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!

​

We’re excited to announce that ElevenLabs is now available inside ComfyUI via Partner Nodes! This brings world-class voice AI directly into your node graph — no external tools, no juggling browser tabs, just drag, connect, and run.

Whether you’re building a podcast pipeline, adding voiceover to AI-generated video, isolating dialogue from noisy footage, or cloning a voice for a character, it all happens right on your canvas now.

ElevenLabs Nodes

🗣️ Text to Speech

Type a prompt, get a voice. Generate natural-sounding speech from any text input — perfect for voiceovers, narration, and automated audio pipelines. Pair it with your video generation nodes for a full end-to-end content workflow.

Try Text to Speech

🔄 Speech to Speech

Feed in one voice, get back another. Transform the style, tone, or identity of a voice recording while keeping the original pacing and emotion. Great for dubbing, voice acting, and creative remixing.

Try Speech to Speech

📝 Speech to Text

Transcribe audio to text directly in your workflow. Use it to create subtitles, feed dialogue into an LLM node for analysis, or build audio-to-text-to-image pipelines that react to spoken content.

Try Speech to Text

🎧 Voice Isolation

Got a noisy recording? This node isolates the voice from background noise, music, or ambient sound. Perfect for cleaning up field recordings or pulling clean dialogue out of complex audio scenes before further processing.

Try Voice Isolation

💬 Text to Dialogue

Generate multi-speaker conversations from a single text input. Assign different speakers, control the back-and-forth, and produce realistic dialogue scenes — ideal for podcasts, audiobooks, explainer content, or game dialogue.

Try Text to Dialogue

🔊 Text to Sound Effects

Describe a sound and generate it. Explosions, footsteps, rain, sci-fi ambience — whatever your project needs. Perfect for adding atmospheric audio to video workflows, building soundscapes, or prototyping game audio without digging through sample libraries.

Try Text to Sound Effects

🎛️ Voice Selector

Browse and select from ElevenLabs’ library of premade voices. Pick the right tone, accent, and style for your project without any setup.

Why This Matters

Audio has been the missing piece in a lot of ComfyUI workflows. You could generate images, video, 3D assets, and text — but voice was always a separate step. With ElevenLabs as a Partner Node, you can now build truly multimodal pipelines:

• Prompt → Image → Video → Voiceover — all in one graph

• Audio cleanup → Transcription → LLM processing — no exports, no context switching

• Generate dialogue → Lay it over generated video — end-to-end character pipelines

These nodes run in parallel with all your other Partner Nodes, so you can trigger multiple generations at once and iterate fast.

Text to Speech Examples

Children’s Story - The Lonely KSampler

Tongue Twister

Sound Effects Examples

Babbling brook, gentle stream

Police siren going by

Train station ambience

Get Started

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

2. Find the ElevenLabs nodes in the Node Library or Templates in the left sidebar.

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

As always, enjoy creating!

We’re excited to announce that Kling Motion Control 3.0 is now available via Partner Nodes in ComfyUI!

Building on the acclaimed Kling 2.6 Motion Control, this upgrade introduces Element Binding — a new facial consistency system that keeps your character's face stable and recognizable across every angle, emotion, and occlusion in the generated video.

Try on Comfy Cloud

Key upgrades:

• Facial consistency from any angle — stable features and smooth expressions even in complex, multi-angle, or long-duration motion

• Accurate complex emotions — upload multiple face reference images to improve expression fidelity across your character’s full emotional range

• Face restoration through occlusions — hats, fans, hands, and other partial obstructions no longer breaks character identity

• Steady clarity during camera movement — face quality stays consistent as the camera zooms, pans, or tracks

Examples

Getting Started

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

2. Search for the “Kling Motion Control” node

3. Prepare your reference image and reference motion video, and run the example workflow

Hi community! We’re excited to announce that LTX-2.3, the latest evolution of Lightricks’ open-source audio-video generation model, is now natively supported in ComfyUI! Building on the foundation of LTX-2, this release delivers major quality improvements across fine details, portrait video, audio, image-to-video, prompt understanding, and text rendering.

Model Highlights

LTX-2.3 brings a comprehensive set of quality upgrades to the LTX family.

