RuntimeError: Given groups=1, weight of size [128, 3, 3, 3], expected input[1, 1, 512, 512] to have 3 channels, but got 1 channels instead

Dmitr: <p>I have problem with AI models.<br>
My code:</p>
<p>import torch<br>
import numpy as np<br>
from PIL import Image<br>
from diffusers import StableDiffusionInpaintPipeline<br>
import cv2<br>
import os<br>
from dotenv import load_dotenv<br>
from segment_anything import sam_model_registry, SamPredictor</p>
<p>load_dotenv()<br>
REV_ANIMATED_MODEL_PATH = os.getenv(‘REV_ANIMATED_MODEL_PATH’)<br>
KANDINSKY_MODEL_PATH = os.getenv(‘KANDINSKY_MODEL_PATH’)<br>
VAE_MODEL_PATH = os.getenv(‘VAE_MODEL_PATH’)<br>
SAM_MODEL_PATH = os.getenv(“SAM_MODEL_PATH”)<br>
MODEL_TYPE = “vit_b”<br>
CHECKPOINT_PATH = ‘sam_vit_b_01ec64.pth’<br>
SDV5_MODEL_PATH = os.getenv(‘SDV5_MODEL_PATH’)<br>
img = ‘inpaint-example.png’</p>
<p><span class="hashtag-raw">#mask</span> generation function<br>
def mask_generator(img):<br>
image = cv2.imread(img)<br>
image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)</p>
<pre><code>sam = sam_model_registry[MODEL_TYPE ](checkpoint=CHECKPOINT_PATH)
sam.to(device='cuda')
mask_predictor = SamPredictor(sam)
mask_predictor.set_image(image_rgb)
input_point = np.array([[250, 250]])
input_label = np.array([1])
masks, scores, logits = mask_predictor.predict(
    point_coords=input_point,
    point_labels=input_label,
    multimask_output=False,
)
mask = masks.astype(float) * 255
mask = np.transpose(mask, (1, 2, 0))
_, bw_image = cv2.threshold(mask, 100, 255, cv2.THRESH_BINARY)
cv2.imwrite('mask.png', bw_image)
</code></pre>
<p><span class="hashtag-raw">#inpainting</span> function<br>
def inpaint(init_img, mask):<br>
init_image = Image.open(init_img)<br>
mask_image = Image.open(mask)<br>
pipe = StableDiffusionInpaintPipeline.from_pretrained(<br>
SDV5_MODEL_PATH,<br>
use_safetensors=True,<br>
torch_dtype=torch.float32<br>
).to(‘cpu’)<br>
negative_prompt = ‘ugly’<br>
prompt = “a grey cat sitting on a bench, high resolution”<br>
image = pipe(prompt=prompt,<br>
negative_prompt=negative_prompt,<br>
image=init_image,<br>
mask_image=mask_image<br>
).images[0]<br>
image.save(‘output.png’)</p>
<p>mask_generator(img)<br>
inpaint(img, ‘mask.png’)</p>
<p>And i can see this problem in terminal: RuntimeError: Given groups=1, weight of size [128, 3, 3, 3], expected input[1, 1, 512, 512] to have 3 channels, but got 1 channels instead</p>
<p>Can anyone help?</p>

KFrank: <p>Hi Dmitr!</p>
<aside class="quote no-group" data-username="Dmitr" data-post="1" data-topic="216781">
<div class="title">
<div class="quote-controls"></div>
<img alt="" width="24" height="24" src="https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/d/13edae/48.png" class="avatar"> Dmitr:</div>
<blockquote>
<p>And i can see this problem in terminal: RuntimeError: Given groups=1, weight of size [128, 3, 3, 3], expected input[1, 1, 512, 512] to have 3 channels, but got 1 channels instead</p>
</blockquote>
</aside>
<p>I haven’t really looked at the code you posted, but I believe the following is happening:</p>
<p>Likely, the first layer of your model is a convolution layer that expects three channels<br>
(e.g., rgb color), while you are inputting a one-channel image (e.g., grayscale).</p>
<p>Consider:</p>
<pre><code class="lang-plaintext">&gt;&gt;&gt; import torch
&gt;&gt;&gt; torch.__version__
'2.6.0+cu126'
&gt;&gt;&gt; conv_1 = torch.nn.Conv2d (1, 128, 3)
&gt;&gt;&gt; conv_3 = torch.nn.Conv2d (3, 128, 3)
&gt;&gt;&gt; t = torch.randn (1, 1, 512, 512)
&gt;&gt;&gt; conv_1 (t).shape
torch.Size([1, 128, 510, 510])
&gt;&gt;&gt; conv_3 (t).shape
Traceback (most recent call last):
  File "&lt;stdin&gt;", line 1, in &lt;module&gt;
  File "&lt;path_to_pytorch_install&gt;/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "&lt;path_to_pytorch_install&gt;/torch/nn/modules/module.py", line 1750, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "&lt;path_to_pytorch_install&gt;/torch/nn/modules/conv.py", line 554, in forward
    return self._conv_forward(input, self.weight, self.bias)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "&lt;path_to_pytorch_install&gt;/torch/nn/modules/conv.py", line 549, in _conv_forward
    return F.conv2d(
           ^^^^^^^^^
RuntimeError: Given groups=1, weight of size [128, 3, 3, 3], expected input[1, 1, 512, 512] to have 3 channels, but got 1 channels instead
</code></pre>
<p>Best.</p>
<p>K. Frank</p>

