recraft Style Transfer on ComfyCloud

Try 1 Click Multi Styles on ComfyCloud

Compare All Edit Models on ComfyCloud

Style transfer is one of the most common techniques used across AI image generation workflows, and the tooling has matured significantly since the days of IP-Adapter. From the rise of many image-editing models, to models built specifically for style transfer, to custom LoRAs.

Today, creators have multiple reliable approaches for applying specific visual styles to their work, each suited to different use cases and levels of specificity. Although there are many options, it is clear that using well-defined styles can elevate your generations and create a cohesive visual language for your images. This article walks through what’s available now, how to think about the different approaches, and when to use each one.

Well-defined reference styles help to create consistent and distinct images

Why Style Transfer Matters

• On-brand content at scale: Maintaining a consistent visual identity across dozens or hundreds of assets requires more than describing a style in a prompt and hoping for consistency.

• Accurate reproduction of unique styles: Clients, studios, and creators often need to match a specific look. Not just “watercolor” but a particular artist’s or brand’s interpretation of it.

• More control than token stuffing: Describing a style with text prompts can get you in the ballpark, but stacking aesthetic descriptors only gets so far. Style transfer techniques give you precision that prompting alone cannot.

• Repeatability: Once a style is captured, whether through a reference image or a trained LoRA, it can be applied reliably across generations.

Key Concepts

A LoRA (Low-Rank Adaptation) is a lightweight model fine-tune that teaches a base model new concepts. In this case, a specific visual style without retraining the entire model.

Style transfer can take several paths depending on what you’re starting with and what you need out of it.

If you’re working with an image-generation model, style lives in the prompt. Without a LoRA, you’re relying on styles the base model already knows from its training data. Generic aesthetics like watercolor, cel shading, or film noir will typically exist in base models but niche or proprietary styles won’t. With a LoRA trained on a specific style, you can activate it with a trigger word in your prompt and the model generates natively in that style. It’s all text-based either way, no input image involved.

If you’re working with an image-edit model, you have more flexibility. Without a LoRA, you can go two directions: provide a style reference image alongside a prompt describing what to generate, and the model creates something new in that style. Or provide an input image with a text prompt describing the style you want applied, and the model transforms it. With a LoRA, it becomes image-to-image: you feed in the image to be transformed with a simple instruction prompt like “Change the image into realcomic style,” and the LoRA handles the rest.

With that framework in mind, let’s look at what’s available today, starting with models you can use right out of the box.

Models Built for Style Transfer

Style transfer is such a core need that some labs have built models specifically around it. Recraft is a standout. Designed with style reference as a primary capability, it accepts 1-5 style reference images and generates new content that faithfully follows the visual language of those references, and is particularly strong with abstract and artistic styles.

For a long time, Midjourney has been the go-to for creators who care about stylistic range and aesthetic quality. Recraft competes directly in that space, offering comparable range while giving creators more explicit control to integrate into existing workflows.

Check out the examples below and try it out the Recraft workflow here.

API models like Recraft give you a lot of power out of the box, but when you need more control or want to work with a style that no existing model covers, the next step is understanding which type of model to build on.

Image Edit vs. Image Gen: Understanding the Difference

This distinction shapes every decision downstream in your style transfer workflow.

Image-edit models accept an input image and work with it. They can transform it into a new style, or use it as a style reference for a new generation. Image-gen models generate from a blank canvas, with style controlled through text prompts and LoRA models. We’ll showcase a few of each in the examples that follow.

They solve different problems. If you have existing images you want to restyle then image-edit models are your path. If you need to create new content natively in a specific style, image-gen models with the right LoRA give you that freedom without being tethered to a source image.

Once you know which type of model fits your use case, the question becomes: what do you do when the style you need isn't something the model already knows? That's where custom LoRAs come in.

Image-Edit Models in Practice

Before reaching for a LoRA, image-edit models can handle a lot of general style transfer on their own through prompting alone.

The first approach is direct image transformation. You provide the image you want restyled and prompt for the style you want. The model takes your input and applies the transformation. This is the more intuitive path when you already have content and just need it in a different aesthetic.

Prompt: Change the style to anime, maintain identity.

This works well for general style transfers, but as you can see, each model interprets the generic style ‘anime’ quite differently and may not be the best if you have a specific style of anime in mind.

The second approach is using a style reference. You provide a style reference image and describe what you want in the prompt.

