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b/COMPARISONS.md @@ -0,0 +1,34 @@ +# All matched comparisons + +BF16 source weights versus the accelerated mixed NVFP4 stack. Same prompts and seeds, 1024x1024, 40 steps, Euler/simple, CFG 1. + +| Subject | Seed | Side by side | +|---|---:|---| +| portrait | 20260920 | [Open](assets/ab_portrait_20260920.jpg) | +| portrait | 20260921 | [Open](assets/ab_portrait_20260921.jpg) | +| landscape | 20260920 | [Open](assets/ab_landscape_20260920.jpg) | +| landscape | 20260921 | [Open](assets/ab_landscape_20260921.jpg) | +| product | 20260920 | [Open](assets/ab_product_20260920.jpg) | +| product | 20260921 | [Open](assets/ab_product_20260921.jpg) | +| typography | 20260920 | [Open](assets/ab_typography_20260920.jpg) | +| typography | 20260921 | [Open](assets/ab_typography_20260921.jpg) | +| spatial | 20260920 | [Open](assets/ab_spatial_20260920.jpg) | +| spatial | 20260921 | [Open](assets/ab_spatial_20260921.jpg) | +| illustration | 20260920 | [Open](assets/ab_illustration_20260920.jpg) | +| illustration | 20260921 | [Open](assets/ab_illustration_20260921.jpg) | +| fashion | 20260920 | [Open](assets/ab_fashion_20260920.jpg) | +| fashion | 20260921 | [Open](assets/ab_fashion_20260921.jpg) | +| night market | 20260920 | [Open](assets/ab_night_market_20260920.jpg) | +| night market | 20260921 | [Open](assets/ab_night_market_20260921.jpg) | +| architecture | 20260920 | [Open](assets/ab_architecture_20260920.jpg) | +| architecture | 20260921 | [Open](assets/ab_architecture_20260921.jpg) | +| macro | 20260920 | [Open](assets/ab_macro_20260920.jpg) | +| macro | 20260921 | [Open](assets/ab_macro_20260921.jpg) | +| astronaut | 20260920 | [Open](assets/ab_astronaut_20260920.jpg) | +| astronaut | 20260921 | [Open](assets/ab_astronaut_20260921.jpg) | +| botanical poster | 20260920 | [Open](assets/ab_botanical_poster_20260920.jpg) | +| botanical poster | 20260921 | [Open](assets/ab_botanical_poster_20260921.jpg) | + +Prompts and image mappings: [comparisons.json](assets/comparisons.json). + +For the interactive slider, download gallery.html and assets together and open gallery.html locally. diff --git a/PROMPTING.md b/PROMPTING.md new file mode 100644 index 0000000000000000000000000000000000000000..f109341a9180d575dc31cdcc3ac39c3c7ec1100b --- /dev/null +++ b/PROMPTING.md @@ -0,0 +1,71 @@ +# Prompting Prism / Qwen Image 2.1 + +Start with a concrete natural-language paragraph: medium, subject, action, +relationships, composition, lighting, materials, then exact lettering. Keep each +attribute next to the object it describes. The supplied workflows use 40 steps, +Euler/simple, CFG 1, denoise 1, and fixed seeds. + +## Text to image + +Describe a scene that can be depicted in one image. For a product photo, specify +the object, material, label, supporting objects, surface, background and lighting. +For a portrait, specify the action and framing as well as the person's appearance. +The included product prompt is a complete starting point. + +Set output width and height in Empty Latent Image. The encoder's resolution field +controls reference-image resizing, not the independent text-to-image canvas. +Changing image dimensions may change composition even with the same seed. + +At CFG 1, the negative conditioning branch does not guide sampling. Put essential +requirements in the positive prompt. Higher CFG is not a validated improvement +for this package. + +## Lettering + +Quote the exact text and specify its position and hierarchy. Example: + +> Large cream headline at the top reads exactly "TAKE THE SCENIC ROUTE". +> A smaller line at the bottom reads exactly "SLOW TRAVEL CLUB". +> Generous margins, flat navy, coral and cream inks, subtle paper grain. + +Proofread the result. Clear headings and short labels worked in the release +examples, but the astronaut test also shows small-label spelling errors in the +BF16 baseline. Neither precision is a guarantee of perfect text. + +## Image editing + +Upload the reference in Load Image. Connect it and the matching VAE to +TextEncodeQwenImage21. Use that node's latent output for sampling, so the output +matches the processed first reference's size. The included editing workflow +already has those connections. + +Name the changes and the elements to preserve: + +> Change the amber glass bottle to deep translucent cobalt-blue glass and the +> brass cap to brushed silver. Replace the orange with lime and rosemary with +> lavender. Keep the bottle shape, cream EMBER label, marble pedestal, camera +> angle, background and lighting. + +For multiple references, refer explicitly to the first and second images. +Reference preservation is semantic, not pixel-locked: small details, hand poses, +clothing coverage and object placement can change. + +## Native transparency + +Use an explicit RGBA instruction around the subject description: + +> This is an RGBA image with transparency. A charming hand-painted gouache sticker +> of a small orange fox curled around a glowing blue book, round spectacles, +> moss-green scarf, clean complete silhouette and generous empty margin. +> The image has alpha channel and the background is transparent. + +Save as PNG. Check the alpha channel or composite over a checkerboard. The hidden +RGB under transparent pixels can be colored; an RGB-only viewer is not an alpha +test. The supplied example has alpha values spanning 0 through 255. + +## Comparisons + +Keep the prompt, seed, dimensions, sampler, steps, backend and encoder mode fixed. +The optional NVFP4 encoder patch changes conditioning arithmetic and may change +the generated image. Compare stock and accelerated modes as distinct settings. +The gallery contains the complete 12-prompt, two-seed comparison set. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c5d286d362b1ec1547470d6d623e86ee9289f668 --- /dev/null +++ b/README.md @@ -0,0 +1,132 @@ +--- +license: other +license_name: qwen-research-license +license_link: LICENSE +base_model: Comfy-Org/Qwen-Image-2.1 +base_model_relation: quantized +pipeline_tag: text-to-image +tags: +- qwen-image +- qwen-image-2.1 +- nvfp4 +- comfyui +- diffusion-single-file +- quantized +- image-editing +- rtx-5090 +--- + +# 🚀 Prism Image 2.1 — NVFP4 for ComfyUI + +**The image model AND the text encoder. 12.42 GB for the complete package.** + +I converted Qwen Image 2.1's image transformer and Qwen3-VL 8B language encoder +directly from the official BF16 weights into native ComfyUI NVFP4. The vision +tower, embeddings, output head, critical image-model tensors, and VAE stay BF16. +Real Blackwell FP4 kernels in both denoising and text conditioning, with an +included small runtime patch for the latter. No new nodes. + +**Built with Qwen.** Research/evaluation use only under the included license. + +**RTX 5090: 6.746 s median for a complete 1024×1024, 40-step run** versus +15.629 s with BF16 and 7.553 s with official INT8. Three warm runs per variant, +every node executed, including text encoding. This one-prompt benchmark used the +encoder patch; [full method and limits](VALIDATION.md). + +![Reference photo and the NVFP4 product edit](assets/editing.jpg) + +## 📦 Download once, wire nothing + +Tested with **ComfyUI 0.36.0**, PyTorch **2.14.0+cu130**, comfy-kitchen **0.2.35**, +and an **RTX 5090**. Use a ComfyUI build with `TextEncodeQwenImage21` and native +NVFP4 support. Older builds without the Qwen Image 2.1 integration will not work. + +| File | Where it goes | Size | +|---|---|---:| +| [prism_image_2.1_nvfp4.safetensors](diffusion_models/prism_image_2.1_nvfp4.safetensors) | `ComfyUI/models/diffusion_models/` | 4.20 GB | +| [prism_qwen3vl_8b_nvfp4.safetensors](text_encoders/prism_qwen3vl_8b_nvfp4.safetensors) | `ComfyUI/models/text_encoders/` | 7.55 GB | +| [qwen_image_2.1_vae_bf16.safetensors](vae/qwen_image_2.1_vae_bf16.safetensors) | `ComfyUI/models/vae/` | 0.68 GB | + +The original BF16 set is 32.44 GB. This package is **61.7% smaller on disk**. +File sizes are decimal GB, not a VRAM requirement. Keep your existing identical +VAE if you already have it. + +1. Put the three files in the folders above and refresh ComfyUI's model list. +2. For accelerated NVFP4 text conditioning, apply the [runtime patch](runtime/README.md) + and restart. Without it, the same models and nodes work, but text conditioning + uses dequantized weights and FP32 activations. Denoising still uses NVFP4. +3. Import one of the workflows below using **Ctrl+O**, then click **Run**. +4. For editing, use **Upload** in Load Image to select the included + [reference image](input/prism_edit_reference.png), or your own image. + +| Working workflow | Purpose | +|---|---| +| [01 — Text to image](workflows/01_Text_to_Image.json) | 1024×1024 product example; change the prompt and latent size | +| [02 — Image editing](workflows/02_Image_Editing.json) | One reference, coherent material/color/object changes | +| [03 — Transparent RGBA](workflows/03_Transparent_RGBA.json) | Native alpha output; no background-removal node | +| [04 — 2K typography](workflows/04_2K_Typography.json) | 2048×2048 poster with exact requested lettering | + +The workflows use core `UNETLoader`, `CLIPLoader` (type **qwen_image**), +`TextEncodeQwenImage21`, `KSampler`, and VAE nodes. Leave diffusion weight dtype +at **default**. Defaults are 40 steps, Euler, simple scheduler, CFG 1, denoise 1. +The encoder node's resolution controls reference resizing; text-to-image output +size comes from Empty Latent Image. API equivalents are in [workflows/api](workflows/api). + +## 👀 Same prompts, same seeds + +![BF16 and NVFP4 portrait comparison](assets/ab_portrait_20260920.jpg) + +![BF16 and NVFP4 typography comparison](assets/ab_typography_20260921.jpg) + +All **24 matched pairs** are in [COMPARISONS.md](COMPARISONS.md). Download +`gallery.html` and its `assets` folder together for the offline comparison slider. +See [PROMPTING.md](PROMPTING.md) for text, editing, lettering and transparency tips. + +See [VALIDATION.md](VALIDATION.md) for the test set, measured results, precision +tradeoffs, and execution requirements. The comparisons use BF16 source weights +against this complete accelerated NVFP4 package. Quantization changes results; +this is not a promise of pixel-identical output or a universal quality ranking. + +## 🧠 What's actually quantized? + +| Component | Policy | +|---|---| +| Image transformer | 192 attention Q/K/V/output and fused MLP gate/up/output matrices: NVFP4 | +| Qwen3-VL language backbone | 252 attention and MLP matrices: NVFP4 | +| Vision tower, token embeddings, language head | Original BF16 tensors retained | +| Image-model input/output, timestep, modulation, normalization | Original BF16 tensors retained | +| VAE | Original BF16 file, unchanged | +| Scales | E4M3 FP8 blocks of 16, FP32 tensor scales | + +Native packed E2M1 FP4, native scale layout, and per-layer `comfy_quant` metadata. +This is a mixed-precision NVFP4 stack, not every parameter forced into four bits. +No GGUF loader, no requantization of the INT8 release, no dropped vision tower. +No calibration or fine-tuning was used for this conversion. + +## 🔧 Reproduce it + +Source repository: [Comfy-Org/Qwen-Image-2.1](https://huggingface.co/Comfy-Org/Qwen-Image-2.1), +revision `ace0edeb3791a594ddfa36ed5f41a178a394e921`. + +Use the Python environment of the tested ComfyUI installation: + +```powershell +python tools/convert.py path/to/qwen_image_2.1_bf16.safetensors diffusion_models/prism_image_2.1_nvfp4.safetensors --component dit --comfy-root path/to/ComfyUI +python tools/convert.py path/to/qwen3vl_8b_bf16.safetensors text_encoders/prism_qwen3vl_8b_nvfp4.safetensors --component encoder --comfy-root path/to/ComfyUI +``` + +The converter uses installed Comfy Kitchen operations, validates matrix alignment, +and emits tensor manifests with source/output hashes and reconstruction error. +`tools/verify.py SOURCE CONVERTED` checks the stored format, checkpoint hash, and +exact equality of every protected tensor. Final file checksums are in `SHA256SUMS`. + +## ⚖️ License & credits + +Qwen created the base model; Comfy-Org provided the native BF16 checkpoint package; +ComfyUI and Comfy Kitchen provide loading and GPU execution. Independent conversion, +workflow packaging, and RTX 5090 validation by **BennyDaBall_OG**. + +The [Qwen Research License](LICENSE) limits use to non-commercial research and +evaluation; commercial use requires a separate license from Qwen. Read the full +license and [NOTICE](NOTICE). The included ComfyUI patch remains GPL-3.0. +This repo is not affiliated with Qwen or Comfy-Org. diff --git a/SHA256SUMS b/SHA256SUMS new file mode 100644 index 0000000000000000000000000000000000000000..69e7600c50ba3b12df976490de66da38d221333f --- /dev/null +++ b/SHA256SUMS @@ -0,0 +1,126 @@ +dbfd3a6bf37d800ab2d5dfbb977fcc6e1b93f4c57ec41ff423598b93a6536ccf assets/01_Text_to_Image.png +4e6fa2ad6423a4bf308707d3d892c3616b614f63d634f4202816e92c8c048910 assets/02_Image_Editing.png +14dbba646932489c0bcbe513ec8151fa5d18005832202abe961d03544c8ef6f8 assets/03_Transparent_RGBA.png +95b2ecdf94cf414f51dbb4402176b3deaaad8d984797658ca013825770f6cdf1 assets/04_2K_Typography.png +22ae98495169b99a864775e7720630cb9efb3de9a99a1b436c209a8508d1eb9e assets/ab_architecture_20260920.jpg +5dc83f4493aabec649da97c4fccb43778e7d408e65f940b8a36b6f7253cde16f assets/ab_architecture_20260921.jpg +3657cd495de80e9a383f71b116aacfb3bb3b708f6cb5a84e6087dd9afd1bcb1d assets/ab_astronaut_20260920.jpg 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+6b8622eb6d6adfd66d0545c327019519ac1a491e878be4d99da46cad5aece479 README.md +230184f60bae2feaf244f10a8bac053c8ff33a183bcc365b4d8b876d2b7f4809 runtime/LICENSE-GPL-3.0 +2c13ec9046ffa720484c569acce153f4759da47e9bc008af56d68d583854e2fa runtime/qwen21-nvfp4-conditioning.patch +524bf47b1f5b061514314d16f6afa6a02d21232545e3e96297c00781fc67733a runtime/README.md +41ac84507beed8d4bf5f46f7c49fa91c12162f73a354598b9943888411d57381 text_encoders/prism_qwen3vl_8b_nvfp4.manifest.json +cdd9b85bcb60d5cb358ddecfd45a48fa15cb728b70d88bd2028840eaef095d16 text_encoders/prism_qwen3vl_8b_nvfp4.safetensors +c9b72630ba5c3a6bb842afe948a778ae7d742e92d94407310976e8c0f2f7f27d tools/convert.py +b5648e901612c6077ed55b93192bf3a152f842736e08b859082af6635e2339f9 tools/run_workflow.py +4363e4224c409a8c8a3099a06e75d9881206fbe4f541d44bde05dc872de2c3dc tools/verify.py +bb21f7473051e1ac368515dd3f2e15cd44d7a11748ee8823e1ddca3e4876b7c9 vae/qwen_image_2.1_vae_bf16.safetensors 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workflows/04_2K_Typography.json +13a543282fae430f5a37b70688741300d022eb9a7adea02396898055c5d273de workflows/api/01_Text_to_Image.api.json +68224d0a803e823f78cf82dc636a6881b75671348b8cdd300f7cf8ac7493c241 workflows/api/02_Image_Editing.api.json +cc742cfda9d55f9ecd5dbd2a6aeb87a8726d8c152f66c0f4b54b629a9ad4e3e2 workflows/api/03_Transparent_RGBA.api.json +ab18f014220866315d52dbb5ff7e5a6f42bd95d6b70219a8c828266b468efff5 workflows/api/04_2K_Typography.api.json diff --git a/VALIDATION.md b/VALIDATION.md new file mode 100644 index 0000000000000000000000000000000000000000..9967597771c7162ddb6ec8089f9193a83c92176b --- /dev/null +++ b/VALIDATION.md @@ -0,0 +1,125 @@ +# Validation — RTX 5090, 20 September 2026 + +## What passed + +- Both native safetensors checkpoints loaded through standard ComfyUI loaders. +- 48 main renders: 12 prompts × two seeds × BF16/NVFP4, all at 1024×1024. +- Eight extended renders: product edit, portrait edit, transparent RGBA and 2K + typography, each with BF16 and NVFP4. +- Six held-out renders: Chinese lettering, 1536×864 landscape and two-reference + image editing, each with BF16 and NVFP4. These prompts were not used to choose + the conversion recipe. +- Four shipped workflows imported and executed through the actual frontend. +- Three subjects tested across all four combinations of BF16/NVFP4 transformer + and encoder weights, using the original stock conditioning path. +- Real SM120 block-scaled FP4 CUDA kernels observed in both language conditioning + and image denoising with the accelerated encoder patch. +- All 571 protected tensors checked for exact equality against the source files. + All 444 quantized matrices checked for packed shape, marker, scale dtype and + finite scales. Source and release file hashes recorded. +- Hub-uploaded weights downloaded into a separate directory and matched against + the release SHA-256 hashes. The VAE hash matches the official original. +- All four API workflows also passed using the Hub-downloaded files on the + unpatched base runtime, with custom nodes disabled. Thus stock compatibility + and optional accelerated conditioning were tested separately. + +No calibration, optimization against the test prompts, or fine-tuning was used. +The quantization recipe remained unchanged after its first conversion. + +## Measured performance + +1024×1024 EMBER product prompt from the shipped generation workflow, seed +20260922, 40 steps, Euler/simple, CFG 1, denoise 1. One first-after-unload run, +then three warm runs per variant. **Every node executed on every repeat** using +`--cache-none`; the histories were checked for an empty cached-node list. +Elapsed time comes from server execution-start/success timestamps, not polling +latency. Includes conditioning, denoising, VAE decode and PNG save. + +| Weights / encoder mode | Warm median | Warm range | First after unload | Largest sampled GPU use, warm | +|---|---:|---:|---:|---:| +| Official BF16 / stock FP32 conditioning | 15.629 s | 15.553–15.650 s | 18.932 s | 31,677 MiB | +| Official INT8 ConvRot / stock conditioning | 7.553 s | 7.496–7.591 s | 9.973 s | 18,433 MiB | +| Prism NVFP4 / patched BF16 + NVFP4 conditioning | **6.746 s** | **6.706–6.749 s** | **8.083 s** | **13,249 MiB** | + +This NVFP4 workflow delivered **2.32× the BF16 