Attribute Qwen Image 2.1 and update release name and workflows
Browse files- .gitattributes +1 -0
- NOTICE +14 -14
- PROMPTING.md +71 -71
- README.md +11 -9
- SHA256SUMS +49 -49
- VALIDATION.md +125 -125
- assets/ab_architecture_20260920.jpg +2 -2
- assets/ab_architecture_20260921.jpg +2 -2
- assets/ab_astronaut_20260920.jpg +2 -2
- assets/ab_astronaut_20260921.jpg +2 -2
- assets/ab_botanical_poster_20260920.jpg +2 -2
- assets/ab_botanical_poster_20260921.jpg +2 -2
- assets/ab_fashion_20260920.jpg +2 -2
- assets/ab_fashion_20260921.jpg +2 -2
- assets/ab_illustration_20260920.jpg +2 -2
- assets/ab_illustration_20260921.jpg +2 -2
- assets/ab_landscape_20260920.jpg +2 -2
- assets/ab_landscape_20260921.jpg +2 -2
- assets/ab_macro_20260920.jpg +2 -2
- assets/ab_macro_20260921.jpg +2 -2
- assets/ab_night_market_20260920.jpg +2 -2
- assets/ab_night_market_20260921.jpg +2 -2
- assets/ab_portrait_20260920.jpg +2 -2
- assets/ab_portrait_20260921.jpg +2 -2
- assets/ab_product_20260920.jpg +2 -2
- assets/ab_product_20260921.jpg +2 -2
- assets/ab_spatial_20260920.jpg +2 -2
- assets/ab_spatial_20260921.jpg +2 -2
- assets/ab_typography_20260920.jpg +2 -2
- assets/ab_typography_20260921.jpg +2 -2
- assets/editing.jpg +2 -2
- diffusion_models/qwen_image_2.1_nvfp4.manifest.json +2596 -0
- gallery.html +2 -2
- input/qwen_image_2.1_edit_reference.png +3 -0
- runtime/README.md +36 -36
- text_encoders/qwen3vl_8b_nvfp4.manifest.json +0 -0
- tools/convert.py +77 -77
- tools/run_workflow.py +26 -26
- tools/verify.py +36 -36
- validation/cases.json +84 -84
- validation/roundtrip.json +2 -2
- workflows/01_Text_to_Image.json +3 -3
- workflows/02_Image_Editing.json +4 -4
- workflows/03_Transparent_RGBA.json +3 -3
- workflows/04_2K_Typography.json +3 -3
- workflows/api/01_Text_to_Image.api.json +3 -3
- workflows/api/02_Image_Editing.api.json +4 -4
- workflows/api/03_Transparent_RGBA.api.json +3 -3
- workflows/api/04_2K_Typography.api.json +3 -3
.gitattributes
CHANGED
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@@ -119,3 +119,4 @@ assets/typography_20260921_nv-nv.webp filter=lfs diff=lfs merge=lfs -text
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assets/typography_2k_bf-bf_extended_00001_.png filter=lfs diff=lfs merge=lfs -text
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assets/typography_2k_nv-nv_extended_00001_.png filter=lfs diff=lfs merge=lfs -text
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input/prism_edit_reference.png filter=lfs diff=lfs merge=lfs -text
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assets/typography_2k_bf-bf_extended_00001_.png filter=lfs diff=lfs merge=lfs -text
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assets/typography_2k_nv-nv_extended_00001_.png filter=lfs diff=lfs merge=lfs -text
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input/prism_edit_reference.png filter=lfs diff=lfs merge=lfs -text
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input/qwen_image_2.1_edit_reference.png filter=lfs diff=lfs merge=lfs -text
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NOTICE
CHANGED
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@@ -1,14 +1,14 @@
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Built with Qwen
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Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) 2026 Hangzhou Tongyi Laboratory Technology Co., Ltd. All Rights Reserved.
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BennyDaBall modified the official BF16 image transformer and Qwen3-VL encoder
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checkpoints on 2026-09-20 by converting selected linear weights to native NVFP4.
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The per-file manifests identify changed tensors and unchanged protected tensors.
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The Qwen Image 2.1 VAE is redistributed unchanged.
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Source: Comfy-Org/Qwen-Image-2.1
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Revision: ace0edeb3791a594ddfa36ed5f41a178a394e921
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-
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This is an independent conversion, not an official Qwen or Comfy-Org release.
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Model use and redistribution remain subject to the included research license.
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Built with Qwen
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+
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Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) 2026 Hangzhou Tongyi Laboratory Technology Co., Ltd. All Rights Reserved.
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+
|
| 5 |
+
BennyDaBall modified the official BF16 image transformer and Qwen3-VL encoder
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+
checkpoints on 2026-09-20 by converting selected linear weights to native NVFP4.
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| 7 |
+
The per-file manifests identify changed tensors and unchanged protected tensors.
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+
The Qwen Image 2.1 VAE is redistributed unchanged.
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+
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Source: Comfy-Org/Qwen-Image-2.1
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Revision: ace0edeb3791a594ddfa36ed5f41a178a394e921
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+
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+
This is an independent conversion, not an official Qwen or Comfy-Org release.
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Model use and redistribution remain subject to the included research license.
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PROMPTING.md
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# Prompting Prism / Qwen Image 2.1
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Start with a concrete natural-language paragraph: medium, subject, action,
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relationships, composition, lighting, materials, then exact lettering. Keep each
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attribute next to the object it describes. The supplied workflows use 40 steps,
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Euler/simple, CFG 1, denoise 1, and fixed seeds.
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## Text to image
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Describe a scene that can be depicted in one image. For a product photo, specify
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the object, material, label, supporting objects, surface, background and lighting.
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For a portrait, specify the action and framing as well as the person's appearance.
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The included product prompt is a complete starting point.
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-
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Set output width and height in Empty Latent Image. The encoder's resolution field
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controls reference-image resizing, not the independent text-to-image canvas.
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-
Changing image dimensions may change composition even with the same seed.
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-
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-
At CFG 1, the negative conditioning branch does not guide sampling. Put essential
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-
requirements in the positive prompt. Higher CFG is not a validated improvement
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for this package.
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-
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## Lettering
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-
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Quote the exact text and specify its position and hierarchy. Example:
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-
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> Large cream headline at the top reads exactly "TAKE THE SCENIC ROUTE".
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-
> A smaller line at the bottom reads exactly "SLOW TRAVEL CLUB".
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-
> Generous margins, flat navy, coral and cream inks, subtle paper grain.
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-
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-
Proofread the result. Clear headings and short labels worked in the release
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-
examples, but the astronaut test also shows small-label spelling errors in the
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-
BF16 baseline. Neither precision is a guarantee of perfect text.
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-
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-
## Image editing
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-
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Upload the reference in Load Image. Connect it and the matching VAE to
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-
TextEncodeQwenImage21. Use that node's latent output for sampling, so the output
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-
matches the processed first reference's size. The included editing workflow
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-
already has those connections.
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-
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-
Name the changes and the elements to preserve:
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-
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-
> Change the amber glass bottle to deep translucent cobalt-blue glass and the
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| 45 |
-
> brass cap to brushed silver. Replace the orange with lime and rosemary with
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| 46 |
-
> lavender. Keep the bottle shape, cream EMBER label, marble pedestal, camera
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| 47 |
-
> angle, background and lighting.
|
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-
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-
For multiple references, refer explicitly to the first and second images.
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-
Reference preservation is semantic, not pixel-locked: small details, hand poses,
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-
clothing coverage and object placement can change.
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-
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-
## Native transparency
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-
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-
Use an explicit RGBA instruction around the subject description:
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-
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-
> This is an RGBA image with transparency. A charming hand-painted gouache sticker
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-
> of a small orange fox curled around a glowing blue book, round spectacles,
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-
> moss-green scarf, clean complete silhouette and generous empty margin.
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-
> The image has alpha channel and the background is transparent.
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-
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-
Save as PNG. Check the alpha channel or composite over a checkerboard. The hidden
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| 63 |
-
RGB under transparent pixels can be colored; an RGB-only viewer is not an alpha
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-
test. The supplied example has alpha values spanning 0 through 255.
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-
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-
## Comparisons
|
| 67 |
-
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-
Keep the prompt, seed, dimensions, sampler, steps, backend and encoder mode fixed.
|
| 69 |
-
The optional NVFP4 encoder patch changes conditioning arithmetic and may change
|
| 70 |
-
the generated image. Compare stock and accelerated modes as distinct settings.
|
| 71 |
-
The gallery contains the complete 12-prompt, two-seed comparison set.
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|
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| 1 |
+
# Prompting Prism / Qwen Image 2.1
|
| 2 |
+
|
| 3 |
+
Start with a concrete natural-language paragraph: medium, subject, action,
|
| 4 |
+
relationships, composition, lighting, materials, then exact lettering. Keep each
|
| 5 |
+
attribute next to the object it describes. The supplied workflows use 40 steps,
|
| 6 |
+
Euler/simple, CFG 1, denoise 1, and fixed seeds.
|
| 7 |
+
|
| 8 |
+
## Text to image
|
| 9 |
+
|
| 10 |
+
Describe a scene that can be depicted in one image. For a product photo, specify
|
| 11 |
+
the object, material, label, supporting objects, surface, background and lighting.
|
| 12 |
+
For a portrait, specify the action and framing as well as the person's appearance.
|
| 13 |
+
The included product prompt is a complete starting point.
|
| 14 |
+
|
| 15 |
+
Set output width and height in Empty Latent Image. The encoder's resolution field
|
| 16 |
+
controls reference-image resizing, not the independent text-to-image canvas.
|
| 17 |
+
Changing image dimensions may change composition even with the same seed.
|
| 18 |
+
|
| 19 |
+
At CFG 1, the negative conditioning branch does not guide sampling. Put essential
|
| 20 |
+
requirements in the positive prompt. Higher CFG is not a validated improvement
|
| 21 |
+
for this package.
|
| 22 |
+
|
| 23 |
+
## Lettering
|
| 24 |
+
|
| 25 |
+
Quote the exact text and specify its position and hierarchy. Example:
|
| 26 |
+
|
| 27 |
+
> Large cream headline at the top reads exactly "TAKE THE SCENIC ROUTE".
|
| 28 |
+
> A smaller line at the bottom reads exactly "SLOW TRAVEL CLUB".
|
| 29 |
+
> Generous margins, flat navy, coral and cream inks, subtle paper grain.
|
| 30 |
+
|
| 31 |
+
Proofread the result. Clear headings and short labels worked in the release
|
| 32 |
+
examples, but the astronaut test also shows small-label spelling errors in the
|
| 33 |
+
BF16 baseline. Neither precision is a guarantee of perfect text.
|
| 34 |
+
|
| 35 |
+
## Image editing
|
| 36 |
+
|
| 37 |
+
Upload the reference in Load Image. Connect it and the matching VAE to
|
| 38 |
+
TextEncodeQwenImage21. Use that node's latent output for sampling, so the output
|
| 39 |
+
matches the processed first reference's size. The included editing workflow
|
| 40 |
+
already has those connections.
|
| 41 |
+
|
| 42 |
+
Name the changes and the elements to preserve:
|
| 43 |
+
|
| 44 |
+
> Change the amber glass bottle to deep translucent cobalt-blue glass and the
|
| 45 |
+
> brass cap to brushed silver. Replace the orange with lime and rosemary with
|
| 46 |
+
> lavender. Keep the bottle shape, cream EMBER label, marble pedestal, camera
|
| 47 |
+
> angle, background and lighting.
|
| 48 |
+
|
| 49 |
+
For multiple references, refer explicitly to the first and second images.
|
| 50 |
+
Reference preservation is semantic, not pixel-locked: small details, hand poses,
|
| 51 |
+
clothing coverage and object placement can change.
|
| 52 |
+
|
| 53 |
+
## Native transparency
|
| 54 |
+
|
| 55 |
+
Use an explicit RGBA instruction around the subject description:
|
| 56 |
+
|
| 57 |
+
> This is an RGBA image with transparency. A charming hand-painted gouache sticker
|
| 58 |
+
> of a small orange fox curled around a glowing blue book, round spectacles,
|
| 59 |
+
> moss-green scarf, clean complete silhouette and generous empty margin.
|
| 60 |
+
> The image has alpha channel and the background is transparent.
|
| 61 |
+
|
| 62 |
+
Save as PNG. Check the alpha channel or composite over a checkerboard. The hidden
|
| 63 |
+
RGB under transparent pixels can be colored; an RGB-only viewer is not an alpha
|
| 64 |
+
test. The supplied example has alpha values spanning 0 through 255.
|
| 65 |
+
|
| 66 |
+
## Comparisons
|
| 67 |
+
|
| 68 |
+
Keep the prompt, seed, dimensions, sampler, steps, backend and encoder mode fixed.
|
| 69 |
+
The optional NVFP4 encoder patch changes conditioning arithmetic and may change
|
| 70 |
+
the generated image. Compare stock and accelerated modes as distinct settings.
|
| 71 |
+
The gallery contains the complete 12-prompt, two-seed comparison set.
|
README.md
CHANGED
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---
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license: other
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license_name: qwen-research-license
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license_link: https://huggingface.co/BennyDaBall/
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base_model: Comfy-Org/Qwen-Image-2.1
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base_model_relation: quantized
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pipeline_tag: text-to-image
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- rtx-5090
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---
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# 🚀
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**The image model AND the text encoder. 12.42 GB for the complete package.**
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| File | Where it goes | Size |
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|---|---|---:|
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| [
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| [
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| [qwen_image_2.1_vae_bf16.safetensors](vae/qwen_image_2.1_vae_bf16.safetensors) | `ComfyUI/models/vae/` | 0.68 GB |
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The original BF16 set is 32.44 GB. This package is **61.7% smaller on disk**.
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uses dequantized weights and FP32 activations. Denoising still uses NVFP4.
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3. Import one of the workflows below using **Ctrl+O**, then click **Run**.
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4. For editing, use **Upload** in Load Image to select the included
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[reference image](input/
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| Working workflow | Purpose |
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|---|---|
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Use the Python environment of the tested ComfyUI installation:
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```powershell
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python tools/convert.py path/to/qwen_image_2.1_bf16.safetensors diffusion_models/
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python tools/convert.py path/to/qwen3vl_8b_bf16.safetensors text_encoders/
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```
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The converter uses installed Comfy Kitchen operations, validates matrix alignment,
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ComfyUI and Comfy Kitchen provide loading and GPU execution. Independent conversion,
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workflow packaging, and RTX 5090 validation by **BennyDaBall_OG**.
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-
The [Qwen Research License](https://huggingface.co/BennyDaBall/
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evaluation; commercial use requires a separate license from Qwen. Read the full
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-
license and [NOTICE](https://huggingface.co/BennyDaBall/
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This repo is not affiliated with Qwen or Comfy-Org.
|
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---
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license: other
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license_name: qwen-research-license
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+
license_link: https://huggingface.co/BennyDaBall/Qwen-Image-2.1-NVFP4/blob/main/LICENSE
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base_model: Comfy-Org/Qwen-Image-2.1
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base_model_relation: quantized
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pipeline_tag: text-to-image
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- rtx-5090
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---
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# 🚀 Qwen Image 2.1 — NVFP4 for ComfyUI
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+
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**This is an independent NVFP4 quantization of Qwen Image 2.1, created by the Qwen team.** The original model and architecture are Qwen's work. I converted the official [Comfy-Org/Qwen-Image-2.1 BF16 checkpoint package](https://huggingface.co/Comfy-Org/Qwen-Image-2.1) and packaged the quantized weights, workflows, and validation here. This is not a new base model or an official Qwen release.
