Text-to-Image
Diffusers
PyTorch
English
Text-to-Image
IP-Adapter
StableDiffusion3Pipeline
image-generation
Stable Diffusion
Instructions to use Runware/SD3.5-ip_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Runware/SD3.5-ip_adapter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Runware/SD3.5-ip_adapter", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 749 Bytes
4604daf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | import torch
from safetensors.torch import save_file
# Load the original .bin file
orig = torch.load("pytorch_model.bin", map_location="cpu")
converted = {}
# Convert ip_adapter weights
for key, value in orig["ip_adapter"].items():
# e.g. '0.to_k_ip.weight' → 'ip_adapter.0.to_k_ip.weight'
parts = key.split(".", 1)
if len(parts) == 2:
new_key = f"ip_adapter.{parts[0]}.{parts[1]}"
else:
new_key = f"ip_adapter.{key}"
converted[new_key] = value
# Convert image_proj weights
for key, value in orig["image_proj"].items():
# Do not rename! Just prepend with 'image_proj.'
new_key = f"image_proj.{key}"
converted[new_key] = value
# Save to safetensors
save_file(converted, "ip_adapter.safetensors")
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