Seedance Playblast2Render
This workflow turns a raw CG playblast into a final rendered shot using Seedance 2.0. It preserves your animation and camera from the playblast, while Nano Banana Pro generates reference images and multi-angle grids to define asset look and improve consistency. A custom Gemini node builds structured prompts, allowing Seedance to reinterpret the shot with a controlled, production-ready style. The result is a fast, repeatable pipeline that replaces traditional rendering with AI-driven output while keeping full control over motion and intent.
About
Seedance Playblast2Render is a ComfyUI workflow that turns a raw CG playblast into a styled, production-ready render using Seedance 2.0. The pipeline preserves your original motion and framing by conditioning the renderer on the playblast video, then layers in controlled art direction through curated references and a structured prompt. VHS_LoadVideo (with VHS_VideoInfo) ingests the playblast and extracts frame rate and duration, while ImageFromBatch+ helps you pick hero frames for style exploration.
Nano Banana Pro (via the GeminiNanoBanana2 node) generates multi-angle look boards and reference grids you can assemble with ImageBatchMulti and LoadImage. PreviewImage/PreviewAny nodes are sprinkled throughout so you can confirm each stage before committing to a render.
A custom GeminiNode builds a clean, shot-aware prompt from your notes, guided by included system instructions for Seedance. Those references and the playblast are fed to ByteDance2ReferenceNode to lock in motion, silhouette, and camera, and then rendered in ByteDance2TextToVideoNode. SetNode/GetNode keep shared parameters (like shot names or FPS) synchronized across groups. When you’re happy, SaveVideo writes out the final clip.
The result is a repeatable, fast path that replaces a traditional offline render pass with AI-driven output—while keeping full control over timing, camera, and intent.
The workflow is organized into clear groups: LOAD PLAYBLAST, (OPTIONAL) GENERATE STYLE FRAMES, GENERATE INDIVIDUAL ASSETS, GENERATE SHOT AND STYLE DESCRIPTION FOR SEEDANCE, and DIFFUSION RENDERER BASED ON REFERENCE IMAGES AND PLAYBLAST ANIMATION. There’s also a GENERATE FAUX-PLAYBLAST group if you want to test the pipeline without a real playblast. Because it leverages structured prompting (GeminiNode), reference conditioning (ByteDance2ReferenceNode), and reference grids (GeminiNanoBanana2 + ImageBatchMulti), it’s especially strong at keeping character/prop continuity and delivering a consistent look across shots.
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What you need
- Runs on
- Comfy Cloud
- Key
- COMFY_API_KEY
Needs a paid Cloud plan and credits.
# Python 3.10+: pip install comfy-sdk==0.4.0
import os
from comfy_sdk import Comfy
api_key = os.environ.get("COMFY_API_KEY", "").strip()
if not api_key: raise RuntimeError("Set COMFY_API_KEY")
os.environ.setdefault("COMFY_BASE_URL", "https://cloud.comfy.org")
client = Comfy(api_key=api_key)
workflow = client.workflows.from_json({
"343": {
"class_type": "ByteDance2TextToVideoNode",
"inputs": {
"model": "Seedance 2.0",
"model.duration": 10,
"model.generate_audio": True,
"model.prompt": "A single sleek sports car (colored red) being chased by a heavy SUV (colored green), drifting aggressively around a tight corner and kicking up particle debris. Low-angle handheld tracking shot, moving backward at high speed to keep both vehicles centered in the frame. A busy city intersection (colored blue). Untextured grey 3D playblast render style, Maya viewport look, flat Lambert shading, ambient occlusion only. No color, no textures, no realistic lighting, maintain solid geometry, no vehicle morphing, no camera cuts.",
"model.ratio": "16:9",
"model.resolution": "720p",
"seed": 675586000,
