AI Image Created By Flux AI Image Generator
![Describe the bug
What error message is this, but it won't cause the task to fail, it will only keep printing, thank you.
Reproduction
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers.image_processor import IPAdapterMaskProcessor
from diffusers.utils import load_image
from transformers import CLIPVisionModelWithProjection
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"./repository/h94-IP-Adapter",
subfolder="models/image_encoder",
torch_dtype=torch.float16
)
pipeline = StableDiffusionXLPipeline.from_pretrained("./repository/stable-diffusion-xl-base-1.0",
image_encoder=image_encoder,
torch_dtype=torch.float16,
use_safetensors=True,
).to("cuda")
pipeline.enable_model_cpu_offload()
pipeline.load_ip_adapter("./repository/h94-IP-Adapter", subfolder="sdxl_models",
weight_name=["ip-adapter-plus-face_sdxl_vit-h.safetensors"] * 2)
pipeline.set_ip_adapter_scale([0.6] * 2)
generator = torch.Generator(device="cpu").manual_seed(4)
mask1 = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_mask1.png")
mask2 = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_mask2.png")
output_height = 1024
output_width = 1024
processor = IPAdapterMaskProcessor()
masks = processor.preprocess([mask1, mask2, ])
face_image1 = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_girl1.png")
face_image2 = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_girl2.png")
ip_images = [[face_image1], [face_image2]]
image = pipeline(
prompt="2 girls",
ip_adapter_image=ip_images,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=20,
num_images_per_prompt=1,
generator=generator,
cross_attention_kwargs={"ip_adapter_masks": masks}
).images[0]
image.save("test.png")
Logs
cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored.
cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored.
cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored.
cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored.
cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored.
cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored.
cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored.
cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored.
System Info
diffusers 0.27.2 python 3.10 windows
Who can help?
No response. - Image](https://r2.fluxaiimagegenerator.com/audio/03aa4e6b-4465-414b-910e-d9526030818a.webp)
Prompt
Describe the bug What error message is this, but it won't cause the task to fail, it will only keep printing, thank you. Reproduction import torch from diffusers import StableDiffusionXLPipeline from diffusers.image_processor import IPAdapterMaskProcessor from diffusers.utils import load_image from transformers import CLIPVisionModelWithProjection image_encoder = CLIPVisionModelWithProjection.from_pretrained( "./repository/h94-IP-Adapter", subfolder="models/image_encoder", torch_dtype=torch.float16 ) pipeline = StableDiffusionXLPipeline.from_pretrained("./repository/stable-diffusion-xl-base-1.0", image_encoder=image_encoder, torch_dtype=torch.float16, use_safetensors=True, ).to("cuda") pipeline.enable_model_cpu_offload() pipeline.load_ip_adapter("./repository/h94-IP-Adapter", subfolder="sdxl_models", weight_name=["ip-adapter-plus-face_sdxl_vit-h.safetensors"] * 2) pipeline.set_ip_adapter_scale([0.6] * 2) generator = torch.Generator(device="cpu").manual_seed(4) mask1 = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_mask1.png") mask2 = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_mask2.png") output_height = 1024 output_width = 1024 processor = IPAdapterMaskProcessor() masks = processor.preprocess([mask1, mask2, ]) face_image1 = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_girl1.png") face_image2 = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_girl2.png") ip_images = [[face_image1], [face_image2]] image = pipeline( prompt="2 girls", ip_adapter_image=ip_images, negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality", num_inference_steps=20, num_images_per_prompt=1, generator=generator, cross_attention_kwargs={"ip_adapter_masks": masks} ).images[0] image.save("test.png") Logs cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored. cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored. cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored. cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored. cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored. cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored. cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored. cross_attention_kwargs ['ip_adapter_masks'] are not expected by AttnProcessor2_0 and will be ignored. System Info diffusers 0.27.2 python 3.10 windows Who can help? No response.
Image Analysis
Emotional Analysis
Application Scenarios
Debugging Stable Diffusion
Description: User debugging a Stable Diffusion pipeline with potential issues.
Potential Use: Identifying bugs in the implementation of the image processing pipeline.
Model Compatibility Enhancement
Description: Developers working on improvements for model compatibility.
Potential Use: Updating model processors to ensure additional parameters are accepted.
ML Framework Training
Description: Training workshops for machine learning frameworks.
Potential Use: Teaching users how to address common errors and warnings.
Documentation Improvement
Description: Documentation updates for model parameters and processor capabilities.
Potential Use: Providing clearer guidelines for expected input parameters in projects.
Technical Analysis
Quality Assessment: The generation quality is decent but lacks clarity in error descriptions.
- Use of advanced image processing techniques.
- Ability to load and preprocess images effectively.
- Integration of multiple models within the image generation pipeline.
- Enhance the error message clarity.
- Add support for additional input parameters in models.
- Improve documentation regarding processor expectations.