Instructions to use Claquasse/Anima-Control-Pose with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Claquasse/Anima-Control-Pose with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Claquasse/Anima-Control-Pose", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Preview-2: multi-resolution pose adapter (512/768/1024) + Anima Pose Control node + workflows
Browse files- README.md +90 -134
- anima_control_tools.zip +3 -0
- anima_pose_preview2.safetensors +3 -0
- comfyui/ComfyUI-anima-pose-control/README.md +34 -0
- comfyui/ComfyUI-anima-pose-control/__init__.py +6 -0
- comfyui/ComfyUI-anima-pose-control/detect.py +37 -0
- comfyui/ComfyUI-anima-pose-control/js/anima_pose_control.js +16 -0
- comfyui/ComfyUI-anima-pose-control/keypoints.py +21 -0
- comfyui/ComfyUI-anima-pose-control/node.py +70 -0
- comfyui/ComfyUI-anima-pose-control/pose_render.py +62 -0
- comfyui/ComfyUI-anima-pose-control/requirements.txt +7 -0
- pose_control.json +598 -234
- pose_control_compare.json +0 -0
- pose_control_demo.json +808 -423
- pose_control_edit.json +1082 -0
- samples/grid.png +2 -2
README.md
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pipeline_tag: text-to-image
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---
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# Anima Control — Pose (Preview-
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> ⚠️ **Preview-
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>
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>
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>
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> **Non-commercial use only** (inherits the Anima base model license). Behavior and weights may change.
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A native **pose control adapter** for the Anima v1.0 image model: condition generation on a
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skeleton pose map so the subject follows a target pose.
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- **Small trainable footprint:** only the control embedder and a rank-16 adapter train; the base stays frozen.
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- **Part of a shared control platform:** the same harness is meant to host other control types (see roadmap below).
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## Method
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**Conditioning.** The base VAE encodes the skeleton pose map into a control latent, the same
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**Fusion (`ControlEmbedder` + `ControlInitialLayer`).** A zero-initialized `ControlEmbedder`
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and the control contribution grows only as it earns loss.
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**Trainable parameters.** The `ControlEmbedder` plus a rank-16 low-rank adapter on the transformer
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**Objective.** The base model's native flow-matching objective, unchanged.
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```
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skeleton ─▶ VAE ─▶ control latent ─┐
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## Training
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**Data.**
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**Configuration.**
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| Setting | Value |
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|---|---|
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| Resolution | 512, aspect-ratio bucketed
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| Adapter rank | 16 |
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| Learning rate | 1e-4 |
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| Epochs |
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| Control dropout | 0.1 |
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| Precision | bf16 |
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| Optimizer | adamw (optimi) |
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>
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## Results
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> target skeleton (PCK@0.1 = fraction of keypoints within 10% of the pose bounding-box diagonal).
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.
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The **model weights are for non-commercial use only.** Generated images (outputs) are not restricted
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by these terms and may be used commercially. See the
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[`LICENSE`](./LICENSE) for the full text.
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## Support
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Building these models means mining and labeling
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## Citation
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```bibtex
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@misc{
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title = {Anima Control --- Pose (Preview-
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author = {Claquasse},
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year = {2026},
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note = {Preview-
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howpublished = {\url{https://huggingface.co/Claquasse/Anima-Control-Pose}}
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}
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```
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pipeline_tag: text-to-image
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---
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# Anima Control — Pose (Preview-2)
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> ⚠️ **Preview-2 — still experimental.** Better than Preview-1, but not finished. It will still
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> miss poses and produce deformed bodies, fused hands, and similar artifacts. Treat it as a
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> work-in-progress preview, not a production tool. **Non-commercial use only** (inherits the Anima
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> base model license). Behaviour and weights may still change.
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A native **pose control adapter** for the Anima v1.0 image model: condition generation on a
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skeleton pose map so the subject follows a target pose. Preview-2 is the same idea as Preview-1,
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trained at higher resolutions on a larger corpus, and shipped with a friendlier ComfyUI node.
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## What changed since Preview-1
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- **Multi-resolution: 512, 768 and 1024.** Preview-1 was 512-only; this runs at all three, and the
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bodies hold together much better at the larger sizes. 1024 looks best.
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- **Larger training corpus** than Preview-1's ~3,900 examples, which shows up as cleaner anatomy and
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steadier poses.
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- **Better pose-following at every resolution**, with the biggest gains at 768 and 1024.
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- **New ComfyUI node, "Anima Pose Control."** Drop in a reference photo; it detects the pose for you
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and renders the skeleton, and you can pick how the skeleton is drawn (thin, thick, puppet, heatmap,
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or with hands/face stripped). The plain thin skeleton is still the best default — it's the only one
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the model trained on — but a different render occasionally lands a stubborn pose when the seed
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alone won't.
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## Method
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Unchanged from Preview-1: a channel-concat control-LoRA on the frozen Anima DiT.
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**Conditioning.** The base VAE encodes the skeleton pose map into a control latent, the same latent
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space as the noisy image, so the control stays spatially aligned with the generation.
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**Fusion (`ControlEmbedder` + `ControlInitialLayer`).** A zero-initialized `ControlEmbedder` produces
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control tokens that are **added to the frozen base patch-embed output**. Zero-init means training
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starts as an exact no-op (output == base) and the control contribution grows only as it earns loss,
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so at `strength = 0` the adapter is exactly the base model.
