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53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
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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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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pipe.dit = pipe.enable_lora_hot_loading(pipe.dit) # Important!
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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="DiffSynth-Studio/Template-KleinBase4B-Aesthetic")],
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)
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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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"lora_ids": list(range(1, 180, 2)),
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"lora_scales": 1.0,
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"merge_type": "mean",
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}],
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negative_template_inputs = [{
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"lora_ids": list(range(1, 180, 2)),
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"lora_scales": 1.0,
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"merge_type": "mean",
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}],
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)
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image.save("image_Aesthetic_1.0.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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"lora_ids": list(range(1, 180, 2)),
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"lora_scales": 2.5,
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"merge_type": "mean",
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}],
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negative_template_inputs = [{
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"lora_ids": list(range(1, 180, 2)),
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"lora_scales": 2.5,
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"merge_type": "mean",
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}],
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)
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image.save("image_Aesthetic_2.5.jpg")
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