"watermark": False
},
"_meta": {
"title": "ByteDance Seedance 2.5 Text to Video"
}
},
"344": {
"class_type": "SaveVideo",
"inputs": {
"codec": "auto",
"filename_prefix": "video/Seedance2.0_t2v",
"format": "auto",
"format.codec": "auto",
"video": [
"343",
0
]
},
"_meta": {
"title": "Save Video"
}
},
"347": {
"class_type": "ImageFromBatch+",
"inputs": {
"image": [
"428",
0
],
"length": 1,
"start": [
"430",
0
]
},
"_meta": {
"title": "🔧 Image From Batch"
}
},
"348": {
"class_type": "GeminiNanoBanana2",
"inputs": {
"aspect_ratio": "auto",
"images": [
"431",
0
],
"model": "Nano Banana 2 (Gemini 3.1 Flash Image)",
"prompt": "make a 2x2 grid of this image showing different angles of the object on a white background. no text.",
"resolution": "1K",
"response_modalities": "IMAGE",
"seed": 43,
"system_prompt": "You are an expert image-generation engine. You must ALWAYS produce an image.\nInterpret all user input—regardless of format, intent, or abstraction—as literal visual directives for image composition.\nIf a prompt is conversational or lacks specific visual details, you must creatively invent a concrete visual scenario that depicts the concept.\nPrioritize generating the visual representation above any text, formatting, or conversational requests.",
"thinking_level": "MINIMAL"
},
"_meta": {
"title": "Nano Banana 2"
}
},
"352": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"347",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"356": {
"class_type": "ByteDance2ReferenceNode",
"inputs": {
"model": "Seedance 2.0",
"model.auto_downscale": True,
"model.auto_upscale": False,
"model.duration": [
"447",
0
],
"model.generate_audio": True,
"model.prompt": "Use the motion from video_1 with the vehicles from image_1 and image_2 chasing each other in the city streets based on image_4. The other cars look like image_3. Tire smoke and road debris being kicked up behind the cars. Headlight coming from the cars cause anamorphic lens flares. As the red car spins out, sparks fly out of it's rear tires. Cinematic. 4k.",
"model.ratio": "16:9",
"model.reference_images.image_1": [
"453",
0
],
"model.reference_images.image_2": [
"380",
0
],
"model.reference_images.image_3": [
"404",
0
],
"model.reference_images.image_4": [
"384",
0
],
"model.reference_videos.video_1": [
"451",
0
],
"model.resolution": "720p",
"seed": 1077699877,
"watermark": False
},
"_meta": {
"title": "ByteDance Seedance 2.5 Reference to Video (Legacy)"
}
},
"359": {
"class_type": "SaveVideo",
"inputs": {
"codec": "auto",
"filename_prefix": "video/Seedance2.0_r2v",
"format": "auto",
"format.codec": "auto",
"video": [
"356",
0
]
},
"_meta": {
"title": "Save Video"
}
},
"361": {
"class_type": "GeminiNode",
"inputs": {
"model": "gemini-3-1-pro",
"prompt": "I have a shot that I want to match the motion of, this is video_1. I want it to have the vehilces from image_1, 2, and 3 and the enviroment should look like image_4.",
"seed": 42,
"system_prompt": "# 🧠 Seedance 2.0 Prompt Assistant\n\n## System Instructions\n\nYou are an expert AI video director and prompt engineer specializing in Seedance 2.0. Your role is to help the user design high-quality prompts for multimodal video generation, including text-to-video, image-to-video, reference-to-video, video-to-video, and audio-to-video workflows.\n\nYou must think like a film director, not a casual prompt writer.\n\n---\n\n## 🎯 Core Objective\n\nAlways generate prompts that:\n\n* Are structured using cinematic language\n* Describe filmable shots, not abstract ideas\n* Maintain subject consistency and motion clarity\n* Use constraints to reduce model drift\n* Are optimized for Seedance’s multimodal behavior\n\n---\n\n## 🧱 Universal Prompt Structure\n\nAlways follow this structure when generating prompts:\n\nSubject → Action → Camera → Scene → Style → Constraints → Timing\n\nDefinitions:\n\n* Subject: who or what is in the scene\n* Action: what is happening (single continuous action)\n* Camera: shot type, framing, and movement\n* Scene: environment and spatial context\n* Style: a single clear visual style reference\n* Constraints: rules to prevent artifacts or drift\n* Timing: optional shot sequencing or time-based structure\n\nDo not change this order.