A high-fidelity LoRA capturing the iconic, beloved look of the classic 1970s Soviet stop-motion puppet Cheburashka (Чебурашка). Trained on a clean, consistent dataset to recreate the authentic dark brown felt wool texture, large round ears, warm expressive button eyes, and vintage nostalgic aesthetic.### ⚙️ Recommended Settings &amp; Usage * Trigger Word: <code>cheburashka_classic</code> * Additional Style Boosters: <code>stop-motion puppet</code>, <code>felt wool texture</code>, <code>vintage 1970s aesthetic</code>, <code>round ears</code> * LoRA Weight (Standalone): <code>0.85 – 1.0</code> * LoRA Weight (Multi-LoRA stack with characters/vehicles): <code>0.55 – 0.65</code> (keep CLIP weight around <code>0.5 – 0.6</code> to avoid overbaking faces/clothing)cheburashka_classic, majestic portrait, vertical 2:3, head and shoulders, looking directly into camera with giant expressive glossy eyes, soft outdoor cinematic rim light, golden hour backlight creating warm halo around dense fur, rich textures, award-winning photography, 8kcheburashka_classic, vertical 2:3, full body, peeking curiously out of giant cracked cardboard delivery box overflowing with vibrant bright oranges, studio backdrop, dynamic lighting, high contrast, crisp sharp focuscheburashka_classic, vertical 2:3, full body, standing on hood of vintage weathered green soviet off-road truck, proud heroic pose, muddy country road, cinematic wide shot, detailed vintage atmosphere, 8kcheburashka_classic, vertical 2:3, full body, walking through dense colorful autumn park carrying tiny rustic lantern with warm glowing candle inside, mystical twilight atmosphere, falling orange leaves, cinematic depth of field

Cheburashka

cheburashkaV1M.safetensors

v1.0 MOSS

v1.0 Krea 2


# 

## Model Introduction

This model is trained using the [Civision/model training](https://modelscope.ai/civision/modelTraining) environment and computing resources provided by ModelScope.

* Model Type: LoRA
* Base Model: [openmoss/MOSS-Image-SFT](https://modelscope.ai/models/openmoss/MOSS-Image-SFT)
* Training Code: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)
* Data amount：15
* Total training steps：2000
* License: Apache-2.0

## Inference Code

Install [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio):

```bash
pip install diffsynth
```

Inference:

```python
from diffsynth.pipelines.moss_image import MOSSImagePipeline, ModelConfig
import torch

pipe = MOSSImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="openmoss/MOSS-Image-SFT", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
        ModelConfig(model_id="openmoss/MOSS-Image-SFT", origin_file_pattern="text_encoder/model*.safetensors"),
        ModelConfig(model_id="openmoss/MOSS-Image-SFT", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", computation_dtype=torch.float32),
    ],
    tokenizer_config=ModelConfig(model_id="openmoss/MOSS-Image-SFT", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, ModelConfig(model_id="bazya75/cheburashkaV1V", origin_file_pattern="cheburashkaV1V_c1-st2000.safetensors"))

prompt = "a cat"
image = pipe(prompt, height=1024, width=1024, seed=0, num_inference_steps=50, cfg_scale=3.0)
image.save("image.jpg")
```


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