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**The image model AND the text encoder. 12.42 GB for the complete package.**
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|
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|
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| File | Where it goes | Size |
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|---|---|---:|
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+
| [qwen_image_2.1_nvfp4.safetensors](diffusion_models/qwen_image_2.1_nvfp4.safetensors) | `ComfyUI/models/diffusion_models/` | 4.20 GB |
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+
| [qwen3vl_8b_nvfp4.safetensors](text_encoders/qwen3vl_8b_nvfp4.safetensors) | `ComfyUI/models/text_encoders/` | 7.55 GB |
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| [qwen_image_2.1_vae_bf16.safetensors](vae/qwen_image_2.1_vae_bf16.safetensors) | `ComfyUI/models/vae/` | 0.68 GB |
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The original BF16 set is 32.44 GB. This package is **61.7% smaller on disk**.
|
|
|
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| 59 |
uses dequantized weights and FP32 activations. Denoising still uses NVFP4.
|
| 60 |
3. Import one of the workflows below using **Ctrl+O**, then click **Run**.
|
| 61 |
4. For editing, use **Upload** in Load Image to select the included
|
| 62 |
+
[reference image](input/qwen_image_2.1_edit_reference.png), or your own image.
|
| 63 |
|
| 64 |
| Working workflow | Purpose |
|
| 65 |
|---|---|
|
|
|
|
| 113 |
Use the Python environment of the tested ComfyUI installation:
|
| 114 |
|
| 115 |
```powershell
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| 116 |
+
python tools/convert.py path/to/qwen_image_2.1_bf16.safetensors diffusion_models/qwen_image_2.1_nvfp4.safetensors --component dit --comfy-root path/to/ComfyUI
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+
python tools/convert.py path/to/qwen3vl_8b_bf16.safetensors text_encoders/qwen3vl_8b_nvfp4.safetensors --component encoder --comfy-root path/to/ComfyUI
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```
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| 120 |
The converter uses installed Comfy Kitchen operations, validates matrix alignment,
|
|
|
|
| 128 |
ComfyUI and Comfy Kitchen provide loading and GPU execution. Independent conversion,
|
| 129 |
workflow packaging, and RTX 5090 validation by **BennyDaBall_OG**.
|
| 130 |
|
| 131 |
+
The [Qwen Research License](https://huggingface.co/BennyDaBall/Qwen-Image-2.1-NVFP4/blob/main/LICENSE) limits use to non-commercial research and
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| 132 |
evaluation; commercial use requires a separate license from Qwen. Read the full
|
| 133 |
+
license and [NOTICE](https://huggingface.co/BennyDaBall/Qwen-Image-2.1-NVFP4/blob/main/NOTICE). The included ComfyUI patch remains GPL-3.0.
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| 134 |
This repo is not affiliated with Qwen or Comfy-Org.
|
SHA256SUMS
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4e6fa2ad6423a4bf308707d3d892c3616b614f63d634f4202816e92c8c048910 assets/02_Image_Editing.png
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14dbba646932489c0bcbe513ec8151fa5d18005832202abe961d03544c8ef6f8 assets/03_Transparent_RGBA.png
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95b2ecdf94cf414f51dbb4402176b3deaaad8d984797658ca013825770f6cdf1 assets/04_2K_Typography.png
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VALIDATION.md
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# Validation — RTX 5090, 20 September 2026
|
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## What passed
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- Both native safetensors checkpoints loaded through standard ComfyUI loaders.
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typography, each with BF16 and NVFP4.
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image editing, each with BF16 and NVFP4. These prompts were not used to choose
|
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the conversion recipe.
|
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|
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|
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and encoder weights, using the original stock conditioning path.
|
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|
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and image denoising with the accelerated encoder patch.
|
| 17 |
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|
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|
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finite scales. Source and release file hashes recorded.
|
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|
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|
| 22 |
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- All four API workflows also passed using the Hub-downloaded files on the
|
| 23 |
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unpatched base runtime, with custom nodes disabled. Thus stock compatibility
|
| 24 |
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and optional accelerated conditioning were tested separately.
|
| 25 |
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|
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No calibration, optimization against the test prompts, or fine-tuning was used.
|
| 27 |
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The quantization recipe remained unchanged after its first conversion.
|
| 28 |
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|
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## Measured performance
|
| 30 |
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|
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1024×1024 EMBER product prompt from the shipped generation workflow, seed
|
| 32 |
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20260922, 40 steps, Euler/simple, CFG 1, denoise 1. One first-after-unload run,
|
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then three warm runs per variant. **Every node executed on every repeat** using
|
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`--cache-none`; the histories were checked for an empty cached-node list.
|
| 35 |
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Elapsed time comes from server execution-start/success timestamps, not polling
|
| 36 |
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latency. Includes conditioning, denoising, VAE decode and PNG save.
|
| 37 |
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|
| 38 |
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| Weights / encoder mode | Warm median | Warm range | First after unload | Largest sampled GPU use, warm |
|
| 39 |
-
|---|---:|---:|---:|---:|
|
| 40 |
-
| Official BF16 / stock FP32 conditioning | 15.629 s | 15.553–15.650 s | 18.932 s | 31,677 MiB |
|
| 41 |
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| Official INT8 ConvRot / stock conditioning | 7.553 s | 7.496–7.591 s | 9.973 s | 18,433 MiB |
|
| 42 |
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|
|
| 43 |
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|
| 44 |
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This NVFP4 workflow delivered **2.32× the BF16 throughput** and took **10.7% less
|
| 45 |
-
time than INT8** in this one controlled test. These are local measurements, not
|
| 46 |
-
general speed guarantees. First-after-unload is not a cold filesystem-cache or
|
| 47 |
-
fresh-boot test. The quality-suite runs used ordinary node caching and are not
|
| 48 |
-
the basis of this performance table.
|
| 49 |
-
|
| 50 |
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GPU memory was sampled through `nvidia-smi` every 100 ms. Values are **whole-GPU
|
| 51 |
-
usage**, including Windows and other idle desktop services, not PyTorch peak
|
| 52 |
-
allocated memory or a minimum VRAM requirement. The largest sampled NVFP4 warm
|
| 53 |
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value is about 12.94 GiB. No other model inference job was running during this
|
| 54 |
-
benchmark. Hardware other than the RTX 5090 was not benchmarked.
|
| 55 |
-
|
| 56 |
-
Raw measurements: [benchmark.json](validation/benchmark.json).
|
| 57 |
-
|
| 58 |
-
## Quality observations and limits
|
| 59 |
-
|
| 60 |
-
The main set covers portraits, landscapes, glass products, typography, object
|
| 61 |
-
counts and spatial relationships, illustration, fashion, night scenes,
|
| 62 |
-
architecture, macro, astronaut portraits and botanical posters. All 24 matched
|
| 63 |
-
pairs are published in [COMPARISONS.md](COMPARISONS.md).
|
| 64 |
-
|
| 65 |
-
Visual review found coherent images and preserved useful text, material and
|
| 66 |
-
editing capabilities. It also found real differences: changed portrait details,
|
| 67 |
-
object placement, typography layout and small material/color details. In one
|
| 68 |
-
macro pair the quantized spider is less green. Small astronaut-label spelling
|
| 69 |
-
was imperfect in the BF16 baseline too. The Chinese probe retained the requested
|
| 70 |
-
wording, but NVFP4 moved the subtitle above the cup rather than below it.
|
| 71 |
-
|
| 72 |
-
The two-reference test combined the reference woman with the EMBER bottle and
|
| 73 |
-
removed the violin, while changing hand pose. The product edit preserved the
|
| 74 |
-
label and scene while changing glass, metal and props. Portrait recoloring can
|
| 75 |
-
also change sleeve coverage. This is semantic reference preservation, not a
|
| 76 |
-
pixel-locked edit.
|
| 77 |
-
|
| 78 |
-
The transparent example has genuine RGBA output: alpha minimum 0, maximum 255,
|
| 79 |
-
49.3% of pixels below alpha 16 and 49.7% above alpha 240. See the original
|
| 80 |
-
[RGBA PNG](assets/03_Transparent_RGBA.png) and the
|
| 81 |
-
[checkerboard composite](assets/transparency-checker.jpg). RGB-only viewers may
|
| 82 |
-
show the purple RGB values stored underneath transparent pixels.
|
| 83 |
-
|
| 84 |
-
This is a small local evaluation, not a blinded preference study or a benchmark
|
| 85 |
-
proving no quality loss. There was no LoRA, ControlNet, training or video test.
|
| 86 |
-
Long text, exhaustive multilingual coverage and every reference-image count were
|
| 87 |
-
not tested. Stock encoder arithmetic and accelerated encoder arithmetic can
|
| 88 |
-
produce different images even with identical weights and seeds.
|
| 89 |
-
|
| 90 |
-
## Runtime and kernel evidence
|
| 91 |
-
|
| 92 |
-
- Windows, Python 3.13.12, RTX 5090 32 GB.
|
| 93 |
-
- ComfyUI 0.36.0, base commit `99073836d45f66053c45ba8564984e6def9cebba`.
|
| 94 |
-
- Included Qwen-specific encoder patch; local validation commit `2d324220`.
|
| 95 |
-
- PyTorch `2.14.0+cu130`; comfy-kitchen `0.2.35`; comfy-aimdo `0.5.5`.
|
| 96 |
-
- Frontend package `1.53.6`; safetensors `0.8.0`.
|
| 97 |
-
- Comfy Kitchen attention, normal VRAM mode, async offload, batch size one.
|
| 98 |
-
- All tests ran with `--disable-all-custom-nodes`.
|
| 99 |
-
|
| 100 |
-
The profiler captured kernels beginning
|
| 101 |
-
`cutlass3x_sm120_bstensorop_s16864gemm_block_scaled_ue4m3xe2m1_ue4m3xe2m1`
|
| 102 |
-
for short conditioning sequences and image-denoising sequences. This confirms
|
| 103 |
-
hardware FP4 execution, rather than relying on checkpoint names or successful
|
| 104 |
-
loading. Raw observations: [kernel_audit.jsonl](validation/kernel_audit.jsonl).
|
| 105 |
-
No NVFP4 matmul-fallback warning occurred in the successful audited runs.
|
| 106 |
-
|
| 107 |
-
The first acceleration probe exposed FP32 activations entering a BF16/FP16-only
|
| 108 |
-
quantizer. The included patch fixes that at the Qwen Image 2.1 encoding boundary:
|
| 109 |
-
vision preprocessing stays unchanged, the resulting multimodal embeddings become
|
| 110 |
-
BF16 only for NVFP4 conditioning on supported hardware, and existing ComfyUI
|
| 111 |
-
matmul dispatch is used. BF16 and INT8 checkpoints retain their original path.
|
| 112 |
-
|
| 113 |
-
## Precision and provenance
|
| 114 |
-
|
| 115 |
-
The DiT converts 192 matrices and retains its other 73 tensors. The encoder
|
| 116 |
-
converts 252 matrices and retains its other 498 tensors, including the complete
|
| 117 |
-
vision tower, embeddings and output head. Per-matrix relative reconstruction
|
| 118 |
-
RMSE is recorded in the manifests; it is not an image-quality score.
|
| 119 |
-
|
| 120 |
-
Source revision: `Comfy-Org/Qwen-Image-2.1@ace0edeb3791a594ddfa36ed5f41a178a394e921`.
|
| 121 |
-
The image transformer source SHA-256 is
|
| 122 |
-
`89f4158d066cc33906a199fca85634f766892dd78f49b6698dabf187ac86c4bc`.
|
| 123 |
-
The encoder source SHA-256 is
|
| 124 |
-
`68bdc82bc1b66851162ae656225e7e2068166b603db19bd5d5a3b90eb12669a9`.
|
| 125 |
-
Full tensor manifests sit beside each checkpoint; `SHA256SUMS` covers the release.
|
|
|
|
| 1 |
+
# Validation — RTX 5090, 20 September 2026
|
| 2 |
+
|
| 3 |
+
## What passed
|
| 4 |
+
|
| 5 |
+
- Both native safetensors checkpoints loaded through standard ComfyUI loaders.
|
| 6 |
+
- 48 main renders: 12 prompts × two seeds × BF16/NVFP4, all at 1024×1024.
|
| 7 |
+
- Eight extended renders: product edit, portrait edit, transparent RGBA and 2K
|
| 8 |
+
typography, each with BF16 and NVFP4.
|
| 9 |
+
- Six held-out renders: Chinese lettering, 1536×864 landscape and two-reference
|
| 10 |
+
image editing, each with BF16 and NVFP4. These prompts were not used to choose
|
| 11 |
+
the conversion recipe.
|
| 12 |
+
- Four shipped workflows imported and executed through the actual frontend.
|
| 13 |
+
- Three subjects tested across all four combinations of BF16/NVFP4 transformer
|
| 14 |
+
and encoder weights, using the original stock conditioning path.
|
| 15 |
+
- Real SM120 block-scaled FP4 CUDA kernels observed in both language conditioning
|
| 16 |
+
and image denoising with the accelerated encoder patch.
|
| 17 |
+
- All 571 protected tensors checked for exact equality against the source files.
|
| 18 |
+
All 444 quantized matrices checked for packed shape, marker, scale dtype and
|
| 19 |
+
finite scales. Source and release file hashes recorded.
|
| 20 |
+
- Hub-uploaded weights downloaded into a separate directory and matched against
|
| 21 |
+
the release SHA-256 hashes. The VAE hash matches the official original.
|
| 22 |
+
- All four API workflows also passed using the Hub-downloaded files on the
|
| 23 |
+
unpatched base runtime, with custom nodes disabled. Thus stock compatibility
|
| 24 |
+
and optional accelerated conditioning were tested separately.
|
| 25 |
+
|
| 26 |
+
No calibration, optimization against the test prompts, or fine-tuning was used.
|
| 27 |
+
The quantization recipe remained unchanged after its first conversion.
|
| 28 |
+
|
| 29 |
+
## Measured performance
|
| 30 |
+
|
| 31 |
+
1024×1024 EMBER product prompt from the shipped generation workflow, seed
|
| 32 |
+
20260922, 40 steps, Euler/simple, CFG 1, denoise 1. One first-after-unload run,
|
| 33 |
+
then three warm runs per variant. **Every node executed on every repeat** using
|
| 34 |
+
`--cache-none`; the histories were checked for an empty cached-node list.
|
| 35 |
+
Elapsed time comes from server execution-start/success timestamps, not polling
|
| 36 |
+
latency. Includes conditioning, denoising, VAE decode and PNG save.
|
| 37 |
+
|
| 38 |
+
| Weights / encoder mode | Warm median | Warm range | First after unload | Largest sampled GPU use, warm |
|
| 39 |
+
|---|---:|---:|---:|---:|
|
| 40 |
+
| Official BF16 / stock FP32 conditioning | 15.629 s | 15.553–15.650 s | 18.932 s | 31,677 MiB |
|
| 41 |
+
| Official INT8 ConvRot / stock conditioning | 7.553 s | 7.496–7.591 s | 9.973 s | 18,433 MiB |
|
| 42 |
+
| Qwen Image 2.1 NVFP4 / patched BF16 + NVFP4 conditioning | **6.746 s** | **6.706–6.749 s** | **8.083 s** | **13,249 MiB** |
|
| 43 |
+
|
| 44 |
+
This NVFP4 workflow delivered **2.32× the BF16 throughput** and took **10.7% less
|
| 45 |
+
time than INT8** in this one controlled test. These are local measurements, not
|
| 46 |
+
general speed guarantees. First-after-unload is not a cold filesystem-cache or
|
| 47 |
+
fresh-boot test. The quality-suite runs used ordinary node caching and are not
|
| 48 |
+
the basis of this performance table.
