Revert incorrect commit

See in [Github action](https://github.com/huggingface/diffusers/actions/runs/9489094498/job/26149588467), the `PATH_IN_REPO` has been incorrectly evaluated, resulting in community pipelines files been uploaded at the root of the folder. This PR fixes this by deleting them.

In parallel, I opened [#8519](https://github.com/huggingface/diffusers/pull/8519) to prevent this in the future.

README_community_scripts.md

@@ -1,232 +0,0 @@
-# Community Scripts
-
-**Community scripts** consist of inference examples using Diffusers pipelines that have been added by the community. 
-Please have a look at the following table to get an overview of all community examples. Click on the **Code Example** to get a copy-and-paste code example that you can try out.
-If a community script doesn't work as expected, please open an issue and ping the author on it.
-
-| Example                                                                                                                               | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | Code Example                                                                              | Colab                                                                                                                                                                                                              |                                                        Author |
-|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
-| Using IP-Adapter with negative noise                                                                                                  | Using negative noise with IP-adapter to better control the generation (see the [original post](https://github.com/huggingface/diffusers/discussions/7167) on the forum for more details)                                                                                                                                                                                                                                                    | [IP-Adapter Negative Noise](#ip-adapter-negative-noise)                                   | | [Álvaro Somoza](https://github.com/asomoza)|
-| asymmetric tiling                                                                                                  |configure seamless image tiling independently for the X and Y axes                                                                                                                                                                                                      | [Asymmetric Tiling](#asymmetric-tiling )                                   | | [alexisrolland](https://github.com/alexisrolland)|
-
-
-## Example usages
-
-### IP Adapter Negative Noise
-
-Diffusers pipelines are fully integrated with IP-Adapter, which allows you to prompt the diffusion model with an image. However, it does not support negative image prompts (there is no `negative_ip_adapter_image` argument) the same way it supports negative text prompts. When you pass an `ip_adapter_image,` it will create a zero-filled tensor as a negative image. This script shows you how to create a negative noise from `ip_adapter_image` and use it to significantly improve the generation quality while preserving the composition of images.
-
-[cubiq](https://github.com/cubiq) initially developed this feature in his [repository](https://github.com/cubiq/ComfyUI_IPAdapter_plus). The community script was contributed by [asomoza](https://github.com/Somoza). You can find more details about this experimentation [this discussion](https://github.com/huggingface/diffusers/discussions/7167)
-
-IP-Adapter without negative noise
-|source|result|
-|---|---|
-|![20240229150812](https://github.com/huggingface/diffusers/assets/5442875/901d8bd8-7a59-4fe7-bda1-a0e0d6c7dffd)|![20240229163923_normal](https://github.com/huggingface/diffusers/assets/5442875/3432e25a-ece6-45f4-a3f4-fca354f40b5b)|
-
-IP-Adapter with negative noise
-|source|result|
-|---|---|
-|![20240229150812](https://github.com/huggingface/diffusers/assets/5442875/901d8bd8-7a59-4fe7-bda1-a0e0d6c7dffd)|![20240229163923](https://github.com/huggingface/diffusers/assets/5442875/736fd15a-36ba-40c0-a7d8-6ec1ac26f788)|
-
-```python
-import torch
-
-from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, StableDiffusionXLPipeline
-from diffusers.models import ImageProjection
-from diffusers.utils import load_image
-
-
-def encode_image(
-    image_encoder,
-    feature_extractor,
-    image,
-    device,
-    num_images_per_prompt,
-    output_hidden_states=None,
-    negative_image=None,
-):
-    dtype = next(image_encoder.parameters()).dtype
-
-    if not isinstance(image, torch.Tensor):
-        image = feature_extractor(image, return_tensors="pt").pixel_values
-
-    image = image.to(device=device, dtype=dtype)
-    if output_hidden_states:
-        image_enc_hidden_states = image_encoder(image, output_hidden_states=True).hidden_states[-2]
-        image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-
-        if negative_image is None:
-            uncond_image_enc_hidden_states = image_encoder(
-                torch.zeros_like(image), output_hidden_states=True
-            ).hidden_states[-2]
-        else:
-            if not isinstance(negative_image, torch.Tensor):
-                negative_image = feature_extractor(negative_image, return_tensors="pt").pixel_values
-            negative_image = negative_image.to(device=device, dtype=dtype)
-            uncond_image_enc_hidden_states = image_encoder(negative_image, output_hidden_states=True).hidden_states[-2]
-
-        uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-        return image_enc_hidden_states, uncond_image_enc_hidden_states
-    else:
-        image_embeds = image_encoder(image).image_embeds
-        image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        uncond_image_embeds = torch.zeros_like(image_embeds)
-
-        return image_embeds, uncond_image_embeds
-
-
-@torch.no_grad()
-def prepare_ip_adapter_image_embeds(
-    unet,
-    image_encoder,
-    feature_extractor,
-    ip_adapter_image,
-    do_classifier_free_guidance,
-    device,
-    num_images_per_prompt,
-    ip_adapter_negative_image=None,
-):
-    if not isinstance(ip_adapter_image, list):
-        ip_adapter_image = [ip_adapter_image]
-
-    if len(ip_adapter_image) != len(unet.encoder_hid_proj.image_projection_layers):
-        raise ValueError(
-            f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
-        )
-
-    image_embeds = []
-    for single_ip_adapter_image, image_proj_layer in zip(
-        ip_adapter_image, unet.encoder_hid_proj.image_projection_layers
-    ):
-        output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
-        single_image_embeds, single_negative_image_embeds = encode_image(
-            image_encoder,
-            feature_extractor,
-            single_ip_adapter_image,
-            device,
-            1,
-            output_hidden_state,
-            negative_image=ip_adapter_negative_image,
-        )
-        single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
-        single_negative_image_embeds = torch.stack([single_negative_image_embeds] * num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
-            single_image_embeds = single_image_embeds.to(device)
-
-        image_embeds.append(single_image_embeds)
-
-    return image_embeds
-
-
-vae = AutoencoderKL.from_pretrained(
-    "madebyollin/sdxl-vae-fp16-fix",
-    torch_dtype=torch.float16,
-).to("cuda")
-
-pipeline = StableDiffusionXLPipeline.from_pretrained(
-    "RunDiffusion/Juggernaut-XL-v9",
-    torch_dtype=torch.float16,
-    vae=vae,
-    variant="fp16",
-).to("cuda")
-
-pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-pipeline.scheduler.config.use_karras_sigmas = True
-
-pipeline.load_ip_adapter(
-    "h94/IP-Adapter",
-    subfolder="sdxl_models",
-    weight_name="ip-adapter-plus_sdxl_vit-h.safetensors",
-    image_encoder_folder="models/image_encoder",
-)
-pipeline.set_ip_adapter_scale(0.7)
-
-ip_image = load_image("source.png")
-negative_ip_image = load_image("noise.png")
-
-image_embeds = prepare_ip_adapter_image_embeds(
-    unet=pipeline.unet,
-    image_encoder=pipeline.image_encoder,
-    feature_extractor=pipeline.feature_extractor,
-    ip_adapter_image=[[ip_image]],
-    do_classifier_free_guidance=True,
-    device="cuda",
-    num_images_per_prompt=1,
-    ip_adapter_negative_image=negative_ip_image,
-)
-
-
-prompt = "cinematic photo of a cyborg in the city, 4k, high quality, intricate, highly detailed"
-negative_prompt = "blurry, smooth, plastic"
-
-image = pipeline(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    ip_adapter_image_embeds=image_embeds,
-    guidance_scale=6.0,
-    num_inference_steps=25,
-    generator=torch.Generator(device="cpu").manual_seed(1556265306),
-).images[0]
-
-image.save("result.png")
-```
-
-### Asymmetric Tiling
-Stable Diffusion is not trained to generate seamless textures. However, you can use this simple script to add tiling to your generation. This script is contributed by [alexisrolland](https://github.com/alexisrolland). See more details in the [this issue](https://github.com/huggingface/diffusers/issues/556)
-
-
-|Generated|Tiled|
-|---|---|
-|![20240313003235_573631814](https://github.com/huggingface/diffusers/assets/5442875/eca174fb-06a4-464e-a3a7-00dbb024543e)|![wall](https://github.com/huggingface/diffusers/assets/5442875/b4aa774b-2a6a-4316-a8eb-8f30b5f4d024)|
-
-
-```py
-import torch
-from typing import Optional
-from diffusers import StableDiffusionPipeline
-from diffusers.models.lora import LoRACompatibleConv
-
-def seamless_tiling(pipeline, x_axis, y_axis):
-    def asymmetric_conv2d_convforward(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
-        self.paddingX = (self._reversed_padding_repeated_twice[0], self._reversed_padding_repeated_twice[1], 0, 0)
-        self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])
-        working = torch.nn.functional.pad(input, self.paddingX, mode=x_mode)
-        working = torch.nn.functional.pad(working, self.paddingY, mode=y_mode)
-        return torch.nn.functional.conv2d(working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups)
-    x_mode = 'circular' if x_axis else 'constant'
-    y_mode = 'circular' if y_axis else 'constant'
-    targets = [pipeline.vae, pipeline.text_encoder, pipeline.unet]
-    convolution_layers = []
-    for target in targets:
-        for module in target.modules():
-            if isinstance(module, torch.nn.Conv2d):
-                convolution_layers.append(module)
-    for layer in convolution_layers:
-        if isinstance(layer, LoRACompatibleConv) and layer.lora_layer is None:
-            layer.lora_layer = lambda * x: 0
-        layer._conv_forward = asymmetric_conv2d_convforward.__get__(layer, torch.nn.Conv2d)
-    return pipeline
-
-pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True)
-pipeline.enable_model_cpu_offload()
-prompt = ["texture of a red brick wall"]
-seed = 123456
-generator = torch.Generator(device='cuda').manual_seed(seed)
-
-pipeline = seamless_tiling(pipeline=pipeline, x_axis=True, y_axis=True)
-image = pipeline(
-    prompt=prompt,
-    width=512,
-    height=512,
-    num_inference_steps=20,
-    guidance_scale=7,
-    num_images_per_prompt=1,
-    generator=generator
-).images[0]
-seamless_tiling(pipeline=pipeline, x_axis=False, y_axis=False)
-
-torch.cuda.empty_cache()
-image.save('image.png')
-```