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**Trainable parameters.** The `ControlEmbedder` plus a rank-16 low-rank adapter on the transformer
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blocks. The base transformer, text encoder, and VAE stay frozen.
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```
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skeleton ─▶ VAE ─▶ control latent ─┐
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## Training
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**Data.** (image, skeleton, caption) triples generated by Anima from a broad prompt distribution;
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skeletons rendered from each image's detected keypoints (DWPose, COCO-WholeBody, black background).
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Preview-2 uses a substantially larger corpus than Preview-1.
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| Setting | Value |
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|---|---|
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| Resolution | **512 + 768 + 1024**, aspect-ratio bucketed |
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| Adapter rank | 16 |
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| Learning rate | 1e-4 |
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| Epochs | 8 |
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| Control dropout | 0.1 |
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| Precision | bf16 |
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> Final training loss ≈ 0.11 (denoising MSE, mean over the final 400 steps).
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## Results
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Measured on held-out full-body poses (fresh generations, not seen in training). Pose agreement is
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body-PCK@0.1: re-detect keypoints on each output, compare to the target skeleton.
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*Each grid: the BASE column shows the reference and the no-control generation (same prompt, different
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seed — it ignores the pose); the remaining columns show the skeleton, drawn in each style, over the
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pose-controlled output. Control follows the pose; no-control doesn't.*
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| body-PCK@0.1 | control off | control on |
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|---|---|---|
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| 512 | ~0.33 | **~0.59** |
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| 768 | ~0.38 | **~0.67** |
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| 1024 | ~0.37 | **~0.83** |
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Preview-1 reached ~0.59 at 512. Preview-2 matches that at 512 and pulls clearly ahead at 768 and
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1024 — the gains grow with resolution.
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**What's still off (honest):**
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- It doesn't always follow the skeleton, even a clean, correct one.
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- A thin stick figure on black isn't how anime is drawn, so the model only half-reads it; the worst
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artifacts (fused hands, mush) cluster where the skeleton is busiest.
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- Dynamic poses — running, jumping, sitting — are the least reliable.
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- On short or vague prompts the style flattens toward a samey default; a richer prompt fixes it.
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## Usage (ComfyUI)
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Preview-2 uses two small custom nodes: **Anima Control Apply** (`AnimaControlApply`) applies the
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adapter, and **Anima Pose Control** (`AnimaPoseControl`) detects the pose from a photo and renders
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the skeleton for you.
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### Install
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1. Download `anima_pose_preview2.safetensors` into `ComfyUI/models/loras/`.
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2. Copy both folders from `comfyui/` in this repo into `ComfyUI/custom_nodes/`:
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`anima_control_lora/` and `ComfyUI-anima-pose-control/`. Restart ComfyUI. ComfyUI-Manager installs
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the second node's `requirements.txt` automatically; otherwise:
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`pip install -r ComfyUI-anima-pose-control/requirements.txt` (rtmlib, opencv-python, onnxruntime,
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numpy; torch and Pillow come with ComfyUI).
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3. Load a workflow from the menu. `pose_control_demo.json` is the easiest — control vs no-control side
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by side. Also included: `pose_control.json` (simple), `pose_control_edit.json` (single node, pick
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the skeleton style), `pose_control_compare.json` (one pose across every style at once).
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`anima_pose_preview2.safetensors` holds both the low-rank adapter (`lora.*` keys) and the control
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embedder (`control_embedder.*` keys); `LoraLoaderModelOnly` reads the first set and Anima Control
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Apply reads the second, both pointing at the same file.
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**Strength.** `0.0` is the base model with no control; `1.0` follows the skeleton (range 0–2). Higher
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tracks the pose more closely but can cost some image quality.
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## Roadmap
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**Preview-3** targets the headline weakness: the model only half-reads the skeleton. The thin
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lines-on-black signal seems foreign to it, so a representation bake-off is testing other renders
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(thicker, puppet/segmentation, depth-mannequin) to find what Anima follows best, plus the smaller
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fixes (default strength, dropping noisy hand points, more varied captions). The other half is the
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detector: the one used here was built for photos, not anime, so on art it produces noisy skeletons.
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Preview-3 will most likely wait on a purpose-built **anime pose detector** ("DWPose for anime"),
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then retrain on the winning representation with a lot more dynamic-pose data.
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**Version 1.0** comes after Preview-3, if it lands clean — the first non-preview release, trained on
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a good deal more data again.
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Pose is the first component on a shared control harness for Anima; planned siblings are image-prompt
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(IP-Adapter) and face-identity conditioning.
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## License
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Cosmos-Predict2 — the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
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The **model weights are for non-commercial use only.** Generated images (outputs) are not restricted
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by these terms and may be used commercially. See the bundled [`LICENSE`](./LICENSE) for the full text.