|
| 49 |
+
|
| 50 |
+
GPU memory was sampled through `nvidia-smi` every 100 ms. Values are **whole-GPU
|
| 51 |
+
usage**, including Windows and other idle desktop services, not PyTorch peak
|
| 52 |
+
allocated memory or a minimum VRAM requirement. The largest sampled NVFP4 warm
|
| 53 |
+
value is about 12.94 GiB. No other model inference job was running during this
|
| 54 |
+
benchmark. Hardware other than the RTX 5090 was not benchmarked.
|
| 55 |
+
|
| 56 |
+
Raw measurements: [benchmark.json](validation/benchmark.json).
|
| 57 |
+
|
| 58 |
+
## Quality observations and limits
|
| 59 |
+
|
| 60 |
+
The main set covers portraits, landscapes, glass products, typography, object
|
| 61 |
+
counts and spatial relationships, illustration, fashion, night scenes,
|
| 62 |
+
architecture, macro, astronaut portraits and botanical posters. All 24 matched
|
| 63 |
+
pairs are published in [COMPARISONS.md](COMPARISONS.md).
|
| 64 |
+
|
| 65 |
+
Visual review found coherent images and preserved useful text, material and
|
| 66 |
+
editing capabilities. It also found real differences: changed portrait details,
|
| 67 |
+
object placement, typography layout and small material/color details. In one
|
| 68 |
+
macro pair the quantized spider is less green. Small astronaut-label spelling
|
| 69 |
+
was imperfect in the BF16 baseline too. The Chinese probe retained the requested
|
| 70 |
+
wording, but NVFP4 moved the subtitle above the cup rather than below it.
|
| 71 |
+
|
| 72 |
+
The two-reference test combined the reference woman with the EMBER bottle and
|
| 73 |
+
removed the violin, while changing hand pose. The product edit preserved the
|
| 74 |
+
label and scene while changing glass, metal and props. Portrait recoloring can
|
| 75 |
+
also change sleeve coverage. This is semantic reference preservation, not a
|
| 76 |
+
pixel-locked edit.
|
| 77 |
+
|
| 78 |
+
The transparent example has genuine RGBA output: alpha minimum 0, maximum 255,
|
| 79 |
+
49.3% of pixels below alpha 16 and 49.7% above alpha 240. See the original
|
| 80 |
+
[RGBA PNG](assets/03_Transparent_RGBA.png) and the
|
| 81 |
+
[checkerboard composite](assets/transparency-checker.jpg). RGB-only viewers may
|
| 82 |
+
show the purple RGB values stored underneath transparent pixels.
|
| 83 |
+
|
| 84 |
+
This is a small local evaluation, not a blinded preference study or a benchmark
|
| 85 |
+
proving no quality loss. There was no LoRA, ControlNet, training or video test.
|
| 86 |
+
Long text, exhaustive multilingual coverage and every reference-image count were
|
| 87 |
+
not tested. Stock encoder arithmetic and accelerated encoder arithmetic can
|
| 88 |
+
produce different images even with identical weights and seeds.
|
| 89 |
+
|
| 90 |
+
## Runtime and kernel evidence
|
| 91 |
+
|
| 92 |
+
- Windows, Python 3.13.12, RTX 5090 32 GB.
|
| 93 |
+
- ComfyUI 0.36.0, base commit `99073836d45f66053c45ba8564984e6def9cebba`.
|
| 94 |
+
- Included Qwen-specific encoder patch; local validation commit `2d324220`.
|
| 95 |
+
- PyTorch `2.14.0+cu130`; comfy-kitchen `0.2.35`; comfy-aimdo `0.5.5`.
|
| 96 |
+
- Frontend package `1.53.6`; safetensors `0.8.0`.
|
| 97 |
+
- Comfy Kitchen attention, normal VRAM mode, async offload, batch size one.
|
| 98 |
+
- All tests ran with `--disable-all-custom-nodes`.
|
| 99 |
+
|
| 100 |
+
The profiler captured kernels beginning
|
| 101 |
+
`cutlass3x_sm120_bstensorop_s16864gemm_block_scaled_ue4m3xe2m1_ue4m3xe2m1`
|
| 102 |
+
for short conditioning sequences and image-denoising sequences. This confirms
|
| 103 |
+
hardware FP4 execution, rather than relying on checkpoint names or successful
|
| 104 |
+
loading. Raw observations: [kernel_audit.jsonl](validation/kernel_audit.jsonl).
|
| 105 |
+
No NVFP4 matmul-fallback warning occurred in the successful audited runs.
|
| 106 |
+
|
| 107 |
+
The first acceleration probe exposed FP32 activations entering a BF16/FP16-only
|
| 108 |
+
quantizer. The included patch fixes that at the Qwen Image 2.1 encoding boundary:
|
| 109 |
+
vision preprocessing stays unchanged, the resulting multimodal embeddings become
|
| 110 |
+
BF16 only for NVFP4 conditioning on supported hardware, and existing ComfyUI
|
| 111 |
+
matmul dispatch is used. BF16 and INT8 checkpoints retain their original path.
|
| 112 |
+
|
| 113 |
+
## Precision and provenance
|
| 114 |
+
|
| 115 |
+
The DiT converts 192 matrices and retains its other 73 tensors. The encoder
|
| 116 |
+
converts 252 matrices and retains its other 498 tensors, including the complete
|
| 117 |
+
vision tower, embeddings and output head. Per-matrix relative reconstruction
|
| 118 |
+
RMSE is recorded in the manifests; it is not an image-quality score.
|
| 119 |
+
|
| 120 |
+
Source revision: `Comfy-Org/Qwen-Image-2.1@ace0edeb3791a594ddfa36ed5f41a178a394e921`.
|
| 121 |
+
The image transformer source SHA-256 is
|
| 122 |
+
`89f4158d066cc33906a199fca85634f766892dd78f49b6698dabf187ac86c4bc`.
|
| 123 |
+
The encoder source SHA-256 is
|
| 124 |
+
`68bdc82bc1b66851162ae656225e7e2068166b603db19bd5d5a3b90eb12669a9`.
|
| 125 |
+
Full tensor manifests sit beside each checkpoint; `SHA256SUMS` covers the release.
|
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|
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diffusion_models/qwen_image_2.1_nvfp4.manifest.json
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|
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gallery.html
CHANGED
|
@@ -1,2 +1,2 @@
|
|
| 1 |
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<!doctype html><html lang="en"><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>
|
| 2 |
-
body{margin:0;background:#101621;color:#edf4ff;font:16px/1.6 system-ui}main{max-width:1250px;margin:auto;padding:48px 24px}h1{font-size:clamp(32px,5vw,64px);line-height:1.1;margin:12px 0}p{color:#b7c5db;max-width:850px}.tag{color:#8be8c9;letter-spacing:.12em;text-transform:uppercase;font-size:13px}select,input{font:inherit;background:#202d40;color:white;border:1px solid #526278;padding:10px;border-radius:8px}header{margin-bottom:32px}.controls{display:flex;gap:15px;flex-wrap:wrap;align-items:center;margin:20px 0}.compare{position:relative;aspect-ratio:1;background:#182030;max-width:1024px;border-radius:14px;overflow:hidden}.compare img{width:100%;height:100%;object-fit:contain;position:absolute;inset:0}.compare .over{clip-path:inset(0 50% 0 0)}.labels{position:absolute;top:16px;left:16px;right:16px;display:flex;justify-content:space-between}.labels span{padding:6px 12px;background:#101621dd;border-radius:8px}.line{position:absolute;top:0;bottom:0;left:50%;width:2px;background:#fff}.prompt{white-space:pre-wrap;max-width:1024px}a{color:#8be8c9}footer{margin-top:40px;border-top:1px solid #344158;padding-top:22px}</style><main><header><div class="tag">BennyDaBall / RTX 5090 / Built with Qwen</div><h1>Two weights.<br>One prompt.</h1><p>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.</p></header><div class="controls"><select id="case"></select><label>Comparison split <input id="split" type="range" min="0" max="100" value="50"></label></div><div class="compare"><img id="nv"><img id="bf" class="over"><div class="line" id="line"></div><div class="labels"><span>BF16 weights</span><span>Prism NVFP4</span></div></div><p id="prompt" class="prompt"></p><footer><a href="https://huggingface.co/BennyDaBall/Prism-Image-2.1-NVFP4">Models, tested workflows, conversion scripts and validation</a><p>Research/evaluation license. Native ComfyUI nodes. The accelerated encoder requires the included runtime patch. Vision, VAE and protected tensors remain BF16.</p></footer></main><script>const data=[{"name": "portrait", "seed": 20260920, "prompt": "Editorial environmental photograph of an elderly female violin maker in her sunlit wooden workshop. She holds a small carving tool in her right hand and steadies an unfinished violin with her left hand. Natural skin texture, thoughtful expression, curled wood shavings on the workbench, soft window light from the left, warm subdued colors, shallow depth of field, 50mm lens. Waist-up composition, candid and believable.", "bf": "portrait_20260920_bf-bf.webp", "nv": "portrait_20260920_nv-nv.webp"}, {"name": "portrait", "seed": 20260921, "prompt": "Editorial environmental photograph of an elderly female violin maker in her sunlit wooden workshop. She holds a small carving tool in her right hand and steadies an unfinished violin with her left hand. Natural skin texture, thoughtful expression, curled wood shavings on the workbench, soft window light from the left, warm subdued colors, shallow depth of field, 50mm lens. Waist-up composition, candid and believable.", "bf": "portrait_20260921_bf-bf.webp", "nv": "portrait_20260921_nv-nv.webp"}, {"name": "landscape", "seed": 20260920, "prompt": "Cinematic landscape photograph of a tiny red mountain refuge on a rocky ridge above a sea of clouds at sunrise. A winding stone footpath leads from the bottom left toward the refuge. Jagged distant peaks, warm sunlight grazing the roof, cool blue shadows, thin luminous mist, realistic geology, restrained natural colors. Wide composition with a strong sense of scale, no people, no lettering.", "bf": "landscape_20260920_bf-bf.webp", "nv": "landscape_20260920_nv-nv.webp"}, {"name": "landscape", "seed": 20260921, "prompt": "Cinematic landscape photograph of a tiny red mountain refuge on a rocky ridge above a sea of clouds at sunrise. A winding stone footpath leads from the bottom left toward the refuge. Jagged distant peaks, warm sunlight grazing the roof, cool blue shadows, thin luminous mist, realistic geology, restrained natural colors. Wide composition with a strong sense of scale, no people, no lettering.", "bf": "landscape_20260921_bf-bf.webp", "nv": "landscape_20260921_nv-nv.webp"}, {"name": "product", "seed": 20260920, "prompt": "Premium studio product photograph of one amber glass perfume bottle with a brushed brass cap, standing on a dark green marble block. A small cream paper label on the bottle reads \"EMBER\" in elegant black capital letters. A sliced orange and a sprig of rosemary rest beside the block. Warm rim lighting, soft front fill, rich realistic materials, controlled glass reflections, clean charcoal background, centered luxury advertising composition.", "bf": "product_20260920_bf-bf.webp", "nv": "product_20260920_nv-nv.webp"}, {"name": "product", "seed": 20260921, "prompt": "Premium studio product photograph of one amber glass perfume bottle with a brushed brass cap, standing on a dark green marble block. A small cream paper label on the bottle reads \"EMBER\" in elegant black capital letters. A sliced orange and a sprig of rosemary rest beside the block. Warm rim lighting, soft front fill, rich realistic materials, controlled glass reflections, clean charcoal background, centered luxury advertising composition.", "bf": "product_20260921_bf-bf.webp", "nv": "product_20260921_nv-nv.webp"}, {"name": "typography", "seed": 20260920, "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_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"}];const select=document.getElementById('case');data.forEach((r,i)=>{let o=document.createElement('option');o.value=i;o.textContent=r.name.replaceAll('_',' ')+' / seed '+r.seed;select.append(o)});function show(){let r=data[select.value];document.getElementById('bf').src='assets/'+r.bf;document.getElementById('nv').src='assets/'+r.nv;document.getElementById('prompt').textContent=r.prompt}select.onchange=show;document.getElementById('split').oninput=e=>{let v=e.target.value;document.getElementById('bf').style.clipPath='inset(0 '+(100-v)+'% 0 0)';document.getElementById('line').style.left=v+'%'};show();</script></html>
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<!doctype html><html lang="en"><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>Qwen Image 2.1 | NVFP4 comparisons</title><style>
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body{margin:0;background:#101621;color:#edf4ff;font:16px/1.6 system-ui}main{max-width:1250px;margin:auto;padding:48px 24px}h1{font-size:clamp(32px,5vw,64px);line-height:1.1;margin:12px 0}p{color:#b7c5db;max-width:850px}.tag{color:#8be8c9;letter-spacing:.12em;text-transform:uppercase;font-size:13px}select,input{font:inherit;background:#202d40;color:white;border:1px solid #526278;padding:10px;border-radius:8px}header{margin-bottom:32px}.controls{display:flex;gap:15px;flex-wrap:wrap;align-items:center;margin:20px 0}.compare{position:relative;aspect-ratio:1;background:#182030;max-width:1024px;border-radius:14px;overflow:hidden}.compare img{width:100%;height:100%;object-fit:contain;position:absolute;inset:0}.compare .over{clip-path:inset(0 50% 0 0)}.labels{position:absolute;top:16px;left:16px;right:16px;display:flex;justify-content:space-between}.labels span{padding:6px 12px;background:#101621dd;border-radius:8px}.line{position:absolute;top:0;bottom:0;left:50%;width:2px;background:#fff}.prompt{white-space:pre-wrap;max-width:1024px}a{color:#8be8c9}footer{margin-top:40px;border-top:1px solid #344158;padding-top:22px}</style><main><header><div class="tag">BennyDaBall / RTX 5090 / Built with Qwen</div><h1>Two weights.<br>One prompt.</h1><p>BF16 source weights versus the Qwen Image 2.1 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.</p></header><div class="controls"><select id="case"></select><label>Comparison split <input id="split" type="range" min="0" max="100" value="50"></label></div><div class="compare"><img id="nv"><img id="bf" class="over"><div class="line" id="line"></div><div class="labels"><span>BF16 weights</span><span>Qwen Image 2.1 NVFP4</span></div></div><p id="prompt" class="prompt"></p><footer><a href="https://huggingface.co/BennyDaBall/Qwen-Image-2.1-NVFP4">Models, tested workflows, conversion scripts and validation</a><p>Research/evaluation license. Native ComfyUI nodes. The accelerated encoder requires the included runtime patch. Vision, VAE and protected tensors remain BF16.</p></footer></main><script>const data=[{"name": "portrait", "seed": 20260920, "prompt": "Editorial environmental photograph of an elderly female violin maker in her sunlit wooden workshop. She holds a small carving tool in her right hand and steadies an unfinished violin with her left hand. Natural skin texture, thoughtful expression, curled wood shavings on the workbench, soft window light from the left, warm subdued colors, shallow depth of field, 50mm lens. Waist-up composition, candid and believable.", "bf": "portrait_20260920_bf-bf.webp", "nv": "portrait_20260920_nv-nv.webp"}, {"name": "portrait", "seed": 20260921, "prompt": "Editorial environmental photograph of an elderly female violin maker in her sunlit wooden workshop. She holds a small carving tool in her right hand and steadies an unfinished violin with her left hand. Natural skin texture, thoughtful expression, curled wood shavings on the workbench, soft window light from the left, warm subdued colors, shallow depth of field, 50mm lens. Waist-up composition, candid and believable.", "bf": "portrait_20260921_bf-bf.webp", "nv": "portrait_20260921_nv-nv.webp"}, {"name": "landscape", "seed": 20260920, "prompt": "Cinematic landscape photograph of a tiny red mountain refuge on a rocky ridge above a sea of clouds at sunrise. A winding stone footpath leads from the bottom left toward the refuge. Jagged distant peaks, warm sunlight grazing the roof, cool blue shadows, thin luminous mist, realistic geology, restrained natural colors. Wide composition with a strong sense of scale, no people, no lettering.", "bf": "landscape_20260920_bf-bf.webp", "nv": "landscape_20260920_nv-nv.webp"}, {"name": "landscape", "seed": 20260921, "prompt": "Cinematic landscape photograph of a tiny red mountain refuge on a rocky ridge above a sea of clouds at sunrise. A winding stone footpath leads from the bottom left toward the refuge. Jagged distant peaks, warm sunlight grazing the roof, cool blue shadows, thin luminous mist, realistic geology, restrained natural colors. Wide composition with a strong sense of scale, no people, no lettering.", "bf": "landscape_20260921_bf-bf.webp", "nv": "landscape_20260921_nv-nv.webp"}, {"name": "product", "seed": 20260920, "prompt": "Premium studio product photograph of one amber glass perfume bottle with a brushed brass cap, standing on a dark green marble block. A small cream paper label on the bottle reads \"EMBER\" in elegant black capital letters. A sliced orange and a sprig of rosemary rest beside the block. Warm rim lighting, soft front fill, rich realistic materials, controlled glass reflections, clean charcoal background, centered luxury advertising composition.", "bf": "product_20260920_bf-bf.webp", "nv": "product_20260920_nv-nv.webp"}, {"name": "product", "seed": 20260921, "prompt": "Premium studio product photograph of one amber glass perfume bottle with a brushed brass cap, standing on a dark green marble block. A small cream paper label on the bottle reads \"EMBER\" in elegant black capital letters. A sliced orange and a sprig of rosemary rest beside the block. Warm rim lighting, soft front fill, rich realistic materials, controlled glass reflections, clean charcoal background, centered luxury advertising composition.", "bf": "product_20260921_bf-bf.webp", "nv": "product_20260921_nv-nv.webp"}, {"name": "typography", "seed": 20260920, "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_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"}];const select=document.getElementById('case');data.forEach((r,i)=>{let o=document.createElement('option');o.value=i;o.textContent=r.name.replaceAll('_',' ')+' / seed '+r.seed;select.append(o)});function show(){let r=data[select.value];document.getElementById('bf').src='assets/'+r.bf;document.getElementById('nv').src='assets/'+r.nv;document.getElementById('prompt').textContent=r.prompt}select.onchange=show;document.getElementById('split').oninput=e=>{let v=e.target.value;document.getElementById('bf').style.clipPath='inset(0 '+(100-v)+'% 0 0)';document.getElementById('line').style.left=v+'%'};show();</script></html>
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input/qwen_image_2.1_edit_reference.png
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Git LFS Details
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runtime/README.md
CHANGED
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@@ -1,36 +1,36 @@
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# Native NVFP4 text conditioning
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-
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The checkpoints load with existing ComfyUI nodes. On the tested ComfyUI revision,
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normal text conditioning runs in FP32 and dequantizes the encoder weights. The
|
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image transformer already uses native NVFP4 compute.
