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Update utils.py
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utils.py
CHANGED
@@ -2,7 +2,7 @@ import os
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os.environ["HF_HOME"] = "/data/huggingface"
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os.environ["TRANSFORMERS_CACHE"] = "/data/huggingface"
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os.makedirs("/data/huggingface/hub", exist_ok=True)
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os.makedirs("/data/huggingface/clip_vision_model", exist_ok=True)
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import torch
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from diffusers import StableDiffusionImg2ImgPipeline
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@@ -40,18 +40,18 @@ pipe.load_ip_adapter(
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weight_name=IPADAPTER_WEIGHT_NAME
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)
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# Load vision encoder and processor for IP-Adapter embedding
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vision_encoder = CLIPVisionModelWithProjection.from_pretrained(
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image_processor = CLIPImageProcessor.from_pretrained(
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)
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def generate_sticker(input_image: Image.Image, prompt: str):
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"""
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@@ -67,13 +67,16 @@ def generate_sticker(input_image: Image.Image, prompt: str):
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# Preprocess the image (resize, etc)
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face_img = input_image.convert("RGB").resize((224, 224))
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inputs = image_processor(images=face_img, return_tensors="pt").to(DEVICE)
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with torch.no_grad():
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# 2. Prepare image for SD pipeline
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init_image = input_image.convert("RGB").resize((512, 512))
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# Run inference (low strength for identity preservation)
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result = pipe(
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prompt=prompt,
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os.environ["HF_HOME"] = "/data/huggingface"
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os.environ["TRANSFORMERS_CACHE"] = "/data/huggingface"
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os.makedirs("/data/huggingface/hub", exist_ok=True)
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# os.makedirs("/data/huggingface/clip_vision_model", exist_ok=True)
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import torch
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from diffusers import StableDiffusionImg2ImgPipeline
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weight_name=IPADAPTER_WEIGHT_NAME
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)
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# # Load vision encoder and processor for IP-Adapter embedding
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# vision_encoder = CLIPVisionModelWithProjection.from_pretrained(
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# "h94/IP-Adapter", # repo_id (main IP-Adapter repo)
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# subfolder="clip_vision_model",# subfolder within the repo!
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# cache_dir=CACHE_DIR
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# )
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# image_processor = CLIPImageProcessor.from_pretrained(
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# "h94/IP-Adapter",
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# subfolder="clip_vision_model",
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# cache_dir=CACHE_DIR
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# )
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def generate_sticker(input_image: Image.Image, prompt: str):
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"""
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# Preprocess the image (resize, etc)
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face_img = input_image.convert("RGB").resize((224, 224))
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# inputs = image_processor(images=face_img, return_tensors="pt").to(DEVICE)
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# with torch.no_grad():
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# image_embeds = vision_encoder(**inputs).image_embeds
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# 2. Prepare image for SD pipeline
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init_image = input_image.convert("RGB").resize((512, 512))
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# IP-Adapter expects the reference image via image_embeds, which is produced by this function:
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image_embeds = pipe.prepare_ip_adapter_image_embeds(face_img)
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# Run inference (low strength for identity preservation)
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result = pipe(
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prompt=prompt,
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