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## Support
|
| 159 |
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Building these models means mining and labeling a lot of images and renting GPUs to train on them. If
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they're useful to you and you want to chip in, it's appreciated and never expected:
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https://ko-fi.com/claquasse
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## Citation
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| 165 |
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```bibtex
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@misc{anima_control_pose_preview2,
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title = {Anima Control --- Pose (Preview-2)},
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author = {Claquasse},
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year = {2026},
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note = {Preview-2 multi-resolution pose control adapter for Anima v1.0},
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howpublished = {\url{https://huggingface.co/Claquasse/Anima-Control-Pose}}
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}
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```
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anima_control_tools.zip
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anima_pose_preview2.safetensors
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comfyui/ComfyUI-anima-pose-control/README.md
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# ComfyUI-anima-pose-control
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A single ComfyUI node — **Anima Pose Control** (`AnimaPoseControl`) — that detects a pose from a photo,
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lets you drag the body joints on a mini-canvas in the node, picks a predefined skeleton style, paints the
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styled skeleton inline, and outputs the control image for a pose-control LoRA.
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## Install
|
| 8 |
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Clone into `ComfyUI/custom_nodes/` and restart ComfyUI (ComfyUI-Manager installs `requirements.txt` for you):
|
| 9 |
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```bash
|
| 10 |
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cd ComfyUI/custom_nodes
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| 11 |
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git clone <this repo> ComfyUI-anima-pose-control
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ComfyUI/venv/bin/python -m pip install -r ComfyUI-anima-pose-control/requirements.txt
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```
|
| 14 |
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Dependencies: `rtmlib` (DWPose Wholebody-133 detection — same model/convention as the training data),
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| 15 |
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`opencv-python`, `onnxruntime`, `numpy`. torch / Pillow are provided by ComfyUI.
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| 16 |
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| 17 |
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## Node: Anima Pose Control
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| 18 |
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**Inputs:** `image` (optional) · widgets `style` (R0_thin / R1_thick / R2_puppet / heatmap),
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| 19 |
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`hands` / `face` / `feet` toggles, `redetect`, `resolution`, hidden `pose_json`.
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| 20 |
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**Outputs:** `skeleton` (IMAGE) · `keypoints` (POSE_KEYPOINT).
|
| 21 |
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|
| 22 |
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### Use
|
| 23 |
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1. Connect a reference photo to `image`, **Queue** once (`redetect` on) — detects the pose, fills the canvas,
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| 24 |
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paints the styled skeleton on the node.
|
| 25 |
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2. **Mute** the node's re-detect (toggle `redetect` off — auto-set when you drag) and drag body joints in the
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| 26 |
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mini-canvas.
|
| 27 |
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3. **Queue** — only this node + downstream re-run (detection is skipped). Output feeds `ImageScale → VAEEncode`.
|
| 28 |
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|
| 29 |
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### Styles & toggles
|
| 30 |
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`R0_thin` = DWPose-style thin skeleton (matches typical pose-LoRA training); `R1_thick` / `R2_puppet` =
|
| 31 |
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thicker limbs; `heatmap` = gaussian joint blobs. `hands`/`face`/`feet` add/remove those parts (R1/R2/heatmap
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| 32 |
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are body-only; `feet` adds foot joints). No color picker — the palette is the fixed DWPose palette.
|
| 33 |
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|
| 34 |
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Pure-Python renderer (`pose_render.py`) is the single source of truth and is unit-tested headless.
|
comfyui/ComfyUI-anima-pose-control/__init__.py
ADDED
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@@ -0,0 +1,6 @@
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|
| 1 |
+
from .node import AnimaPoseControl
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| 2 |
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| 3 |
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NODE_CLASS_MAPPINGS = {"AnimaPoseControl": AnimaPoseControl}
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| 4 |
+
NODE_DISPLAY_NAME_MAPPINGS = {"AnimaPoseControl": "Anima Pose Control (detect+edit+style)"}
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| 5 |
+
WEB_DIRECTORY = "./js"
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| 6 |
+
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
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comfyui/ComfyUI-anima-pose-control/detect.py
ADDED
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@@ -0,0 +1,37 @@
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| 1 |
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"""DWPose detection wrapper. Uses rtmlib Wholebody — the same model + 133-keypoint convention
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| 2 |
+
v2 trained on (render_skeletons.py / eval used the identical call). Injectable for tests.
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| 3 |
+
|
| 4 |
+
Wholebody-133: body 0-16, feet 17-22, face 23-90, L-hand 91-111, R-hand 112-132."""
|
| 5 |
+
import numpy as np
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
class NoPersonError(RuntimeError):
|
| 9 |
+
pass
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| 10 |
+
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| 11 |
+
|
| 12 |
+
_POSE = None
|
| 13 |
+
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| 14 |
+
|
| 15 |
+
def _default_estimator(resolution):
|
| 16 |
+
"""Lazily build the rtmlib Wholebody estimator (ComfyUI venv). Returns a callable
|
| 17 |
+
img_rgb -> (kp[N,133,2], sc[N,133]) in pixel coords on a `resolution`-square canvas."""
