Instructions to use mash2005/Qwen-Image-Edit-2511-MultiAngles-FullQ4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mash2005/Qwen-Image-Edit-2511-MultiAngles-FullQ4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mash2005/Qwen-Image-Edit-2511-MultiAngles-FullQ4", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
base_model: Qwen/Qwen-Image-Edit-2511 library_name: diffusers license: apache-2.0 tags: - qwen-image-edit - lora - multi-angle - camera-angles - image-to-image - quantized - nf4
Qwen-Image-Edit-2511 + Multiple Angles LoRA (Full NF4 โ Transformer + Text Encoder)
Merged and fully quantized version of Qwen/Qwen-Image-Edit-2511 with fal/Qwen-Image-Edit-2511-Multiple-Angles-LoRA fused into the weights.
Both the DiT transformer and the Qwen2.5-VL text encoder are quantized to NF4 4-bit with double quantization. Runs on ~15GB VRAM โ fits a T4.
Usage
import torch
from diffusers import QwenImageEditPlusPipeline, QwenImageTransformer2DModel
from transformers import Qwen2_5_VLForConditionalGeneration, BitsAndBytesConfig
from PIL import Image
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
)
transformer = QwenImageTransformer2DModel.from_pretrained(
"mash2005/Qwen-Image-Edit-2511-MultiAngles-FullQ4",
subfolder="transformer",
torch_dtype=torch.bfloat16,
device_map="cuda"
)
text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"mash2005/Qwen-Image-Edit-2511-MultiAngles-FullQ4",
subfolder="text_encoder",
torch_dtype=torch.bfloat16,
device_map="cuda"
)
pipeline = QwenImageEditPlusPipeline.from_pretrained(
"mash2005/Qwen-Image-Edit-2511-MultiAngles-FullQ4",
transformer=transformer,
text_encoder=text_encoder,
torch_dtype=torch.bfloat16,
)
pipeline.enable_model_cpu_offload()
image = Image.open("your_image.jpg").convert("RGB")
result = pipeline(
image=[image],
prompt="Show this object from the back view, azimuth 180 degrees",
negative_prompt=" ",
num_inference_steps=30,
guidance_scale=1.0,
true_cfg_scale=4.0,
).images[0]
result.save("output.png")
Prompt format
"front view, azimuth 0 degrees""back view, azimuth 180 degrees""side view, azimuth 90 degrees""top-down view, elevation 90 degrees"
Credits
- Base model: Qwen/Qwen-Image-Edit-2511
- LoRA: fal/Qwen-Image-Edit-2511-Multiple-Angles-LoRA
- Downloads last month
- -