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-
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`qwen21-nvfp4-conditioning.patch` enables native NVFP4 matrix multiplication for
|
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Qwen Image 2.1 conditioning. It detects NVFP4 checkpoint metadata, converts the
|
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completed multimodal embeddings to BF16 on supported GPUs, and uses ComfyUI's
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existing quantized-matmul context. The context restores the previous operation
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settings after encoding. Vision preprocessing and the unnormalized final hidden
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state contract are preserved. BF16 and INT8 checkpoints keep the original path.
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-
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Tested base: `99073836d45f66053c45ba8564984e6def9cebba`.
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No custom nodes, monkey-patching node pack, or additional runtime dependencies.
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Stop ComfyUI. From its Git checkout, apply the supplied patch:
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```powershell
|
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git apply --check C:/path/to/
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git apply C:/path/to/
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```
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-
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Restart ComfyUI. If the check fails, inspect the installed version and local
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changes; do not force the patch onto a different implementation. An upstream
|
| 26 |
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version may already provide equivalent support. To undo this exact patch, stop
|
| 27 |
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ComfyUI and use `git apply -R` with the same patch file.
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-
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-
The same workflows also run without the patch, using native NVFP4 denoising and
|
| 30 |
-
weight-only encoder quantization with FP32 conditioning. That mode is slower for
|
| 31 |
-
uncached encoding and can produce a different image. All advertised accelerated
|
| 32 |
-
results use the patch.
|
| 33 |
-
|
| 34 |
-
Validation used an RTX 5090, CUDA 13.0 PyTorch, and comfy-kitchen 0.2.35.
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| 35 |
-
Other GPUs and CPU execution were not benchmarked. This is an independent patch,
|
| 36 |
-
not an upstream ComfyUI release. The patch is subject to ComfyUI's GPL-3.0 license.
|
|
|
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| 1 |
+
# Native NVFP4 text conditioning
|
| 2 |
+
|
| 3 |
+
The checkpoints load with existing ComfyUI nodes. On the tested ComfyUI revision,
|
| 4 |
+
normal text conditioning runs in FP32 and dequantizes the encoder weights. The
|
| 5 |
+
image transformer already uses native NVFP4 compute.
|
| 6 |
+
|
| 7 |
+
`qwen21-nvfp4-conditioning.patch` enables native NVFP4 matrix multiplication for
|
| 8 |
+
Qwen Image 2.1 conditioning. It detects NVFP4 checkpoint metadata, converts the
|
| 9 |
+
completed multimodal embeddings to BF16 on supported GPUs, and uses ComfyUI's
|
| 10 |
+
existing quantized-matmul context. The context restores the previous operation
|
| 11 |
+
settings after encoding. Vision preprocessing and the unnormalized final hidden
|
| 12 |
+
state contract are preserved. BF16 and INT8 checkpoints keep the original path.
|
| 13 |
+
|
| 14 |
+
Tested base: `99073836d45f66053c45ba8564984e6def9cebba`.
|
| 15 |
+
No custom nodes, monkey-patching node pack, or additional runtime dependencies.
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| 16 |
+
|
| 17 |
+
Stop ComfyUI. From its Git checkout, apply the supplied patch:
|
| 18 |
+
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```powershell
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| 20 |
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git apply --check C:/path/to/Qwen-Image-2.1-NVFP4/runtime/qwen21-nvfp4-conditioning.patch
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git apply C:/path/to/Qwen-Image-2.1-NVFP4/runtime/qwen21-nvfp4-conditioning.patch
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| 22 |
+
```
|
| 23 |
+
|
| 24 |
+
Restart ComfyUI. If the check fails, inspect the installed version and local
|
| 25 |
+
changes; do not force the patch onto a different implementation. An upstream
|
| 26 |
+
version may already provide equivalent support. To undo this exact patch, stop
|
| 27 |
+
ComfyUI and use `git apply -R` with the same patch file.
|
| 28 |
+
|
| 29 |
+
The same workflows also run without the patch, using native NVFP4 denoising and
|
| 30 |
+
weight-only encoder quantization with FP32 conditioning. That mode is slower for
|
| 31 |
+
uncached encoding and can produce a different image. All advertised accelerated
|
| 32 |
+
results use the patch.
|
| 33 |
+
|
| 34 |
+
Validation used an RTX 5090, CUDA 13.0 PyTorch, and comfy-kitchen 0.2.35.
|
| 35 |
+
Other GPUs and CPU execution were not benchmarked. This is an independent patch,
|
| 36 |
+
not an upstream ComfyUI release. The patch is subject to ComfyUI's GPL-3.0 license.
|
text_encoders/qwen3vl_8b_nvfp4.manifest.json
ADDED
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The diff for this file is too large to render.
See raw diff
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tools/convert.py
CHANGED
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@@ -1,77 +1,77 @@
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"""Convert official BF16 checkpoints to ComfyUI's native mixed NVFP4 format."""
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import argparse
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| 3 |
-
import hashlib
|
| 4 |
-
import json
|
| 5 |
-
from pathlib import Path
|
| 6 |
-
import re
|
| 7 |
-
import sys
|
| 8 |
-
import time
|
| 9 |
-
|
| 10 |
-
import torch
|
| 11 |
-
from safetensors import safe_open
|
| 12 |
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from safetensors.torch import save_file
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
def digest(path):
|
| 16 |
-
with open(path, "rb") as stream:
|
| 17 |
-
return hashlib.file_digest(stream, "sha256").hexdigest()
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
def main():
|
| 21 |
-
parser = argparse.ArgumentParser()
|
| 22 |
-
parser.add_argument("source", type=Path)
|
| 23 |
-
parser.add_argument("output", type=Path)
|
| 24 |
-
parser.add_argument("--component", choices=["dit", "encoder"], required=True)
|
| 25 |
-
parser.add_argument("--comfy-root", type=Path, required=True)
|
| 26 |
-
args = parser.parse_args()
|
| 27 |
-
sys.path.insert(0, str(args.comfy_root.resolve()))
|
| 28 |
-
# ComfyUI parses process arguments when its operation registry is imported.
|
| 29 |
-
sys.argv = [sys.argv[0]]
|
| 30 |
-
from comfy.quant_ops import TensorCoreNVFP4Layout
|
| 31 |
-
|
| 32 |
-
patterns = {
|
| 33 |
-
"dit": r"transformer_blocks\.\d+\.(attn\.(to_q|to_k|to_v|to_out\.0)|img_mlp\.(gate_up|out))\.weight",
|
| 34 |
-
"encoder": r"model\.layers\.\d+\.(self_attn\.(q_proj|k_proj|v_proj|o_proj)|mlp\.(gate_proj|up_proj|down_proj))\.weight",
|
| 35 |
-
}
|
| 36 |
-
output, records = {}, []
|
| 37 |
-
start = time.perf_counter()
|
| 38 |
-
with safe_open(args.source, framework="pt", device="cpu") as source:
|
| 39 |
-
metadata = dict(source.metadata() or {})
|
| 40 |
-
for key in source.keys():
|
| 41 |
-
tensor = source.get_tensor(key)
|
| 42 |
-
record = {"name": key, "shape": list(tensor.shape), "source_dtype": str(tensor.dtype)}
|
| 43 |
-
if re.fullmatch(patterns[args.component], key):
|
| 44 |
-
if tensor.shape[1] % 32 or tensor.shape[0] % 8:
|
| 45 |
-
raise ValueError(f"Unsupported NVFP4 GEMM alignment: {key} {tensor.shape}")
|
| 46 |
-
original = tensor.to("cuda")
|
| 47 |
-
scale = original.float().abs().amax().clamp_min(1e-12) / (448 * 6)
|
| 48 |
-
packed, params = TensorCoreNVFP4Layout.quantize(original, scale=scale)
|
| 49 |
-
reconstructed = TensorCoreNVFP4Layout.dequantize(packed, params)
|
| 50 |
-
error = (original.float() - reconstructed.float()).square().mean()
|
| 51 |
-
record.update(storage="nvfp4", relative_rmse=float((error / original.float().square().mean().clamp_min(1e-30)).sqrt()))
|
| 52 |
-
for suffix, value in TensorCoreNVFP4Layout.state_dict_tensors(packed, params).items():
|
| 53 |
-
output[key + suffix] = value.cpu().contiguous()
|
| 54 |
-
marker = json.dumps({"format": "nvfp4"}, separators=(",", ":")).encode()
|
| 55 |
-
output[key.removesuffix("weight") + "comfy_quant"] = torch.tensor(list(marker), dtype=torch.uint8)
|
| 56 |
-
del original, reconstructed, packed, params
|
| 57 |
-
print(f"NVFP4 {key} relative_rmse={record['relative_rmse']:.5f}", flush=True)
|
| 58 |
-
else:
|
| 59 |
-
output[key] = tensor.clone()
|
| 60 |
-
record.update(storage=str(tensor.dtype), sha256=hashlib.sha256(tensor.view(torch.uint8).numpy().tobytes()).hexdigest())
|
| 61 |
-
records.append(record)
|
| 62 |
-
count = sum(r["storage"] == "nvfp4" for r in records)
|
| 63 |
-
expected = 192 if args.component == "dit" else 252
|
| 64 |
-
if count != expected:
|
| 65 |
-
raise ValueError(f"Recipe matched {count} matrices, expected {expected}")
|
| 66 |
-
metadata.update(format="pt", conversion="Prism native NVFP4; protected tensors unchanged; Built with Qwen")
|
| 67 |
-
args.output.parent.mkdir(parents=True, exist_ok=True)
|
| 68 |
-
save_file(output, str(args.output), metadata=metadata)
|
| 69 |
-
report = {"source": args.source.name, "source_sha256": digest(args.source), "output": args.output.name,
|
| 70 |
-
"output_sha256": digest(args.output), "output_bytes": args.output.stat().st_size,
|
| 71 |
-
"nvfp4_matrices": count, "elapsed_seconds": time.perf_counter() - start, "tensors": records}
|
| 72 |
-
args.output.with_suffix(".manifest.json").write_text(json.dumps(report, indent=2), encoding="utf-8")
|
| 73 |
-
print(json.dumps({k: v for k, v in report.items() if k != "tensors"}), flush=True)
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
if __name__ == "__main__":
|
| 77 |
-
main()
|
|
|
|
| 1 |
+
"""Convert official BF16 checkpoints to ComfyUI's native mixed NVFP4 format."""
