CLIP Guided Img2Img Stable Diffusion
CLIP guided Img2Img stable diffusion can help to generate more realistic images with an initial image by guiding stable diffusion at every denoising step with an additional CLIP model.
The following code requires roughly 12GB of GPU RAM. THis script was contributed by Nipun Jindal and the notebook by Parag Ekbote.
In [1]:
pip install diffusers transformers pillow torch
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/system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from diffusers) (1.26.4) Requirement already satisfied: regex!=2019.12.17 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from diffusers) (2024.11.6) Requirement already satisfied: requests in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from diffusers) (2.32.3) Requirement already satisfied: safetensors>=0.3.1 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from diffusers) (0.4.5) Requirement already satisfied: packaging>=20.0 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from transformers) (24.1) Requirement already satisfied: pyyaml>=5.1 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from transformers) (6.0.2) Requirement already satisfied: tokenizers<0.21,>=0.20 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from transformers) (0.20.3) Requirement already satisfied: tqdm>=4.27 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from transformers) (4.66.6) Requirement already satisfied: typing-extensions>=4.8.0 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (4.12.2) Requirement already satisfied: sympy in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (1.13.3) Requirement already satisfied: networkx in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (3.4.2) Requirement already satisfied: jinja2 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (3.1.4) Requirement already satisfied: fsspec in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (2024.9.0) Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (12.1.105) Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (12.1.105) Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (12.1.105) Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (8.9.2.26) Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (12.1.3.1) Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (11.0.2.54) Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from torch) (10.3.2.106) Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in 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/system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from importlib-metadata->diffusers) (3.21.0) Requirement already satisfied: MarkupSafe>=2.0 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from jinja2->torch) (3.0.2) Requirement already satisfied: charset-normalizer<4,>=2 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from requests->diffusers) (3.4.0) Requirement already satisfied: idna<4,>=2.5 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from requests->diffusers) (3.10) Requirement already satisfied: urllib3<3,>=1.21.1 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from requests->diffusers) (2.2.3) Requirement already satisfied: certifi>=2017.4.17 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from requests->diffusers) (2024.8.30) Requirement already satisfied: mpmath<1.4,>=1.1.0 in /system/conda/miniconda3/envs/cloudspace/lib/python3.10/site-packages (from sympy->torch) (1.3.0) Note: you may need to restart the kernel to use updated packages.
In [3]:
from io import BytesIO
import requests
import torch
from diffusers import DiffusionPipeline
from PIL import Image
from transformers import CLIPImageProcessor, CLIPModel
# Load CLIP model and feature extractor
feature_extractor = CLIPImageProcessor.from_pretrained(
"laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
)
clip_model = CLIPModel.from_pretrained(
"laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
)
# Load guided pipeline
guided_pipeline = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
custom_pipeline="clip_guided_stable_diffusion_img2img",
clip_model=clip_model,
feature_extractor=feature_extractor,
torch_dtype=torch.float16,
)
guided_pipeline.enable_attention_slicing()
guided_pipeline = guided_pipeline.to("cuda")
# Define prompt and fetch image
prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
response = requests.get(url)
edit_image = Image.open(BytesIO(response.content)).convert("RGB")
# Run the pipeline
image = guided_pipeline(
prompt=prompt,
height=512, # Height of the output image
width=512, # Width of the output image
image=edit_image, # Input image to guide the diffusion
strength=0.75, # How much to transform the input image
num_inference_steps=30, # Number of diffusion steps
guidance_scale=7.5, # Scale of the classifier-free guidance
clip_guidance_scale=100, # Scale of the CLIP guidance
num_images_per_prompt=1, # Generate one image per prompt
eta=0.0, # Noise scheduling parameter
num_cutouts=4, # Number of cutouts for CLIP guidance
use_cutouts=False, # Whether to use cutouts
output_type="pil", # Output as PIL image
).images[0]
# Display the generated image
image.show()
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