Prompt: A serene gondola in the Venice canals that follow the same style and colour palette as image 1, generate a completely unique image, do not reuse any from the input image.

This approach tends to be more hit or miss as it is heavily dependent on the input images style. Nano Banana Pro, Grok Image Edit and Seedream 5.0-lite typically perform well for this approach, especially with more detailed prompting and multiple generations.

Play around and this workflow to test multiple styles transfers with one click!

LoRAs for Style Transfer

When a style is too niche or specific for a model to know from its training data, custom LoRAs become the answer. But not all style LoRAs work the same way, and the distinction mirrors the image-edit vs. image-gen divide.

Image-edit LoRAs are trained on before/after pairs. Each example in the dataset shows an image in its original form alongside the same image transformed into the target style. Open-source contributors have curated these kinds of paired datasets for models like Qwen Image Edit, producing LoRAs such as infl8, make wojak, 3d animation, and realcomic. Once trained, you apply the style with a trigger word in an instruction prompt, and the model reliably transforms input images into that style. The power here is control. Because the training data defines the exact transformation, the output is predictable and faithful.

The tradeoff is dataset creation. Building before/after pairs isn’t particularly intuitive. If you have a collection of images in a style you love, you still need to source or create the “unstyled” counterparts to pair them with. Contributors in the community have found ways to curate these datasets, but it’s a less obvious starting point than simply collecting style examples.

Image-gen LoRAs are much simpler in their creation. Your dataset is just a collection of images in the target style, no pairs needed. The model learns what the style looks like and can then generate anything in that style from a prompt and trigger word. You’re not transforming an existing image; you’re creating new compositions, scenes, and subjects that natively live in that style.

Prompt: {trigger word}, a side profile composition of a human cyborg with a black bobcut and a high black neckline turtleneck and black jeans, with a sky as the background, and the visual focus concentrated on the vegetation and the model walking through it.

Style Training Your Own LoRAs

If the style you need doesn’t exist as a LoRA yet, training your own is more accessible than you might think. Several tools and services support LoRA training today, each with different levels of ease and control.

For the fastest path, Fal.ai offers training endpoints for models like Z-Image, Flux Klein, and Qwen Image Edit. It’s the most hands-off option and a good place to start. For those who want more control over the training process, AI Toolkit can be run on RunPod or locally.

One practical note on model selection: Flux Klein at 9B parameters hits a sweet spot for LoRA training. It’s fast to train and learns well from reasonably sized datasets, making it a good default choice if you’re getting started.

There is a lot of nuance when it comes to training a high quality LoRA. Expect to do multiple training runs, test thoroughly, and invest in a high quality dataset. The dataset quality alone can make or break your results.

The style transfer landscape offers more options than ever. Whether you need an out-of-the-box solution like Recraft, the flexibility of image-edit models, or the precision of a custom trained LoRA, the right approach depends on your style, your content, and how much control you need.

The relationship between art and technology has always had a fundamental tension: scarcity vs abundance. Historically the value of art has been synonymous with lack of access, with the opposite being true of software and tools.But a new wave of creator-led AI projects is proving these philosophies don’t have to be at odds — and the results are both culturally resonant and commercially viable.

Doodles, the entertainment brand built around the iconic pastel-palette artwork of Canadian illustrator Scott Martin (known as Burnt Toast), is about to launch Doodles AI — a generative platform powered by PRISM 1.0, a generative image model trained on Doodles’ extensive body of work that can reimagine people and objects in the unmistakable Doodles visual language. Behind the scenes, the engineering is being handled by SYSTMS, an AI studio whose tagline — “Engineering the Impossible” — reflects their approach to building bespoke creative pipelines using open-source infrastructure, including node-based workflow tools like ComfyUI.

The story of how these pieces came together offers a compelling blueprint for anyone watching the intersection of open-source, AI, artist-driven brands, and the emerging concept the Doodles team is calling “open story.”

IP Without Walls

Artists have traditionally been protective of their IP, and for good reason. But the Doodles team is exploring a new model where the community doesn’t just consume the brand — they co-create it. Every generation a user produces on the Doodles AI platform makes the model stronger. Through reinforcement learning user generated content becomes part of the training data for future iterations of the PRISM. Users aren’t just customers; they’re collaborators shaping the brand’s visual DNA.

