mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
import torch
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from diffsynth import ModelManager, FluxImagePipeline, download_customized_models, FluxImageLoraPipeline
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from examples.EntityControl.utils import visualize_masks
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import os
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import json
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from PIL import Image
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import requests
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from io import BytesIO
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# download and load model
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lora_path = 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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)[0]
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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(lora_path, lora_alpha=1.)
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pipe = FluxImagePipeline.from_model_manager(model_manager)
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# prepare inputs
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image_shape = 1024
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seed = 0
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# set True to apply regional attention in negative prompt prediction for better results with more time
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use_seperated_negtive_prompt = False
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mask_urls = [
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'https://github.com/user-attachments/assets/0cf78663-5314-4280-a065-31ded7a24a46',
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'https://github.com/user-attachments/assets/bd3938b8-72a8-4d56-814f-f6445971b91d'
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]
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# prepare entity masks, entity prompts, global prompt and negative prompt
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masks = []
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for url in mask_urls:
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response = requests.get(url)
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mask = Image.open(BytesIO(response.content)).resize((image_shape, image_shape), resample=Image.NEAREST)
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masks.append(mask)
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entity_prompts = ["A person wear red shirt", "Airplane"]
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global_prompt = "A person walking on the path in front of a house; An airplane in the sky"
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negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw, blur"
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response = requests.get('https://github.com/user-attachments/assets/fa4d6ba5-08fd-4fc7-adbb-19898d839364')
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inpaint_input = Image.open(BytesIO(response.content)).convert('RGB').resize((image_shape, image_shape))
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# generate image
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torch.manual_seed(seed)
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image = pipe(
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prompt=global_prompt,
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cfg_scale=3.0,
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negative_prompt=negative_prompt,
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num_inference_steps=50,
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embedded_guidance=3.5,
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height=image_shape,
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width=image_shape,
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entity_prompts=entity_prompts,
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entity_masks=masks,
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inpaint_input=inpaint_input,
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use_seperated_negtive_prompt=use_seperated_negtive_prompt,
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)
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image.save(f"entity_inpaint.png")
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visualize_masks(image, masks, entity_prompts, f"entity_inpaint_with_mask.png") |