# Nvidia driver update - reactor node **URL:** \ **Category:** Beginners\ **Created:** [May 25, 2026, 12:01pm UTC](https://discuss.huggingface.co/t/nvidia-driver-update-reactor-node/176221 "2026-05-25T12:01:31Z")\ **Posts on this page:** 1\ **Showing post:** 10
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [June 13, 2026, 9:03am UTC](https://discuss.huggingface.co/t/nvidia-driver-update-reactor-node/176221/10 "2026-06-13T09:03:41Z")
Hmm, this is not really a Wan-only thing, but there is a pretty hard ceiling on what prompt changes alone can fix in the model output: * * * I think this is probably **not just a prompt problem**. What you are describing sounds like a mix of: ```plaintext image-to-video continuity drift + occlusion / out-of-frame re-entry + small-detail resampling + clip-to-clip artifact accumulation ``` In simpler terms: ```plaintext hand is visible with no rings / nude nails / no bracelet ↓ hand leaves the frame, becomes hidden, or crosses a clip boundary ↓ model no longer has a strong visual anchor for the exact hand details ↓ hand re-enters the frame ↓ model re-invents plausible "pretty hand" details ↓ rings, nail polish, bracelets, manicured nails, etc. appear ``` That is frustrating, but it is also a very normal kind of failure in AI video workflows. I would not interpret it as: ```plaintext The negative prompt is wrong. ``` I would interpret it more like: ```plaintext The model is not being visually constrained strongly enough after the hand disappears. ``` So I would treat the hands, nails, rings, and bracelets as **continuity props** , not just prompt words. If the hands matter to the scene, manage them the same way you would manage a face, outfit, tattoo, logo, or important object. ## Direct answer For this case, I would not try to solve it only with: ```plaintext no rings no nail polish ((nude nails:1.4)) ``` That can help, but it is not a hard constraint. A better practical stack is: | Layer | What it does | 8GB VRAM suitability | How much I would rely on it | | --- | --- | --- | --- | | **Positive continuity prompt** | Tells Wan what must remain stable | Safe | Medium | | **Negative prompt** | Reduces unwanted concepts | Safe | Low/medium | | **Shot design** | Avoids giving the model a chance to re-invent the hand | Safe | High | | **Short clips** | Reduces drift and error accumulation | Safe | High | | **Clean first frames** | Gives each new clip a corrected visual anchor | Safe | Very high | | **Still-image I2I / inpaint** | Fixes bad keyframes before continuing | Usually realistic | Very high | | **Video inpaint / VACE** | Repairs a region across frames | Later/experimental on 8GB | Medium, but not first | | **Fun Control / pose/depth/canny** | Controls motion/structure more than accessories | Later/experimental | Medium | | **Reference / LoRA / identity workflows** | Helps character/object consistency | Later | Case-dependent | The short version: ```plaintext Prompt can bias the model. Clean frames and shot planning constrain the model. ``` For your particular artifact, I would start with: ```plaintext structured positive prompt + shorter shots + avoid hand leaving frame + clean keyframe before each new clip + still-image inpaint if the hand becomes wrong ``` Only after that would I try heavier video-editing workflows. ## Why this is not only a Wan issue This is a broader image-to-video problem. Image-to-video generation starts from an image and tries to extend it into a temporally coherent video. But maintaining visual consistency across the subject, background, style, and details is still a known hard problem. Useful background: - [ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation](https://arxiv.org/abs/2402.04324) - [AIGCBench: Comprehensive Evaluation of Image-to-Video Generation](https://arxiv.org/abs/2401.01651) - [ConsID-Gen: View-Consistent and Identity-Preserving Image-to-Video Generation](https://arxiv.org/abs/2602.10113) The exact research papers are not necessary for your workflow, but they are useful context: even research systems treat consistency as a central problem. So if Wan invents a ring after the hand disappears, that is not surprising. It is a practical version of the same general issue. ## Why hands, nails, and jewelry are especially fragile Some details are much harder to preserve than others. | Detail type | Usually easier/harder | Why | | --- | --- | --- | | Character