This represents a fundamental shift. As Scott Martin put it when he returned as CEO in early 2025, the goal is to recalibrate — creativity first, community at the center, art driving everything. Martin, who built his career as an illustrator working with Google, Snapchat, Dropbox, and Adobe before co-founding Doodles in 2021 alongside Evan Keast and Jordan Castro, understands both the commercial and artistic sides of this equation. His role as what some call an “artist CEO” is central to the credibility of the entire endeavor.

The Last Mile Is the Whole Game

Doodles AI represents something powerful: proof that open source tools can power commercially successful, brand-quality products.

The SYSTMS team uses open-source tools in their rawest form, prioritizing control and innovation at the bleeding edge of the space. The fact that these same tools are now producing output with the kind of brand fidelity that differentiates Doodles from generalized platforms like MidJourney or Sora is significant. It’s the “last mile” problem in creative AI — getting from 85% to 100% fidelity — and it’s where the real value lies.

Doodles AI is a showcase of what’s possible when open-source workflows meet professional creative direction. ComfyUI’s powerful node-based platforms allows users to package complex systems of open-source models, APIs and other tools into consumer-facing applications, making it a natural fit for projects like this, and the SYSTMS team is exploring ways to make this collaboration more visible to the broader community.

Coded DNA

Doodles AI launches with PRISM 1.0 as an image to image model, but the roadmap is ambitious: 2D and 3D output generation, video with sound, real-time AR, and gaming applications. Original Doodles holders receive 100 free generations on launch day — a deliberate move to seed the community and let them flood every timeline with the platform’s output.

The deeper play is alignment with the speed and scale of the entire AI industry. By building on open-source infrastructure and fostering a community of co-creators, Doodles has positioned itself to plug its “coded DNA” into future technologies that don’t yet exist. It’s a bet that openness — open source, open story, open creation — isn’t just philosophically appealing but strategically sound.

For artists watching from the sidelines, the message is clear: the building blocks are here, the community is building, and the line between creator and consumer is disappearing. The question isn’t whether open source will reshape creative industries. It’s whether you’ll be building with it when it does.

Doodles: doodles.app | SYSTMS: systms.ai | ComfyUI: comfy.org

Take a look at some of the examples below to get a feel for Reve Create, Reve Remix and Reve Edit nodes and their strengths!

Reve Create

Reve’s trained in aesthetics make the model very good with fashion editorial style prompts. The model can generate unique lighting conditions, natural textures, and works very well with keywords that evoke a sense of movement.

Reve can generate images at up to 4k resolution. Every image is generated with clarity and sharpness, with fine details prominent in high resolution generations.

Try on Comfy Cloud

Download Workflow

Reve Edit

Leaning on the strengths of the Reve Create model, the image edit model excels at adding movement and editing lighting conditions and color grading to input images.

Try on Comfy Cloud

Download Workflow

Reve Remix

While using Reve Remix node, you can instruct the model to use elements from reference images and treat them as layers to be remixed into the target image. The model does very well at applying these remixes while maintaining consistency of the target image.

Try on Comfy Cloud

Download Workflow

Get Started

1. Update ComfyUI to the latest version or visit Comfy Cloud

2. Find the Reve Image Create, Reve Image Remix, and Reve Image Edit nodes in the Node Library or Templates in the left sidebar.

3. Drop a node on your canvas and start creating.
   

From Workflow to App: Introducing App Mode, App Builder, and ComfyHub

ComfyUI is a workflow tool for people who want control. Not sliders and presets, but the actual graph — every node, every connection, every parameter exposed and wired exactly how you want it.

We’ve heard from thousands of users that there are some instances when they want to share their workflows — with a client, a teammate, a collaborator who doesn’t live in the node graph but the learning curve for them to understand how node graphs work is too steep. Today, this changes.

We’re announcing App Mode, App Builder, shareable app URLs, and ComfyHub — a complete system for turning any ComfyUI workflow into a shareable application that anyone can run.

App Mode

App Mode is a new way to view and run any ComfyUI workflow. When you enter App Mode, the node graph disappears and is replaced by a clean, purpose-built interface: just the inputs your user needs.

The switch is a single click. From any open workflow, press Enter App Mode in the top bar. If you haven’t configured the app yet, you’ll land in the App Builder. If you have, you’ll see the finished app — exactly what your users will see when they open it.

App Mode isn’t a separate product. It runs on the same ComfyUI instance, against the same backend, using the same queue. The workflow doesn’t change. What changes is the surface.

