

Workflow name: Flux model fast face-changing ins-style filter
[Workflow introduction]
Open the workflow and upload 2 images, one of which is your own face image and the other is the image of the face-changing. Combine the FLUX model and the fulid node, and give it to the sampler to perform face-changing processing on the two images. Then combine the ins-style filter to output the image (if there is no filter requirement, you can close the entire group). The parameters are built-in, just click Run, come and try it!
[Usage scenario]
You only need to upload 2 images to get the face-changing and ins-style filter image, which is vivid and interesting. Come and use your creativity!
[Key nodes]
PULIDFLUX
[Model version]
FLUX
Model name: flux1-dev-Q8_0.gguf
Model name: pulid_flux_v0.9.0.safetensors
[ControlNet]
Flux.1-dev-Controlnet-Upscaler-jasperai.safetensors
Start time: 0
End time: 1.0
Intensity: 0.7
[LoRA model]
None
[K sampler]
Scheduler: normal
Noise reduction: 0.63
Sampler: euler ancestral
Scheduler: sgm uniform
Noise reduction: 0.5
Sampler: euler ancestral
Workflow name: Flux model fast face-changing ins-style filter
[Workflow introduction]
Open the workflow and upload 2 images, one of which is your own face image and the other is the image of the face-changing. Combine the FLUX model and the fulid node, and give it to the sampler to perform face-changing processing on the two images. Then combine the ins-style filter to output the image (if there is no filter requirement, you can close the entire group). The parameters are built-in, just click Run, come and try it!
[Usage scenario]
You only need to upload 2 images to get the face-changing and ins-style filter image, which is vivid and interesting. Come and use your creativity!
[Key nodes]
PULIDFLUX
[Model version]
FLUX
Model name: flux1-dev-Q8_0.gguf
Model name: pulid_flux_v0.9.0.safetensors
[ControlNet]
Flux.1-dev-Controlnet-Upscaler-jasperai.safetensors
Start time: 0
End time: 1.0
Intensity: 0.7
[LoRA model]
None
[K sampler]
Scheduler: normal
Noise reduction: 0.63
Sampler: euler ancestral
Scheduler: sgm uniform
Noise reduction: 0.5
Sampler: euler ancestral