Restore And Upscale

Improve image quality and resolution. Use when the user says "upscale this", "make it sharper / higher-res", "deblur", "denoise", "dehaze", "restore this old photo", "fix this low-res / pixelated image", "clean up this scan", or "enhance". Covers super-resolution and restoration of damaged, blurry, noisy, or small images, with no change to the content. To add, remove, or replace things in the image, use edit-image. For video, use video-upscale.

Take a degraded or low-resolution image and return a cleaner, sharper, larger one: deblur, denoise, dehaze, recover detail, and enlarge 2x to 4x. The lever is matching the kind of damage to the right model, not running everything through one upscaler. For video, a dedicated temporal model exists. For worked end-to-end recipes (straight upscale, old-photo restore, video upscale), see references/examples.md.

Inputs to collect

• The source image (or video). A URL or upload. (Ask only if none provided.)
• What's wrong with it: soft/blurry, noisy/grainy, hazy, compressed/JPEG artifacts, just too small, or an old/damaged photo. This routes the model.
• Target size or factor: 2x or 4x, or a target resolution. The supported factors vary by model, so confirm against the schema before promising one.
• Fidelity vs invention: "stay faithful to the original" (transformer/GAN upscalers) or "add believable detail" (diffusion upscalers). They behave differently and this is the most important routing question.
• Content type: photo, document/text, artwork, or screenshot. Documents and text demand a faithful (non-diffusion) path so characters don't get reinvented.

Models

• Default still-image upscaler: Real-ESRGAN (runware:504@1). Practical GAN upscaler for real-world photos, 2x or 4x, denoises while enlarging. Reliable general pick.
• Sharp, faithful, low-artifact: SwinIR (runware:503@1). Transformer restorer, strong texture recovery with minimal hallucination. Good when you must stay true to the source. 2x or 4x.
• Creative detail boost: Clarity (runware:500@1). Enhances perceived detail during enlargement, accepts a guiding positivePrompt. 2x. Best when the input is clean and you want more pop.
• Diffusion super-resolution for rough real-world inputs: CCSR (runware:501@1). Restores detail on genuinely low-quality images while keeping artifacts controlled. 2x only.
• General "restore / fix this photo" (edit-style): Bria FIBO Edit (bria:21@1). Instruction-driven editing. Describe the restoration ("remove scratches, fix faded color, sharpen") in natural language. Use when the ask is broader than pure upscaling.
• Video enhancement: Topaz Starlight Precise 2.5 (topazlabs:starlight-precise@2.5). Diffusion video upscaler that denoises, de-aliases, and sharpens with full temporal consistency. This one is video-to-video, not images.
• All four still upscalers are Runware-hosted (Optimized). Confirm the live model + its schema via the runware-models and runware-run skills before calling. Never hardcode a stale choice.

No dedicated face restoration

There is currently no face-specific restoration model on Runware (no GFPGAN / CodeFormer-class model). Faces do improve as a side effect of the general upscalers above, but there is no model that targets faces, fixes eyes/teeth, or reconstructs identity from a degraded portrait. Set this expectation plainly before running:

• For a blurry or low-res face, run a general upscaler and report that the result on the face is best-effort, not a dedicated restore.
• Don't claim a face was "restored." It was upscaled, and fine facial detail may stay soft or shift slightly.
• If true identity recovery is the goal (a barely-recognizable face that must come back sharp and correct), that is a different workflow: reference-guided regeneration, see character-consistency. That trades faithfulness to the original pixels for a believable, sharp face.

Workflow

1. Diagnose the damage from the input and the user's words, then route to the model above.
2. Resolve the schema (runware-run) and confirm the input field and the allowed upscaleFactor values for that model. They differ (some are 2x only, some 2x/4x).
3. Upload the source into the model's input field (inputs.image for the still upscalers, inputs.video for Topaz).
4. Run with the right taskType and delivery mode:
   • Still upscalers (Real-ESRGAN, SwinIR, Clarity, CCSR) → taskType: "upscale", synchronous. The call returns the upscaled image.
   • Bria FIBO Edit → taskType: "imageInference", synchronous, with the restoration written as an instruction.
   • Topaz Starlight (video) → taskType: "upscale" over video, asynchronous + poll getResponse. It returns a videoURL. Don't block a sync call on it.
5. Inspect the output at full resolution before returning. If it over-smoothed or invented wrong detail, switch model class (see Technique) and retry.