throughput** and took **10.7% less +time than INT8** in this one controlled test. These are local measurements, not +general speed guarantees. First-after-unload is not a cold filesystem-cache or +fresh-boot test. The quality-suite runs used ordinary node caching and are not +the basis of this performance table. + +GPU memory was sampled through `nvidia-smi` every 100 ms. Values are **whole-GPU +usage**, including Windows and other idle desktop services, not PyTorch peak +allocated memory or a minimum VRAM requirement. The largest sampled NVFP4 warm +value is about 12.94 GiB. No other model inference job was running during this +benchmark. Hardware other than the RTX 5090 was not benchmarked. + +Raw measurements: [benchmark.json](validation/benchmark.json). + +## Quality observations and limits + +The main set covers portraits, landscapes, glass products, typography, object +counts and spatial relationships, illustration, fashion, night scenes, +architecture, macro, astronaut portraits and botanical posters. All 24 matched +pairs are published in [COMPARISONS.md](COMPARISONS.md). + +Visual review found coherent images and preserved useful text, material and +editing capabilities. It also found real differences: changed portrait details, +object placement, typography layout and small material/color details. In one +macro pair the quantized spider is less green. Small astronaut-label spelling +was imperfect in the BF16 baseline too. The Chinese probe retained the requested +wording, but NVFP4 moved the subtitle above the cup rather than below it. + +The two-reference test combined the reference woman with the EMBER bottle and +removed the violin, while changing hand pose. The product edit preserved the +label and scene while changing glass, metal and props. Portrait recoloring can +also change sleeve coverage. This is semantic reference preservation, not a +pixel-locked edit. + +The transparent example has genuine RGBA output: alpha minimum 0, maximum 255, +49.3% of pixels below alpha 16 and 49.7% above alpha 240. See the original +[RGBA PNG](assets/03_Transparent_RGBA.png) and the +[checkerboard composite](assets/transparency-checker.jpg). RGB-only viewers may +show the purple RGB values stored underneath transparent pixels. + +This is a small local evaluation, not a blinded preference study or a benchmark +proving no quality loss. There was no LoRA, ControlNet, training or video test. +Long text, exhaustive multilingual coverage and every reference-image count were +not tested. Stock encoder arithmetic and accelerated encoder arithmetic can +produce different images even with identical weights and seeds. + +## Runtime and kernel evidence + +- Windows, Python 3.13.12, RTX 5090 32 GB. +- ComfyUI 0.36.0, base commit `99073836d45f66053c45ba8564984e6def9cebba`. +- Included Qwen-specific encoder patch; local validation commit `2d324220`. +- PyTorch `2.14.0+cu130`; comfy-kitchen `0.2.35`; comfy-aimdo `0.5.5`. +- Frontend package `1.53.6`; safetensors `0.8.0`. +- Comfy Kitchen attention, normal VRAM mode, async offload, batch size one. +- All tests ran with `--disable-all-custom-nodes`. + +The profiler captured kernels beginning +`cutlass3x_sm120_bstensorop_s16864gemm_block_scaled_ue4m3xe2m1_ue4m3xe2m1` +for short conditioning sequences and image-denoising sequences. This confirms +hardware FP4 execution, rather than relying on checkpoint names or successful +loading. Raw observations: [kernel_audit.jsonl](validation/kernel_audit.jsonl). +No NVFP4 matmul-fallback warning occurred in the successful audited runs. + +The first acceleration probe exposed FP32 activations entering a BF16/FP16-only +quantizer. The included patch fixes that at the Qwen Image 2.1 encoding boundary: +vision preprocessing stays unchanged, the resulting multimodal embeddings become +BF16 only for NVFP4 conditioning on supported hardware, and existing ComfyUI +matmul dispatch is used. BF16 and INT8 checkpoints retain their original path. + +## Precision and provenance + +The DiT converts 192 matrices and retains its other 73 tensors. The encoder +converts 252 matrices and retains its other 498 tensors, including the complete +vision tower, embeddings and output head. Per-matrix relative reconstruction +RMSE is recorded in the manifests; it is not an image-quality score. + +Source revision: `Comfy-Org/Qwen-Image-2.1@ace0edeb3791a594ddfa36ed5f41a178a394e921`. +The image transformer source SHA-256 is +`89f4158d066cc33906a199fca85634f766892dd78f49b6698dabf187ac86c4bc`. +The encoder source SHA-256 is +`68bdc82bc1b66851162ae656225e7e2068166b603db19bd5d5a3b90eb12669a9`. +Full tensor manifests sit beside each checkpoint; `SHA256SUMS` covers the release. diff --git a/assets/01_Text_to_Image.png b/assets/01_Text_to_Image.png new file mode 100644 index 0000000000000000000000000000000000000000..38f0638d331e0c0117a44e2dc4a9fd4648bc7eaf --- /dev/null +++ b/assets/01_Text_to_Image.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dbfd3a6bf37d800ab2d5dfbb977fcc6e1b93f4c57ec41ff423598b93a6536ccf +size 1186885 diff --git a/assets/02_Image_Editing.png b/assets/02_Image_Editing.png new file mode 100644 index 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All lettering sharp and clearly legible.", + "bf": "typography_20260920_bf-bf.webp", + "nv": "typography_20260920_nv-nv.webp" + }, + { + "name": "typography", + "seed": 20260921, + "prompt": "Design a polished square travel poster with a restrained mid-century screen-print illustration of a red tram crossing a stone bridge over a blue river at sunset. Large cream headline at the top reads exactly \"TAKE THE SCENIC ROUTE\". A smaller line at the bottom reads exactly \"SLOW TRAVEL CLUB\". Strong typographic hierarchy, generous margins, flat navy, coral and cream inks, subtle paper grain. All lettering sharp and clearly legible.", + "bf": "typography_20260921_bf-bf.webp", + "nv": "typography_20260921_nv-nv.webp" + }, + { + "name": "spatial", + "seed": 20260920, + "prompt": "A clean studio photograph on a light gray tabletop showing exactly three objects arranged in a straight horizontal row: a red ceramic cube on the left, a blue glass sphere in the center, and a yellow wooden cone on the right. The sphere is slightly larger than the cube, and the cone is tallest. A single soft light from the upper left casts shadows toward the lower right. Neutral pale gray background, eye-level camera, every object fully visible and separated by equal gaps, no other objects, no text.", + "bf": "spatial_20260920_bf-bf.webp", + "nv": "spatial_20260920_nv-nv.webp" + }, + { + "name": "spatial", + "seed": 20260921, + "prompt": "A clean studio photograph on a light gray tabletop showing exactly three objects arranged in a straight horizontal row: a red ceramic cube on the left, a blue glass sphere in the center, and a yellow wooden cone on the right. The sphere is slightly larger than the cube, and the cone is tallest. A single soft light from the upper left casts shadows toward the lower right. Neutral pale gray background, eye-level camera, every object fully visible and separated by equal gaps, no other objects, no text.", + "bf": "spatial_20260921_bf-bf.webp", + "nv": "spatial_20260921_nv-nv.webp" + }, + { + "name": "illustration", + "seed": 20260920, + "prompt": "A richly detailed storybook gouache illustration of a small fox librarian wearing round spectacles and a moss-green waistcoat, standing on a rolling wooden ladder in a towering circular library inside an ancient tree. The fox reaches toward a glowing blue book with one paw. Curving shelves, warm amber lamps, tiny floating dust motes, a rainy moonlit window, hand-painted brush texture, charming expressive face, coherent architecture, dramatic but cozy composition, no lettering.", + "bf": "illustration_20260920_bf-bf.webp", + "nv": "illustration_20260920_nv-nv.webp" + }, + { + "name": "illustration", + "seed": 20260921, + "prompt": "A richly detailed storybook gouache illustration of a small fox librarian wearing round spectacles and a moss-green waistcoat, standing on a rolling wooden ladder in a towering circular library inside an ancient tree. The fox reaches toward a glowing blue book with one paw. Curving shelves, warm amber lamps, tiny floating dust motes, a rainy moonlit window, hand-painted brush texture, charming expressive face, coherent architecture, dramatic but cozy composition, no lettering.", + "bf": "illustration_20260921_bf-bf.webp", + "nv": "illustration_20260921_nv-nv.webp" + }, + { + "name": "fashion", + "seed": 20260920, + "prompt": "A candid editorial photograph of an adult woman with short dark curly hair and freckles, wearing a cobalt-blue tailored wool coat over a cream turtleneck, standing outside a small Parisian cafe on a rainy evening. She glances back over her shoulder with a slight natural smile, holding a folded red umbrella by its wooden handle. Fine raindrops on the coat, realistic