• Finer Details: New latent space & updated VAE for sharper textures, cleaner edges, and more precise visuals.

• 9:16 Portrait Support: Greatly improved quality for vertical portrait videos, perfect for social media & mobile.

• Better Audio: Cleaner sound with reduced noise, enhanced dialogue, music, and ambient audio.

• Improved Image-to-Video: More consistent motion and fewer glitches, such as frozen frames, for smoother, more natural animations.

• Smarter Prompt Understanding: Improved text encoder for more accurate interpretation of complex prompts.

• Clearer Text Rendering: More accurate text and letter rendering in videos.

Example outputs

Image to Video

Try on Comfy Cloud

Download LTX-2.3 I2V workflow

Text to Video

Try on Comfy Cloud

Download LTX-2.3 T2V Workflow

Getting Started

1. Update ComfyUI to the latest version (0.16.1) or visit Comfy Cloud

2. Access Workflows: Go to Template Library → Search → LTX-2.3

3. Download Models: Follow the prompts to download the required models

4. Start Creating: Configure your prompts and inputs, then run the workflow
   
   As always, enjoy creating!

​

When we launched Comfy Cloud’s private beta, we had one goal: bring the full power of ComfyUI to anyone, anywhere, without the setup headache.

Today, we’re proud to announce that Comfy Cloud has officially exited beta.

This milestone belongs to the thousands of creators, developers, and artists in this community who tested, broke, filed bugs, and pushed us to build something worth shipping. Thank you. Genuinely.

Now let’s talk about what you’re getting.

Your favorite custom nodes are now on Cloud.

The custom nodes powering ~90% of local workflows are now fully available in the cloud — no setup or installs needed.

Bring your most-used pipelines to the cloud, exactly as you built them.

Pay for running your workflows, not idle time

Most cloud platforms bill you from the moment a machine spins up, whether you’re actively generating or just staring at the screen deciding which node to connect next. That idle time adds up fast, and it quietly punishes experimentation.

Comfy Cloud works differently: you’re only charged for active GPU time while a workflow is actually running.

Building your graph? Free. Tweaking a node parameter? Free. The meter starts when you hit run, and stops the moment your workflow finishes.

Great workflows are built through iteration, not on the first try. We wanted our pricing to reflect that reality.

The Latest Models, Ready to Go

One of the most time-consuming parts of running AI workflows locally is hunting down models, checking compatibility, and managing your disk space.

With ComfyCloud, you get instant access to all the leading open-source (i.e. Qwen, LTX 2 and Hunyuan) and closed source models (i.e. Nano Banana 2, Grok and Kling 3.0).

These models are powerful out of the box but to create something truly unique and matching your creative vision, you might need to fine-tune them using LoRAs.

Comfy Cloud supports custom LoRA uploads with full integration from HuggingFace and Civitai. We also have the most popular LoRAs trained by the community, so you can experiment and infinitely customize your outputs.

Whether you’ve trained a custom character, a distinctive lighting style, or a brand-specific aesthetic, you can bring it into the cloud and apply it to any workflow.

Powerful GPUs in Your Browser

We’ve powered Comfy Cloud with NVIDIA Blackwell RTX 6000 Pro GPUs — the same class of hardware used in cutting-edge AI research labs and production studios. Each one comes with:

• 96GB of VRAM — enough to run the most demanding video generation, upscaling, and multi-model workflows.

• 180GB of system RAM — so even the heaviest pipelines can be executed without slowdowns.

Try It Free

We want to lower the barrier as much as possible for new creators who are curious but not ready to commit. That’s why earlier this week we introduced a free tier — to give you a real taste of what Comfy Cloud can do.

Try on Comfy Cloud

When you’re ready to go deeper, our paid plans unlock longer workflow runtimes, more credits for API nodes (like Sora, Veo, Nano Banana 2, and more) and team-level billing plans.

What’s Next

Exiting beta is a milestone but we are just getting started. Here’s a preview of what we’re building toward:

• Workflow API deployment — run your ComfyUI workflows as production APIs

• Parallel workflow execution — run multiple jobs at once

• Team plans — collaborate with your studio or agency

• Expanded model library — we’re adding new open and closed-source models constantly

• Ever growing support of custom nodes - community-built nodes that scale creative infrastructure the way no single company can ever do.