|
| 18 |
+
global _POSE
|
| 19 |
+
if _POSE is None:
|
| 20 |
+
from rtmlib import Wholebody
|
| 21 |
+
_POSE = Wholebody(to_openpose=False, mode="balanced", backend="onnxruntime", device="cpu")
|
| 22 |
+
pose = _POSE
|
| 23 |
+
res = int(resolution)
|
| 24 |
+
|
| 25 |
+
def run(img):
|
| 26 |
+
import cv2
|
| 27 |
+
bgr = cv2.cvtColor(cv2.resize(img, (res, res)), cv2.COLOR_RGB2BGR)
|
| 28 |
+
return pose(bgr) # (N,133,2), (N,133)
|
| 29 |
+
return run
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
def detect(image_rgb, resolution, estimator=None):
|
| 33 |
+
est = _default_estimator(resolution) if estimator is None else estimator
|
| 34 |
+
kp_all, sc_all = est(image_rgb)
|
| 35 |
+
if kp_all is None or len(kp_all) == 0:
|
| 36 |
+
raise NoPersonError("no person detected")
|
| 37 |
+
return np.asarray(kp_all[0], float), np.asarray(sc_all[0], float)
|
comfyui/ComfyUI-anima-pose-control/js/anima_pose_control.js
ADDED
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@@ -0,0 +1,16 @@
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|
| 1 |
+
// js/anima_pose_control.js — Anima Pose Control: hide the machine-managed pose_json text box.
|
| 2 |
+
// (Joint-drag editing removed for now; the node shows the styled skeleton via ComfyUI's image preview.)
|
| 3 |
+
import { app } from "../../scripts/app.js";
|
| 4 |
+
|
| 5 |
+
app.registerExtension({
|
| 6 |
+
name: "anima.pose.control",
|
| 7 |
+
async beforeRegisterNodeDef(nodeType, nodeData) {
|
| 8 |
+
if (nodeData.name !== "AnimaPoseControl") return;
|
| 9 |
+
const onNodeCreated = nodeType.prototype.onNodeCreated;
|
| 10 |
+
nodeType.prototype.onNodeCreated = function () {
|
| 11 |
+
onNodeCreated?.apply(this, arguments);
|
| 12 |
+
const pj = this.widgets?.find((w) => w.name === "pose_json");
|
| 13 |
+
if (pj) { pj.hidden = true; pj.computeSize = () => [0, -4]; pj.type = "hidden_pose_json"; }
|
| 14 |
+
};
|
| 15 |
+
},
|
| 16 |
+
});
|
comfyui/ComfyUI-anima-pose-control/keypoints.py
ADDED
|
@@ -0,0 +1,21 @@
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|
| 1 |
+
"""Codec between the JS canvas pose JSON and numpy keypoint arrays, plus body-17 edit merge."""
|
| 2 |
+
import json
|
| 3 |
+
import numpy as np
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
def to_json(kp, sc, canvas):
|
| 7 |
+
kp = np.asarray(kp, float); sc = np.asarray(sc, float)
|
| 8 |
+
pts = [[float(kp[i][0]), float(kp[i][1]), float(sc[i])] for i in range(133)]
|
| 9 |
+
return json.dumps({"canvas": int(canvas), "points": pts})
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
def from_json(s):
|
| 13 |
+
d = json.loads(s)
|
| 14 |
+
pts = np.asarray(d["points"], float) # (133,3)
|
| 15 |
+
return pts[:, :2].copy(), pts[:, 2].copy(), int(d["canvas"])
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
def merge_body(full_kp, full_sc, body_kp):
|
| 19 |
+
out = np.asarray(full_kp, float).copy()
|
| 20 |
+
out[:17] = np.asarray(body_kp, float)
|
| 21 |
+
return out
|
comfyui/ComfyUI-anima-pose-control/node.py
ADDED
|
@@ -0,0 +1,70 @@
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|
| 1 |
+
"""AnimaPoseControl ComfyUI node: detect -> edit (JS) -> styled skeleton -> control IMAGE."""
|
| 2 |
+
import numpy as np
|
| 3 |
+
|
| 4 |
+
try: # keep the helper importable without the package context (tests)
|
| 5 |
+
from .pose_render import render, STYLES
|
| 6 |
+
from .keypoints import from_json, to_json
|
| 7 |
+
from .detect import detect
|
| 8 |
+
except ImportError: # flat import when running tests inside the package dir
|
| 9 |
+
from pose_render import render, STYLES
|
| 10 |
+
from keypoints import from_json, to_json
|
| 11 |
+
from detect import detect
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
def compose(image, pose_json, style, hands, face, feet, redetect, resolution, estimator=None):
|
| 15 |
+
"""Pure branch logic. Returns (skeleton uint8 [H,W,3] RGB, pose_json_out str)."""