Dmitr: <p>Thanks for the answer.</p>
<p>Did I understand correctly that I need to convert 1 channel img to 3 channel img?</p>

J_Johnson: <p>If black and white is all you have, you could just repeat it on the channels dim 3 times.</p>
<pre><code class="lang-auto">if image.size()[1]==1: #check if it's single channel
    image = image.expand(-1, 3, -1, -1) #expand to 3 channels
</code></pre>

Dmitr: <p>Hi. Is image OpenCV object?</p>

J_Johnson: <p>I’m providing you with an example. You’ll have to figure where in your code the image is first entering the model. And then you can apply that code to it.</p>
<p>By the way, your current code display is very difficult to read. You need to wrap all code with three backticks before and after. (Same key as the ~ is located.)</p>

Dmitr: <p>Hello!<br>
Here is my main inpainting function</p>
<pre><code class="lang-auto">def inpaint(init_img, mask):
    init_image = Image.open(init_img)
    mask_image = Image.open(mask)

#Your code
    if mask_image.size()[1] == 1:  # check if it's single channel
        mask_image = mask_image.expand(-1, 3, -1, -1)
#

    pipe = StableDiffusionInpaintPipeline.from_pretrained(
        SDV5_MODEL_PATH,
        use_safetensors=True,
        torch_dtype=torch.float32
    ).to('cpu')

    negative_prompt = 'ugly'
    prompt = "a grey cat sitting on a bench, high resolution"
    image = pipe(prompt=prompt,
                 negative_prompt=negative_prompt,
                 image=init_image,
                 mask_image=mask_image
                 ).images[0]
    image.save('output.png')

</code></pre>
<p>When i inserted your code, pycharm started show an errors.<br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/1/6/163ddf7811f210fc6d63a280892b97091b7c2aaa.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/163ddf7811f210fc6d63a280892b97091b7c2aaa" title="2025-02-20_15-58-53"><img src="https://discuss.pytorch.org/uploads/default/original/3X/1/6/163ddf7811f210fc6d63a280892b97091b7c2aaa.jpeg" alt="2025-02-20_15-58-53" data-base62-sha1="3aL47yzfvk9pL05FmnjK6P0WzMK" width="690" height="151" data-dominant-color="3C3D3E"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">2025-02-20_15-58-53</span><span class="informations">745×164 38.9 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div><br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/b/b/bb7d9ccf934f1989f4728d22f49d4607a3a26c8d.jpeg" data-download-href="https://discuss.pytorch.org/uploads/default/bb7d9ccf934f1989f4728d22f49d4607a3a26c8d" title="2025-02-20_15-58-27"><img src="https://discuss.pytorch.org/uploads/default/original/3X/b/b/bb7d9ccf934f1989f4728d22f49d4607a3a26c8d.jpeg" alt="2025-02-20_15-58-27" data-base62-sha1="qKCpsmC4HZ2Mh7a5PohsnwCeLpj" width="638" height="256"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">2025-02-20_15-58-27</span><span class="informations">638×256 42.5 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>

J_Johnson: <p>Ah. Much better. <img src="https://discuss.pytorch.org/images/emoji/apple/slightly_smiling_face.png?v=12" title=":slightly_smiling_face:" class="emoji" alt=":slightly_smiling_face:" loading="lazy" width="20" height="20"> Try this:</p>
<pre><code class="lang-auto">
def inpaint(init_img, mask):
    init_image = Image.open(init_img).convert('RGB') #&lt;&lt;&lt;
    mask_image = Image.open(mask).convert('RGB') #&lt;&lt;&lt;

    pipe = StableDiffusionInpaintPipeline.from_pretrained(
        SDV5_MODEL_PATH,
        use_safetensors=True,
        torch_dtype=torch.float32
    ).to('cpu')

    negative_prompt = 'ugly'
    prompt = "a grey cat sitting on a bench, high resolution"
    image = pipe(prompt=prompt,
                 negative_prompt=negative_prompt,
                 image=init_image,
                 mask_image=mask_image
                 ).images[0]
    image.save('output.png')
</code></pre>
<p>That will do the same thing, except for a PIL image object.</p>

Dmitr: <p>Thank you very much!<br>
You solved my problem.</p>