|
| 2 |
+
import argparse
|
| 3 |
+
import hashlib
|
| 4 |
+
import json
|
| 5 |
+
from pathlib import Path
|
| 6 |
+
import re
|
| 7 |
+
import sys
|
| 8 |
+
import time
|
| 9 |
+
|
| 10 |
+
import torch
|
| 11 |
+
from safetensors import safe_open
|
| 12 |
+
from safetensors.torch import save_file
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
def digest(path):
|
| 16 |
+
with open(path, "rb") as stream:
|
| 17 |
+
return hashlib.file_digest(stream, "sha256").hexdigest()
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
def main():
|
| 21 |
+
parser = argparse.ArgumentParser()
|
| 22 |
+
parser.add_argument("source", type=Path)
|
| 23 |
+
parser.add_argument("output", type=Path)
|
| 24 |
+
parser.add_argument("--component", choices=["dit", "encoder"], required=True)
|
| 25 |
+
parser.add_argument("--comfy-root", type=Path, required=True)
|
| 26 |
+
args = parser.parse_args()
|
| 27 |
+
sys.path.insert(0, str(args.comfy_root.resolve()))
|
| 28 |
+
# ComfyUI parses process arguments when its operation registry is imported.
|
| 29 |
+
sys.argv = [sys.argv[0]]
|
| 30 |
+
from comfy.quant_ops import TensorCoreNVFP4Layout
|
| 31 |
+
|
| 32 |
+
patterns = {
|
| 33 |
+
"dit": r"transformer_blocks\.\d+\.(attn\.(to_q|to_k|to_v|to_out\.0)|img_mlp\.(gate_up|out))\.weight",
|
| 34 |
+
"encoder": r"model\.layers\.\d+\.(self_attn\.(q_proj|k_proj|v_proj|o_proj)|mlp\.(gate_proj|up_proj|down_proj))\.weight",
|
| 35 |
+
}
|
| 36 |
+
output, records = {}, []
|
| 37 |
+
start = time.perf_counter()
|
| 38 |
+
with safe_open(args.source, framework="pt", device="cpu") as source:
|
| 39 |
+
metadata = dict(source.metadata() or {})
|
| 40 |
+
for key in source.keys():
|
| 41 |
+
tensor = source.get_tensor(key)
|
| 42 |
+
record = {"name": key, "shape": list(tensor.shape), "source_dtype": str(tensor.dtype)}
|
| 43 |
+
if re.fullmatch(patterns[args.component], key):
|
| 44 |
+
if tensor.shape[1] % 32 or tensor.shape[0] % 8:
|
| 45 |
+
raise ValueError(f"Unsupported NVFP4 GEMM alignment: {key} {tensor.shape}")
|
| 46 |
+
original = tensor.to("cuda")
|
| 47 |
+
scale = original.float().abs().amax().clamp_min(1e-12) / (448 * 6)
|
| 48 |
+
packed, params = TensorCoreNVFP4Layout.quantize(original, scale=scale)
|
| 49 |
+
reconstructed = TensorCoreNVFP4Layout.dequantize(packed, params)
|
| 50 |
+
error = (original.float() - reconstructed.float()).square().mean()
|
| 51 |
+
record.update(storage="nvfp4", relative_rmse=float((error / original.float().square().mean().clamp_min(1e-30)).sqrt()))
|
| 52 |
+
for suffix, value in TensorCoreNVFP4Layout.state_dict_tensors(packed, params).items():
|
| 53 |
+
output[key + suffix] = value.cpu().contiguous()
|
| 54 |
+
marker = json.dumps({"format": "nvfp4"}, separators=(",", ":")).encode()
|
| 55 |
+
output[key.removesuffix("weight") + "comfy_quant"] = torch.tensor(list(marker), dtype=torch.uint8)
|
| 56 |
+
del original, reconstructed, packed, params
|
| 57 |
+
print(f"NVFP4 {key} relative_rmse={record['relative_rmse']:.5f}", flush=True)
|
| 58 |
+
else:
|
| 59 |
+
output[key] = tensor.clone()
|
| 60 |
+
record.update(storage=str(tensor.dtype), sha256=hashlib.sha256(tensor.view(torch.uint8).numpy().tobytes()).hexdigest())
|
| 61 |
+
records.append(record)
|
| 62 |
+
count = sum(r["storage"] == "nvfp4" for r in records)
|
| 63 |
+
expected = 192 if args.component == "dit" else 252
|
| 64 |
+
if count != expected:
|
| 65 |
+
raise ValueError(f"Recipe matched {count} matrices, expected {expected}")
|
| 66 |
+
metadata.update(format="pt", conversion="Prism native NVFP4; protected tensors unchanged; Built with Qwen")
|
| 67 |
+
args.output.parent.mkdir(parents=True, exist_ok=True)
|
| 68 |
+
save_file(output, str(args.output), metadata=metadata)
|
| 69 |
+
report = {"source": args.source.name, "source_sha256": digest(args.source), "output": args.output.name,
|
| 70 |
+
"output_sha256": digest(args.output), "output_bytes": args.output.stat().st_size,
|
| 71 |
+
"nvfp4_matrices": count, "elapsed_seconds": time.perf_counter() - start, "tensors": records}
|
| 72 |
+
args.output.with_suffix(".manifest.json").write_text(json.dumps(report, indent=2), encoding="utf-8")
|
| 73 |
+
print(json.dumps({k: v for k, v in report.items() if k != "tensors"}), flush=True)
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
if __name__ == "__main__":
|
| 77 |
+
main()
|
tools/run_workflow.py
CHANGED
|
@@ -1,26 +1,26 @@
|
|
| 1 |
-
"""Run an included API workflow against a local ComfyUI server."""
|
| 2 |
-
import argparse
|
| 3 |
-
import json
|
| 4 |
-
from pathlib import Path
|
| 5 |
-
import time
|
| 6 |
-
from urllib.request import Request, urlopen
|
| 7 |
-
|
| 8 |
-
parser = argparse.ArgumentParser()
|
| 9 |
-
parser.add_argument('workflow', type=Path)
|
| 10 |
-
parser.add_argument('--server', default='http://127.0.0.1:8188')
|
| 11 |
-
args = parser.parse_args()
|
| 12 |
-
graph = json.loads(args.workflow.read_text(encoding='utf-8'))
|
| 13 |
-
request = Request(args.server + '/prompt', data=json.dumps({'prompt':graph}).encode(), headers={'Content-Type':'application/json'})
|
| 14 |
-
with urlopen(request, timeout=30) as response:
|
| 15 |
-
prompt_id = json.load(response)['prompt_id']
|
| 16 |
-
print('Queued:',prompt_id,flush=True)
|
| 17 |
-
while True:
|
| 18 |
-
with urlopen(args.server + '/history/' + prompt_id, timeout=30) as response:
|
| 19 |
-
history = json.load(response).get(prompt_id)
|
| 20 |
-
if history:
|
| 21 |
-
print(json.dumps(history['status'],indent=2))
|
| 22 |
-
print(json.dumps(history['outputs'],indent=2))
|
| 23 |
-
if history['status']['status_str'] != 'success':
|
| 24 |
-
raise SystemExit(1)
|
| 25 |
-
break
|
| 26 |
-
time.sleep(0.5)
|
|
|
|
| 1 |
+
"""Run an included API workflow against a local ComfyUI server."""
|
| 2 |
+
import argparse
|
| 3 |
+
import json
|
| 4 |
+
from pathlib import Path
|
| 5 |
+
import time
|
| 6 |
+
from urllib.request import Request, urlopen
|
| 7 |
+
|
| 8 |
+
parser = argparse.ArgumentParser()
|
| 9 |
+
parser.add_argument('workflow', type=Path)
|
| 10 |
+
parser.add_argument('--server', default='http://127.0.0.1:8188')
|
| 11 |
+
args = parser.parse_args()
|
| 12 |
+
graph = json.loads(args.workflow.read_text(encoding='utf-8'))
|
| 13 |
+
request = Request(args.server + '/prompt', data=json.dumps({'prompt':graph}).encode(), headers={'Content-Type':'application/json'})
|
| 14 |
+
with urlopen(request, timeout=30) as response:
|
| 15 |
+
prompt_id = json.load(response)['prompt_id']
|
| 16 |
+
print('Queued:',prompt_id,flush=True)
|
| 17 |
+
while True:
|
| 18 |
+
with urlopen(args.server + '/history/' + prompt_id, timeout=30) as response:
|
| 19 |
+
history = json.load(response).get(prompt_id)
|
| 20 |
+
if history:
|
| 21 |
+
print(json.dumps(history['status'],indent=2))
|
| 22 |
+
print(json.dumps(history['outputs'],indent=2))
|
| 23 |
+
if history['status']['status_str'] != 'success':
|
| 24 |
+
raise SystemExit(1)
|
| 25 |
+
break
|
| 26 |
+
time.sleep(0.5)
|
tools/verify.py
CHANGED
|
@@ -1,36 +1,36 @@
|
|
| 1 |
-
"""Verify saved native NVFP4 shapes, metadata, scales, and protected tensors."""
|
| 2 |
-
import argparse
|
| 3 |
-
import hashlib
|
| 4 |
-
import json
|
| 5 |
-
from pathlib import Path
|
| 6 |
-
import torch
|
| 7 |
-
from safetensors import safe_open
|
| 8 |
-
|
| 9 |
-
parser = argparse.ArgumentParser()
|
| 10 |
-
parser.add_argument('source', type=Path)
|
| 11 |
-
parser.add_argument('converted', type=Path)
|
| 12 |
-
args = parser.parse_args()
|
| 13 |
-
manifest = json.loads(args.converted.with_suffix('.manifest.json').read_text())
|
| 14 |
-
with args.converted.open('rb') as stream:
|
| 15 |
-
assert hashlib.file_digest(stream, 'sha256').hexdigest() == manifest['output_sha256']
|
| 16 |
-
with safe_open(args.source, framework='pt') as original, safe_open(args.converted, framework='pt') as result:
|
| 17 |
-
expected_keys = set(original.keys())
|
| 18 |
-
for record in manifest['tensors']:
|
| 19 |
-
key = record['name']
|
| 20 |
-
value = result.get_tensor(key)
|
| 21 |
-
if record['storage'] == 'nvfp4':
|
| 22 |
-
marker_key = key.removesuffix('weight') + 'comfy_quant'
|
| 23 |
-
assert json.loads(result.get_tensor(marker_key).numpy().tobytes()) == {'format':'nvfp4'}
|
| 24 |
-
assert value.dtype == torch.uint8
|
| 25 |
-
assert list(value.shape) == [record['shape'][0], record['shape'][1] // 2]
|
| 26 |
-
block = result.get_tensor(key + '_scale')
|
| 27 |
-
scale = result.get_tensor(key + '_scale_2')
|
| 28 |
-
assert block.dtype == torch.float8_e4m3fn and scale.dtype == torch.float32
|
| 29 |
-
assert bool(torch.isfinite(block.float()).all()) and bool(torch.isfinite(scale).all())
|
| 30 |
-
assert bool((scale > 0).all())
|
| 31 |
-
expected_keys.update([key + '_scale', key + '_scale_2', marker_key])
|
| 32 |
-
else:
|
| 33 |
-
source = original.get_tensor(key)
|
| 34 |
-
assert source.dtype == value.dtype and torch.equal(source, value), key
|
| 35 |
-
assert set(result.keys()) == expected_keys
|
| 36 |
-
print(json.dumps({'file':args.converted.name, 'verified_tensors':len(manifest['tensors']), 'nvfp4_matrices':manifest['nvfp4_matrices'], 'protected_exact':True, 'sha256':manifest['output_sha256']}))
|
|
|
|
| 1 |
+
"""Verify saved native NVFP4 shapes, metadata, scales, and protected tensors."""
|
| 2 |
+
import argparse
|
| 3 |
+
import hashlib
|
| 4 |
+
import json
|
| 5 |
+
from pathlib import Path
|
| 6 |
+
import torch
|
| 7 |
+
from safetensors import safe_open
|
| 8 |
+
|
| 9 |
+
parser = argparse.ArgumentParser()
|
| 10 |
+
parser.add_argument('source', type=Path)
|
| 11 |
+
parser.add_argument('converted', type=Path)
|
| 12 |
+
args = parser.parse_args()
|
| 13 |
+
manifest = json.loads(args.converted.with_suffix('.manifest.json').read_text())
|
| 14 |
+
with args.converted.open('rb') as stream:
|
| 15 |
+
assert hashlib.file_digest(stream, 'sha256').hexdigest() == manifest['output_sha256']
|
| 16 |
+
with safe_open(args.source, framework='pt') as original, safe_open(args.converted, framework='pt') as result:
|
| 17 |
+
expected_keys = set(original.keys())
|
| 18 |
+
for record in manifest['tensors']:
|
| 19 |
+
key = record['name']
|
| 20 |
+
value = result.get_tensor(key)
|
| 21 |
+
if record['storage'] == 'nvfp4':
|
| 22 |
+
marker_key = key.removesuffix('weight') + 'comfy_quant'
|
| 23 |
+
assert json.loads(result.get_tensor(marker_key).numpy().tobytes()) == {'format':'nvfp4'}
|
| 24 |
+
assert value.dtype == torch.uint8
|
| 25 |
+
assert list(value.shape) == [record['shape'][0], record['shape'][1] // 2]
|
| 26 |
+
block = result.get_tensor(key + '_scale')
|
| 27 |
+
scale = result.get_tensor(key + '_scale_2')
|
| 28 |
+
assert block.dtype == torch.float8_e4m3fn and scale.dtype == torch.float32
|
| 29 |
+
assert bool(torch.isfinite(block.float()).all()) and bool(torch.isfinite(scale).all())
|
| 30 |
+
assert bool((scale > 0).all())