gender/body outline | Easier | Large global feature | | Hair color | Medium | Visible but can drift | | Clothing color | Medium | Large but texture can change | | Face identity | Hard | Needs identity preservation | | Hands/fingers | Hard | Complex anatomy, small shapes | | Nails | Very hard | Tiny detail, easily stylized | | Rings/bracelets | Very hard | Small accessories, often “beautified” by model | | Logos/text | Very hard | Fine detail and exact symbols | The model can preserve: ```plaintext same woman same room same general outfit same cinematic look ``` while still changing: ```plaintext nails rings bracelets finger details small clothing trim ``` That is why prompt changes may improve the probability but not guarantee continuity. ## Wan-specific prompt advice For Wan, I would use a more structured prompt rather than only short tokens. A useful Wan prompting discussion: - [How to get the most out of prompts for WAN Models](https://discuss.huggingface.co/t/how-to-get-the-most-out-of-prompts-for-wan-models/170354) The practical idea is to describe: ```plaintext subject scene camera motion style continuity constraints ``` not only a list of forbidden objects. For example, instead of only: ```plaintext no rings, nude nails ``` I would include a positive continuity block: ```plaintext Her hands remain plain and bare throughout the entire shot. Her fingernails are short, natural, nude, and unpainted. She wears no rings, no bracelets, no bangles, no gemstones, and no hand jewelry. When her hand moves or re-enters the frame, it remains the same plain bare hand as in the first frame. The hand details remain visually consistent from the first frame to the last frame. ``` Then use a supporting negative prompt: ```plaintext rings, ring, bracelet, bracelets, bangle, bangles, hand jewelry, gemstones, nail polish, painted nails, colorful nails, red nails, pink nails, black nails, long nails, fake nails, acrylic nails, decorated nails, manicure, inconsistent hands, changing jewelry ``` But I would still think of this as only **Layer 1**. ## Prompt template to try Positive prompt: ```plaintext A cinematic realistic shot of the same woman in the same outfit and the same environment. Her hands remain plain and bare throughout the entire shot. Her fingernails are short, natural, nude, and unpainted. She wears no rings, no bracelets, no bangles, no gemstones, and no hand jewelry. When her hand moves, partially leaves view, or re-enters the frame, it remains the same plain bare hand as in the first frame. The hands, fingernails, sleeves, and skin texture remain visually consistent from the first frame to the last frame. Natural realistic fingers, no added accessories, no cosmetic nail styling. ``` Negative prompt: ```plaintext rings, ring, bracelet, bracelets, bangle, bangles, hand jewelry, gemstones, jewel, jewels, nail polish, painted nails, colorful nails, red nails, pink nails, black nails, glossy nails, long nails, fake nails, acrylic nails, decorated nails, manicure, jeweled hands, inconsistent hands, changing jewelry, extra fingers, deformed fingers, distorted hands ``` This is worth trying. But again: ```plaintext This can reduce the issue. It probably cannot fully guarantee the issue disappears. ``` ## Why negative prompts are weak here Negative prompts are not the same as a hard mask or a hard rule. They can reduce probability, but they do not force exact pixel-level continuity. This is especially true when the artifact is a small plausible detail. In cinematic/fashion/beauty-like imagery, the model may have a learned tendency to add things like: ```plaintext manicured nails glossy nails rings bracelets jewelry ``` because those are visually common in similar training contexts. There are also practical discussions around Wan negative-prompt behavior: - [ComfyUI-WanVideoWrapper issue: Weighted Negative Prompting Feels Chaotic](https://github.com/kijai/ComfyUI-WanVideoWrapper/issues/1834) I would still use negatives, but I would not make them the main fix. ## The main failure trigger: hand leaves frame The most important practical observation in your case is probably this: ```plaintext The hand leaves the frame, then returns changed. ``` That suggests the problem is not simply: ```plaintext Wan cannot draw plain hands. ``` It is more likely: ```plaintext Wan can draw the current hand, but does not preserve the exact small details after