skin pores, wet pavement reflections, warm cafe windows against cool blue dusk, waist-up framing, 85mm lens, subtle film grain, believable unretouched photography.", + "bf": "fashion_20260920_bf-bf.webp", + "nv": "fashion_20260920_nv-nv.webp" + }, + { + "name": "fashion", + "seed": 20260921, + "prompt": "A candid editorial photograph of an adult woman with short dark curly hair and freckles, wearing a cobalt-blue tailored wool coat over a cream turtleneck, standing outside a small Parisian cafe on a rainy evening. She glances back over her shoulder with a slight natural smile, holding a folded red umbrella by its wooden handle. Fine raindrops on the coat, realistic skin pores, wet pavement reflections, warm cafe windows against cool blue dusk, waist-up framing, 85mm lens, subtle film grain, believable unretouched photography.", + "bf": "fashion_20260921_bf-bf.webp", + "nv": "fashion_20260921_nv-nv.webp" + }, + { + "name": "night_market", + "seed": 20260920, + "prompt": "A cinematic street photograph of a crowded night market in Taipei during gentle rain. In the foreground an elderly cook in a white apron lifts a steaming bamboo basket from a street-food stall, with hands clearly visible. A small illuminated sign above him reads exactly \"NIGHT BITES\". Rich red lanterns and teal shop lights reflect in wet asphalt; layered pedestrians with umbrellas recede into the scene. Realistic steam, appetizing food texture, natural human proportions, 35mm lens, documentary photography, atmospheric but restrained color.", + "bf": "night_market_20260920_bf-bf.webp", + "nv": "night_market_20260920_nv-nv.webp" + }, + { + "name": "night_market", + "seed": 20260921, + "prompt": "A cinematic street photograph of a crowded night market in Taipei during gentle rain. In the foreground an elderly cook in a white apron lifts a steaming bamboo basket from a street-food stall, with hands clearly visible. A small illuminated sign above him reads exactly \"NIGHT BITES\". Rich red lanterns and teal shop lights reflect in wet asphalt; layered pedestrians with umbrellas recede into the scene. Realistic steam, appetizing food texture, natural human proportions, 35mm lens, documentary photography, atmospheric but restrained color.", + "bf": "night_market_20260921_bf-bf.webp", + "nv": "night_market_20260921_nv-nv.webp" + }, + { + "name": "architecture", + "seed": 20260920, + "prompt": "Architectural interior photograph of a sunken conversation pit in a restored 1970s coastal house. Rust-orange corduroy sofas surround a low circular walnut table holding a clear glass vase with three white tulips. Floor-to-ceiling windows reveal a stormy ocean. Rain beads on the windows; a suspended black fireplace glows softly at the left. Pale travertine floor, detailed walnut ceiling, soft overcast daylight, balanced wide-angle composition, realistic materials and straight architectural lines, no people or lettering.", + "bf": "architecture_20260920_bf-bf.webp", + "nv": "architecture_20260920_nv-nv.webp" + }, + { + "name": "architecture", + "seed": 20260921, + "prompt": "Architectural interior photograph of a sunken conversation pit in a restored 1970s coastal house. Rust-orange corduroy sofas surround a low circular walnut table holding a clear glass vase with three white tulips. Floor-to-ceiling windows reveal a stormy ocean. Rain beads on the windows; a suspended black fireplace glows softly at the left. Pale travertine floor, detailed walnut ceiling, soft overcast daylight, balanced wide-angle composition, realistic materials and straight architectural lines, no people or lettering.", + "bf": "architecture_20260921_bf-bf.webp", + "nv": "architecture_20260921_nv-nv.webp" + }, + { + "name": "macro", + "seed": 20260920, + "prompt": "Extreme macro nature photograph of a tiny emerald jumping spider perched on the curled edge of a copper-colored autumn leaf. A single spherical dew drop beside the spider reflects a miniature garden. Fine individual hairs on the spider, realistic eight-legged anatomy, sharp jewel-like eyes, translucent dew, intricate leaf veins. Soft warm backlight, muted olive background with circular bokeh, narrow but carefully placed focus plane, natural colors, scientifically plausible wildlife photography, no lettering.", + "bf": "macro_20260920_bf-bf.webp", + "nv": "macro_20260920_nv-nv.webp" + }, + { + "name": "macro", + "seed": 20260921, + "prompt": "Extreme macro nature photograph of a tiny emerald jumping spider perched on the curled edge of a copper-colored autumn leaf. A single spherical dew drop beside the spider reflects a miniature garden. Fine individual hairs on the spider, realistic eight-legged anatomy, sharp jewel-like eyes, translucent dew, intricate leaf veins. Soft warm backlight, muted olive background with circular bokeh, narrow but carefully placed focus plane, natural colors, scientifically plausible wildlife photography, no lettering.", + "bf": "macro_20260921_bf-bf.webp", + "nv": "macro_20260921_nv-nv.webp" + }, + { + "name": "astronaut", + "seed": 20260920, + "prompt": "A cinematic close-up photograph of an adult female astronaut inside a weathered lunar habitat, looking directly into the camera through a clear helmet visor. Her brown eyes, small freckles and loose strands of hair remain clearly visible behind subtle reflections. White spacesuit with stitched fabric, orange fittings and a small rectangular chest patch reading \"LUNA 09\". Warm practical light on one cheek and cool Earthlight on the other, Earth visible through a round window in the background, restrained analog science-fiction realism, finely detailed materials, no illustration style.", + "bf": "astronaut_20260920_bf-bf.webp", + "nv": "astronaut_20260920_nv-nv.webp" + }, + { + "name": "astronaut", + "seed": 20260921, + "prompt": "A cinematic close-up photograph of an adult female astronaut inside a weathered lunar habitat, looking directly into the camera through a clear helmet visor. Her brown eyes, small freckles and loose strands of hair remain clearly visible behind subtle reflections. White spacesuit with stitched fabric, orange fittings and a small rectangular chest patch reading \"LUNA 09\". Warm practical light on one cheek and cool Earthlight on the other, Earth visible through a round window in the background, restrained analog science-fiction realism, finely detailed materials, no illustration style.", + "bf": "astronaut_20260921_bf-bf.webp", + "nv": "astronaut_20260921_nv-nv.webp" + }, + { + "name": "botanical_poster", + "seed": 20260920, + "prompt": "An elegant contemporary botanical exhibition poster on textured ivory paper. A lifelike ink-and-watercolor illustration of one flowering magnolia branch curves diagonally across the center, pale pink petals with delicate translucent edges and deep green leaves. At the top, a large refined serif headline reads exactly \"THE QUIET GARDEN\". Beneath it, smaller text reads exactly \"BOTANICAL STUDIES\". At the bottom, a small centered line reads exactly \"APRIL 12 - MAY 30\". Generous negative space, exceptional typography, restrained burgundy and sage palette, museum-quality printed design.", + "bf": "botanical_poster_20260920_bf-bf.webp", + "nv": "botanical_poster_20260920_nv-nv.webp" + }, + { + "name": "botanical_poster", + "seed": 20260921, + "prompt": "An elegant contemporary botanical exhibition poster on textured ivory paper. A lifelike ink-and-watercolor illustration of one flowering magnolia branch curves diagonally across the center, pale pink petals with delicate translucent edges and deep green leaves. At the top, a large refined serif headline reads exactly \"THE QUIET GARDEN\". Beneath it, smaller text reads exactly \"BOTANICAL STUDIES\". At the bottom, a small centered line reads exactly \"APRIL 12 - MAY 30\". Generous negative space, exceptional typography, restrained burgundy and sage palette, museum-quality printed design.", + "bf": "botanical_poster_20260921_bf-bf.webp", + "nv": "botanical_poster_20260921_nv-nv.webp" + } +] \ No newline at end of file diff --git a/assets/edit_portrait_bf-bf_extended_00001_.png b/assets/edit_portrait_bf-bf_extended_00001_.png new file mode 100644 index 0000000000000000000000000000000000000000..60f9b7964afb38f91e8fd0082468d3e114328795 --- /dev/null +++ b/assets/edit_portrait_bf-bf_extended_00001_.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:883d3436df3c78142c7590f4b1c73518308498a98bb3b1343cf0ef2c80b85ad2 +size 1875427 diff --git a/assets/edit_portrait_nv-nv_extended_00001_.png b/assets/edit_portrait_nv-nv_extended_00001_.png new file mode 100644 index 0000000000000000000000000000000000000000..3d53604db34b01b453cedf8a7a975d5f3902459f --- /dev/null +++ b/assets/edit_portrait_nv-nv_extended_00001_.png @@ -0,0 +1,3 @@ +version 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-0,0 +1,2 @@ +Prism Image 2.1 | NVFP4 comparisons
BennyDaBall / RTX 5090 / Built with Qwen

Two weights.
