mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
from diffsynth import ModelManager, FluxImagePipeline, download_customized_models
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from diffsynth.data.video import crop_and_resize
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from modelscope import dataset_snapshot_download
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from examples.EntityControl.utils import visualize_masks
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from PIL import Image
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import numpy as np
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import torch
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def build_pipeline():
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model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
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model_manager.load_lora(
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download_customized_models(
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model_id="DiffSynth-Studio/Eligen",
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origin_file_path="model_bf16.safetensors",
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local_dir="models/lora/entity_control"
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),
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lora_alpha=1
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)
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model_manager.load_lora(
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download_customized_models(
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model_id="iic/In-Context-LoRA",
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origin_file_path="visual-identity-design.safetensors",
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local_dir="models/lora/In-Context-LoRA"
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),
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lora_alpha=1
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)
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pipe = FluxImagePipeline.from_model_manager(model_manager)
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return pipe
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def generate(pipe: FluxImagePipeline, logo_image, target_image, mask, height, width, prompt, logo_prompt, image_save_path, mask_save_path):
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mask = Image.fromarray(np.concatenate([
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np.ones((height, width, 3), dtype=np.uint8) * 0,
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np.array(crop_and_resize(mask, height, width)),
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], axis=1))
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input_image = Image.fromarray(np.concatenate([
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np.array(crop_and_resize(logo_image, height, width)),
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np.array(crop_and_resize(target_image, height, width)),
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], axis=1))
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image = pipe(
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prompt=prompt,
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input_image=input_image,
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cfg_scale=3.0,
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negative_prompt="",
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num_inference_steps=50,
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embedded_guidance=3.5,
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seed=0,
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height=height,
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width=width * 2,
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eligen_entity_prompts=[logo_prompt],
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eligen_entity_masks=[mask],
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enable_eligen_on_negative=False,
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enable_eligen_inpaint=True,
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)
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image.save(image_save_path)
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visualize_masks(image, [mask], [logo_prompt], mask_save_path)
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pipe = build_pipeline()
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dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern="data/examples/eligen/logo_transfer*")
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logo_image = Image.open("data/examples/eligen/logo_transfer_logo.png")
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target_image = Image.open("data/examples/eligen/logo_transfer_target_image.png")
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prompt="The two-panel image showcases the joyful identity, with the left panel showing a rabbit graphic; [LEFT] while the right panel translates the design onto a shopping tote with the rabbit logo in black, held by a person in a market setting, emphasizing the brand's approachable and eco-friendly vibe."
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logo_prompt="a rabbit logo"
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mask = Image.open("data/examples/eligen/logo_transfer_mask_1.png")
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generate(
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pipe, logo_image, target_image, mask,
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height=1024, width=736,
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prompt=prompt, logo_prompt=logo_prompt,
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image_save_path="entity_transfer_1.png",
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mask_save_path="entity_transfer_with_mask_1.png"
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)
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mask = Image.open("data/examples/eligen/logo_transfer_mask_2.png")
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generate(
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pipe, logo_image, target_image, mask,
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height=1024, width=736,
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prompt=prompt, logo_prompt=logo_prompt,
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image_save_path="entity_transfer_2.png",
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mask_save_path="entity_transfer_with_mask_2.png"
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)
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