text2img.py works for me but img2img.py doesn't work why?

[tonymacx86PRO]

python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img sketch-mountains-input.jpg --strength 0.8 --skip_grid --n_samples 1

Global seed set to 42 Loading model from models/ldm/stable-diffusion-v1/model.ckpt Global Step: 470000 LatentDiffusion: Running in eps-prediction mode DiffusionWrapper has 859.52 M params. making attention of type 'vanilla' with 512 in_channels Working with z of shape (1, 4, 32, 32) = 4096 dimensions. making attention of type 'vanilla' with 512 in_channels Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.22.self_attn.k_proj.weight', 'vision_model.encoder.layers.6.self_attn.k_proj.weight', 'vision_model.encoder.layers.18.self_attn.v_proj.weight', 'vision_model.encoder.layers.20.layer_norm2.bias', 'vision_model.encoder.layers.18.layer_norm1.bias', 'vision_model.encoder.layers.11.self_attn.out_proj.weight', 'vision_model.encoder.layers.2.self_attn.k_proj.bias', 'vision_model.encoder.layers.12.mlp.fc1.bias', 'vision_model.encoder.layers.10.mlp.fc1.bias', 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• This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
• This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). loaded input image of size (1024, 512) from sketch-mountains-input.jpg scripts/img2img.py:53: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead. image = image.resize((w, h), resample=PIL.Image.LANCZOS) target t_enc is 40 steps Sampling: 0%| | 0/1 [00:00<?, ?it/s]Running DDIM Sampling with 40 timesteps | 0/1 [00:00<?, ?it/s] Decoding image: 0%| | 0/40 [00:04<?, ?it/s] data: 0%| | 0/1 [00:04<?, ?it/s] Sampling: 0%| | 0/1 [00:04<?, ?it/s] Traceback (most recent call last): File "scripts/img2img.py", line 293, in main() File "scripts/img2img.py", line 261, in main samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale, File "C:\Users\Coder.conda\envs\ldm\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(*args, kwargs) File "c:\users\coder\desktop\stable-diffusion-main\ldm\models\diffusion\ddim.py", line 238, in decode xdec, = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, File "C:\Users\Coder.conda\envs\ldm\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(args, kwargs) File "c:\users\coder\desktop\stable-diffusion-main\ldm\models\diffusion\ddim.py", line 177, in p_sample_ddim e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) File "c:\users\coder\desktop\stable-diffusion-main\ldm\models\diffusion\ddpm.py", line 987, in apply_model x_recon = self.model(x_noisy, t, cond) File "C:\Users\Coder.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(input, kwargs) File "c:\users\coder\desktop\stable-diffusion-main\ldm\models\diffusion\ddpm.py", line 1410, in forward out = self.diffusion_model(x, t, context=cc) File "C:\Users\Coder.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, kwargs) File "c:\users\coder\desktop\stable-diffusion-main\ldm\modules\diffusionmodules\openaimodel.py", line 732, in forward h = module(h, emb, context) File "C:\Users\Coder.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, *kwargs) File "c:\users\coder\desktop\stable-diffusion-main\ldm\modules\diffusionmodules\openaimodel.py", line 85, in forward x = layer(x, context) File "C:\Users\Coder.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(input, kwargs) File "c:\users\coder\desktop\stable-diffusion-main\ldm\modules\attention.py", line 258, in forward x = block(x, context=context) File "C:\Users\Coder.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, *kwargs) File "c:\users\coder\desktop\stable-diffusion-main\ldm\modules\attention.py", line 209, in forward return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) File "c:\users\coder\desktop\stable-diffusion-main\ldm\modules\diffusionmodules\util.py", line 114, in checkpoint return CheckpointFunction.apply(func, len(inputs), args) File "c:\users\coder\desktop\stable-diffusion-main\ldm\modules\diffusionmodules\util.py", line 127, in forward output_tensors = ctx.run_function(ctx.input_tensors) File "c:\users\coder\desktop\stable-diffusion-main\ldm\modules\attention.py", line 212, in _forward x = self.attn1(self.norm1(x)) + x File "C:\Users\Coder.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(input, **kwargs) File "c:\users\coder\desktop\stable-diffusion-main\ldm\modules\attention.py", line 191, in forward out = einsum('b i j, b j d -> b i d', attn, v) File "C:\Users\Coder.conda\envs\ldm\lib\site-packages\torch\functional.py", line 330, in einsum return _VF.einsum(equation, operands) # type: ignore[attr-defined] RuntimeError: CUDA out of memory. Tried to allocate 2.00 GiB (GPU 0; 12.00 GiB total capacity; 10.08 GiB already allocated; 0 bytes free; 10.37 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

[nerdyrodent]

Your GPU doesn't have enough VRAM to complete your request - RuntimeError: CUDA out of memory. Tried to allocate 2.00 GiB

Try using a smaller image.

[nonetrix]

It seems img2img uses much more VRAM is there other a ways to reduce this while keeping at least 512x512? 256x256 is way to low and the AI seems to not know what to do at that point and gives much worse results

I have a GTX 1080 with 8GB of VRAM

[tonymacx86PRO]

It works with a small image but what is it? Original: Output (Prompt: Add realistic details to this image): Is that okay?

[nonetrix]

It works with a small image but what is it? Original: Output (Prompt: Add realistic details to this image): Is that okay?

User error prompt shouldn't be like "add realistic details to this image" needs to be like "a realistic photo of a RPG sitting on sand background at day time"(or whatever this is supposed to be) to my knowledge when it hears realistic details might be thinking of trees or grass or anything in nature

However something like that would be interesting although don't know if it would be practical as it doesn't know much(e.g. material, lighting)

[tonymacx86PRO]

a realistic photo of a RPG sitting on sand background at day time It doesn't work

[nonetrix]

a realistic photo of a RPG sitting on sand background at day time It doesn't work

Try tweaking your settings mainly --n_samples or --n_iter or one of those two I think don't know which but you're probably not giving it enough time in the oven