One prompt.

BF16 source weights versus Prism's mixed NVFP4 image transformer and accelerated language encoder. Same prompt, seed, size, 40-step Euler/simple sampler, CFG 1. Quantization can change composition and detail. Every pair is included, not just the best-looking ones.

BF16 weightsPrism NVFP4

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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/runtime/README.md b/runtime/README.md new file mode 100644 index 0000000000000000000000000000000000000000..18dd6816305e7c8fc6adaf5b8f9d767a43b2c03a --- /dev/null +++ b/runtime/README.md @@ -0,0 +1,36 @@ +# Native NVFP4 text conditioning + +The checkpoints load with existing ComfyUI nodes. On the tested ComfyUI revision, +normal text conditioning runs in FP32 and dequantizes the encoder weights. The +image transformer already uses native NVFP4 compute. + +`qwen21-nvfp4-conditioning.patch` enables native NVFP4 matrix multiplication for +Qwen Image 2.1 conditioning. It detects NVFP4 checkpoint metadata, converts the +completed multimodal embeddings to BF16 on supported GPUs, and uses ComfyUI's +existing quantized-matmul context. The context restores the previous operation +settings after encoding. Vision preprocessing and the unnormalized final hidden +state contract are preserved. BF16 and INT8 checkpoints keep the original path. + +Tested base: `99073836d45f66053c45ba8564984e6def9cebba`. +No custom nodes, monkey-patching node pack, or additional runtime dependencies. + +Stop ComfyUI. From its Git checkout, apply the supplied patch: + +```powershell +git apply --check C:/path/to/Prism-Image-2.1-NVFP4/runtime/qwen21-nvfp4-conditioning.patch +git apply C:/path/to/Prism-Image-2.1-NVFP4/runtime/qwen21-nvfp4-conditioning.patch +``` + +Restart ComfyUI. If the check fails, inspect the installed version and local +changes; do not force the patch onto a different implementation. An upstream +version may already provide equivalent support. To undo this exact patch, stop +ComfyUI and use `git apply -R` with the same patch file. + +The same workflows also run without the patch, using native NVFP4 denoising and +weight-only encoder quantization with FP32 conditioning. That mode is slower for +uncached encoding and can produce a different image. All advertised accelerated +results use the patch. + +Validation used an RTX 5090, CUDA 13.0 PyTorch, and comfy-kitchen 0.2.35. +Other GPUs and CPU execution were not benchmarked. This is an independent patch, +not an upstream ComfyUI release. The patch is subject to ComfyUI's GPL-3.0 license. diff --git a/runtime/qwen21-nvfp4-conditioning.patch b/runtime/qwen21-nvfp4-conditioning.patch new file mode 100644 index 0000000000000000000000000000000000000000..5888f0519b23d0aba37c3d40e6ea7fbfafa29c99 --- /dev/null +++ b/runtime/qwen21-nvfp4-conditioning.patch @@ -0,0 +1,41 @@ +diff --git a/comfy/text_encoders/qwen_image21.py b/comfy/text_encoders/qwen_image21.py +index de0d147d..8ee21129 100644 +--- a/comfy/text_encoders/qwen_image21.py ++++ b/comfy/text_encoders/qwen_image21.py +@@ -1,7 +1,10 @@ ++import json + import numbers + + import torch + ++import comfy.ops ++import comfy.model_management + import comfy.text_encoders.qwen3vl + from comfy import sd1_clip + +@@ -33,9 +36,25 @@ class QwenImage21Qwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel): + # last layer without the final RMSNorm: transformers 4.57 hidden_states[-1], which Qwen's results are tuned to (5.x norms it) + self.layer_norm_hidden_state = False + self.image_spans = [] ++ self.nvfp4_conditioning = False ++ ++ def load_sd(self, sd): ++ self.nvfp4_conditioning = any(json.loads(v.numpy().tobytes()).get("format") == "nvfp4" for k, v in sd.items() if k.endswith(".comfy_quant")) ++ return super().load_sd(sd) ++ ++ def forward(self, tokens): ++ device = self.execution_device ++ if device is None: ++ device = self.transformer.get_input_embeddings().weight.device ++ if not self.nvfp4_conditioning or not comfy.model_management.supports_nvfp4_compute(device): ++ return super().forward(tokens) ++ with comfy.ops.use_quantized_matmul(self.transformer, device): ++ return super().forward(tokens) + + def process_tokens(self, tokens, device): + embeds, attention_mask, num_tokens, embeds_info = super().process_tokens(tokens, device) ++ if self.nvfp4_conditioning and comfy.model_management.supports_nvfp4_compute(device): ++ embeds = embeds.to(dtype=torch.bfloat16) + self.image_spans = [(e["index"], e["size"]) for e in embeds_info if e["type"] == "image"] + return embeds, attention_mask, num_tokens, embeds_info + diff --git a/tools/convert.py b/tools/convert.py new file mode 100644 index 0000000000000000000000000000000000000000..a5171f16d31676ca94bb1baddd3c247d956b8b09 --- /dev/null +++ b/tools/convert.py @@ -0,0 +1,77 @@ +"""Convert official BF16 checkpoints to ComfyUI's native mixed NVFP4 format.""" +import argparse +import hashlib +import json +from pathlib import Path +import re +import sys +import time + +import torch +from safetensors import safe_open +from safetensors.torch import save_file + + +def digest(path): + with open(path, "rb") as stream: + return hashlib.file_digest(stream, "sha256").hexdigest() + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("source", type=Path) + parser.add_argument("output", type=Path) + parser.add_argument("--component", choices=["dit", "encoder"], required=True) + parser.add_argument("--comfy-root", type=Path, required=True) + args = parser.parse_args() + sys.path.insert(0, str(args.comfy_root.resolve())) + # ComfyUI parses process arguments when its operation registry is imported. + sys.argv = [sys.argv[0]] + from comfy.quant_ops import TensorCoreNVFP4Layout + + patterns = { + "dit": r"transformer_blocks\.\d+\.(attn\.(to_q|to_k|to_v|to_out\.0)|img_mlp\.(gate_up|out))\.weight", + "encoder": r"model\.layers\.\d+\.(self_attn\.(q_proj|k_proj|v_proj|o_proj)|mlp\.(gate_proj|up_proj|down_proj))\.weight", + } + output, records = {}, [] + start = time.perf_counter() + with safe_open(args.source, framework="pt", device="cpu") as source: + metadata = dict(source.metadata() or {}) + for key in source.keys(): + tensor = source.get_tensor(key) + record = {"name": key, "shape": list(tensor.shape), "source_dtype": str(tensor.dtype)} + if re.fullmatch(patterns[args.component], key): + if tensor.shape[1] % 32 or tensor.shape[0] % 8: + raise ValueError(f"Unsupported NVFP4 GEMM alignment: {key} {tensor.shape}") + original = tensor.to("cuda") + scale = original.float().abs().amax().clamp_min(1e-12) / (448 * 6) + packed, params = TensorCoreNVFP4Layout.quantize(original, scale=scale) + reconstructed = TensorCoreNVFP4Layout.dequantize(packed, params) + error = (original.float() - reconstructed.float()).square().mean() + record.update(storage="nvfp4", relative_rmse=float((error / original.float().square().mean().clamp_min(1e-30)).sqrt())) + for suffix, value in TensorCoreNVFP4Layout.state_dict_tensors(packed, params).items(): + output[key + suffix] = value.cpu().contiguous() + marker = json.dumps({"format": "nvfp4"}, separators=(",", ":")).encode() + output[key.removesuffix("weight") + "comfy_quant"] = torch.tensor(list(marker), dtype=torch.uint8) + del original, reconstructed, packed, params + print(f"NVFP4 {key} relative_rmse={record['relative_rmse']:.5f}", flush=True) + else: + output[key] = tensor.clone() + record.update(storage=str(tensor.dtype), sha256=hashlib.sha256(tensor.view(torch.uint8).numpy().tobytes()).hexdigest()) + records.append(record) + count = sum(r["storage"] == "nvfp4" for r in records) + expected = 192 if args.component == "dit" else 252 + if count != expected: + raise ValueError(f"Recipe matched {count} matrices, expected {expected}") + metadata.update(format="pt", conversion="Prism native NVFP4; protected tensors unchanged; Built with Qwen") + args.output.parent.mkdir(parents=True, exist_ok=True) + save_file(output, str(args.output), metadata=metadata) + report = {"source": args.source.name, "source_sha256": digest(args.source), "output": args.output.name, + "output_sha256": digest(args.output), "output_bytes": args.output.stat().st_size, + "nvfp4_matrices": count, "elapsed_seconds": time.perf_counter() - start, "tensors": records} + args.output.with_suffix(".manifest.json").write_text(json.dumps(report, indent=2), encoding="utf-8") + print(json.dumps({k: v for k, v in report.items() if k != "tensors"}), flush=True) + + +if __name__ == "__main__": + main() diff --git a/tools/run_workflow.py b/tools/run_workflow.py new file mode 100644 index 0000000000000000000000000000000000000000..e7ffac51eb33f9810afb8d24286dda06be998c17 --- /dev/null +++ b/tools/run_workflow.py @@ -0,0 +1,26 @@ +"""Run an included API workflow against a local ComfyUI server.""" +import argparse +import json +from pathlib import Path +import time +from urllib.request import Request, urlopen + +parser = argparse.ArgumentParser() +parser.add_argument('workflow', type=Path) +parser.add_argument('--server', default='http://127.0.0.1:8188') +args = parser.parse_args() +graph = json.loads(args.workflow.read_text(encoding='utf-8')) +request = Request(args.server + '/prompt', data=json.dumps({'prompt':graph}).encode(), headers={'Content-Type':'application/json'}) +with urlopen(request, timeout=30) as response: + prompt_id = json.load(response)['prompt_id'] +print('Queued:',prompt_id,flush=True) +while True: + with urlopen(args.server + '/history/' + prompt_id, timeout=30) as response: + history = json.load(response).get(prompt_id) + if history: + print(json.dumps(history['status'],indent=2)) + print(json.dumps(history['outputs'],indent=2)) + if history['status']['status_str'] != 'success': + raise SystemExit(1) + break + time.sleep(0.5) diff --git a/tools/verify.py b/tools/verify.py new file mode 100644 index 0000000000000000000000000000000000000000..79ca03f0682f3b4a00cda0698dab548b55f6ab4b --- /dev/null +++ b/tools/verify.py @@ -0,0 +1,36 @@ +"""Verify saved native NVFP4 shapes, metadata, scales, and protected tensors.""" +import argparse +import hashlib +import json +from pathlib import Path +import torch +from safetensors import safe_open + +parser = argparse.ArgumentParser() +parser.add_argument('source', type=Path) +parser.add_argument('converted', type=Path) +args = parser.parse_args() +manifest = json.loads(args.converted.with_suffix('.manifest.json').read_text()) +with args.converted.open('rb') as stream: + assert hashlib.file_digest(stream, 'sha256').hexdigest() == manifest['output_sha256'] +with safe_open(args.source, framework='pt') as original, safe_open(args.converted, framework='pt') as result: + expected_keys = set(original.keys()) + for record in manifest['tensors']: + key = record['name'] + value = result.get_tensor(key) + if record['storage'] == 'nvfp4': + marker_key = key.removesuffix('weight') + 'comfy_quant' + assert json.loads(result.get_tensor(marker_key).numpy().tobytes()) == {'format':'nvfp4'} + assert value.dtype == torch.uint8 + assert list(value.shape) == [record['shape'][0], record['shape'][1] // 2] + block = result.get_tensor(key + '_scale') + scale = result.get_tensor(key + '_scale_2') + assert block.dtype == torch.float8_e4m3fn and scale.dtype == torch.float32 + assert bool(torch.isfinite(block.float()).all()) and bool(torch.isfinite(scale).all()) + assert bool((scale > 0).all()) + expected_keys.update([key + '_scale', key + '_scale_2', marker_key]) + else: + source = original.get_tensor(key) + assert source.dtype == value.dtype and torch.equal(source, value), key + assert set(result.keys()) == expected_keys +print(json.dumps({'file':args.converted.name, 'verified_tensors':len(manifest['tensors']), 'nvfp4_matrices':manifest['nvfp4_matrices'], 'protected_exact':True, 'sha256':manifest['output_sha256']})) diff --git a/validation/alpha.json b/validation/alpha.json new file mode 100644 index 0000000000000000000000000000000000000000..7d50858baf2714e52a3d8ee481bb963fe7e9b49d --- /dev/null +++ b/validation/alpha.json @@ -0,0 +1,11 @@ +{ + "mode": "RGBA", + "size": [ + 1024, + 1024 + ], + "alpha_min": 0, + "alpha_max": 255, + "fraction_alpha_under_16": 0.49314022064208984, + "fraction_alpha_over_240": 0.49694347381591797 +} \ No newline at end of file diff --git a/validation/benchmark.json b/validation/benchmark.json new file mode 100644 index 0000000000000000000000000000000000000000..06e61cb935c8f0630ecedb34682fa2db9d859ba2 --- /dev/null +++ b/validation/benchmark.json @@ -0,0 +1,98 @@ +[ + { + "variant": "bf16", + "repeat": 0, + "kind": "first_after_unload", + "server_seconds": 18.932, + "peak_gpu_used_mib_sampled_100ms": 31566.0, + "prompt_id": "3ef713e0-ca6c-48a9-8c15-c788aec7465d" + }, + { + "variant": "bf16", + "repeat": 1, + "kind": "warm_uncached", + "server_seconds": 15.65, + "peak_gpu_used_mib_sampled_100ms": 31671.0, + "prompt_id": "824f54e3-3f72-4464-b63d-ea875d60cb03" + }, + { + "variant": "bf16", + "repeat": 2, + "kind": "warm_uncached", + "server_seconds": 15.553, + "peak_gpu_used_mib_sampled_100ms": 31669.0, + "prompt_id": "24022bdf-db7d-45f7-8b9b-12c5afa2e5da" + }, + { + "variant": "bf16", + "repeat": 3, + "kind": "warm_uncached", + "server_seconds": 15.629, + "peak_gpu_used_mib_sampled_100ms": 31677.0, + "prompt_id": "7928a85f-29b9-4a09-a11a-82047c634848" + }, + { + "variant": "int8", + "repeat": 0, + "kind": "first_after_unload", + "server_seconds": 9.973, + "peak_gpu_used_mib_sampled_100ms": 18436.0, + "prompt_id": "fa61d057-1cb9-4b2b-b964-c706926102d6" + }, + { + "variant": "int8", + "repeat": 1, + "kind": "warm_uncached", + "server_seconds": 7.591, + "peak_gpu_used_mib_sampled_100ms": 18433.0, + "prompt_id": "f15f1028-f516-481e-aa86-42d8139f8c1c" + }, + { + "variant": "int8", + "repeat": 2, + "kind": "warm_uncached", + "server_seconds": 7.496, + "peak_gpu_used_mib_sampled_100ms": 17857.0, + "prompt_id": "0c85ba2d-95f8-407a-b6a9-e9df634307dd" + }, + { + "variant": "int8", + "repeat": 3, + "kind": "warm_uncached", + "server_seconds": 7.553, + "peak_gpu_used_mib_sampled_100ms": 18433.0, + "prompt_id": "b6ef131b-9109-4c63-8647-289adbe47bbc" + }, + { + "variant": "nvfp4", + "repeat": 0, + "kind": "first_after_unload", + "server_seconds": 8.083, + "peak_gpu_used_mib_sampled_100ms": 12668.0, + "prompt_id": "3ddc23a3-c125-4fe2-bf8c-59cf7502ec20" + }, + { + "variant": "nvfp4", + "repeat": 1, + "kind": "warm_uncached", + "server_seconds": 6.749, + "peak_gpu_used_mib_sampled_100ms": 12675.0, + "prompt_id": "b152e5c9-bc56-4da2-88f8-456e100a18e2" + }, + { + "variant": "nvfp4", + "repeat": 2, + "kind": "warm_uncached", + "server_seconds": 6.746, + "peak_gpu_used_mib_sampled_100ms": 12678.0, + "prompt_id": "1308e64c-c68d-407e-acf4-92918d6cb668" + }, + { + "variant": "nvfp4", + "repeat": 3, + "kind": "warm_uncached", + "server_seconds": 6.706, + "peak_gpu_used_mib_sampled_100ms": 