\n\n---\n\n## 🎬 Mode-Specific Behavior\n\n### 1. Text-to-Video (T2V)\n\n* Generate everything from scratch\n* Use a single subject and a single action\n* Avoid multiple ideas or branching events\n* Treat the prompt as one continuous shot unless explicitly told otherwise\n\n---\n\n### 2. Image-to-Video (I2V)\n\n* Assume the image defines identity\n* Do not redefine appearance in detail\n* Focus on motion, camera, and environment\n* Include constraints to preserve facial and structural consistency\n\nExample constraints:\n\n* maintain exact facial structure\n* preserve clothing and proportions\n\n---\n\n### 3. Reference-to-Video (Multi-input)\n\n* Assign clear roles to each input\n\nUse this pattern:\n\n* Image = identity\n* Video = motion reference\n* Audio = timing and emotion\n\nExplicitly describe how each input is used:\n\n* perform motion from reference video\n* maintain identity from reference image\n\n---\n\n### 4. Video-to-Video (V2V)\n\n* Preserve motion and timing from the source video\n* Only modify:\n\n * subject\n * environment\n * style\n* Avoid redefining motion unless necessary\n\n---\n\n### 5. Audio-to-Video (A2V)\n\n* Use audio to drive:\n\n * pacing\n * rhythm\n * emotional tone\n* Sync actions and camera movement to audio cues\n\n---\n\n## 🎥 Shot Design Rules\n\n* Default to a single shot unless multi-shot is requested\n* If multi-shot is used, structure clearly:\n\nShot 1:\nShot 2:\nShot 3:\n\nEach shot must follow the full prompt structure.\n\n---\n\n## 🎯 Constraints (Critical Requirement)\n\nAlways include constraints when generating prompts.\n\nTypes of constraints:\n\n* identity: no face distortion, consistent character\n* motion: no jitter, no morphing\n* camera: no cuts unless specified\n* environment: consistent lighting and geometry\n\nConstraints must be short, direct, and explicit.\n\n---\n\n## ⚖️ Prompt Quality Rules\n\n* Keep prompts between 30 and 120 words unless user asks otherwise\n* Use clear, literal language\n* Avoid vague descriptors like:\n\n * cool\n * cinematic (unless supported with specifics)\n * dynamic\n\nReplace vague language with:\n\n* specific camera moves\n* lighting descriptions\n* physical actions\n\n---\n\n## 🎨 Style Rules\n\n* Use only ONE strong style anchor\n* Prefer:\n\n * film references\n * lighting styles\n * real-world cinematography\n\nAvoid stacking multiple styles.\n\n---\n\n## 🎥 Camera Rules\n\nAlways define camera behavior when possible:\n\n* shot type: wide, medium, close-up\n* movement: dolly, pan, tilt, handheld\n* perspective: eye-level, low angle, overhead\n\nCamera language is more important than visual adjectives.\n\n---\n\n## 🧠 Iteration Strategy\n\nWhen refining prompts:\n\n* Change only one major variable at a time\n* Preserve working elements\n* Improve clarity, not complexity\n\n---\n\n## 🚫 What NOT to Do\n\nDo not:\n\n* write long narrative paragraphs\n* include multiple competing subjects\n* mix conflicting styles\n* omit camera direction\n* rely on abstract or emotional-only descriptions\n\n---\n\n## ✅ Output Format\n\nWhen the user asks for a prompt, return:\n\n1. Final Prompt (clean, ready to paste)\n2. Optional: Short explanation (if helpful)\n\nDo not over-explain unless asked.\n\n---\n\n## 🔥 Guiding Principle\n\nEvery prompt should describe something that could realistically be filmed on a set.\n\nIf it would not make sense to a cinematographer, rewrite it."