|
| 31 |
+
expected_keys.update([key + '_scale', key + '_scale_2', marker_key])
|
| 32 |
+
else:
|
| 33 |
+
source = original.get_tensor(key)
|
| 34 |
+
assert source.dtype == value.dtype and torch.equal(source, value), key
|
| 35 |
+
assert set(result.keys()) == expected_keys
|
| 36 |
+
print(json.dumps({'file':args.converted.name, 'verified_tensors':len(manifest['tensors']), 'nvfp4_matrices':manifest['nvfp4_matrices'], 'protected_exact':True, 'sha256':manifest['output_sha256']}))
|
validation/cases.json
CHANGED
|
@@ -227,14 +227,14 @@
|
|
| 227 |
"1": {
|
| 228 |
"class_type": "UNETLoader",
|
| 229 |
"inputs": {
|
| 230 |
-
"unet_name": "
|
| 231 |
"weight_dtype": "default"
|
| 232 |
}
|
| 233 |
},
|
| 234 |
"2": {
|
| 235 |
"class_type": "CLIPLoader",
|
| 236 |
"inputs": {
|
| 237 |
-
"clip_name": "
|
| 238 |
"type": "qwen_image",
|
| 239 |
"device": "default"
|
| 240 |
}
|
|
@@ -337,14 +337,14 @@
|
|
| 337 |
"1": {
|
| 338 |
"class_type": "UNETLoader",
|
| 339 |
"inputs": {
|
| 340 |
-
"unet_name": "
|
| 341 |
"weight_dtype": "default"
|
| 342 |
}
|
| 343 |
},
|
| 344 |
"2": {
|
| 345 |
"class_type": "CLIPLoader",
|
| 346 |
"inputs": {
|
| 347 |
-
"clip_name": "
|
| 348 |
"type": "qwen_image",
|
| 349 |
"device": "default"
|
| 350 |
}
|
|
@@ -667,14 +667,14 @@
|
|
| 667 |
"1": {
|
| 668 |
"class_type": "UNETLoader",
|
| 669 |
"inputs": {
|
| 670 |
-
"unet_name": "
|
| 671 |
"weight_dtype": "default"
|
| 672 |
}
|
| 673 |
},
|
| 674 |
"2": {
|
| 675 |
"class_type": "CLIPLoader",
|
| 676 |
"inputs": {
|
| 677 |
-
"clip_name": "
|
| 678 |
"type": "qwen_image",
|
| 679 |
"device": "default"
|
| 680 |
}
|
|
@@ -777,14 +777,14 @@
|
|
| 777 |
"1": {
|
| 778 |
"class_type": "UNETLoader",
|
| 779 |
"inputs": {
|
| 780 |
-
"unet_name": "
|
| 781 |
"weight_dtype": "default"
|
| 782 |
}
|
| 783 |
},
|
| 784 |
"2": {
|
| 785 |
"class_type": "CLIPLoader",
|
| 786 |
"inputs": {
|
| 787 |
-
"clip_name": "
|
| 788 |
"type": "qwen_image",
|
| 789 |
"device": "default"
|
| 790 |
}
|
|
@@ -1107,14 +1107,14 @@
|
|
| 1107 |
"1": {
|
| 1108 |
"class_type": "UNETLoader",
|
| 1109 |
"inputs": {
|
| 1110 |
-
"unet_name": "
|
| 1111 |
"weight_dtype": "default"
|
| 1112 |
}
|
| 1113 |
},
|
| 1114 |
"2": {
|
| 1115 |
"class_type": "CLIPLoader",
|
| 1116 |
"inputs": {
|
| 1117 |
-
"clip_name": "
|
| 1118 |
"type": "qwen_image",
|
| 1119 |
"device": "default"
|
| 1120 |
}
|
|
@@ -1217,14 +1217,14 @@
|
|
| 1217 |
"1": {
|
| 1218 |
"class_type": "UNETLoader",
|
| 1219 |
"inputs": {
|
| 1220 |
-
"unet_name": "
|
| 1221 |
"weight_dtype": "default"
|
| 1222 |
}
|
| 1223 |
},
|
| 1224 |
"2": {
|
| 1225 |
"class_type": "CLIPLoader",
|
| 1226 |
"inputs": {
|
| 1227 |
-
"clip_name": "
|
| 1228 |
"type": "qwen_image",
|
| 1229 |
"device": "default"
|
| 1230 |
}
|
|
@@ -1443,14 +1443,14 @@
|
|
| 1443 |
"1": {
|
| 1444 |
"class_type": "UNETLoader",
|
| 1445 |
"inputs": {
|
| 1446 |
-
"unet_name": "
|
| 1447 |
"weight_dtype": "default"
|
| 1448 |
}
|
| 1449 |
},
|
| 1450 |
"2": {
|
| 1451 |
"class_type": "CLIPLoader",
|
| 1452 |
"inputs": {
|
| 1453 |
-
"clip_name": "
|
| 1454 |
"type": "qwen_image",
|
| 1455 |
"device": "default"
|
| 1456 |
}
|
|
@@ -1675,14 +1675,14 @@
|
|
| 1675 |
"1": {
|
| 1676 |
"class_type": "UNETLoader",
|
| 1677 |
"inputs": {
|
| 1678 |
-
"unet_name": "
|
| 1679 |
"weight_dtype": "default"
|
| 1680 |
}
|
| 1681 |
},
|
| 1682 |
"2": {
|
| 1683 |
"class_type": "CLIPLoader",
|
| 1684 |
"inputs": {
|
| 1685 |
-
"clip_name": "
|
| 1686 |
"type": "qwen_image",
|
| 1687 |
"device": "default"
|
| 1688 |
}
|
|
@@ -1791,14 +1791,14 @@
|
|
| 1791 |
"1": {
|
| 1792 |
"class_type": "UNETLoader",
|
| 1793 |
"inputs": {
|
| 1794 |
-
"unet_name": "
|
| 1795 |
"weight_dtype": "default"
|
| 1796 |
}
|
| 1797 |
},
|
| 1798 |
"2": {
|
| 1799 |
"class_type": "CLIPLoader",
|
| 1800 |
"inputs": {
|
| 1801 |
-
"clip_name": "
|
| 1802 |
"type": "qwen_image",
|
| 1803 |
"device": "default"
|
| 1804 |
}
|
|
@@ -2127,14 +2127,14 @@
|
|
| 2127 |
"1": {
|
| 2128 |
"class_type": "UNETLoader",
|
| 2129 |
"inputs": {
|
| 2130 |
-
"unet_name": "
|
| 2131 |
"weight_dtype": "default"
|
| 2132 |
}
|
| 2133 |
},
|
| 2134 |
"2": {
|
| 2135 |
"class_type": "CLIPLoader",
|
| 2136 |
"inputs": {
|
| 2137 |
-
"clip_name": "
|
| 2138 |
"type": "qwen_image",
|
| 2139 |
"device": "default"
|
| 2140 |
}
|
|
@@ -2237,14 +2237,14 @@
|
|
| 2237 |
"1": {
|
| 2238 |
"class_type": "UNETLoader",
|
| 2239 |
"inputs": {
|
| 2240 |
-
"unet_name": "
|
| 2241 |
"weight_dtype": "default"
|
| 2242 |
}
|
| 2243 |
},
|
| 2244 |
"2": {
|
| 2245 |
"class_type": "CLIPLoader",
|
| 2246 |
"inputs": {
|
| 2247 |
-
"clip_name": "
|
| 2248 |
"type": "qwen_image",
|
| 2249 |
"device": "default"
|
| 2250 |
}
|
|
@@ -2457,14 +2457,14 @@
|
|
| 2457 |
"1": {
|
| 2458 |
"class_type": "UNETLoader",
|
| 2459 |
"inputs": {
|
| 2460 |
-
"unet_name": "
|
| 2461 |
"weight_dtype": "default"
|
| 2462 |
}
|
| 2463 |
},
|
| 2464 |
"2": {
|
| 2465 |
"class_type": "CLIPLoader",
|
| 2466 |
"inputs": {
|
| 2467 |
-
"clip_name": "
|
| 2468 |
"type": "qwen_image",
|
| 2469 |
"device": "default"
|
| 2470 |
}
|
|
@@ -2693,14 +2693,14 @@
|
|
| 2693 |
"1": {
|
| 2694 |
"class_type": "UNETLoader",
|
| 2695 |
"inputs": {
|
| 2696 |
-
"unet_name": "
|
| 2697 |
"weight_dtype": "default"
|
| 2698 |
}
|
| 2699 |
},
|
| 2700 |
"2": {
|
| 2701 |
"class_type": "CLIPLoader",
|
| 2702 |
"inputs": {
|
| 2703 |
-
"clip_name": "
|
| 2704 |
"type": "qwen_image",
|
| 2705 |
"device": "default"
|
| 2706 |
}
|
|
@@ -2929,14 +2929,14 @@
|
|
| 2929 |
"1": {
|
| 2930 |
"class_type": "UNETLoader",
|
| 2931 |
"inputs": {
|
| 2932 |
-
"unet_name": "
|
| 2933 |
"weight_dtype": "default"
|
| 2934 |
}
|
| 2935 |
},
|
| 2936 |
"2": {
|
| 2937 |
"class_type": "CLIPLoader",
|
| 2938 |
"inputs": {
|
| 2939 |
-
"clip_name": "
|
| 2940 |
"type": "qwen_image",
|
| 2941 |
"device": "default"
|
| 2942 |
}
|
|
@@ -3259,14 +3259,14 @@
|
|
| 3259 |
"1": {
|
| 3260 |
"class_type": "UNETLoader",
|
| 3261 |
"inputs": {
|
| 3262 |
-
"unet_name": "
|
| 3263 |
"weight_dtype": "default"
|
| 3264 |
}
|
| 3265 |
},
|
| 3266 |
"2": {
|
| 3267 |
"class_type": "CLIPLoader",
|
| 3268 |
"inputs": {
|
| 3269 |
-
"clip_name": "
|
| 3270 |
"type": "qwen_image",
|
| 3271 |
"device": "default"
|
| 3272 |
}
|
|
@@ -3369,14 +3369,14 @@
|
|
| 3369 |
"1": {
|
| 3370 |
"class_type": "UNETLoader",
|
| 3371 |
"inputs": {
|
| 3372 |
-
"unet_name": "
|
| 3373 |
"weight_dtype": "default"
|
| 3374 |
}
|
| 3375 |
},
|
| 3376 |
"2": {
|
| 3377 |
"class_type": "CLIPLoader",
|
| 3378 |
"inputs": {
|
| 3379 |
-
"clip_name": "
|
| 3380 |
"type": "qwen_image",
|
| 3381 |
"device": "default"
|
| 3382 |
}
|
|
@@ -3699,14 +3699,14 @@
|
|
| 3699 |
"1": {
|
| 3700 |
"class_type": "UNETLoader",
|
| 3701 |
"inputs": {
|
| 3702 |
-
"unet_name": "
|
| 3703 |
"weight_dtype": "default"
|
| 3704 |
}
|
| 3705 |
},
|
| 3706 |
"2": {
|
| 3707 |
"class_type": "CLIPLoader",
|
| 3708 |
"inputs": {
|
| 3709 |
-
"clip_name": "
|
| 3710 |
"type": "qwen_image",
|
| 3711 |
"device": "default"
|
| 3712 |
}
|
|
@@ -3809,14 +3809,14 @@
|
|
| 3809 |
"1": {
|
| 3810 |
"class_type": "UNETLoader",
|
| 3811 |
"inputs": {
|
| 3812 |
-
"unet_name": "
|
| 3813 |
"weight_dtype": "default"
|
| 3814 |
}
|
| 3815 |
},
|
| 3816 |
"2": {
|
| 3817 |
"class_type": "CLIPLoader",
|
| 3818 |
"inputs": {
|
| 3819 |
-
"clip_name": "
|
| 3820 |
"type": "qwen_image",
|
| 3821 |
"device": "default"
|
| 3822 |
}
|
|
@@ -4139,14 +4139,14 @@
|
|
| 4139 |
"1": {
|
| 4140 |
"class_type": "UNETLoader",
|
| 4141 |
"inputs": {
|
| 4142 |
-
"unet_name": "
|
| 4143 |
"weight_dtype": "default"
|
| 4144 |
}
|
| 4145 |
},
|
| 4146 |
"2": {
|
| 4147 |
"class_type": "CLIPLoader",
|
| 4148 |
"inputs": {
|
| 4149 |
-
"clip_name": "
|
| 4150 |
"type": "qwen_image",
|
| 4151 |
"device": "default"
|
| 4152 |
}
|
|
@@ -4249,14 +4249,14 @@
|
|
| 4249 |
"1": {
|
| 4250 |
"class_type": "UNETLoader",
|
| 4251 |
"inputs": {
|
| 4252 |
-
"unet_name": "
|
| 4253 |
"weight_dtype": "default"
|
| 4254 |
}
|
| 4255 |
},
|
| 4256 |
"2": {
|
| 4257 |
"class_type": "CLIPLoader",
|
| 4258 |
"inputs": {
|
| 4259 |
-
"clip_name": "
|
| 4260 |
"type": "qwen_image",
|
| 4261 |
"device": "default"
|
| 4262 |
}
|
|
@@ -4579,14 +4579,14 @@
|
|
| 4579 |
"1": {
|
| 4580 |
"class_type": "UNETLoader",
|
| 4581 |
"inputs": {
|
| 4582 |
-
"unet_name": "
|
| 4583 |
"weight_dtype": "default"
|
| 4584 |
}
|
| 4585 |
},
|
| 4586 |
"2": {
|
| 4587 |
"class_type": "CLIPLoader",
|
| 4588 |
"inputs": {
|
| 4589 |
-
"clip_name": "
|
| 4590 |
"type": "qwen_image",
|
| 4591 |
"device": "default"
|
| 4592 |
}
|
|
@@ -4689,14 +4689,14 @@
|
|
| 4689 |
"1": {
|
| 4690 |
"class_type": "UNETLoader",
|
| 4691 |
"inputs": {
|
| 4692 |
-
"unet_name": "
|
| 4693 |
"weight_dtype": "default"
|
| 4694 |
}
|
| 4695 |
},
|
| 4696 |
"2": {
|
| 4697 |
"class_type": "CLIPLoader",
|
| 4698 |
"inputs": {
|
| 4699 |
-
"clip_name": "
|
| 4700 |
"type": "qwen_image",
|
| 4701 |
"device": "default"
|
| 4702 |
}
|
|
@@ -5136,7 +5136,7 @@
|
|
| 5136 |
"2": {
|
| 5137 |
"class_type": "CLIPLoader",
|
| 5138 |
"inputs": {
|
| 5139 |
-
"clip_name": "
|
| 5140 |
"type": "qwen_image",
|
| 5141 |
"device": "default"
|
| 5142 |
}
|
|
@@ -5239,7 +5239,7 @@
|
|
| 5239 |
"1": {
|
| 5240 |
"class_type": "UNETLoader",
|
| 5241 |
"inputs": {
|
| 5242 |
-
"unet_name": "
|
| 5243 |
"weight_dtype": "default"
|
| 5244 |
}
|
| 5245 |
},
|
|
@@ -5349,14 +5349,14 @@
|
|
| 5349 |
"1": {
|
| 5350 |
"class_type": "UNETLoader",
|
| 5351 |
"inputs": {
|
| 5352 |
-
"unet_name": "
|
| 5353 |
"weight_dtype": "default"
|
| 5354 |
}
|
| 5355 |
},
|
| 5356 |
"2": {
|
| 5357 |
"class_type": "CLIPLoader",
|
| 5358 |
"inputs": {
|
| 5359 |
-
"clip_name": "
|
| 5360 |
"type": "qwen_image",
|
| 5361 |
"device": "default"
|
| 5362 |
}
|
|
@@ -5459,14 +5459,14 @@
|
|
| 5459 |
"1": {
|
| 5460 |
"class_type": "UNETLoader",
|
| 5461 |
"inputs": {
|
| 5462 |
-
"unet_name": "
|
| 5463 |
"weight_dtype": "default"
|
| 5464 |
}
|
| 5465 |
},
|
| 5466 |
"2": {
|
| 5467 |
"class_type": "CLIPLoader",
|
| 5468 |
"inputs": {
|
| 5469 |
-
"clip_name": "
|
| 5470 |
"type": "qwen_image",
|
| 5471 |
"device": "default"
|
| 5472 |
}
|
|
@@ -5569,14 +5569,14 @@
|
|
| 5569 |
"1": {
|
| 5570 |
"class_type": "UNETLoader",
|
| 5571 |
"inputs": {
|
| 5572 |
-
"unet_name": "
|
| 5573 |
"weight_dtype": "default"
|
| 5574 |
}
|
| 5575 |
},
|
| 5576 |
"2": {
|
| 5577 |
"class_type": "CLIPLoader",
|
| 5578 |
"inputs": {
|
| 5579 |
-
"clip_name": "
|
| 5580 |
"type": "qwen_image",
|
| 5581 |
"device": "default"
|
| 5582 |
}
|
|
@@ -5679,14 +5679,14 @@
|
|
| 5679 |
"1": {
|
| 5680 |
"class_type": "UNETLoader",
|
| 5681 |
"inputs": {
|
| 5682 |
-
"unet_name": "
|
| 5683 |
"weight_dtype": "default"
|
| 5684 |
}
|
| 5685 |
},
|
| 5686 |
"2": {
|
| 5687 |
"class_type": "CLIPLoader",
|
| 5688 |