13249.0, + "prompt_id": "847c74d0-3806-4ecb-bc5f-8a5b28d35c02" + } +] \ No newline at end of file diff --git a/validation/cases.json b/validation/cases.json new file mode 100644 index 0000000000000000000000000000000000000000..3335979dda490f5932e5d518a867816d6c0f3c31 --- /dev/null +++ b/validation/cases.json @@ -0,0 +1,8754 @@ +[ + { + "id": "architecture_bf-bf_suite_20260920", + "case": "architecture", + "variant": "bf-bf", + "graph": { + "1": { + "class_type": "UNETLoader", + "inputs": { + "unet_name": "qwen_image_2.1_bf16.safetensors", + "weight_dtype": "default" + } + }, + "2": { + "class_type": "CLIPLoader", + "inputs": { + "clip_name": "qwen3vl_8b_bf16.safetensors", + "type": "qwen_image", + "device": "default" + } + }, + "3": { + "class_type": "VAELoader", + "inputs": { + "vae_name": "qwen_image_2.1_vae_bf16.safetensors" + } + }, + "4": { + "class_type": "TextEncodeQwenImage21", + "inputs": { + "clip": [ + "2", + 0 + ], + "prompt": "Architectural interior photograph of a sunken conversation pit in a restored 1970s coastal house. Rust-orange corduroy sofas surround a low circular walnut table holding a clear glass vase with three white tulips. Floor-to-ceiling windows reveal a stormy ocean. Rain beads on the windows; a suspended black fireplace glows softly at the left. Pale travertine floor, detailed walnut ceiling, soft overcast daylight, balanced wide-angle composition, realistic materials and straight architectural lines, no people or lettering.", + "negative_prompt": "", + "resolution": 1024 + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "width": 1024, + "height": 1024, + "batch_size": 1 + } + }, + "6": { + "class_type": "KSampler", + "inputs": { + "model": [ + "1", + 0 + ], + "positive": [ + "4", + 0 + ], + "negative": [ + "4", + 1 + ], + "latent_image": [ + "5", + 0 + ], + "seed": 20260920, + "steps": 40, + "cfg": 1.0, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1.0 + } + }, + "7": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "6", + 0 + ], + "vae": [ + "3", + 0 + ] + } + }, + "8": { + "class_type": "SaveImage", + "inputs": { + "images": [ + "7", + 0 + ], + "filename_prefix": "prism_validation/architecture_bf-bf_suite_20260920" + } + } + }, + "status": "success", + "outputs": { + "8": { + "images": [ + { + "filename": "architecture_bf-bf_suite_20260920_00001_.png", + "subfolder": "prism_validation", + "type": "output" + } + ] + } + } + }, + { + "id": "architecture_bf-bf_suite_20260921", + "case": "architecture", + "variant": "bf-bf", + "graph": { + "1": { + "class_type": "UNETLoader", + "inputs": { + "unet_name": "qwen_image_2.1_bf16.safetensors", + "weight_dtype": "default" + } + }, + "2": { + "class_type": "CLIPLoader", + "inputs": { + "clip_name": "qwen3vl_8b_bf16.safetensors", + "type": "qwen_image", + "device": "default" + } + }, + "3": { + "class_type": "VAELoader", + "inputs": { + "vae_name": "qwen_image_2.1_vae_bf16.safetensors" + } + }, + "4": { + "class_type": "TextEncodeQwenImage21", + "inputs": { + "clip": [ + "2", + 0 + ], + "prompt": "Architectural interior photograph of a sunken conversation pit in a restored 1970s coastal house. Rust-orange corduroy sofas surround a low circular walnut table holding a clear glass vase with three white tulips. Floor-to-ceiling windows reveal a stormy ocean. Rain beads on the windows; a suspended black fireplace glows softly at the left. Pale travertine floor, detailed walnut ceiling, soft overcast daylight, balanced wide-angle composition, realistic materials and straight architectural lines, no people or lettering.", + "negative_prompt": "", + "resolution": 1024 + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "width": 1024, + "height": 1024, + "batch_size": 1 + } + }, + "6": { + "class_type": "KSampler", + "inputs": { + "model": [ + "1", + 0 + ], + "positive": [ + "4", + 0 + ], + "negative": [ + "4", + 1 + ], + "latent_image": [ + "5", + 0 + ], + "seed": 20260921, + "steps": 40, + "cfg": 1.0, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1.0 + } + }, + "7": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "6", + 0 + ], + "vae": [ + "3", + 0 + ] + } + }, + "8": { + "class_type": "SaveImage", + "inputs": { + "images": [ + "7", + 0 + ], + "filename_prefix": "prism_validation/architecture_bf-bf_suite_20260921" + } + } + }, + "status": "success", + "outputs": { + "8": { + "images": [ + { + "filename": "architecture_bf-bf_suite_20260921_00001_.png", + "subfolder": "prism_validation", + "type": "output" + } + ] + } + } + }, + { + "id": "architecture_nv-nv_suite_20260920", + "case": "architecture", + "variant": "nv-nv", + "graph": { + "1": { + "class_type": "UNETLoader", + "inputs": { + "unet_name": "prism_image_2.1_nvfp4.safetensors", + "weight_dtype": "default" + } + }, + "2": { + "class_type": "CLIPLoader", + "inputs": { + "clip_name": "prism_qwen3vl_8b_nvfp4.safetensors", + "type": "qwen_image", + "device": "default" + } + }, + "3": { + "class_type": "VAELoader", + "inputs": { + "vae_name": "qwen_image_2.1_vae_bf16.safetensors" + } + }, + "4": { + "class_type": "TextEncodeQwenImage21", + "inputs": { + "clip": [ + "2", + 0 + ], + "prompt": "Architectural interior photograph of a sunken conversation pit in a restored 1970s coastal house. Rust-orange corduroy sofas surround a low circular walnut table holding a clear glass vase with three white tulips. Floor-to-ceiling windows reveal a stormy ocean. Rain beads on the windows; a suspended black fireplace glows softly at the left. Pale travertine floor, detailed walnut ceiling, soft overcast daylight, balanced wide-angle composition, realistic materials and straight architectural lines, no people or lettering.", + "negative_prompt": "", + "resolution": 1024 + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "width": 1024, + "height": 1024, + "batch_size": 1 + } + }, + "6": { + "class_type": "KSampler", + "inputs": { + "model": [ + "1", + 0 + ], + "positive": [ + "4", + 0 + ], + "negative": [ + "4", + 1 + ], + "latent_image": [ + "5", + 0 + ], + "seed": 20260920, + "steps": 40, + "cfg": 1.0, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1.0 + } + }, + "7": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "6", + 0 + ], + "vae": [ + "3", + 0 + ] + } + }, + "8": { + "class_type": "SaveImage", + "inputs": { + "images": [ + "7", + 0 + ], + "filename_prefix": "prism_validation/architecture_nv-nv_suite_20260920" + } + } + }, + "status": "success", + "outputs": { + "8": { + "images": [ + { + "filename": "architecture_nv-nv_suite_20260920_00001_.png", + "subfolder": "prism_validation", + "type": "output" + } + ] + } + } + }, + { + "id": "architecture_nv-nv_suite_20260921", + "case": "architecture", + "variant": "nv-nv", + "graph": { + "1": { + "class_type": "UNETLoader", + "inputs": { + "unet_name": "prism_image_2.1_nvfp4.safetensors", + "weight_dtype": "default" + } + }, + "2": { + "class_type": "CLIPLoader", + "inputs": { + "clip_name": "prism_qwen3vl_8b_nvfp4.safetensors", + "type": "qwen_image", + "device": "default" + } + }, + "3": { + "class_type": "VAELoader", + "inputs": { + "vae_name": "qwen_image_2.1_vae_bf16.safetensors" + } + }, + "4": { + "class_type": "TextEncodeQwenImage21", + "inputs": { + "clip": [ + "2", + 0 + ], + "prompt": "Architectural interior photograph of a sunken conversation pit in a restored 1970s coastal house. 