Announcing #comfyrewind challenge

To mark ComfyCloud exiting beta, we’re giving away 100,000 in free Cloud credits to creators who can take us back in time.

The Challenge: create a 30 second reel using ComfyUI in a pre-90s retro style that feels like it was pulled straight from another era (60s, 70s, 80s, 90s).

How to Enter

• Create your reel using ComfyUI

• Post it as an Instagram Reel

• Tag @Comfycreators + use #comfyrewind

• Submissions close March 31

How Winners Are Chosen

We’ll factor in the number of views your reel generates and creative use of ComfyUI to choose the top 3 creators. 1st prize is 100K, 2nd is 50K, 3rd is 30K of ComfyCloud credits.

For anyone without a subscription today, we’re giving you 400 credits every month, completely free. Just sign in with Google, and your account is automatically upgraded to our Free Tier. No payments. No credit card required.

We think everyone should be able to try the latest public and private models, and we’re putting our money where our mouth is.

Get Comfy Cloud Free Tier

Whether you want to try the best image models like Nano Banana Pro, Seedream 5.0, Flux, Qwen-Image, and Grok Image, or experience cutting-edge video generation, including Seedance, Wan, Kling, Lightricks, Vidu, and more, you can now experiment with image, video, audio, and 3D entirely through our Free Tier plan.

Not just now, but when the next one drops next month!

Your workflows will run on the same RTX Pro 6000 GPUs that thousands of Comfy Cloud users already enjoy, with an ever-growing list of supported models, template workflows, and custom nodepacks.

Getting started takes about 10 seconds. Sign up and start running workflows. It’s that easy.

Whether you’re a ComfyUI veteran or just starting to dip your toes into AI generation, we want Comfy Cloud to be an easy place to get started. When you first land on Comfy Cloud, you have the best-in-class templates with the credits to actually run them! Free Tier users have already enjoyed hundreds of hours of GPU time and thousands of generations from open and closed-source models.

Get Comfy Cloud Free Tier

Not sure what to try first? Here are some of the latest workflows our creative technologists have been cooking up!

Try on Comfy Cloud

Try on Comfy Cloud

Try on Comfy Cloud

Try Grok Imagine on Comfy Cloud

Try on Comfy Cloud

Try on Comfy Cloud

FAQ

1. Do my 400 credits roll over? No. Your credits will refresh each month so that you will always have some credits to try the latest models.

2. What happens when I use all my free credits? If you want to run more workflows, you will have to subscribe (plans start at $20/month) or wait for your credits to refresh. In the meantime, you will still have access to all your uploaded inputs and generated outputs.

3. Is there a catch? No credit card required, no trial expiration. It’s just a good deal!

4. Can I use my own custom workflows? Yes. You’ll have access to the same models and nodepacks as paid tiers. Import, save, and run any workflow that’s supported on Comfy Cloud.

P.S. There really are no caveats. Sign up with Google. Get Free Credits. Run Workflows!

Happy Generating!

If you’ve used Nano Banana or Nano Banana Pro before, this release fundamentally changes the default choice for most creators.

Try Nano Banana 2 on ComfyCloud!

Upgraded Highlights and Examples

Pro-level quality at Flash speed

Nano Banana 2 closes the gap between Nano Banana and Nano Banana Pro. You get richer textures, sharper detail, stronger lighting, and noticeably better prompt adherence — without the latency tradeoff.
For creators, this means faster iteration without sacrificing output quality. You no longer need to switch models mid-workflow just to get something production-ready.

Search-grounded world knowledge

When your prompt references lesser-known landmarks, niche objects, or specific real-world details, the model can intelligently trigger Google Image Search grounding to improve factual accuracy and reduce hallucination.
It stays efficient for common subjects, but anchors results when precision matters — especially useful for travel visuals, architectural references, product concepts, or educational graphics.

Subject consistency

If you’re storyboarding or building narrative sequences, subject consistency is stronger — maintain character resemblance of up to five characters and the fidelity of up to 14 objects in a single workflow, allowing you to storyboard and build narratives without altering the appearance of your inputs.

