frosty-vl

Frosty Studio

Qwen Image 2.1 at the center. Video and agent workflows alongside it.

Frosty brings local image creation, reference editing, video production, and MCP agent tools into one project. Work in a browser, connect your own assistant, or use the job APIs directly. The Image and VL workspaces share Frosty's charcoal, ice-cyan, and mint design, with controls tailored to each engine.

Start here · Image setup · Connect an agent · Agent installation instructions

Image features · Video features · Video installation · Troubleshooting

Choose your workspace

Component	What you get	What it needs
Frosty Image	Qwen Image 2.1 creation, editing, compositing, RGBA, masks and official prompt enhancement	Local model files and a compatible NVIDIA/CUDA installation; a Windows RTX 4080 16 GB NF4 profile has been tested
Frosty VL	Queued video clips, Scene Lab, opening frames, engine-aware controls and a recoverable video library	A separately configured video backend; requirements depend on the model
Frosty MCP	12 tools that let an agent use both workspaces	Python 3.10+ and a reachable Studio; no GPU or weights on the companion machine

Install only the parts you need. The repository's frosty-vl name is retained for existing links, but it now houses the complete Studio. Model weights are supplied separately. Configuring two engines does not make both models fit on one GPU.

Frosty Image: create, edit, and compose

Current UI captured with a local interface fixture. No generated artwork or GPU benchmark is represented by this screenshot.

• One composer for creation and editing. Start with text, or add references to switch automatically into editing. Drop, paste, preview, reorder, replace, or append images from your gallery.
• Up to ten ordered references. Refer to “image 1” and “image 2” in your direction. Mask editing reserves one slot for the mask: nine references plus one mask.
• Official Qwen prompt enhancement. Preview the expanded direction, edit it, and apply it; or enhance automatically before rendering. Creation and editing use separate optional PE checkpoints. Aspect-ratio suggestions are opt-in.
• Transparent PNG workflows. Generate RGBA, edit transparent layers, or extract a subject with a prompt.
• Local editing tools. Paint a mask or annotate a reference. Mask mode can composite the result over the resized original to preserve pixels outside the selected region.
• Detailed generation controls. Seven aspect ratios, 512/1K/2K presets, bounded custom dimensions, steps, seed, negative prompt, guidance, reference detail, context caching and one to four sequential variations.
• A library you can recover. Download, reuse, delete one or many images, Undo, and restore from Trash with metadata retained. No permanent-purge command.
• Image DWM runtime bank included. The compact 1.2 MB inference vectors enable runtime controls with the documented profile. Calibration pairs and captures stay private. Omitted MCP strength preserves the engine's default; strength 0 is the clean baseline.

Read the Image guide for parameters, limits, persistence and API examples. Start installation with the Windows walkthrough.

Frosty VL: from a shot to a sequence

Current UI with an explicitly labeled interface-preview backend. Live VL model inference has not been requalified for the 1.2 workflow.

• Queue clips and keep working. Jobs show status, scene progress, errors and result links. Refresh reconnects to watched jobs while Studio remains running.
• Shape the shot. Use a natural-language prompt, structured JSON, or the editable shot builder for camera, light, motion and sound. The shot builder is a local writing aid; it does not run a separate enhancement model.
• Build 2–8 scenes. Reorder, duplicate, choose durations, and assemble clips into a movie with FFmpeg. Match-cut continuity uses the previous clip's last frame.
• Bring images into motion. Upload an opening frame or choose a saved Frosty Image output. Identity references and native audio appear only where supported.
• Keep useful drafts and results. Browser drafts retain text and settings; references must be reattached. Search the gallery, reuse settings, download, Delete, Undo and Restore.

The existing modular Qwen3-VL video adapter uses a specific compatible diffusion pipeline layout; an ordinary Qwen3-VL chat checkpoint is insufficient. Its bundled Compose topology targets two B200-class GPUs. The optional Wan 2.2 TI2V 5B adapter has a separate loader and capabilities. These are not the hardware requirements for Image or MCP. See video installation.

Your agent can use the same Studio

The MCP companion translates agent tool calls into Studio jobs. It does not load models. Start locally over stdio, or use authenticated Streamable HTTP on a specific private/VPN address.