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Her brown eyes, small freckles and loose strands of hair remain clearly visible behind subtle reflections. White spacesuit with stitched fabric, orange fittings and a small rectangular chest patch reading \"LUNA 09\". Warm practical light on one cheek and cool Earthlight on the other, Earth visible through a round window in the background, restrained analog science-fiction realism, finely detailed materials, no illustration style.", + "negative_prompt": "", + "resolution": 1024 + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "width": 1024, + "height": 1024, + "batch_size": 1 + } + }, + "6": { + "class_type": "KSampler", + "inputs": { + "model": [ + "1", + 0 + ], + "positive": [ + "4", + 0 + ], + "negative": [ + "4", + 1 + ], + "latent_image": [ + "5", + 0 + ], + "seed": 20260920, + "steps": 40, + "cfg": 1.0, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1.0 + } + }, + "7": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "6", + 0 + ], + "vae": [ + "3", + 0 + ] + } + }, + "8": { + "class_type": "SaveImage", + "inputs": { + "images": [ + "7", + 0 + ], + "filename_prefix": "prism_validation/astronaut_bf-bf_suite_20260920" + } + } + }, + "status": "success", + "outputs": { + "8": { + "images": [ + { + "filename": "astronaut_bf-bf_suite_20260920_00001_.png", + "subfolder": "prism_validation", + "type": "output" + } + ] + } + } + }, + { + "id": "astronaut_bf-bf_suite_20260921", + "case": "astronaut", + "variant": "bf-bf", + "graph": { + "1": { + "class_type": "UNETLoader", + "inputs": { + "unet_name": "qwen_image_2.1_bf16.safetensors", + "weight_dtype": "default" + } + }, + "2": { + "class_type": "CLIPLoader", + "inputs": { + "clip_name": "qwen3vl_8b_bf16.safetensors", + "type": "qwen_image", + "device": "default" + } + }, + "3": { + "class_type": "VAELoader", + "inputs": { + "vae_name": "qwen_image_2.1_vae_bf16.safetensors" + } + }, + "4": { + "class_type": "TextEncodeQwenImage21", + "inputs": { + "clip": [ + "2", + 0 + ], + "prompt": "A cinematic close-up photograph of an adult female astronaut inside a weathered lunar habitat, looking directly into the camera through a clear helmet visor. Her brown eyes, small freckles and loose strands of hair remain clearly visible behind subtle reflections. White spacesuit with stitched fabric, orange fittings and a small rectangular chest patch reading \"LUNA 09\". Warm practical light on one cheek and cool Earthlight on the other, Earth visible through a round window in the background, restrained analog science-fiction realism, finely detailed materials, no illustration style.", + "negative_prompt": "", + "resolution": 1024 + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "width": 1024, + "height": 1024, + "batch_size": 1 + } + }, + "6": { + "class_type": "KSampler", + "inputs": { + "model": [ + "1", + 0 + ], + "positive": [ + "4", + 0 + ], + "negative": [ + "4", + 1 + ], + "latent_image": [ + "5", + 0 + ], + "seed": 20260921, + "steps": 40, + "cfg": 1.0, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1.0 + } + }, + "7": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "6", + 0 + ], + "vae": [ + "3", + 0 + ] + } + }, + "8": { + "class_type": "SaveImage", + "inputs": { + "images": [ + "7", + 0 + ], + "filename_prefix": "prism_validation/astronaut_bf-bf_suite_20260921" + } + } + }, + "status": "success", + "outputs": { + "8": { + "images": [ + { + "filename": "astronaut_bf-bf_suite_20260921_00001_.png", + "subfolder": "prism_validation", + "type": "output" + } + ] + } + } + }, + { + "id": "astronaut_nv-nv_suite_20260920", + "case": "astronaut", + "variant": "nv-nv", + "graph": { + "1": { + "class_type": "UNETLoader", + "inputs": { + "unet_name": "prism_image_2.1_nvfp4.safetensors", + "weight_dtype": "default" + } + }, + "2": { + "class_type": "CLIPLoader", + "inputs": { + "clip_name": "prism_qwen3vl_8b_nvfp4.safetensors", + "type": "qwen_image", + "device": "default" + } + }, + "3": { + "class_type": "VAELoader", + "inputs": { + "vae_name": "qwen_image_2.1_vae_bf16.safetensors" + } + }, + "4": { + "class_type": "TextEncodeQwenImage21", + "inputs": { + "clip": [ + "2", + 0 + ], + "prompt": "A cinematic close-up photograph of an adult female astronaut inside a weathered lunar habitat, looking directly into the camera through a clear helmet visor. 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Preserve the composition and realistic product photography.", + "negative_prompt": "", + "resolution": 1024, + "images.image_1": [ + "10", + 0 + ], + "vae": [ + "3", + 0 + ] + } + }, + "6": { + "class_type": "KSampler", + "inputs": { + "model": [ + "1", + 0 + ], + "positive": [ + "4", + 0 + ], + "negative": [ + "4", + 1 + ], + "latent_image": [ + "4", + 2 + ], + "seed": 20261010, + "steps": 40, + "cfg": 1.0, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1.0 + } + }, + "7": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "6", + 0 + ], + "vae": [ + "3", + 0 + ] + } + }, + "8": { + "class_type": "SaveImage", + "inputs": { + "images": [ + "7", + 0 + ], + "filename_prefix": "Prism/02_Image_Editing" + } + }, + "10": { + "class_type": "LoadImage", + "inputs": { + "image": "prism_edit_reference.png" + } + } +} \ No newline at end of file diff --git a/workflows/api/03_Transparent_RGBA.api.json b/workflows/api/03_Transparent_RGBA.api.json new file mode 100644 index 0000000000000000000000000000000000000000..6c6c36a64defe31b4228a9cf7250f064b5a2f7d8 --- /dev/null +++ b/workflows/api/03_Transparent_RGBA.api.json @@ -0,0 +1,93 @@ +{ + "1": { + "class_type": "UNETLoader", + "inputs": { + "unet_name": "prism_image_2.1_nvfp4.safetensors", + "weight_dtype": "default" + } + }, + "2": { + "class_type": "CLIPLoader", + "inputs": { + "clip_name": "prism_qwen3vl_8b_nvfp4.safetensors", + "type": "qwen_image", + "device": "default" + } + }, + "3": { + "class_type": "VAELoader", + "inputs": { + "vae_name": "qwen_image_2.1_vae_bf16.safetensors" + } + }, + "4": { + "class_type": "TextEncodeQwenImage21", + "inputs": { + "clip": [ + "2", + 0 + ], + "prompt": "This is an RGBA image with transparency. A charming hand-painted gouache sticker of a small orange fox curled around a glowing blue book, round spectacles, moss-green scarf, expressive eyes, clean complete silhouette, generous empty margin around the subject. 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Large cream headline at the top reads exactly \"TAKE THE SCENIC ROUTE\". A smaller line at the bottom reads exactly \"SLOW TRAVEL CLUB\". Strong typographic hierarchy, generous margins, flat navy, coral and cream inks, subtle paper grain. All lettering sharp and clearly legible.", + "negative_prompt": "", + "resolution": 2048 + } + }, + "5": { + "class_type": "EmptyLatentImage", + "inputs": { + "width": 2048, + "height": 2048, + "batch_size": 1 + } + }, + "6": { + "class_type": "KSampler", + "inputs": { + "model": [ + "1", + 0 + ], + "positive": [ + "4", + 0 + ], + "negative": [ + "4", + 1 + ], + "latent_image": [ + "5", + 0 + ], + "seed": 20260923, + "steps": 40, + "cfg": 1.0, + "sampler_name": "euler", + "scheduler": "simple", + "denoise": 1.0 + } + }, + "7": { + "class_type": "VAEDecode", + "inputs": { + "samples": [ + "6", + 0 + ], + "vae": [ + "3", + 0 + ] + } + }, + "8": { + "class_type": "SaveImage", + "inputs": { + "images": [ + "7", + 0 + ], + "filename_prefix": "Prism/04_2K_Typography" + } + } +} \ No newline at end of file