occlusion/out-of-frame/re-entry. ``` So the easiest practical fix is to avoid the failure trigger. If hands/nails/jewelry continuity matters, avoid shots where: ```plaintext the hand exits the frame the hand goes behind the body the hand is hidden by hair/clothing/object the hand becomes tiny the hand crosses the edge of the frame the hand returns after several seconds the clip boundary happens while the hand is missing or distorted ``` Better shot planning: ```plaintext keep the hands visible keep the hands relaxed cut before the hand disappears start a new clip from a clean hand frame avoid long hand re-entry movements do not make the hand the focus unless necessary ``` This is less exciting than a technical fix, but often more reliable. A useful practical rule: > If the model keeps re-inventing something after it disappears, avoid making it disappear. ## Shot design table | If you want… | Avoid… | Prefer… | | --- | --- | --- | | No rings / no bracelets | Hand leaving frame and returning | Hand stays visible | | Nude natural nails | Close-up beauty hand gestures | Relaxed hands, less emphasis | | Stable sleeve/hand relation | Hand crossing body/face/hair | Simpler arm motion | | Stable hands across clips | Last frame with bad hand | Clean corrected first frame | | Long scene continuity | One long uncontrolled generation | Several short controlled clips | | Fewer hand artifacts | Tiny/blurred hands | Larger, clearer, simpler hands | ## First/last frame is useful, but not magic First/last frame workflows are useful, but they are not a total continuity solution. Relevant links: - [ComfyUI Wan2.2 tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2) - [Wan 2.2 14B First-Last Frame to Video - ComfyUI Workflow](https://www.comfy.org/workflows/video_wan2_2_14B_flf2v-7016f027bcf1/) - [ComfyUI-WanVideoWrapper issue #1342 - First and Last Frame Pingponging Wan 2.2](https://github.com/kijai/ComfyUI-WanVideoWrapper/issues/1342) The issue is that first/last frames give boundary guidance, but they do not guarantee that every tiny detail will remain stable inside the generated video. Also, if your last frame already contains a bad ring/nail/bracelet artifact, and you feed it into the next clip, you may accidentally tell the next generation: ```plaintext This ring is now part of the scene. ``` So I would not do this blindly: ```plaintext clip A last frame ↓ clip B first frame ``` I would do this: ```plaintext clip A last frame candidate ↓ inspect it ↓ fix hands/nails/jewelry as a still image if needed ↓ use the corrected clean image as clip B first frame ``` That gives the model a cleaner anchor. ## Clean keyframe strategy This is probably the most important workflow change. A practical clip-chaining workflow: ```plaintext 1. Generate clip A. 2. Pick the best final frame or near-final frame. 3. Inspect hands, nails, jewelry, sleeves, face, clothing. 4. If anything is wrong, fix the frame as a still image. 5. Use the corrected frame as clip B's first frame. 6. Generate clip B. 7. Repeat. ``` This is slower, but it avoids artifact accumulation. Bad pipeline: ```plaintext generate video ↓ use last frame automatically ↓ artifact enters next clip ↓ artifact becomes stronger ↓ continuity gets worse ``` Better pipeline: ```plaintext generate short video ↓ select clean boundary frame ↓ repair boundary frame ↓ continue from repaired frame ``` ## Still-image inpainting before video inpainting Since you are on 8GB VRAM, I would start with still-image repair before video repair. ComfyUI has basic inpainting workflows: - [ComfyUI Inpaint Examples](https://docs.comfy.org/tutorials/basic/inpaint) The simple idea: ```plaintext bad hand frame ↓ mask rings / nails / bracelet area ↓ inpaint as plain bare hand ↓ use repaired image as next first frame ``` Still-image inpainting is not perfect, but it is usually easier to debug than video inpainting. For your case, I would try: ```plaintext I2I / inpaint keyframes first VACE / video inpaint later ``` ## What to do when the bad detail appears inside the clip If a bad ring/bracelet/nail appears inside an otherwise good clip, you have several options. | Option | Best when | 8GB friendliness | Notes | | --- | --- | --- | --- | | Regenerate the short clip | Clip is short | High | Often easiest | | Change seed | Artifact is seed-dependent | High | Quick test | | Change shot | Hand re-entry is causing it | High | Best structural