Configurable thinking levels

You now control how much the model “thinks” before generating.

• Minimal for fast exploration

• High/Dynamic for complex layouts, multi-object scenes, or detailed instructions

In ComfyUI workflows, this becomes a practical control: speed when experimenting, deeper reasoning when refining.

Precision text rendering and translation

Nano Banana 2 allows you to generate accurate, legible text for marketing mockups or greeting cards. You can even translate and localize text within an image to share your ideas globally.

If you’re building infographics, Nano Banana 2 handles structured layouts and legible text far better than the original Nano Banana. If you’re creating marketing mockups, greeting cards, or localized assets, text rendering and translation inside images are significantly improved.

How to Use It in ComfyUI

1. Update ComfyUI, double-click the canvas to search for the “Nano Banana 2” node, or visit Comfy Cloud

2. Or go to the Template Library to use the ready-to-go workflow

3. Choose your resolution, set your aspect ratio, and decide whether the prompt needs deeper reasoning.

Nano Banana 2 Workflow

We’ve also added this new model version to the previous “Nano Banana Pro” node. So if you have old workflows with this node, you don’t need to rebuild them. Just simply reselect the model version.

Which Model Should You Pick?

Nano Banana Pro remains ideal for maximum factual precision and high-stakes edge cases.

But for most creators, Nano Banana 2 will likely become the new default: fast enough to explore, smart enough to refine, and polished enough to ship.

How do you make generative AI work at architectural scale? Moment Factory used ComfyUI to fundamentally transform how they handle early concept, look development, and design exploration for architectural projection mapping.

Before ComfyUI, this phase was slower, more abstract, and carried greater risk. After ComfyUI, it became faster, more concrete, and spatially grounded from the start.

Before ComfyUI: Slow iteration, abstract decisions, late risk

Early concept and look development traditionally relied on:

• Static sketches

• Reference decks

• Moodboards

• Abstract discussions about intent

For architectural projection mapping, this creates a problem. You do not really know if something works until it is projected at scale. Seams, pixel density, spatial drift, and composition issues usually reveal themselves later in the process, when changes have a massive impact on production.

Traditionally, this means:

• Fewer directions explored

• Longer back-and-forth cycles

• Creative decisions made without spatial proof

• Risk pushed downstream into production

What changed with ComfyUI

Moment Factory built a custom ComfyUI workflow and used it to enhance and accelerate large parts of early concept sketching, look-dev exploration, and part of the design phase.

They did not just generate images. They changed how decisions were made.

1. Iteration stopped being the bottleneck

ComfyUI, transformed the iteration process, making it faster, sharper, and more intentional

Grounded in real production parameters, they explored:

• Over 20 main artistic directions

• 20 to 40 iterations per direction

• Styles ranging from hyper-realism to illustrative engraving

The studio used batching and parameter tweaks to move quickly, while intentionally stress-testing the system to understand its limits.

“With any GenAI tool, it’s easy to over-iterate, to believe the best result is always one click away. Imposing real production constraints, whether financial or time-based, was essential to ensure these explorations remained meaningful and truly impacted our pipelines” - Guillaume Borgomano | Senior Multimedia Directory & Innovation Creative Lead @ Moment Factory

That volume of exploration would not have been realistic in their previous workflow.

2. Concept work moved from days to hours

The biggest acceleration happened early.

What would normally involve days of back-and-forth between static concepts and reference decks could happen within a few hours.

They generated intentionally low-resolution outputs around 2K, reviewed them quickly, and even generated new variations live on site. Those outputs could be checked directly in the media server timeline minutes later.

This low-resolution stage was not about polish. It was about validation and decision-making. What would normally involve days could happen within a few hours.

That shift alone changed the pace of the entire project.

3. Spatial credibility came first, not last

A major reason this worked is that every generation was already spatially constrained.

Moment Factory built the entire workflow around architectural surface templates, so outputs were pre-mapped from the start. The pipeline supported multiple template types in parallel, including flat UVs, 360 layouts, and camera-projection setups.

ControlNet injected structural information from those templates directly into the diffusion process, enforcing scale, layout, and spatial logic early.

Because of this, visuals were already spatially credible during the concept phase. That made alignment easier.