This matters because anything you can do in ComfyUI can be done in apps. New nodes, model updates, custom extensions — they’re all available in apps the moment they’re available in ComfyUI.

App Builder

App Builder is the interface for configuring what an App Mode view looks like. Open it from the Build button inside App Mode, or from the top bar at any time.

The core concept is simple: you pick which node inputs become app inputs, and which node outputs become app outputs. Everything else stays in the graph where it belongs — invisible to the end user, locked in place.

A text-to-image workflow might have dozens of configurable parameters — sampler, steps, CFG, scheduler, LoRA weight, seed, resolution — but a given use case might only need three of them exposed. App Builder lets you choose exactly which three. You can rename them, reorder them, and group them however makes sense for your user. The rest of the workflow runs exactly as configured, every time.

Watch this tutorial to learn how to create your first app using App Builder.

Sharing Apps by URL

Once you’ve built an app, you can share it with a single URL.

The URL encodes everything a recipient needs to open and run your app: the workflow configuration, the app layout, the node bindings. Anyone with the link can open the app in their browser, run generations, and interact with the interface without ever seeing the underlying graph and without needing to install anything themselves (on Comfy Cloud).

ComfyHub

We are also excited to introduce ComfyHub, a dedicated place where apps and node graph workflows can be shared with the community.

Think of it as the public layer of the ComfyUI output ecosystem. Where the Comfy Node Registry is for developers publishing custom nodes, ComfyHub is for creators publishing finished apps and workflows.

Today, we are launching a preview of ComfyHub with select workflows for you to browse and run.

For users, ComfyHub is the fastest way to find and use the workflows the community has built — without needing to understand what’s powering them.

For workflow authors, ComfyHub is a place to build a portfolio and an audience. The best workflows in the ecosystem have always been community creations. Now those creations have a home.

If you would like to create your profile page and publish your workflows on ComfyHub, fill out this form https://links.comfy.org/hubearlyaccess. We’re working to make ComfyHub available to more creators to publish their workflows over the following weeks.

Try ComfyHub preview today at comfy.org/workflows

Availability

App Mode, App Builder, and URL sharing are available today on both Comfy Cloud and Comfy Local.

With Comfy Local, deployment is fully optimized for the latest consumer GPU hardware, including the NVIDIA GeForce RTX 5090 and AMD Radeon RX 9070 XT.

ComfyHub Preview is available to everyone at comfy.org/workflows.

ComfyUI has always been built on the idea that the best tools are the ones you can fully control. App Mode extends that principle outward — from the creator who controls the workflow, to the user who runs the app, to the community that discovers and builds on both.

Following our initial HY 3D 3.0 integration, we’re excited to bring HY 3D's advanced processing features directly into ComfyUI via Partner Nodes

For many 3D creators, generation is only the beginning. The real bottleneck is turning a raw mesh into something usable for texturing, animation, game engines, or production pipelines. These new HY 3D workflows help close that gap within ComfyUI by bringing key post-processing steps into a single workflow.
This update introduces support for three key capabilities:

• 3D Parts Decomposition for splitting assets into editable components

• UV Unwrapping for preparing models for texturing

• Smart Topology for converting dense geometry into cleaner, production-friendly meshes

Supported Features

3D Parts Decomposition

Split a complete 3D model into meaningful structural parts, such as armor pieces, accessories, wheels, or other distinct components. This makes it easier to edit specific regions of an asset, swap parts for variations, and prepare models for modular workflows, animation, or downstream assembly.

Try on Comfy Cloud

Download Workflow

UV Unwrapping

Automatically generate UV layouts for supported 3D models, turning raw geometry into assets that are much easier to texture. Instead of manually cutting seams and organizing UV islands, creators can move more quickly into painting, baking, and material work with a cleaner starting point.

Try on Comfy Cloud

Download Workflow

Smart Topology

Convert dense geometry into cleaner meshes with more organized edge flow, helping generated models become easier to optimize and reuse in real production pipelines. This is especially useful when preparing assets for game engines, real-time rendering, or any workflow that benefits from lower-density, better-structured geometry.

Try on Comfy Cloud

Download Workflow

Get Started in ComfyUI

1. Update ComfyUI to the latest version, or go to Comfy Cloud

2. Open Template Library -> 3D model -> Choose the HY 3D advanced template that matches your task

3. Upload your model input and run the workflow

As always, enjoy creating!

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