mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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118 lines
5.3 KiB
Python
118 lines
5.3 KiB
Python
import torch
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import random
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from PIL import Image, ImageDraw, ImageFont
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from modelscope import dataset_snapshot_download, snapshot_download
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from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
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def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False):
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# Create a blank image for overlays
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overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
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colors = [
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(165, 238, 173, 80),
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(76, 102, 221, 80),
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(221, 160, 77, 80),
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(204, 93, 71, 80),
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(145, 187, 149, 80),
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(134, 141, 172, 80),
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(157, 137, 109, 80),
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(153, 104, 95, 80),
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(165, 238, 173, 80),
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(76, 102, 221, 80),
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(221, 160, 77, 80),
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(204, 93, 71, 80),
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(145, 187, 149, 80),
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(134, 141, 172, 80),
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(157, 137, 109, 80),
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(153, 104, 95, 80),
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]
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# Generate random colors for each mask
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if use_random_colors:
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colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
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# Font settings
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try:
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font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed
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except IOError:
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font = ImageFont.load_default(font_size)
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# Overlay each mask onto the overlay image
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for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
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# Convert mask to RGBA mode
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mask_rgba = mask.convert('RGBA')
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mask_data = mask_rgba.getdata()
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new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
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mask_rgba.putdata(new_data)
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# Draw the mask prompt text on the mask
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draw = ImageDraw.Draw(mask_rgba)
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mask_bbox = mask.getbbox() # Get the bounding box of the mask
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text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position
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draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
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# Alpha composite the overlay with this mask
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overlay = Image.alpha_composite(overlay, mask_rgba)
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# Composite the overlay onto the original image
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result = Image.alpha_composite(image.convert('RGBA'), overlay)
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# Save or display the resulting image
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result.save(output_path)
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return result
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def example(pipe, seeds, example_id, global_prompt, entity_prompts):
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dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png")
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masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB').resize((1024, 1024)) for i in range(len(entity_prompts))]
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negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴"
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for seed in seeds:
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# generate image
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image = pipe(
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prompt=global_prompt,
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cfg_scale=4.0,
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negative_prompt=negative_prompt,
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num_inference_steps=40,
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seed=seed,
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height=1024,
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width=1024,
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eligen_entity_prompts=entity_prompts,
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eligen_entity_masks=masks,
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)
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image.save(f"eligen_example_{example_id}_{seed}.png")
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visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png")
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vram_config = {
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"offload_dtype": "disk",
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"offload_device": "disk",
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"onload_dtype": torch.float8_e4m3fn,
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"onload_device": "cpu",
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"preparing_dtype": torch.float8_e4m3fn,
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"preparing_device": "cuda",
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"computation_dtype": torch.bfloat16,
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"computation_device": "cuda",
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}
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pipe = QwenImagePipeline.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="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
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],
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tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
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vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
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)
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snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen-V2", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-V2", allow_file_pattern="model.safetensors")
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pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-V2/model.safetensors", hotload=True)
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seeds = [0]
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global_prompt = "写实摄影风格. A beautiful asia woman wearing white dress, she is holding a mirror with her right arm, with a beach background."
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entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"]
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example(pipe, seeds, 7, global_prompt, entity_prompts)
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global_prompt = "写实摄影风格, 细节丰富。街头一位漂亮的女孩，穿着衬衫和短裤，手持写有“实体控制”的标牌，背景是繁忙的城市街道，阳光明媚，行人匆匆。"
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entity_prompts = ["一个漂亮的女孩", "标牌 '实体控制'", "短裤", "衬衫"]
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example(pipe, seeds, 4, global_prompt, entity_prompts)
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