|
| 16 |
+
if redetect and image is not None:
|
| 17 |
+
kp, sc = detect(image, int(resolution), estimator=estimator)
|
| 18 |
+
elif pose_json and pose_json.strip():
|
| 19 |
+
kp, sc, _ = from_json(pose_json)
|
| 20 |
+
elif image is not None:
|
| 21 |
+
kp, sc = detect(image, int(resolution), estimator=estimator) # no pose yet -> detect
|
| 22 |
+
else:
|
| 23 |
+
raise ValueError("AnimaPoseControl: no pose to render — connect an image, or Queue once with redetect on first")
|
| 24 |
+
img = render(kp, sc, style, int(resolution), hands=hands, face=face, feet=feet)
|
| 25 |
+
return img, to_json(kp, sc, int(resolution))
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
class AnimaPoseControl:
|
| 29 |
+
@classmethod
|
| 30 |
+
def INPUT_TYPES(cls):
|
| 31 |
+
return {
|
| 32 |
+
"required": {
|
| 33 |
+
"style": (list(STYLES),),
|
| 34 |
+
"hands": ("BOOLEAN", {"default": True}),
|
| 35 |
+
"face": ("BOOLEAN", {"default": True}),
|
| 36 |
+
"feet": ("BOOLEAN", {"default": True}),
|
| 37 |
+
"redetect": ("BOOLEAN", {"default": True}),
|
| 38 |
+
"resolution": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 64}),
|
| 39 |
+
"pose_json": ("STRING", {"multiline": True, "default": ""}),
|
| 40 |
+
},
|
| 41 |
+
"optional": {"image": ("IMAGE",)},
|
| 42 |
+
}
|
| 43 |
+
|
| 44 |
+
RETURN_TYPES = ("IMAGE", "POSE_KEYPOINT")
|
| 45 |
+
RETURN_NAMES = ("skeleton", "keypoints")
|
| 46 |
+
FUNCTION = "run"
|
| 47 |
+
OUTPUT_NODE = True
|
| 48 |
+
CATEGORY = "Anima/pose"
|
| 49 |
+
|
| 50 |
+
def run(self, style, hands, face, feet, redetect, resolution, pose_json, image=None):
|
| 51 |
+
import torch
|
| 52 |
+
img_in = None
|
| 53 |
+
if image is not None:
|
| 54 |
+
img_in = (image[0].cpu().numpy() * 255).astype(np.uint8) # ComfyUI IMAGE -> HWC uint8
|
| 55 |
+
skel, pj = compose(img_in, pose_json, style, hands, face, feet, redetect, resolution)
|
| 56 |
+
t = torch.from_numpy(skel.astype(np.float32) / 255.0)[None, ...]
|
| 57 |
+
import json
|
| 58 |
+
kpts = json.loads(pj)
|
| 59 |
+
# Show the styled render inline via ComfyUI's standard image preview.
|
| 60 |
+
return {"ui": {"images": _save_preview(skel), "pose_json": [pj]}, "result": (t, kpts)}
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
def _save_preview(skel_uint8):
|
| 64 |
+
"""Write the styled skeleton to ComfyUI's temp dir; return a /view descriptor for the JS to load."""
|
| 65 |
+
import folder_paths, os, uuid
|
| 66 |
+
from PIL import Image
|
| 67 |
+
d = folder_paths.get_temp_directory(); os.makedirs(d, exist_ok=True)
|
| 68 |
+
fn = "anima_pose_%s.png" % uuid.uuid4().hex[:8]
|
| 69 |
+
Image.fromarray(skel_uint8).save(os.path.join(d, fn))
|
| 70 |
+
return [{"filename": fn, "subfolder": "", "type": "temp"}]
|
comfyui/ComfyUI-anima-pose-control/pose_render.py
ADDED
|
@@ -0,0 +1,62 @@
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|
| 1 |
+
"""Styled pose-skeleton renderer for AnimaPoseControl. Pure numpy + cv2 (no torch).
|
| 2 |
+
Single source of truth for the predefined skeleton looks.
|
| 3 |
+
Wholebody-133: body 0-16, feet 17-22, face 23-90, L-hand 91-111, R-hand 112-132."""
|
| 4 |
+
import numpy as np
|
| 5 |
+
import cv2
|
| 6 |
+
|
| 7 |
+
BODY = list(range(17)); FEET = list(range(17, 23)); FACE = list(range(23, 91)); HAND = list(range(91, 133))
|
| 8 |
+
COCO_LIMBS = [(5, 7), (7, 9), (6, 8), (8, 10), (11, 13), (13, 15), (12, 14), (14, 16),
|
| 9 |
+
(5, 6), (11, 12), (5, 11), (6, 12), (0, 5), (0, 6)]
|
| 10 |
+
_PAL = [(255, 0, 0), (255, 128, 0), (255, 255, 0), (128, 255, 0), (0, 255, 0), (0, 255, 128), (0, 255, 255),
|
| 11 |
+
(0, 128, 255), (0, 0, 255), (128, 0, 255), (255, 0, 255), (255, 0, 128), (180, 180, 180), (220, 220, 220)]
|
| 12 |
+
LIMB_COLOR = {l: _PAL[i % len(_PAL)] for i, l in enumerate(COCO_LIMBS)}
|
| 13 |
+
JOINT_COLOR = [_PAL[i % len(_PAL)] for i in range(17)]
|
| 14 |
+
STYLES = ("R0_thin", "R1_thick", "R2_puppet", "heatmap")
|
| 15 |
+
_W = {"R0_thin": {"stick": 2, "joint": 3}, "R1_thick": {"stick": 8, "joint": 6}, "R2_puppet": {"stick": 22, "joint": 11}, "heatmap": {"stick": 2, "joint": 3}}
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
def _toggle(sc, hands, face, feet):
|
| 19 |
+
sc = np.asarray(sc, float).copy()
|
| 20 |
+
if not hands: sc[HAND] = 0.0
|
| 21 |
+
if not face: sc[FACE] = 0.0
|
| 22 |
+
if not feet: sc[FEET] = 0.0
|
| 23 |
+
return sc
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
def render(kp, sc, style, resolution, hands=True, face=True, feet=True, kpt_thr=0.3):
|
| 27 |
+
if style not in STYLES:
|
| 28 |
+
raise ValueError(f"unknown style {style!r}")
|
| 29 |
+
H = W = int(resolution)
|
| 30 |
+
kp = np.asarray(kp, float); sc = _toggle(sc, hands, face, feet)
|
| 31 |
+
if style == "heatmap":
|
| 32 |
+
return _heatmap(kp, sc, H, W, feet, kpt_thr)
|
| 33 |
+
cfg = _W[style]; cv = np.zeros((H, W, 3), np.uint8)
|
| 34 |
+
for a, b in COCO_LIMBS:
|
| 35 |
+
if sc[a] >= kpt_thr and sc[b] >= kpt_thr:
|
| 36 |
+
cv2.line(cv, (int(kp[a][0]), int(kp[a][1])), (int(kp[b][0]), int(kp[b][1])), LIMB_COLOR[(a, b)], cfg["stick"])
|
| 37 |
+
for i in BODY:
|
| 38 |
+
if sc[i] >= kpt_thr:
|
| 39 |
+
cv2.circle(cv, (int(kp[i][0]), int(kp[i][1])), cfg["joint"], (255, 255, 255), -1)
|
| 40 |
+
for i in FEET:
|
| 41 |
+
if sc[i] >= kpt_thr:
|
| 42 |
+
cv2.circle(cv, (int(kp[i][0]), int(kp[i][1])), max(1, cfg["joint"] // 2), (255, 255, 255), -1)
|
| 43 |
+
if style == "R0_thin": # only R0 draws hands + face (DWPose-style small visible dots)
|
| 44 |
+
for i in HAND:
|
| 45 |
+
if sc[i] >= kpt_thr:
|
| 46 |
+
cv2.circle(cv, (int(kp[i][0]), int(kp[i][1])), 2, (0, 255, 255), -1)
|
| 47 |
+
for i in FACE:
|
| 48 |
+
if sc[i] >= kpt_thr:
|
| 49 |
+
cv2.circle(cv, (int(kp[i][0]), int(kp[i][1])), 2, (255, 255, 255), -1)
|
| 50 |
+
return cv
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
def _heatmap(kp, sc, H, W, feet, thr, sig=11):
|
| 54 |
+
acc = np.zeros((H, W, 3), np.float32)
|
| 55 |
+
for i in BODY + (FEET if feet else []):
|
| 56 |
+
if sc[i] >= thr:
|
| 57 |
+
x, y = int(round(kp[i][0])), int(round(kp[i][1]))
|
| 58 |
+
if 0 <= x < W and 0 <= y < H:
|
| 59 |
+
b = np.zeros((H, W), np.float32); b[y, x] = 1.0
|
| 60 |
+
b = cv2.GaussianBlur(b, (0, 0), sig); b /= (b.max() + 1e-9)
|
| 61 |
+
acc += b[..., None] * np.array(JOINT_COLOR[i % 17], np.float32)
|
| 62 |
+
return np.clip(acc, 0, 255).astype(np.uint8)
|
comfyui/ComfyUI-anima-pose-control/requirements.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
| 1 |
+
# ComfyUI-anima-pose-control runtime dependencies.
|
| 2 |
+
# ComfyUI already provides torch, Pillow and numpy; these are the extras the node needs.
|
| 3 |
+
# ComfyUI-Manager installs this automatically on first load.
|
| 4 |
+
rtmlib
|
| 5 |
+
opencv-python
|
| 6 |
+
onnxruntime
|
| 7 |
+
numpy
|
pose_control.json
CHANGED
|
@@ -1,6 +1,8 @@
|
|
| 1 |
{
|
| 2 |
-
"
|
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},
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"type": "CLIPTextEncode",
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"pos": [
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],
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"size": [
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"flags": {},
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"inputs": [
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"name": "clip",
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"type": "CLIP",
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"link": 2
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"outputs": [
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|