Technique

• Match the model to the damage. Noise/grain and "just enlarge a real photo" → Real-ESRGAN. Must stay faithful with crisp texture (documents, product shots, anything where invented detail is wrong) → SwinIR. Clean input, want more punch → Clarity. Genuinely degraded real-world capture (heavy compression, very low res) → CCSR, where diffusion fills missing detail. Broad "fix this old photo" with scratches, fading, and color casts → Bria FIBO Edit as an edit instruction, then upscale the result if it's still small.
• GAN/transformer vs diffusion is the core tradeoff. GAN (Real-ESRGAN) and transformer (SwinIR) upscalers stay close to the pixels and rarely invent. Diffusion upscalers (CCSR, and Clarity when prompted) recover or imagine detail, which is great for soft inputs and risky for documents, faces, or anything where invented detail is wrong. When fidelity is non-negotiable, prefer the non-diffusion path. When the input is so soft there's no real detail to recover, diffusion is the only thing that helps.
• Upscale once, at the right factor. Don't chain 2x then 2x again hoping for better. Pick the factor the model supports and run it once. Re-upscaling compounds artifacts.
• Restore before you enlarge. If an image is both damaged and small, fix the damage first (denoise, descratch, color-correct) and upscale last, so you're not enlarging the artifacts. For a one-shot "fix and enlarge" on a clean-ish photo, a single upscaler pass is fine.
• Guide Clarity with a short prompt. Clarity accepts a positivePrompt to steer the enhancement (e.g. "sharp natural skin texture, fine fabric detail"). Keep it descriptive of texture, not a new scene, or it starts inventing.
• For restoration via Bria FIBO Edit, write the fix as an instruction and preserve everything else: "restore this old photograph: remove scratches and dust, correct faded color, sharpen, keep the original composition and faces unchanged." Don't ask it to re-render the scene. A mask scopes the fix to a damaged region without touching the rest.
• Faces: state up front there is no face-restore model. Run a general upscaler, and if a face still looks wrong, that's the ceiling here, not a tuning problem. The escape hatch is regeneration, not a different upscaler.

Parameters that matter

• inputs.image (still upscalers) / inputs.video (Topaz) is the source. Confirm the exact field against the live schema and never guess.
• upscaleFactor is 2 or 4 on Real-ESRGAN and SwinIR, and 2 only on CCSR and Clarity. Check the schema enum before sending an unsupported value.
• settings.positivePrompt is available on Clarity to steer enhancement. Optional, texture-focused.
• Topaz video: width / height (target, aspect preserved from the shorter edge, up to 3840x2160) and fps (15 to 120, at least the input's rate). Both width and height are required.
• Bria FIBO Edit runs as a standard imageInference edit. Put the restoration in the instruction prompt, optionally with a mask for localized fixes.

Quality bar

• Output is sharper and cleaner and still looks like the original subject. No plastic over-smoothing, no invented detail where fidelity matters (text, documents, faces).
• The enlargement hit the requested factor/size and the schema accepted the upscaleFactor.
• For diffusion upscalers, confirm hallucinated detail is plausible and wanted. If not, rerun on a GAN/transformer model.
• Faces were handled honestly: improved via a general upscaler, with the no-dedicated-face-restore limitation communicated, not silently papered over.
• Video upscales were run async and the videoURL was retrieved, not blocked on a sync call.

Related skills

runware-run, runware-models, runware-prompting; edit-image (instruction edits and broader fixes), product-photography (clean, high-res product shots), character-consistency (reference-guided regeneration when a degraded face needs identity recovery).