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[Docs] Fix typos (#7118)

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Fix typos, formatting and remove trailing whitespace
This commit is contained in:
M. Tolga Cangöz
2024-02-27 23:38:00 +03:00
committed by GitHub
parent 8492db2332
commit e51862bbed
2 changed files with 24 additions and 22 deletions
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38
docs/source/en/using-diffusers/ip_adapter.md
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@@ -48,10 +48,10 @@ Create a text prompt and load an image prompt before passing them to the pipelin
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_diner.png")
generator = torch.Generator(device="cpu").manual_seed(0)
images = pipeline(
prompt="a polar bear sitting in a chair drinking a milkshake",
prompt="a polar bear sitting in a chair drinking a milkshake",
ip_adapter_image=image,
negative_prompt="deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality",
num_inference_steps=100,
num_inference_steps=100,
generator=generator,
).images
images[0]
@@ -270,7 +270,7 @@ generator = torch.Generator(device="cpu").manual_seed(26)
image = pipeline(
prompt="A photo of Einstein as a chef, wearing an apron, cooking in a French restaurant",
ip_adapter_image=image,
negative_prompt="lowres, bad anatomy, worst quality, low quality",
negative_prompt="lowres, bad anatomy, worst quality, low quality",
num_inference_steps=100,
generator=generator,
).images[0]
@@ -304,7 +304,7 @@ from transformers import CLIPVisionModelWithProjection
from diffusers.utils import load_image
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter",
"h94/IP-Adapter",
subfolder="models/image_encoder",
torch_dtype=torch.float16,
)
@@ -323,8 +323,8 @@ pipeline = AutoPipelineForText2Image.from_pretrained(
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.load_ip_adapter(
"h94/IP-Adapter",
subfolder="sdxl_models",
"h94/IP-Adapter",
subfolder="sdxl_models",
weight_name=["ip-adapter-plus_sdxl_vit-h.safetensors", "ip-adapter-plus-face_sdxl_vit-h.safetensors"]
)
pipeline.set_ip_adapter_scale([0.7, 0.3])
@@ -336,7 +336,7 @@ Load an image prompt and a folder containing images of a certain style you want
```py
face_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/women_input.png")
style_folder = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy"
style_images = [load_image(f"{style_folder}/img{i}.png") for i in range(10)]
style_images = [load_image(f"{style_folder}/img{i}.png") for i in range(10)]
```
<div class="flex flex-row gap-4">
@@ -358,10 +358,11 @@ generator = torch.Generator(device="cpu").manual_seed(0)
image = pipeline(
prompt="wonderwoman",
ip_adapter_image=[style_images, face_image],
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50, num_images_per_prompt=1,
generator=generator,
).images[0]
image
```
<div class="flex justify-center">
@@ -379,14 +380,14 @@ from diffusers import DiffusionPipeline, LCMScheduler
import torch
from diffusers.utils import load_image
model_id = "sd-dreambooth-library/herge-style"
model_id = "sd-dreambooth-library/herge-style"
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
pipeline.load_lora_weights(lcm_lora_id)
pipeline.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config)
pipeline.enable_model_cpu_offload()
```
@@ -455,13 +456,13 @@ Pass the depth map and IP-Adapter image to the pipeline to generate an image.
```py
generator = torch.Generator(device="cpu").manual_seed(33)
image = pipeline(
prompt="best quality, high quality",
prompt="best quality, high quality",
image=depth_map,
ip_adapter_image=ip_adapter_image,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50,
generator=generator,
).image[0]
).images[0]
image
```
@@ -511,8 +512,7 @@ If you have more than one IP-Adapter image, load them into a list, ensuring each
face_image1 = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_mask_girl1.png")
face_image2 = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_mask_girl2.png")
ip_images =[[image1], [image2]]
ip_images = [[face_image1], [face_image2]]
```
<div class="flex flex-row gap-4">
@@ -529,19 +529,19 @@ ip_images =[[image1], [image2]]
Pass preprocessed masks to the pipeline using `cross_attention_kwargs` as shown below:
```py
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name=["ip-adapter-plus-face_sdxl_vit-h.safetensors"] * 2)
pipeline.set_ip_adapter_scale([0.7] * 2)
generator = torch.Generator(device="cpu").manual_seed(0)
num_images=1
num_images = 1
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=num_images,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=20, num_images_per_prompt=num_images,
generator=generator, cross_attention_kwargs={"ip_adapter_masks": masks}
).images[0]
image
```
<div class="flex justify-center">

8
docs/source/en/using-diffusers/loading_adapters.md
View File

@@ -340,9 +340,9 @@ Once loaded, you can use the pipeline with an image and text prompt to guide the
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png")
generator = torch.Generator(device="cpu").manual_seed(33)
images = pipeline(
    prompt='best quality, high quality, wearing sunglasses',
    prompt='best quality, high quality, wearing sunglasses',
    ip_adapter_image=image,
    negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
    negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
    num_inference_steps=50,
    generator=generator,
).images[0]
@@ -355,11 +355,13 @@ images
### IP-Adapter Plus
IP-Adapter relies on an image encoder to generate image features. If the IP-Adapter repository contains a `image_encoder` subfolder, the image encoder is automatically loaded and registed to the pipeline. Otherwise, you'll need to explicitly load the image encoder with a [`~transformers.CLIPVisionModelWithProjection`] model and pass it to the pipeline.
IP-Adapter relies on an image encoder to generate image features. If the IP-Adapter repository contains an `image_encoder` subfolder, the image encoder is automatically loaded and registered to the pipeline. Otherwise, you'll need to explicitly load the image encoder with a [`~transformers.CLIPVisionModelWithProjection`] model and pass it to the pipeline.
This is the case for *IP-Adapter Plus* checkpoints which use the ViT-H image encoder.
```py
from transformers import CLIPVisionModelWithProjection
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter",
subfolder="models/image_encoder",