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"outputs": [
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"type": "CONDITIONING",
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"links": [
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"properties": {
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@@ -216,76 +322,253 @@
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"widgets_values": [
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"worst quality, low quality, score_1, score_2, blurry, jpeg artifacts"
|
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-
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},
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{
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"id": 7,
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"outputs": [
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"links": [
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|
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"type": "ImageScale",
|
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"pos": [
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],
|
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"size": [
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"flags": {},
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"inputs": [
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"name": "image",
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"outputs": [
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"type": "IMAGE",
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"links": [
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-
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-
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"properties": {
|
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@@ -293,45 +576,48 @@
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},
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"widgets_values": [
|
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"nearest-exact",
|
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"disabled"
|
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]
|
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},
|
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{
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"id":
|
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"type": "VAEEncode",
|
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"pos": [
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],
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"size": [
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"flags": {},
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"mode": 0,
|
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"inputs": [
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{
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"name": "pixels",
|
| 318 |
"type": "IMAGE",
|
| 319 |
-
"link":
|
| 320 |
},
|
| 321 |
{
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| 322 |
"name": "vae",
|
| 323 |
"type": "VAE",
|
| 324 |
-
"link":
|
| 325 |
}
|
| 326 |
],
|
| 327 |
"outputs": [
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{
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| 329 |
"name": "LATENT",
|
| 330 |
"type": "LATENT",
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"links": [
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| 332 |
-
|
| 333 |
-
]
|
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"slot_index": 0
|
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}
|
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|
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"properties": {
|
|
@@ -340,65 +626,87 @@
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"widgets_values": []
|
| 341 |
},
|
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{
|
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-
"id":
|
| 344 |
"type": "AnimaControlApply",
|
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"pos": [
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],
|
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"size": [
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],
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"flags": {},
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|
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|
| 356 |
"inputs": [
|
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{
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"name": "model",
|
| 359 |
"type": "MODEL",
|
| 360 |
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"link":
|
| 361 |
},
|
| 362 |
{
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"name": "control_latent",
|
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"type": "LATENT",
|
| 365 |
-
"link":
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}
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],
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"outputs": [
|
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{
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| 370 |
"name": "MODEL",
|
| 371 |
"type": "MODEL",
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| 372 |
"links": [
|
| 373 |
10
|
| 374 |
-
]
|
| 375 |
-
"slot_index": 0
|
| 376 |
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"properties": {
|
| 379 |
"Node name for S&R": "AnimaControlApply"
|
| 380 |
},
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"widgets_values": [
|
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| 383 |
-
1
|
| 384 |
]
|
| 385 |
},
|
| 386 |
{
|
| 387 |
-
"id":
|
| 388 |
"type": "CFGZeroStar",
|
| 389 |
"pos": [
|
| 390 |
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|
| 391 |
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|
| 392 |
],
|
| 393 |
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@@ -406,12 +714,13 @@
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@@ -420,132 +729,160 @@
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| 536 |
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@@ -554,24 +891,34 @@
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|
pose_control_compare.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
pose_control_demo.json
CHANGED
|
@@ -1,4 +1,6 @@
|
|
| 1 |
{
|
|
|
|
|
|
|
| 2 |
"last_node_id": 21,
|
| 3 |
"last_link_id": 26,
|
| 4 |
"nodes": [
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|
@@ -6,26 +8,46 @@
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|
| 6 |
"id": 1,
|
| 7 |
"type": "UNETLoader",
|
| 8 |
"pos": [
|
| 9 |
-
|
| 10 |
-
|
| 11 |
],
|
| 12 |
"size": [
|
| 13 |
300,
|
| 14 |
120
|
| 15 |
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|
| 16 |
"flags": {},
|
| 17 |
-
"order":
|
| 18 |
"mode": 0,
|
| 19 |
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 20 |
"outputs": [
|
| 21 |
{
|
|
|
|
| 22 |
"name": "MODEL",
|
| 23 |
"type": "MODEL",
|
|
|
|
| 24 |
"links": [
|
| 25 |
1,
|
| 26 |
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|
| 27 |
-
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|
| 28 |
-
"slot_index": 0
|
| 29 |
}
|
| 30 |
],
|
| 31 |
"properties": {
|
|
@@ -38,28 +60,117 @@
|
|
| 38 |
},
|
| 39 |
{
|
| 40 |
"id": 2,
|
|
|
|
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|
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|
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|
|
|
|
|
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|
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|
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|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