Abstract intent turned into shared reference points. The team could react to something grounded instead of imagining how it might look later.

4. Approval no longer meant starting over

Once a direction was approved, the workflow did not reset.

They could:

• Inpaint specific regions

• Preserve composition

• Upscale selected outputs to 18K in ~20 minutes

This completely changed how fast ideas moved from concept to projection-ready content.

Previously, approval often meant rebuilding work. With ComfyUI, approval meant pushing forward.

5. Fewer people, better collaboration

Once the system was stable, one main artist operated inside ComfyUI.

Around that setup, two additional team members were continuously involved in art direction, prompt tuning, selection, and alignment discussions.

They had to define a new working methodology to keep creative intent at the center, but in practice, ComfyUI functioned as a shared exploration tool, not a solo technical setup.

6. The moment it became undeniable

Within Moment Factory’s innovation team, it felt like a breakthrough early on, the level of malleability and control simply wasn’t achievable with more rigid tools. But the real turning point came during a in-situ live demo, held at 25 Broadway. Late in the process, Moment Factory swapped the surface template and reran the entire pipeline without re-authoring a single asset. The composition held & the spatial logic remained intact. The content dropped straight into the media server timeline.

The room went quiet.

In that moment, it stopped being a promising experiment and became a shared realization. People weren’t asking what if anymore, they were asking how to prompt, and in what other context it could apply.

That’s when it became undeniable: this wasn’t just a powerful tool for R&D. It was a shift in how teams across Moment Factory could think, iterate, and produce.

Why ComfyUI Was Critical at Architectural Scale

Moment Factory had been exploring diffusion-based workflows for projection mapping for years.

The ambition was clear: use generative systems not just for images, but as structured spatial material within complex, large-scale environments.

What architectural scale demanded, however, was not just image generation. It required:

• Precise control over spatial conditioning

• The ability to inject UV layouts and depth constraints directly into inference

• Rapid template switching without breaking composition

• Iterative refinement without rebuilding from scratch

• A pipeline that could evolve as constraints changed.

This level of structural malleability was essential.

ComfyUI’s node-based architecture allowed the team to design and reshape the workflow itself, not just the outputs. Conditioning logic, batching strategies, template inputs, and upscaling stages could be reconfigured as the project evolved.

Rather than adapting the project to fit a tool, the tool could be adapted to fit the architecture.

At that point, it became clear: achieving reliable architectural-scale generative workflows required a system flexible enough to be re-authored alongside the creative process. ComfyUI provided that flexibility.

The takeaway

ComfyUI did not make the creative decisions.

The vision stayed human. The constraints were architectural, and the expectations were production-level from the start.

What ComfyUI brought to the table was structural flexibility. It allowed the workflow itself to be shaped and reshaped as the project evolved. Spatial inputs could be injected directly into inference. Templates could be swapped without collapsing the composition. Refinements could happen without rebuilding entire directions.

Generative systems stopped behaving like black boxes and started behaving like controllable material. Spatial logic was embedded early, and scaling to architectural resolution became a managed step rather than a gamble.

The impact was not just speed. Decisions could be validated earlier, directly against geometry and projection conditions. Spatial alignment became part of concept development instead of a late-stage correction. That shift reduced uncertainty before entering production.

In that sense, ComfyUI did more than accelerate exploration. It made architectural-scale generative workflows structurally viable within real production constraints.

Moment Factory Contributors :

Guillaume Borgomano | Senior Multimedia Directory & Innovation Creative Lead

Conner Tozier | Lead Motion Designer & Generative AI Lead

Read more on this topic : https://momentfactory.com/blogs/lab/exploration-at-25-broadway

Seedream 5.0 lite is the latest image generation model from BytePlus. It is the first in the Seedream family to bring in web-connected retrieval, which means the model can draw on live online information when generating images. The result is visuals that stay current and relevant without extra effort from the user. Reasoning and comprehension have also been upgraded across the board, with improvements in world knowledge coverage, reference consistency, instruction following, and overall performance in professional creative workflows.

Try Seedream 5.0 lite on Comfy Cloud

What’s New in Seedream 5.0 lite?