Workflow	MCP tools
Discover and generate	frosty_status, frosty_generate_image, frosty_enhance_prompt, frosty_generate_video
Track and cancel	frosty_job, frosty_video_jobs, frosty_cancel_job
Find and use media	frosty_gallery, frosty_asset, frosty_preview_image
Recoverable cleanup	frosty_trash, frosty_restore

A normal agent loop is discover → submit → poll → inspect → deliver. A returned job ID means queued work, not a successful render. References can be gallery IDs, data URLs, or explicitly allowed local paths. Download links and native image previews let the agent return results to its user.

MCP installation and client examples · Give these instructions to your setup agent

How the pieces connect

• Studio :8890 serves /image, /video, galleries and job APIs. / follows the default in the engine configuration.
• Image engine :8899 owns image jobs, files and image Trash in the native example. Video uses another port when both run together.
• MCP connects to Studio, not directly to a render engine. Stdio needs no listening port; optional HTTP uses :8891/mcp by default.
• Each backend owns its loader and GPU needs. The video gallery requires shared access to the video output directory; image files are proxied from the image engine.

Use the combined configuration as an editable example. It does not install engines, load models, allocate GPUs or create a network tunnel. The native example addresses are not Docker service names.

Setup in three decisions

1. Choose Image, video, MCP only, or a combination. Use the setup matrix to select the right instructions.
2. Choose storage and compute. Keep weights, output libraries, environments and private configuration outside the Git checkout where practical. Image enhancement adds two checkpoints and model-switching time.
3. Choose access. Loopback for one machine; an existing WireGuard/private network or SSH tunnel for remote use. Studio and the raw engines do not add user accounts or authentication. MCP's HTTP token protects only MCP.

An agent can handle installation using AGENTS.md and the detailed runbook. The runbook tells it to inspect your environment, reuse answers you already supplied, preserve existing data, and verify the actual browser/agent path before claiming success.

What is verified, and what still depends on your machine

The 1.2 implementation passed 76 Python tests and 8 UI workflow tests, including real MCP stdio/HTTP clients, authentication checks, lifecycle and Trash recovery, and synthetic FFmpeg scene assembly. These are software-workflow checks.

The earlier native Windows Image profile was exercised on an RTX 4080 16 GB with NF4, references, both official enhancers, masks and transparency. Resolution, reference count, memory pressure and checkpoint switching affect performance. There is no universal VRAM-fit or speed guarantee. Live VL generation for the new workflow still requires a loaded model and an installation smoke test.

Job records and unfinished queues are in memory and do not resume after a process restart. Saved media, sidecars and Trash persist. This project does not currently provide hosted accounts, multi-user isolation, public sharing, or automatic model management. An agent should report those boundaries clearly.

Documentation and development

Guide	Use it for
Getting started	Select the installation path and understand prerequisites
Windows Image setup	Clone, environment, verified model downloads, engine, Studio, first image
Image Studio	References, enhancement, editing, controls, DWM and Image API
Video setup	Model compatibility, separate artifacts, Docker topology and verification
Video Studio	Queue, Scene Lab, gallery, persistence and video API
MCP	Companion installation, client configuration, tools and network access
Agent instructions	End-to-end installation and handoff for assistants
Troubleshooting	Common setup, model, queue and networking failures
Development	Source map, local checks, updates and contribution guidance
Visual assets	Branding, screenshot provenance and asset descriptions

The ComfyUI integration remains an optional video steering node. It is separate from the browser Studio and is not required for Image or MCP.

Upstream projects and distribution

Frosty is a Blackfrost project built around separately supplied model runtimes. See the official Qwen Image 2.1 model, Qwen implementation, official demo, Diffusers, and MCP Python SDK.

Read LICENSE, NOTICE and LICENSE-NOTICE.md for the repository's existing distribution terms. Model licenses remain separate. The public Git clone includes only the compact image DWM runtime vectors and their manifest, not calibration data, base-model weights or licensed video direction .npy files. The vendored Qwen enhancer adapter has its own provenance and license.