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DRAFT

Intro

App: https://github.com/vladmandic/sdnext/tree/dev Version 2025-07-040 (ipex)

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Execution: Time: 18m 39.49s | total 1120.96 pipeline 1083.47 decode 35.96 preview 1.47 | RAM 61.37 GB 65%



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Part 3 - Legs and ribbon

Prompt: Generate a photo of a woman's legs, with her feet crossed and wearing white high-heeled shoes with ribbons tied around her ankles. The shoes should have a pointed toe and a stiletto heel. The woman's legs should be smooth and tanned, with a slight sheen to them. The background should be a light gray color. The photo should be taken from a low angle, looking up at the woman's legs. The ribbons should be tied in a bow shape around the ankles. The shoes should have a red sole. The woman's legs should be slightly bent at the knee.

Parameters: Steps: 20| Size: 1024x1024| Seed: 3033194654| CFG scale: 8| Model: Kolors-diffusers| App: SD.Next| Version: 1a3b6e3| Operations: txt2img| Pipeline: KolorsPipeline


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Is there way to draw legs correct?

  1. trying several random seeds - may get a better result
  2. trying different resolutions
  3. trying different sampler
  4. trying negative prompts

Prompt: Generate a photo of a woman's legs, with her feet crossed and wearing white high-heeled shoes with ribbons tied around her ankles. The shoes should have a pointed toe and a stiletto heel. The woman's legs should be smooth and tanned, with a slight sheen to them. The background should be a light gray color. The photo should be taken from a low angle, looking up at the woman's legs. The ribbons should be tied in a bow shape around the ankles. The shoes should have a red sole. The woman's legs should be slightly bent at the knee.

trying different resolutions and samplers

Seed 101684518 found to be better than 3033194654 just by run several generation with random seeds 


Steps: 12| Size: 1024x1024| Seed: 101684518| CFG scale: 3Steps: 12| Size: 768x768| Seed: 101684518| CFG scale: 3Steps: 12| Size: 512x512| Seed: 101684518| CFG scale: 3

Default

(Euler)

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Time: 11m 22.99s
total 683.75 pipeline 645.62 decode 37.33 preview 0.76
RAM 41.54 GB 44%

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Time: 6m 38.81s
total 399.49 pipeline 378.25 decode 20.54 preview 0.68
RAM 41.79 GB 44%

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Heun



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Time: 5m 53.04s
total 353.95 pipeline 343.03 decode 9.98 preview 0.91
RAM 41.76 GB 44%

LCM



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Time: 3m 17.69s
total 198.13 pipeline 187.76 decode 9.89 preview 0.44
RAM 41.76 GB 44%

DDIM



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Time: 3m 17.08s
total 197.57 pipeline 187.34 decode 9.71 preview 0.49
RAM 41.76 GB 44%

DPM++ 2M



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Time: 3m 13.02s
total 193.43 pipeline 183.74 decode 9.26 preview 0.41
RAM 41.75 GB 44%

DPM++ 1S



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Time: 3m 15.26s
total 195.58 pipeline 185.46 decode 9.76 preview 0.32
RAM 41.78 GB 44%

DPM++ 2M SDE



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Time: 3m 18.49s
total 198.93 pipeline 188.45 decode 10.00 preview 0.44
RAM 41.79 GB 44%

KDPM2



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Time: 5m 55.50s
total 356.34 pipeline 345.08 decode 9.76 preview 0.85 prepare 0.63
RAM 41.76 GB 44%

System Info

Code Block
app: sdnext updated: 2025-07-04 hash: 1a3b6e3b url: https://github.com/vladmandic/sdnext/tree/dev
arch: x86_64 cpu: x86_64 system: Linux release: 6.11.0-28-generic
python: 3.12.3 Torch 2.7.1+xpu
device: Intel(R) Iris(R) Xe Graphics (iGPU) openvino: 2025.2.0
ram: free:31.06 used:62.91 total:93.97 gpu: total:93.97
xformers: diffusers: 0.35.0.dev0 transformers: 4.53.0
active: cpu dtype: torch.float32 vae: torch.float32 unet: torch.float32
base: Diffusers/Kwai-Kolors/Kolors-diffusers [7e091c7519] refiner: none vae: none te: none unet: none

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