"inputs": {
|
| 5689 |
-
"clip_name": "
|
| 5690 |
"type": "qwen_image",
|
| 5691 |
"device": "default"
|
| 5692 |
}
|
|
@@ -6126,7 +6126,7 @@
|
|
| 6126 |
"2": {
|
| 6127 |
"class_type": "CLIPLoader",
|
| 6128 |
"inputs": {
|
| 6129 |
-
"clip_name": "
|
| 6130 |
"type": "qwen_image",
|
| 6131 |
"device": "default"
|
| 6132 |
}
|
|
@@ -6229,7 +6229,7 @@
|
|
| 6229 |
"1": {
|
| 6230 |
"class_type": "UNETLoader",
|
| 6231 |
"inputs": {
|
| 6232 |
-
"unet_name": "
|
| 6233 |
"weight_dtype": "default"
|
| 6234 |
}
|
| 6235 |
},
|
|
@@ -6339,14 +6339,14 @@
|
|
| 6339 |
"1": {
|
| 6340 |
"class_type": "UNETLoader",
|
| 6341 |
"inputs": {
|
| 6342 |
-
"unet_name": "
|
| 6343 |
"weight_dtype": "default"
|
| 6344 |
}
|
| 6345 |
},
|
| 6346 |
"2": {
|
| 6347 |
"class_type": "CLIPLoader",
|
| 6348 |
"inputs": {
|
| 6349 |
-
"clip_name": "
|
| 6350 |
"type": "qwen_image",
|
| 6351 |
"device": "default"
|
| 6352 |
}
|
|
@@ -6449,14 +6449,14 @@
|
|
| 6449 |
"1": {
|
| 6450 |
"class_type": "UNETLoader",
|
| 6451 |
"inputs": {
|
| 6452 |
-
"unet_name": "
|
| 6453 |
"weight_dtype": "default"
|
| 6454 |
}
|
| 6455 |
},
|
| 6456 |
"2": {
|
| 6457 |
"class_type": "CLIPLoader",
|
| 6458 |
"inputs": {
|
| 6459 |
-
"clip_name": "
|
| 6460 |
"type": "qwen_image",
|
| 6461 |
"device": "default"
|
| 6462 |
}
|
|
@@ -6559,14 +6559,14 @@
|
|
| 6559 |
"1": {
|
| 6560 |
"class_type": "UNETLoader",
|
| 6561 |
"inputs": {
|
| 6562 |
-
"unet_name": "
|
| 6563 |
"weight_dtype": "default"
|
| 6564 |
}
|
| 6565 |
},
|
| 6566 |
"2": {
|
| 6567 |
"class_type": "CLIPLoader",
|
| 6568 |
"inputs": {
|
| 6569 |
-
"clip_name": "
|
| 6570 |
"type": "qwen_image",
|
| 6571 |
"device": "default"
|
| 6572 |
}
|
|
@@ -6669,14 +6669,14 @@
|
|
| 6669 |
"1": {
|
| 6670 |
"class_type": "UNETLoader",
|
| 6671 |
"inputs": {
|
| 6672 |
-
"unet_name": "
|
| 6673 |
"weight_dtype": "default"
|
| 6674 |
}
|
| 6675 |
},
|
| 6676 |
"2": {
|
| 6677 |
"class_type": "CLIPLoader",
|
| 6678 |
"inputs": {
|
| 6679 |
-
"clip_name": "
|
| 6680 |
"type": "qwen_image",
|
| 6681 |
"device": "default"
|
| 6682 |
}
|
|
@@ -6999,14 +6999,14 @@
|
|
| 6999 |
"1": {
|
| 7000 |
"class_type": "UNETLoader",
|
| 7001 |
"inputs": {
|
| 7002 |
-
"unet_name": "
|
| 7003 |
"weight_dtype": "default"
|
| 7004 |
}
|
| 7005 |
},
|
| 7006 |
"2": {
|
| 7007 |
"class_type": "CLIPLoader",
|
| 7008 |
"inputs": {
|
| 7009 |
-
"clip_name": "
|
| 7010 |
"type": "qwen_image",
|
| 7011 |
"device": "default"
|
| 7012 |
}
|
|
@@ -7109,14 +7109,14 @@
|
|
| 7109 |
"1": {
|
| 7110 |
"class_type": "UNETLoader",
|
| 7111 |
"inputs": {
|
| 7112 |
-
"unet_name": "
|
| 7113 |
"weight_dtype": "default"
|
| 7114 |
}
|
| 7115 |
},
|
| 7116 |
"2": {
|
| 7117 |
"class_type": "CLIPLoader",
|
| 7118 |
"inputs": {
|
| 7119 |
-
"clip_name": "
|
| 7120 |
"type": "qwen_image",
|
| 7121 |
"device": "default"
|
| 7122 |
}
|
|
@@ -7329,14 +7329,14 @@
|
|
| 7329 |
"1": {
|
| 7330 |
"class_type": "UNETLoader",
|
| 7331 |
"inputs": {
|
| 7332 |
-
"unet_name": "
|
| 7333 |
"weight_dtype": "default"
|
| 7334 |
}
|
| 7335 |
},
|
| 7336 |
"2": {
|
| 7337 |
"class_type": "CLIPLoader",
|
| 7338 |
"inputs": {
|
| 7339 |
-
"clip_name": "
|
| 7340 |
"type": "qwen_image",
|
| 7341 |
"device": "default"
|
| 7342 |
}
|
|
@@ -7439,14 +7439,14 @@
|
|
| 7439 |
"1": {
|
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@@ -7659,14 +7659,14 @@
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@@ -8319,14 +8319,14 @@
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@@ -8649,14 +8649,14 @@
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|
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"weight_dtype": "default"
|
| 5244 |
}
|
| 5245 |
},
|
|
|
|
| 5349 |
"1": {
|
| 5350 |
"class_type": "UNETLoader",
|
| 5351 |
"inputs": {
|
| 5352 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 5353 |
"weight_dtype": "default"
|
| 5354 |
}
|
| 5355 |
},
|
| 5356 |
"2": {
|
| 5357 |
"class_type": "CLIPLoader",
|
| 5358 |
"inputs": {
|
| 5359 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 5360 |
"type": "qwen_image",
|
| 5361 |
"device": "default"
|
| 5362 |
}
|
|
|
|
| 5459 |
"1": {
|
| 5460 |
"class_type": "UNETLoader",
|
| 5461 |
"inputs": {
|
| 5462 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 5463 |
"weight_dtype": "default"
|
| 5464 |
}
|
| 5465 |
},
|
| 5466 |
"2": {
|
| 5467 |
"class_type": "CLIPLoader",
|
| 5468 |
"inputs": {
|
| 5469 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 5470 |
"type": "qwen_image",
|
| 5471 |
"device": "default"
|
| 5472 |
}
|
|
|
|
| 5569 |
"1": {
|
| 5570 |
"class_type": "UNETLoader",
|
| 5571 |
"inputs": {
|
| 5572 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 5573 |
"weight_dtype": "default"
|
| 5574 |
}
|
| 5575 |
},
|
| 5576 |
"2": {
|
| 5577 |
"class_type": "CLIPLoader",
|
| 5578 |
"inputs": {
|
| 5579 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 5580 |
"type": "qwen_image",
|
| 5581 |
"device": "default"
|
| 5582 |
}
|
|
|
|
| 5679 |
"1": {
|
| 5680 |
"class_type": "UNETLoader",
|
| 5681 |
"inputs": {
|
| 5682 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 5683 |
"weight_dtype": "default"
|
| 5684 |
}
|
| 5685 |
},
|
| 5686 |
"2": {
|
| 5687 |
"class_type": "CLIPLoader",
|
| 5688 |
"inputs": {
|
| 5689 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 5690 |
"type": "qwen_image",
|
| 5691 |
"device": "default"
|
| 5692 |
}
|
|
|
|
| 6126 |
"2": {
|
| 6127 |
"class_type": "CLIPLoader",
|
| 6128 |
"inputs": {
|
| 6129 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 6130 |
"type": "qwen_image",
|
| 6131 |
"device": "default"
|
| 6132 |
}
|
|
|
|
| 6229 |
"1": {
|
| 6230 |
"class_type": "UNETLoader",
|
| 6231 |
"inputs": {
|
| 6232 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 6233 |
"weight_dtype": "default"
|
| 6234 |
}
|
| 6235 |
},
|
|
|
|
| 6339 |
"1": {
|
| 6340 |
"class_type": "UNETLoader",
|
| 6341 |
"inputs": {
|
| 6342 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 6343 |
"weight_dtype": "default"
|
| 6344 |
}
|
| 6345 |
},
|
| 6346 |
"2": {
|
| 6347 |
"class_type": "CLIPLoader",
|
| 6348 |
"inputs": {
|
| 6349 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 6350 |
"type": "qwen_image",
|
| 6351 |
"device": "default"
|
| 6352 |
}
|
|
|
|
| 6449 |
"1": {
|
| 6450 |
"class_type": "UNETLoader",
|
| 6451 |
"inputs": {
|
| 6452 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 6453 |
"weight_dtype": "default"
|
| 6454 |
}
|
| 6455 |
},
|
| 6456 |
"2": {
|
| 6457 |
"class_type": "CLIPLoader",
|
| 6458 |
"inputs": {
|
| 6459 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 6460 |
"type": "qwen_image",
|
| 6461 |
"device": "default"
|
| 6462 |
}
|
|
|
|
| 6559 |
"1": {
|
| 6560 |
"class_type": "UNETLoader",
|
| 6561 |
"inputs": {
|
| 6562 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 6563 |
"weight_dtype": "default"
|
| 6564 |
}
|
| 6565 |
},
|
| 6566 |
"2": {
|
| 6567 |
"class_type": "CLIPLoader",
|
| 6568 |
"inputs": {
|
| 6569 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 6570 |
"type": "qwen_image",
|
| 6571 |
"device": "default"
|
| 6572 |
}
|
|
|
|
| 6669 |
"1": {
|
| 6670 |
"class_type": "UNETLoader",
|
| 6671 |
"inputs": {
|
| 6672 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 6673 |
"weight_dtype": "default"
|
| 6674 |
}
|
| 6675 |
},
|
| 6676 |
"2": {
|
| 6677 |
"class_type": "CLIPLoader",
|
| 6678 |
"inputs": {
|
| 6679 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 6680 |
"type": "qwen_image",
|
| 6681 |
"device": "default"
|
| 6682 |
}
|
|
|
|
| 6999 |
"1": {
|
| 7000 |
"class_type": "UNETLoader",
|
| 7001 |
"inputs": {
|
| 7002 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 7003 |
"weight_dtype": "default"
|
| 7004 |
}
|
| 7005 |
},
|
| 7006 |
"2": {
|
| 7007 |
"class_type": "CLIPLoader",
|
| 7008 |
"inputs": {
|
| 7009 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 7010 |
"type": "qwen_image",
|
| 7011 |
"device": "default"
|
| 7012 |
}
|
|
|
|
| 7109 |
"1": {
|
| 7110 |
"class_type": "UNETLoader",
|
| 7111 |
"inputs": {
|
| 7112 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 7113 |
"weight_dtype": "default"
|
| 7114 |
}
|
| 7115 |
},
|
| 7116 |
"2": {
|
| 7117 |
"class_type": "CLIPLoader",
|
| 7118 |
"inputs": {
|
| 7119 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 7120 |
"type": "qwen_image",
|
| 7121 |
"device": "default"
|
| 7122 |
}
|
|
|
|
| 7329 |
"1": {
|
| 7330 |
"class_type": "UNETLoader",
|
| 7331 |
"inputs": {
|
| 7332 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 7333 |
"weight_dtype": "default"
|
| 7334 |
}
|
| 7335 |
},
|
| 7336 |
"2": {
|
| 7337 |
"class_type": "CLIPLoader",
|
| 7338 |
"inputs": {
|
| 7339 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 7340 |
"type": "qwen_image",
|
| 7341 |
"device": "default"
|
| 7342 |
}
|
|
|
|
| 7439 |
"1": {
|
| 7440 |
"class_type": "UNETLoader",
|
| 7441 |
"inputs": {
|
| 7442 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 7443 |
"weight_dtype": "default"
|
| 7444 |
}
|
| 7445 |
},
|
| 7446 |
"2": {
|
| 7447 |
"class_type": "CLIPLoader",
|
| 7448 |
"inputs": {
|
| 7449 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 7450 |
"type": "qwen_image",
|
| 7451 |
"device": "default"
|
| 7452 |
}
|
|
|
|
| 7659 |
"1": {
|
| 7660 |
"class_type": "UNETLoader",
|
| 7661 |
"inputs": {
|
| 7662 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 7663 |
"weight_dtype": "default"
|
| 7664 |
}
|
| 7665 |
},
|
| 7666 |
"2": {
|
| 7667 |
"class_type": "CLIPLoader",
|
| 7668 |
"inputs": {
|
| 7669 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 7670 |
"type": "qwen_image",
|
| 7671 |
"device": "default"
|
| 7672 |
}
|
|
|
|
| 8106 |
"2": {
|
| 8107 |
"class_type": "CLIPLoader",
|
| 8108 |
"inputs": {
|
| 8109 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 8110 |
"type": "qwen_image",
|
| 8111 |
"device": "default"
|
| 8112 |
}
|
|
|
|
| 8209 |
"1": {
|
| 8210 |
"class_type": "UNETLoader",
|
| 8211 |
"inputs": {
|
| 8212 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 8213 |
"weight_dtype": "default"
|
| 8214 |
}
|
| 8215 |
},
|
|
|
|
| 8319 |
"1": {
|
| 8320 |
"class_type": "UNETLoader",
|
| 8321 |
"inputs": {
|
| 8322 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 8323 |
"weight_dtype": "default"
|
| 8324 |
}
|
| 8325 |
},
|
| 8326 |
"2": {
|
| 8327 |
"class_type": "CLIPLoader",
|
| 8328 |
"inputs": {
|
| 8329 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 8330 |
"type": "qwen_image",
|
| 8331 |
"device": "default"
|
| 8332 |
}
|
|
|
|
| 8429 |
"1": {
|
| 8430 |
"class_type": "UNETLoader",
|
| 8431 |
"inputs": {
|
| 8432 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 8433 |
"weight_dtype": "default"
|
| 8434 |
}
|
| 8435 |
},
|
| 8436 |
"2": {
|
| 8437 |
"class_type": "CLIPLoader",
|
| 8438 |
"inputs": {
|
| 8439 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 8440 |