| 41 |
"type": "CLIPLoader",
|
| 42 |
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|
| 43 |
-
|
| 44 |
-
|
| 45 |
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|
| 46 |
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|
| 47 |
300,
|
| 48 |
120
|
| 49 |
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|
| 50 |
"flags": {},
|
| 51 |
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| 52 |
"mode": 0,
|
| 53 |
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"properties": {
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|
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"widgets_values": [
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|
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|
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1.0
|
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]
|
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},
|
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{
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"size": [
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|
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|
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"flags": {},
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|
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},
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{
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"name": "vae",
|
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"type": "VAE",
|
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"link": 7
|
|
@@ -380,93 +617,26 @@
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| 380 |
],
|
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"outputs": [
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{
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"links": [
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|
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|
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],
|
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"properties": {
|
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-
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|
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|
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"widgets_values": []
|
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},
|
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{
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|
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-
"type": "SaveImage",
|
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"pos": [
|
| 400 |
-
2080,
|
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0
|
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-
],
|
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"size": [
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|
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],
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"flags": {},
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|
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|
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"inputs": [
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{
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"name": "images",
|
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"type": "IMAGE",
|
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"link": 26
|
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}
|
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],
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"outputs": [],
|
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"properties": {
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|
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},
|
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"widgets_values": [
|
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"anima_control_BASE"
|
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]
|
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},
|
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{
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"id": 10,
|
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"type": "LoraLoaderModelOnly",
|
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"pos": [
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520,
|
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|
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],
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"size": [
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|
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|
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"flags": {},
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"order": 11,
|
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"mode": 0,
|
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"inputs": [
|
| 440 |
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{
|
| 441 |
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"name": "model",
|
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-
"type": "MODEL",
|
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"link": 1
|
| 444 |
-
}
|
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-
],
|
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"outputs": [
|
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{
|
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-
"name": "MODEL",
|
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"type": "MODEL",
|
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"links": [
|
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-
13
|
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-
],
|
| 453 |
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"slot_index": 0
|
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}
|
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],
|
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"properties": {
|
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"Node name for S&R": "LoraLoaderModelOnly"
|
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},
|
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"widgets_values": [
|
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-
"adapter_model.safetensors",
|
| 461 |
-
1.0
|
| 462 |
-
]
|
| 463 |
-
},
|
| 464 |
-
{
|
| 465 |
-
"id": 11,
|
| 466 |
"type": "AnimaControlApply",
|
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"pos": [
|
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-
|
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|
| 470 |
],
|
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"size": [
|
| 472 |
300,
|
|
@@ -477,40 +647,61 @@
|
|
| 477 |
"mode": 0,
|
| 478 |
"inputs": [
|
| 479 |
{
|
|
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|
| 480 |
"name": "model",
|
| 481 |
"type": "MODEL",
|
| 482 |
-
"link":
|
| 483 |
},
|
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{
|
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| 485 |
"name": "control_latent",
|
| 486 |
"type": "LATENT",
|
| 487 |
-
"link":
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|
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"outputs": [
|
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{
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"name": "MODEL",
|
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"type": "MODEL",
|
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"links": [
|
