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README.md

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+---
+license: apache-2.0
+base_model: "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+tags:
+  - WanPipeline
+  - WanPipeline-diffusers
+  - text-to-image
+  - image-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - lora
+  - template:sd-lora
+  - standard
+pipeline_tag: text-to-image
+inference: true
+widget:
+- text: 'A black and white animated scene unfolds featuring a distressed upright cow with prominent horns and expressive eyes, suspended by its legs from a hook on a static background wall. A smaller Mickey Mouse-like character enters, standing near a wooden bench, initiating interaction between the two. The cow''s posture changes as it leans, stretches, and falls, while the mouse watches with a concerned expression, its face a mixture of curiosity and worry, in a world devoid of color.'
+  parameters:
+    negative_prompt: '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走'
+  output:
+    url: ./assets/image_0_0.gif
+---
+
+# wan-disney-DCM-distilled
+
+This is a DCM-distilled PEFT LoRA derived from [Wan-AI/Wan2.1-T2V-1.3B-Diffusers](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers).
+
+The main validation prompt used during training was:
+```
+A black and white animated scene unfolds featuring a distressed upright cow with prominent horns and expressive eyes, suspended by its legs from a hook on a static background wall. A smaller Mickey Mouse-like character enters, standing near a wooden bench, initiating interaction between the two. The cow's posture changes as it leans, stretches, and falls, while the mouse watches with a concerned expression, its face a mixture of curiosity and worry, in a world devoid of color.
+```
+
+
+## Validation settings
+- CFG: `1.0`
+- CFG Rescale: `0.0`
+- Steps: `8`
+- Sampler: `FlowMatchEulerDiscreteScheduler`
+- Seed: `42`
+- Resolution: `832x480`
+
+
+Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
+
+You can find some example images in the following gallery:
+
+
+<Gallery />
+
+The text encoder **was not** trained.
+You may reuse the base model text encoder for inference.
+
+
+## Training settings
+
+- Training epochs: 0
+- Training steps: 100
+- Learning rate: 5e-05
+  - Learning rate schedule: cosine
+  - Warmup steps: 400000
+- Max grad value: 0.01
+- Effective batch size: 2
+  - Micro-batch size: 2
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 1
+- Gradient checkpointing: True
+- Prediction type: flow_matching (extra parameters=['shift=17.0'])
+- Optimizer: adamw_bf16
+- Trainable parameter precision: Pure BF16
+- Base model precision: `int8-quanto`
+- Caption dropout probability: 0.1%
+
+
+- LoRA Rank: 128
+- LoRA Alpha: 128.0
+- LoRA Dropout: 0.1
+- LoRA initialisation style: default
+    
+
+## Datasets
+
+### disney-black-and-white-wan
+- Repeats: 10
+- Total number of images: 68
+- Total number of aspect buckets: 1
+- Resolution: 0.2304 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+model_id = 'Wan-AI/Wan2.1-T2V-1.3B-Diffusers'
+adapter_id = 'bghira/wan-disney-DCM-distilled'
+pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
+pipeline.load_lora_weights(adapter_id)
+
+prompt = "A black and white animated scene unfolds featuring a distressed upright cow with prominent horns and expressive eyes, suspended by its legs from a hook on a static background wall. A smaller Mickey Mouse-like character enters, standing near a wooden bench, initiating interaction between the two. The cow's posture changes as it leans, stretches, and falls, while the mouse watches with a concerned expression, its face a mixture of curiosity and worry, in a world devoid of color."
+negative_prompt = '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走'
+
+## Optional: quantise the model to save on vram.
+## Note: The model was quantised during training, and so it is recommended to do the same during inference time.
+from optimum.quanto import quantize, freeze, qint8
+quantize(pipeline.transformer, weights=qint8)
+freeze(pipeline.transformer)
+    
+pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
+model_output = pipeline(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_inference_steps=8,
+    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
+    width=832,
+    height=480,
+    guidance_scale=1.0,
+).images[0]
+
+from diffusers.utils.export_utils import export_to_gif
+export_to_gif(model_output, "output.gif", fps=15)
+
+```
+
+
+