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Diffusion Templates framework
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Artiprocher
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from diffsynth.diffusion.skills import SkillsPipeline
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from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
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import torch
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from PIL import Image
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pipe = Flux2ImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="transformer/*.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
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],
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tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
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)
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skills = SkillsPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="DiffSynth-Studio/F2KB4B-Skills-ControlNet"),
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ModelConfig(model_id="DiffSynth-Studio/F2KB4B-Skills-Brightness"),
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],
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)
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skill_cache = skills(
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positive_inputs = [
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{
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"model_id": 0,
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"image": Image.open("xxx.jpg"),
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"prompt": "一位长发少女，四周环绕着魔法粒子",
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},
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{
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"model_id": 1,
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"scale": 0.6,
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},
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],
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negative_inputs = [
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{
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"model_id": 0,
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"image": Image.open("xxx.jpg"),
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"prompt": "一位长发少女，四周环绕着魔法粒子",
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},
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{
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"model_id": 1,
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"scale": 0.5,
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},
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],
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pipe=pipe,
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)
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image = pipe(
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prompt="一位长发少女，四周环绕着魔法粒子",
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seed=0, rand_device="cuda", num_inference_steps=50, cfg_scale=4,
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height=1024, width=1024,
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**skill_cache,
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)
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image.save("image.jpg")
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256
examples/flux2/model_inference/Template-KleinBase4B.py
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256
examples/flux2/model_inference/Template-KleinBase4B.py
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from diffsynth.diffusion.template import TemplatePipeline
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from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
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import torch
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from PIL import Image
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import numpy as np
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def load_template_pipeline(model_ids):
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template = TemplatePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[ModelConfig(model_id=model_id) for model_id in model_ids],
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)
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return template
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# Base Model
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pipe = Flux2ImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="black-forest-labs/FLUX.2-klein-base-4B", origin_file_pattern="transformer/*.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
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],
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tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
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)
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# image = pipe(
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# )
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# image.save("image_base.jpg")
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# template = load_template_pipeline(["DiffSynth-Studio/Template-KleinBase4B-Brightness"])
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{"scale": 0.7}],
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# negative_template_inputs = [{"scale": 0.5}]
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# )
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# image.save("image_Brightness_light.jpg")
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{"scale": 0.5}],
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# negative_template_inputs = [{"scale": 0.5}]
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# )
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# image.save("image_Brightness_normal.jpg")
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{"scale": 0.3}],
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# negative_template_inputs = [{"scale": 0.5}]
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# )
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# image.save("image_Brightness_dark.jpg")
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# template = load_template_pipeline(["DiffSynth-Studio/Template-KleinBase4B-ControlNet"])
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone, bathed in bright sunshine.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "image": Image.open("data/assets/image_depth.jpg"),
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# "prompt": "A cat is sitting on a stone, bathed in bright sunshine.",
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# }],
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# negative_template_inputs = [{
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# "image": Image.open("data/assets/image_depth.jpg"),
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# "prompt": "",
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# }],
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# )
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# image.save("image_ControlNet_sunshine.jpg")
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone, surrounded by colorful magical particles.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "image": Image.open("data/assets/image_depth.jpg"),
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# "prompt": "A cat is sitting on a stone, surrounded by colorful magical particles.",
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# }],
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# negative_template_inputs = [{
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# "image": Image.open("data/assets/image_depth.jpg"),
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# "prompt": "",
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# }],
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# )
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# image.save("image_ControlNet_magic.jpg")
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# template = load_template_pipeline(["DiffSynth-Studio/Template-KleinBase4B-Edit"])
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# image = template(
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# pipe,
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# prompt="Put a hat on this cat.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "image": Image.open("data/assets/image_reference.jpg"),
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# "prompt": "Put a hat on this cat.",
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# }],
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# negative_template_inputs = [{
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# "image": Image.open("data/assets/image_reference.jpg"),
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# "prompt": "",
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# }],
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# )