},
"_meta": {
"title": "Google Gemini"
}
},
"364": {
"class_type": "PreviewAny",
"inputs": {
"source": [
"361",
0
]
},
"_meta": {
"title": "Preview as Text"
}
},
"380": {
"class_type": "LoadImage",
"inputs": {
"image": "bc1a1a3b41888b23907817f550a86cd1d4d332574a66307ef5e7e4407f1d6684.png"
},
"_meta": {
"title": "Load Image"
}
},
"384": {
"class_type": "LoadImage",
"inputs": {
"image": "efaf62dab3fc7e992bb6b628df69b781112830794beb98facb695ca810922772.png"
},
"_meta": {
"title": "Load Image"
}
},
"404": {
"class_type": "LoadImage",
"inputs": {
"image": "c62d4ddf874ebc4e076d63f1f3de34b0355052470bd3acd2dced26262449f3b2.png"
},
"_meta": {
"title": "Load Image"
}
},
"428": {
"class_type": "VHS_LoadVideo",
"inputs": {
"custom_height": 0,
"custom_width": 0,
"force_rate": 0,
"format": "AnimateDiff",
"frame_load_cap": 0,
"select_every_nth": 1,
"skip_first_frames": 0,
"video": "69e04c97120a65e3f3739e9b68cdfa58e06addc39cfe1417ac56b4798828e2fe.mp4"
},
"_meta": {
"title": "Load Video (Upload) 🎥🅥🅗🅢"
}
},
"430": {
"class_type": "Int",
"inputs": {
"value": 1
},
"_meta": {
"title": "CHOOSE FRAME"
}
},
"431": {
"class_type": "GeminiNanoBanana2",
"inputs": {
"aspect_ratio": "auto",
"model": "Nano Banana 2 (Gemini 3.1 Flash Image)",
"prompt": "A stylized synthwave race car. Red.",
"resolution": "1K",
"response_modalities": "IMAGE",
"seed": 43,
"system_prompt": "You are an expert image-generation engine. You must ALWAYS produce an image.\nInterpret all user input—regardless of format, intent, or abstraction—as literal visual directives for image composition.\nIf a prompt is conversational or lacks specific visual details, you must creatively invent a concrete visual scenario that depicts the concept.\nPrioritize generating the visual representation above any text, formatting, or conversational requests.",
"thinking_level": "MINIMAL"
},
"_meta": {
"title": "Nano Banana 2"
}
},
"432": {
"class_type": "GeminiNanoBanana2",
"inputs": {
"aspect_ratio": "auto",
"images": [
"436",
0
],
"model": "Nano Banana 2 (Gemini 3.1 Flash Image)",
"prompt": "Change this 3D render into a photorealistic, high quality, still frame of a sythnwave style environment.",
"resolution": "1K",
"response_modalities": "IMAGE",
"seed": 43,
"system_prompt": "You are an expert image-generation engine. You must ALWAYS produce an image.\nInterpret all user input—regardless of format, intent, or abstraction—as literal visual directives for image composition.\nIf a prompt is conversational or lacks specific visual details, you must creatively invent a concrete visual scenario that depicts the concept.\nPrioritize generating the visual representation above any text, formatting, or conversational requests.",
"thinking_level": "MINIMAL"
},
"_meta": {
"title": "Nano Banana 2"
}
},
"433": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"432",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"436": {
"class_type": "ImageBatchMulti",
"inputs": {
"image_1": [
"347",
0
],
"inputcount": 2
},
"_meta": {
"title": "Image Batch Multi"
}
},
"443": {
"class_type": "VHS_VideoInfo",
"inputs": {
"video_info": [
"428",
3
]
},
"_meta": {
"title": "Video Info 🎥🅥🅗🅢"
}
},
"447": {
"class_type": "CM_FloatToInt",
"inputs": {
"a": [
"443",
7
]
},
"_meta": {
"title": "FloatToInt"
}
},
"451": {
"class_type": "LoadVideo",
"inputs": {
"file": "video/69e04c97120a65e3f3739e9b68cdfa58e06addc39cfe1417ac56b4798828e2fe.mp4 [output]"
},
"_meta": {
"title": "Load Video"
}
},
"452": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"348",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"453": {
"class_type": "LoadImage",
"inputs": {
"image": "56598a0bbd51a7ff5dc6f7557906032006c8e4b7c5d0c6e14cf8e019dc018761.png [output]"
},
"_meta": {
"title": "Load Image"
}
}
})
# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)Questions & answers
Your playblast video is fed into ByteDance2ReferenceNode as a conditioning source. That node guides ByteDance2TextToVideoNode so timing, motion, and framing from the playblast are respected while Seedance 2.0 reinterprets the appearance with your prompt and references.
Both work. GeminiNanoBanana2 can generate multi-angle boards to establish a consistent look, but you can also load your own refs with LoadImage and assemble them with ImageBatchMulti. The key is to provide clear, angle-diverse references for stable asset identity.
Use VHS_VideoInfo to read the source FPS and frame count, then propagate those values to the renderer. If you encounter fractional values, CM_FloatToInt and Int help align integer frame counts so the output matches the playblast length.
Increase reference influence in ByteDance2ReferenceNode, simplify or clarify the structured prompt from GeminiNode, and ensure your ImageBatchMulti contains clean, multi-angle references. Also fix the random seed in ByteDance2TextToVideoNode for repeatable results when iterating.