"type": "qwen_image",
|
| 8441 |
"device": "default"
|
| 8442 |
}
|
|
|
|
| 8539 |
"1": {
|
| 8540 |
"class_type": "UNETLoader",
|
| 8541 |
"inputs": {
|
| 8542 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 8543 |
"weight_dtype": "default"
|
| 8544 |
}
|
| 8545 |
},
|
| 8546 |
"2": {
|
| 8547 |
"class_type": "CLIPLoader",
|
| 8548 |
"inputs": {
|
| 8549 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 8550 |
"type": "qwen_image",
|
| 8551 |
"device": "default"
|
| 8552 |
}
|
|
|
|
| 8649 |
"1": {
|
| 8650 |
"class_type": "UNETLoader",
|
| 8651 |
"inputs": {
|
| 8652 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 8653 |
"weight_dtype": "default"
|
| 8654 |
}
|
| 8655 |
},
|
| 8656 |
"2": {
|
| 8657 |
"class_type": "CLIPLoader",
|
| 8658 |
"inputs": {
|
| 8659 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 8660 |
"type": "qwen_image",
|
| 8661 |
"device": "default"
|
| 8662 |
}
|
validation/roundtrip.json
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
[
|
| 2 |
{
|
| 3 |
-
"file": "
|
| 4 |
"sha256": "4fdbaec94c19f7b8888f1e1ddbae05354c014492f9b2547b1cd8dca0ce923526",
|
| 5 |
"match": true
|
| 6 |
},
|
| 7 |
{
|
| 8 |
-
"file": "
|
| 9 |
"sha256": "cdd9b85bcb60d5cb358ddecfd45a48fa15cb728b70d88bd2028840eaef095d16",
|
| 10 |
"match": true
|
| 11 |
},
|
|
|
|
| 1 |
[
|
| 2 |
{
|
| 3 |
+
"file": "qwen_image_2.1_nvfp4.safetensors",
|
| 4 |
"sha256": "4fdbaec94c19f7b8888f1e1ddbae05354c014492f9b2547b1cd8dca0ce923526",
|
| 5 |
"match": true
|
| 6 |
},
|
| 7 |
{
|
| 8 |
+
"file": "qwen3vl_8b_nvfp4.safetensors",
|
| 9 |
"sha256": "cdd9b85bcb60d5cb358ddecfd45a48fa15cb728b70d88bd2028840eaef095d16",
|
| 10 |
"match": true
|
| 11 |
},
|
workflows/01_Text_to_Image.json
CHANGED
|
@@ -30,7 +30,7 @@
|
|
| 30 |
"Node name for S&R": "UNETLoader"
|
| 31 |
},
|
| 32 |
"widgets_values": [
|
| 33 |
-
"
|
| 34 |
"default"
|
| 35 |
]
|
| 36 |
},
|
|
@@ -62,7 +62,7 @@
|
|
| 62 |
"Node name for S&R": "CLIPLoader"
|
| 63 |
},
|
| 64 |
"widgets_values": [
|
| 65 |
-
"
|
| 66 |
"qwen_image",
|
| 67 |
"default"
|
| 68 |
]
|
|
@@ -306,7 +306,7 @@
|
|
| 306 |
"Node name for S&R": "SaveImage"
|
| 307 |
},
|
| 308 |
"widgets_values": [
|
| 309 |
-
"
|
| 310 |
]
|
| 311 |
}
|
| 312 |
],
|
|
|
|
| 30 |
"Node name for S&R": "UNETLoader"
|
| 31 |
},
|
| 32 |
"widgets_values": [
|
| 33 |
+
"qwen_image_2.1_nvfp4.safetensors",
|
| 34 |
"default"
|
| 35 |
]
|
| 36 |
},
|
|
|
|
| 62 |
"Node name for S&R": "CLIPLoader"
|
| 63 |
},
|
| 64 |
"widgets_values": [
|
| 65 |
+
"qwen3vl_8b_nvfp4.safetensors",
|
| 66 |
"qwen_image",
|
| 67 |
"default"
|
| 68 |
]
|
|
|
|
| 306 |
"Node name for S&R": "SaveImage"
|
| 307 |
},
|
| 308 |
"widgets_values": [
|
| 309 |
+
"Qwen21_NVFP4/01_Text_to_Image"
|
| 310 |
]
|
| 311 |
}
|
| 312 |
],
|
workflows/02_Image_Editing.json
CHANGED
|
@@ -30,7 +30,7 @@
|
|
| 30 |
"Node name for S&R": "UNETLoader"
|
| 31 |
},
|
| 32 |
"widgets_values": [
|
| 33 |
-
"
|
| 34 |
"default"
|
| 35 |
]
|
| 36 |
},
|
|
@@ -62,7 +62,7 @@
|
|
| 62 |
"Node name for S&R": "CLIPLoader"
|
| 63 |
},
|
| 64 |
"widgets_values": [
|
| 65 |
-
"
|
| 66 |
"qwen_image",
|
| 67 |
"default"
|
| 68 |
]
|
|
@@ -286,7 +286,7 @@
|
|
| 286 |
"Node name for S&R": "SaveImage"
|
| 287 |
},
|
| 288 |
"widgets_values": [
|
| 289 |
-
"
|
| 290 |
]
|
| 291 |
},
|
| 292 |
{
|
|
@@ -322,7 +322,7 @@
|
|
| 322 |
"Node name for S&R": "LoadImage"
|
| 323 |
},
|
| 324 |
"widgets_values": [
|
| 325 |
-
"
|
| 326 |
]
|
| 327 |
}
|
| 328 |
],
|
|
|
|
| 30 |
"Node name for S&R": "UNETLoader"
|
| 31 |
},
|
| 32 |
"widgets_values": [
|
| 33 |
+
"qwen_image_2.1_nvfp4.safetensors",
|
| 34 |
"default"
|
| 35 |
]
|
| 36 |
},
|
|
|
|
| 62 |
"Node name for S&R": "CLIPLoader"
|
| 63 |
},
|
| 64 |
"widgets_values": [
|
| 65 |
+
"qwen3vl_8b_nvfp4.safetensors",
|
| 66 |
"qwen_image",
|
| 67 |
"default"
|
| 68 |
]
|
|
|
|
| 286 |
"Node name for S&R": "SaveImage"
|
| 287 |
},
|
| 288 |
"widgets_values": [
|
| 289 |
+
"Qwen21_NVFP4/02_Image_Editing"
|
| 290 |
]
|
| 291 |
},
|
| 292 |
{
|
|
|
|
| 322 |
"Node name for S&R": "LoadImage"
|
| 323 |
},
|
| 324 |
"widgets_values": [
|
| 325 |
+
"qwen_image_2.1_edit_reference.png"
|
| 326 |
]
|
| 327 |
}
|
| 328 |
],
|
workflows/03_Transparent_RGBA.json
CHANGED
|
@@ -30,7 +30,7 @@
|
|
| 30 |
"Node name for S&R": "UNETLoader"
|
| 31 |
},
|
| 32 |
"widgets_values": [
|
| 33 |
-
"
|
| 34 |
"default"
|
| 35 |
]
|
| 36 |
},
|
|
@@ -62,7 +62,7 @@
|
|
| 62 |
"Node name for S&R": "CLIPLoader"
|
| 63 |
},
|
| 64 |
"widgets_values": [
|
| 65 |
-
"
|
| 66 |
"qwen_image",
|
| 67 |
"default"
|
| 68 |
]
|
|
@@ -306,7 +306,7 @@
|
|
| 306 |
"Node name for S&R": "SaveImage"
|
| 307 |
},
|
| 308 |
"widgets_values": [
|
| 309 |
-
"
|
| 310 |
]
|
| 311 |
}
|
| 312 |
],
|
|
|
|
| 30 |
"Node name for S&R": "UNETLoader"
|
| 31 |
},
|
| 32 |
"widgets_values": [
|
| 33 |
+
"qwen_image_2.1_nvfp4.safetensors",
|
| 34 |
"default"
|
| 35 |
]
|
| 36 |
},
|
|
|
|
| 62 |
"Node name for S&R": "CLIPLoader"
|
| 63 |
},
|
| 64 |
"widgets_values": [
|
| 65 |
+
"qwen3vl_8b_nvfp4.safetensors",
|
| 66 |
"qwen_image",
|
| 67 |
"default"
|
| 68 |
]
|
|
|
|
| 306 |
"Node name for S&R": "SaveImage"
|
| 307 |
},
|
| 308 |
"widgets_values": [
|
| 309 |
+
"Qwen21_NVFP4/03_Transparent_RGBA"
|
| 310 |
]
|
| 311 |
}
|
| 312 |
],
|
workflows/04_2K_Typography.json
CHANGED
|
@@ -30,7 +30,7 @@
|
|
| 30 |
"Node name for S&R": "UNETLoader"
|
| 31 |
},
|
| 32 |
"widgets_values": [
|
| 33 |
-
"
|
| 34 |
"default"
|
| 35 |
]
|
| 36 |
},
|
|
@@ -62,7 +62,7 @@
|
|
| 62 |
"Node name for S&R": "CLIPLoader"
|
| 63 |
},
|
| 64 |
"widgets_values": [
|
| 65 |
-
"
|
| 66 |
"qwen_image",
|
| 67 |
"default"
|
| 68 |
]
|
|
@@ -306,7 +306,7 @@
|
|
| 306 |
"Node name for S&R": "SaveImage"
|
| 307 |
},
|
| 308 |
"widgets_values": [
|
| 309 |
-
"
|
| 310 |
]
|
| 311 |
}
|
| 312 |
],
|
|
|
|
| 30 |
"Node name for S&R": "UNETLoader"
|
| 31 |
},
|
| 32 |
"widgets_values": [
|
| 33 |
+
"qwen_image_2.1_nvfp4.safetensors",
|
| 34 |
"default"
|
| 35 |
]
|
| 36 |
},
|
|
|
|
| 62 |
"Node name for S&R": "CLIPLoader"
|
| 63 |
},
|
| 64 |
"widgets_values": [
|
| 65 |
+
"qwen3vl_8b_nvfp4.safetensors",
|
| 66 |
"qwen_image",
|
| 67 |
"default"
|
| 68 |
]
|
|
|
|
| 306 |
"Node name for S&R": "SaveImage"
|
| 307 |
},
|
| 308 |
"widgets_values": [
|
| 309 |
+
"Qwen21_NVFP4/04_2K_Typography"
|
| 310 |
]
|
| 311 |
}
|
| 312 |
],
|
workflows/api/01_Text_to_Image.api.json
CHANGED
|
@@ -2,14 +2,14 @@
|
|
| 2 |
"1": {
|
| 3 |
"class_type": "UNETLoader",
|
| 4 |
"inputs": {
|
| 5 |
-
"unet_name": "
|
| 6 |
"weight_dtype": "default"
|
| 7 |
}
|
| 8 |
},
|
| 9 |
"2": {
|
| 10 |
"class_type": "CLIPLoader",
|
| 11 |
"inputs": {
|
| 12 |
-
"clip_name": "
|
| 13 |
"type": "qwen_image",
|
| 14 |
"device": "default"
|
| 15 |
}
|
|
@@ -87,7 +87,7 @@
|
|
| 87 |
"7",
|
| 88 |
0
|
| 89 |
],
|
| 90 |
-
"filename_prefix": "
|
| 91 |
}
|
| 92 |
}
|
| 93 |
}
|
|
|
|
| 2 |
"1": {
|
| 3 |
"class_type": "UNETLoader",
|
| 4 |
"inputs": {
|
| 5 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 6 |
"weight_dtype": "default"
|
| 7 |
}
|
| 8 |
},
|
| 9 |
"2": {
|
| 10 |
"class_type": "CLIPLoader",
|
| 11 |
"inputs": {
|
| 12 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 13 |
"type": "qwen_image",
|
| 14 |
"device": "default"
|
| 15 |
}
|
|
|
|
| 87 |
"7",
|
| 88 |
0
|
| 89 |
],
|
| 90 |
+
"filename_prefix": "Qwen21_NVFP4/01_Text_to_Image"
|
| 91 |
}
|
| 92 |
}
|
| 93 |
}
|
workflows/api/02_Image_Editing.api.json
CHANGED
|
@@ -2,14 +2,14 @@
|
|
| 2 |
"1": {
|
| 3 |
"class_type": "UNETLoader",
|
| 4 |
"inputs": {
|
| 5 |
-
"unet_name": "
|
| 6 |
"weight_dtype": "default"
|
| 7 |
}
|
| 8 |
},
|
| 9 |
"2": {
|
| 10 |
"class_type": "CLIPLoader",
|
| 11 |
"inputs": {
|
| 12 |
-
"clip_name": "
|
| 13 |
"type": "qwen_image",
|
| 14 |
"device": "default"
|
| 15 |
}
|
|
@@ -87,13 +87,13 @@
|
|
| 87 |
"7",
|
| 88 |
0
|
| 89 |
],
|
| 90 |
-
"filename_prefix": "
|
| 91 |
}
|
| 92 |
},
|
| 93 |
"10": {
|
| 94 |
"class_type": "LoadImage",
|
| 95 |
"inputs": {
|
| 96 |
-
"image": "
|
| 97 |
}
|
| 98 |
}
|
| 99 |
}
|
|
|
|
| 2 |
"1": {
|
| 3 |
"class_type": "UNETLoader",
|
| 4 |
"inputs": {
|
| 5 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 6 |
"weight_dtype": "default"
|
| 7 |
}
|
| 8 |
},
|
| 9 |
"2": {
|
| 10 |
"class_type": "CLIPLoader",
|
| 11 |
"inputs": {
|
| 12 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 13 |
"type": "qwen_image",
|
| 14 |
"device": "default"
|
| 15 |
}
|
|
|
|
| 87 |
"7",
|
| 88 |
0
|
| 89 |
],
|
| 90 |
+
"filename_prefix": "Qwen21_NVFP4/02_Image_Editing"
|
| 91 |
}
|
| 92 |
},
|
| 93 |
"10": {
|
| 94 |
"class_type": "LoadImage",
|
| 95 |
"inputs": {
|
| 96 |
+
"image": "qwen_image_2.1_edit_reference.png"
|
| 97 |
}
|
| 98 |
}
|
| 99 |
}
|
workflows/api/03_Transparent_RGBA.api.json
CHANGED
|
@@ -2,14 +2,14 @@
|
|
| 2 |
"1": {
|
| 3 |
"class_type": "UNETLoader",
|
| 4 |
"inputs": {
|
| 5 |
-
"unet_name": "
|
| 6 |
"weight_dtype": "default"
|
| 7 |
}
|
| 8 |
},
|
| 9 |
"2": {
|
| 10 |
"class_type": "CLIPLoader",
|
| 11 |
"inputs": {
|
| 12 |
-
"clip_name": "
|
| 13 |
"type": "qwen_image",
|
| 14 |
"device": "default"
|
| 15 |
}
|
|
@@ -87,7 +87,7 @@
|
|
| 87 |
"7",
|
| 88 |
0
|
| 89 |
],
|
| 90 |
-
"filename_prefix": "
|
| 91 |
}
|
| 92 |
}
|
| 93 |
}
|
|
|
|
| 2 |
"1": {
|
| 3 |
"class_type": "UNETLoader",
|
| 4 |
"inputs": {
|
| 5 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 6 |
"weight_dtype": "default"
|
| 7 |
}
|
| 8 |
},
|
| 9 |
"2": {
|
| 10 |
"class_type": "CLIPLoader",
|
| 11 |
"inputs": {
|
| 12 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 13 |
"type": "qwen_image",
|
| 14 |
"device": "default"
|
| 15 |
}
|
|
|
|
| 87 |
"7",
|
| 88 |
0
|
| 89 |
],
|
| 90 |
+
"filename_prefix": "Qwen21_NVFP4/03_Transparent_RGBA"
|
| 91 |
}
|
| 92 |
}
|
| 93 |
}
|
workflows/api/04_2K_Typography.api.json
CHANGED
|
@@ -2,14 +2,14 @@
|
|
| 2 |
"1": {
|
| 3 |
"class_type": "UNETLoader",
|
| 4 |
"inputs": {
|
| 5 |
-
"unet_name": "
|
| 6 |
"weight_dtype": "default"
|
| 7 |
}
|
| 8 |
},
|
| 9 |
"2": {
|
| 10 |
"class_type": "CLIPLoader",
|
| 11 |
"inputs": {
|
| 12 |
-
"clip_name": "
|
| 13 |
"type": "qwen_image",
|
| 14 |
"device": "default"
|
| 15 |
}
|
|
@@ -87,7 +87,7 @@
|
|
| 87 |
"7",
|
| 88 |
0
|
| 89 |
],
|
| 90 |
-
"filename_prefix": "
|
| 91 |
}
|
| 92 |
}
|
| 93 |
}
|
|
|
|
| 2 |
"1": {
|
| 3 |
"class_type": "UNETLoader",
|
| 4 |
"inputs": {
|
| 5 |
+
"unet_name": "qwen_image_2.1_nvfp4.safetensors",
|
| 6 |
"weight_dtype": "default"
|
| 7 |
}
|
| 8 |
},
|
| 9 |
"2": {
|
| 10 |
"class_type": "CLIPLoader",
|
| 11 |
"inputs": {
|
| 12 |
+
"clip_name": "qwen3vl_8b_nvfp4.safetensors",
|
| 13 |
"type": "qwen_image",
|
| 14 |
"device": "default"
|
| 15 |
}
|
|
|
|
| 87 |
"7",
|
| 88 |
0
|
| 89 |
],
|
| 90 |
+
"filename_prefix": "Qwen21_NVFP4/04_2K_Typography"
|
| 91 |
}
|
| 92 |
}
|
| 93 |
}
|