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# image.save("image_Edit_hat.jpg")
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# image = template(
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# pipe,
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# prompt="Make the cat turn its head to look to the right.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "image": Image.open("data/assets/image_reference.jpg"),
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# "prompt": "Make the cat turn its head to look to the right.",
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# }],
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# negative_template_inputs = [{
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# "image": Image.open("data/assets/image_reference.jpg"),
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# "prompt": "",
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# }],
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# )
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# image.save("image_Edit_head.jpg")
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# template = load_template_pipeline(["DiffSynth-Studio/Template-KleinBase4B-Upscaler"])
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "image": Image.open("data/assets/image_lowres_512.jpg"),
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# "prompt": "A cat is sitting on a stone.",
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# }],
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# negative_template_inputs = [{
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# "image": Image.open("data/assets/image_lowres_512.jpg"),
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# "prompt": "",
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# }],
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# )
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# image.save("image_Upscaler_1.png")
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "image": Image.open("data/assets/image_lowres_100.jpg"),
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# "prompt": "A cat is sitting on a stone.",
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# }],
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# negative_template_inputs = [{
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# "image": Image.open("data/assets/image_lowres_100.jpg"),
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# "prompt": "",
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# }],
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# )
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# image.save("image_Upscaler_2.png")
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# template = load_template_pipeline(["DiffSynth-Studio/Template-KleinBase4B-SoftRGB"])
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "R": 128/255,
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# "G": 128/255,
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# "B": 128/255
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# }],
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# )
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# image.save("image_rgb_normal.jpg")
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "R": 208/255,
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# "G": 185/255,
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# "B": 138/255
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# }],
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# )
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# image.save("image_rgb_warm.jpg")
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "R": 94/255,
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# "G": 163/255,
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# "B": 174/255
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# }],
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# )
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# image.save("image_rgb_cold.jpg")
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# template = load_template_pipeline(["DiffSynth-Studio/Template-KleinBase4B-PandaMeme"])
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# image = template(
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# pipe,
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# prompt="A meme with a sleepy expression.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{}],
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# negative_template_inputs = [{}],
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# )
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# image.save("image_PandaMeme_sleepy.jpg")
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# image = template(
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# pipe,
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# prompt="A meme with a happy expression.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{}],
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# negative_template_inputs = [{}],
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# )
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# image.save("image_PandaMeme_happy.jpg")
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# image = template(
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# pipe,
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# prompt="A meme with a surprised expression.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{}],
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# negative_template_inputs = [{}],
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# )
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# image.save("image_PandaMeme_surprised.jpg")
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# template = load_template_pipeline(["DiffSynth-Studio/Template-KleinBase4B-Sharpness"])
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{"scale": 0.1}],
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# negative_template_inputs = [{"scale": 0.5}],
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# )
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# image.save("image_Sharpness_0.1.jpg")
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# image = template(
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# pipe,
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# prompt="A cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{"scale": 0.8}],
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# negative_template_inputs = [{"scale": 0.5}],
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# )
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# image.save("image_Sharpness_0.8.jpg")
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# template = load_template_pipeline(["DiffSynth-Studio/Template-KleinBase4B-Inpaint"])
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# image = template(
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# pipe,
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# prompt="An orange cat is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "image": Image.open("data/assets/image_reference.jpg"),
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# "mask": Image.open("data/assets/image_mask_1.jpg"),
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# "force_inpaint": True,
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# }],
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# negative_template_inputs = [{
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# "image": Image.open("data/assets/image_reference.jpg"),
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# "mask": Image.open("data/assets/image_mask_1.jpg"),
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# }],
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# )
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# image.save("image_Inpaint_1.jpg")
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# image = template(
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# pipe,
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# prompt="A cat wearing sunglasses is sitting on a stone.",
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# seed=0, cfg_scale=4, num_inference_steps=50,
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# template_inputs = [{
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# "image": Image.open("data/assets/image_reference.jpg"),
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# "mask": Image.open("data/assets/image_mask_2.jpg"),
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# }],
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# negative_template_inputs = [{
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# "image": Image.open("data/assets/image_reference.jpg"),
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# "mask": Image.open("data/assets/image_mask_2.jpg"),
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# }],
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# )
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# image.save("image_Inpaint_2.jpg")
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