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|
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|
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|
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}
|
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],
|
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"properties": {
|
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"Node name for S&R": "AnimaControlApply"
|
| 502 |
},
|
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"widgets_values": [
|
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-
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|
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-
1
|
| 506 |
]
|
| 507 |
},
|
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{
|
| 509 |
-
"id":
|
| 510 |
"type": "CFGZeroStar",
|
| 511 |
"pos": [
|
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-
|
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|
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],
|
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"size": [
|
| 516 |
300,
|
|
@@ -521,19 +712,21 @@
|
|
| 521 |
"mode": 0,
|
| 522 |
"inputs": [
|
| 523 |
{
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|
| 524 |
"name": "model",
|
| 525 |
"type": "MODEL",
|
| 526 |
-
"link":
|
| 527 |
}
|
| 528 |
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2780,
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{
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"type": "IMAGE",
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"name": "filename_prefix"
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| 926 |
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| 927 |
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|
| 932 |
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},
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],
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| 938 |
},
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{
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1880,
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580
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{
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| 982 |
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580
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| 993 |
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{
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| 1000 |
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| 1001 |
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},
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{
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{
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| 1013 |
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{
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| 1020 |
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| 1021 |
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"name": "seed"
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| 1022 |
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},
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| 1023 |
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},
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| 1025 |
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{
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| 1026 |
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| 1027 |
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| 1030 |
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"name": "steps"
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| 1031 |
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},
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| 1032 |
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| 1033 |
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| 1034 |
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{
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| 1035 |
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| 1039 |
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| 1040 |
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},
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| 1043 |
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{
|
| 1044 |
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| 1045 |
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"widget": {
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| 1048 |
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"name": "sampler_name"
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| 1049 |
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| 1050 |
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| 1051 |
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},
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| 1052 |
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{
|
| 1053 |
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| 1054 |
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| 1055 |
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| 1057 |
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"name": "scheduler"
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| 1058 |
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| 1059 |
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| 1060 |
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},
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| 1061 |
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{
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| 1062 |
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| 1063 |
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| 1066 |
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"name": "denoise"
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| 1067 |
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| 1068 |
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| 1073 |
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|
| 1406 |
24,
|
| 1407 |
+
4,
|
| 1408 |
0,
|
| 1409 |
19,
|
| 1410 |
+
1,
|
| 1411 |
+
"VAE"
|
| 1412 |
],
|
| 1413 |
[
|
| 1414 |
25,
|
| 1415 |
+
19,
|
| 1416 |
0,
|
| 1417 |
+
20,
|
| 1418 |
0,
|
| 1419 |
"IMAGE"
|
| 1420 |
],
|
| 1421 |
[
|
| 1422 |
26,
|
| 1423 |
+
9,
|
| 1424 |
0,
|
| 1425 |
+
21,
|
| 1426 |
0,
|
| 1427 |
"IMAGE"
|
| 1428 |
]
|
| 1429 |
],
|
| 1430 |
"groups": [
|
| 1431 |
{
|
| 1432 |
+
"id": 1,
|
| 1433 |
+
"title": "CONTROL (pose LoRA)",
|
| 1434 |
"bounding": [
|
| 1435 |
+
1560,
|
| 1436 |
+
60,
|
| 1437 |
+
1260,
|
| 1438 |
300
|
| 1439 |
],
|
| 1440 |
+
"color": "#2a3f2a",
|
| 1441 |
+
"font_size": 22,
|
| 1442 |
+
"flags": {}
|
| 1443 |
},
|
| 1444 |
{
|
| 1445 |
+
"id": 2,
|
| 1446 |
+
"title": "BASE (no control) \u2014 same seed/prompt",
|
| 1447 |
"bounding": [
|
| 1448 |
+
1560,
|
| 1449 |
+
520,
|
| 1450 |
+
1260,
|
| 1451 |
300
|
| 1452 |
],
|
| 1453 |
+
"color": "#3f2a2a",
|
| 1454 |
+
"font_size": 22,
|
| 1455 |
+
"flags": {}
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|
|
|
| 1456 |
}
|
| 1457 |
],
|
| 1458 |
"config": {},
|
| 1459 |
+
"extra": {
|
| 1460 |
+
"ds": {
|
| 1461 |
+
"scale": 1.2284597357367357,
|
| 1462 |
+
"offset": [
|
| 1463 |
+
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|
| 1464 |
+
-2098.6519356376084
|
| 1465 |
+
]
|
| 1466 |
+
}
|
| 1467 |
+
},
|
| 1468 |
"version": 0.4
|
| 1469 |
}
|
pose_control_edit.json
ADDED
|
@@ -0,0 +1,1082 @@
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|
samples/grid.png
CHANGED
|
Git LFS Details
|
|
Git LFS Details
|