fix | | Cut around the bad frames | Artifact is brief | High | Normal editing approach | | Repair a keyframe and regenerate | You need continuity | Medium/high | Good compromise | | Still-image inpaint frames | Few frames matter | Medium | Manual but controllable | | Video inpaint / VACE | Good clip except local hand issue | Lower | More advanced | For a first pass, I would not attempt to perfectly repair a long clip. I would make the clip shorter and regenerate. ## 8GB VRAM reality check This matters because some solutions are technically relevant but may not be the best first move for your setup. Wan2.1 has a 1.3B model that is described as consumer-grade and around 8.19GB VRAM: - [Wan-AI/Wan2.1-T2V-1.3B](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) - [Wan paper](https://arxiv.org/abs/2503.20314) But that does **not** automatically mean every Wan editing workflow, VACE workflow, 14B workflow, or video-inpainting setup will be comfortable on an 8GB card. So I would keep the first-line plan lightweight: ```plaintext prompt shot design short clips clean first frames still-image inpaint ``` and treat heavier tools as experiments. ## VACE / video inpainting VACE is relevant, but I would not put it first for an 8GB setup. Relevant links: - [ComfyUI Wan VACE tutorial](https://docs.comfy.org/tutorials/video/wan/vace) - [Wan2.1-VACE native support announcement](https://blog.comfy.org/p/wan21-vace-native-support-and-ace) - [Wan2.1 VACE Inpainting - ComfyUI Workflow](https://www.comfy.org/workflows/video_wan_vace_inpainting-a1bb55d904c9/) - [VACE GitHub](https://github.com/ali-vilab/VACE) The concept is exactly relevant: ```plaintext mask the bad hand/nail/jewelry area ↓ inpaint the local region ↓ keep the rest of the video ``` For your case: ```plaintext mask the hand area remove ring / nail polish / bracelet restore plain bare hand ``` However, video inpainting can be harder than it sounds. There are practical reports of VACE/video inpainting damaging areas outside the mask or creating texture issues: - [Reddit: Wan 2.2 VACE inpainting corrupting outside mask](https://www.reddit.com/r/comfyui/comments/1tezktt/wan_22vace_inpainting_corrupting_outside_mask_and/) So I would phrase VACE like this: ```plaintext Very relevant idea. Worth testing later on a short clip. Not my first recommendation on 8GB VRAM. ``` If you do try it: ```plaintext test 2–3 seconds low resolution first small mask one problem area compare before/after carefully do not start with a full scene ``` ## Fun Control / control workflows Wan Fun Control workflows may be useful if the problem is hand movement, pose, or structure. Relevant links: - [ComfyUI Wan2.1 Fun Control tutorial](https://docs.comfy.org/tutorials/video/wan/fun-control) - [ComfyUI Wan2.2 Fun Control tutorial](https://docs.comfy.org/tutorials/video/wan/wan2-2-fun-control) These workflows can use control signals like: ```plaintext Canny Depth OpenPose MLSD trajectory ``` This can help with: ```plaintext hand position body movement camera structure silhouette motion path ``` But it probably does not directly solve: ```plaintext no rings no nail polish no bracelet ``` So I would put Fun Control in this category: ```plaintext Useful for motion/shape control. Not a direct jewelry/nail continuity fix. Try later if hand motion itself is unstable. ``` ## Reference / subject consistency workflows Reference-based workflows may help with general character and clothing consistency. Relevant links: - [Phantom GitHub](https://github.com/Phantom-video/Phantom) - [Phantom paper](https://arxiv.org/abs/2502.11079) - [RunComfy: Wan2.1 Stand-In character-consistent video workflow](https://www.runcomfy.com/comfyui-workflows/wan2-1-stand-in-in-comfyui-character-consistent-video-workflow) These are interesting for: ```plaintext same character same outfit same subject same visual identity ``` But I would be cautious about expecting them to guarantee: ```plaintext exact nail color no tiny ring same bracelet absence ``` They may help the whole image stay more consistent, but tiny hand accessories are still a very hard target. ## LoRA / training route A LoRA or character-specific workflow may help if you need repeated scenes with the same character and outfit. But for your immediate problem, I would not go there first. Training or reference workflows are more useful when the problem is: ```plaintext the character keeps changing