This is the first Seedream model to have Web-Connected Retrieval. The model pulls from live web data during generation. This allows for creations to be tied to real-world trends, current events, and up-to-date knowledge without any manual research on the user’s side.

Smarter Instruction Following

The model handles complex, multi-step instructional prompts far more reliably than its predecessors. Multi-image inputs, local editing instructions, and batch style requests all land closer to the intended output on the first try.

Change the glasses in the green box to circular lenses, the orange purse in the blue box to a purple oval purse, and her shoes in the red box are patterned with leopard print.

While maintaining the original composition, change the overall color tone to navy and white colors. Change "RTX PRO 6000" to "SEEDREAM 5.0 LITE" in the image, and change "2025" to "NOW LIVE".

Refer to the second image and change the style of the first picture to match that of the second image.

Try Seedream 5.0 lite on Comfy Cloud

Seedream 5.0 Lite Image Edit workflow

Stronger Consistency

Character identity, product appearance, style, and lighting all hold up much better across a set of generated images. Whether you are preserving a product across multiple scenes or maintaining a consistent visual style across a campaign, the outputs are noticeably more stable.

Refer to the design and layout style of Figure 2 and adapt the style of Figure 1 accordingly.

Merge Figure 1 and Figure 2 into a high-angle top-down selfie of two people, compact composition with them close together, heads slightly tilted up to the camera. Left keeps Figure 1's look, right retains Figure 2's look and facial similarity with slightly inward body. Figure 1's height reaches Figure 2's waist, pure white background, phone front camera

Use the flowers shown in Figure 1‑4 for flower arrangement. There is no limit to the number of flowers; you may add other foliage for matching. Arrange all the flowers in the vase shown in Figure 5, then place the vase on the table shown in Figure 6.

Using the bottle from the reference image, generate six product photos in different seasonal settings. Consistent bottle design across all six.

First: clean white studio background, minimal, soft shadow. Second: winter, snow-dusted stone, pine branches, star anise, cool blue tones. Third: spring rainforest, mossy stone, ferns, wildflowers, dewdrop on a leaf, lush green. Fourth: summer, sandstone surface, sliced lemons, oranges, rosemary, warm golden sunlight, Mediterranean feel. Fifth: cherry blossom season, pink petals falling, soft pink bokeh, mossy stone, dreamy romantic light. Sixth: autumn, fallen maple leaves, cinnamon sticks, dried figs, warm amber afternoon light.

Editorial product photography, shallow depth of field, luxury fragrance aesthetic.

Deeper World Knowledge

The expanded knowledge base makes the model genuinely useful for technical illustration, educational diagrams, and knowledge-heavy creative work. Things that previously required a lot of prompt tuning now come out well with a straightforward description.

Pixel art infographic for explaining gravity, rendered in crisp 16-bit style with a dark navy/charcoal background and bold cinematic palette tones — deep cosmic purple, electric blue, molten gold, and stark white; key info displayed in pixelated rectangular dialogue boxes and 8-bit speech bubbles linked by dotted pixel arrows and dashed lines; matched with proportioned pixel sprite figure wearing an astronaut suit and doodle icons (pixel stars, 8-bit falling objects, tiny pixel planets and orbit rings); chunky pixel font with clean spacing, concise captions explaining gravitational pull, mass, and acceleration, with generous breathing room between each concept panel.

Comic book style infographic for explaining the Big Bang and the age of dinosaurs, with a deep crimson and ink-black background and bold comic palette tones — volcanic orange, prehistoric green, cosmic yellow, and stark white; key info displayed in rectangular dialogue boxes and speech bubbles linked by dotted arrows and dashed panel borders; matched with proportioned comic-style dinosaur illustrations (T-Rex, Brachiosaurus) and doodle icons (asteroids, explosion bursts, tiny galaxies and primordial Earth); chunky comic font with clean spacing, concise captions explaining the singularity, cosmic expansion, and mass extinction event, with bold halftone dot textures and action lines radiating between each concept panel.

Try Seedream 5.0 Lite on ComfyCloud

Seedream 5.0 Lite text to Image workflow

Get Started

1. Update ComfyUI to the latest (0.15.0) or visit Comfy Cloud

2. Go to Template Library, search for Seedream 5.0 lite