the outfit keeps changing the face keeps changing ``` Your current issue is narrower: ```plaintext hands/nails/accessories are being invented after motion/occlusion ``` So I would first solve it with: ```plaintext shot design keyframes still repair short clips ``` not training. ## Suggested practical order If this were my workflow, I would test in this order: | Order | Test | Why | | --- | --- | --- | | 1 | Add positive hand-continuity wording | Easy and free | | 2 | Keep negative prompt, but simplify expectations | It may help but not guarantee | | 3 | Generate a shot where the hands never leave frame | Tests whether re-entry is the trigger | | 4 | Shorten the clip | Reduces drift | | 5 | Change seed | Checks if artifact is seed-specific | | 6 | Use a manually cleaned first frame | Stronger continuity anchor | | 7 | Fix bad boundary frames with still inpaint | Prevents artifact accumulation | | 8 | Regenerate only the short bad clip | Cheaper than fixing a long clip | | 9 | Try VACE/video inpaint on 2–3 seconds | Later experiment | | 10 | Try Fun Control/reference workflows | Later experiment | ## Tiny test matrix Before investing in the full scene, I would run a tiny experiment. | Test | Setup | What it tells you | | --- | --- | --- | | A | Original prompt | Baseline | | B | Add positive hand continuity prompt | Prompt sensitivity | | C | Same prompt, hand never leaves frame | Whether re-entry causes the artifact | | D | Same shot, shorter clip | Whether duration causes drift | | E | Clean corrected first frame | Whether visual anchor helps | | F | Different seed | Whether it is random seed dependent | | G | Still-inpainted first frame + shorter clip | Best lightweight solution | | H | VACE/inpaint short test | Whether local video repair is worth the complexity | If test C is much better than A, then your main enemy is: ```plaintext hand disappears → model re-invents details ``` If test E/G is much better, then your best tool is: ```plaintext clean keyframes ``` If none of them helps, then the prompt/model combination may simply be biased toward styled hands/jewelry and you may need a different shot or workflow. ## A concrete “safe” 8GB workflow ```plaintext 1. Keep the current working Wan workflow. 2. Do not add several new heavy systems at once. 3. Generate only a short clip. 4. Avoid hand leaving frame. 5. Use positive hand continuity wording. 6. Inspect final frames. 7. If hands/nails/jewelry are wrong, fix the still frame. 8. Use the fixed still frame as the next clip's first frame. 9. Only after this works, test VACE/Fun Control/reference workflows separately. ``` That keeps your current setup from getting unstable again. ## What I would avoid for now I would avoid: ```plaintext long clips multiple chained clips without inspection automatically feeding bad last frames into the next clip trying VACE on a full scene first installing several new video custom nodes at once treating "no rings" as a hard rule forcing everything through one giant workflow ``` Especially after your earlier ReActor/ONNX/environment trouble, I would keep each new experiment isolated. ## My practical expectation I would expect roughly this: | Method | Expected effect | | --- | --- | | Negative prompt only | Small improvement, not reliable | | Better positive continuity prompt | Useful, but not enough | | Keeping hands visible | Very useful | | Shorter clips | Very useful | | Clean first frames | Very useful | | Still-image inpaint on boundary frames | Very useful | | VACE/video inpaint | Potentially useful, but more complex | | Fun Control | Useful for motion/pose, not directly jewelry | | Reference workflows | Useful for character consistency, uncertain for nails/rings | So my best guess is: ```plaintext Do not fight this only at the prompt level. Make the continuity visible and explicit. Then repair/anchor the frames where continuity matters. ``` ## Final mental model I would think of it like film continuity: ```plaintext hands = continuity prop nails = continuity prop rings/bracelets = forbidden props clean first frame = continuity anchor short clip = safer shot inpainted keyframe = reset point ``` Once a bad ring appears, do not let it become part of the next generation. Go back to the last clean frame, repair it if needed, and continue from there. --- _[View the full topic](https://discuss.huggingface.co/t/nvidia-driver-update-reactor-node/176221)._