Update README.md

README.md

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   </div>
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-<Gallery />
+<Gallery />
+
+# Model File and Inference Workflow
+
+## 📥 Download Links:
+
+- [squish_18.safetensors](./squish_18.safetensors) - LoRA Model File
+- [wan_img2video_lora_workflow.json](./workflow/wan_img2video_lora_workflow.json) - Wan I2V with LoRA Workflow for ComfyUI
+
+## Using with Diffusers
+```py
+pip install git+https://github.com/huggingface/diffusers.git
+```
+
+```py
+import torch
+from diffusers.utils import export_to_video, load_image
+from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
+from transformers import CLIPVisionModel
+import numpy as np
+
+model_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+pipe.load_lora_weights("Remade/Squish")
+
+pipe.enable_model_cpu_offload() #for low-vram environments
+
+prompt = "In the video, a miniature cat toy is presented. The cat toy is held in a person's hands. The person then presses on the cat toy, causing a sq41sh squish effect. The person keeps pressing down on the cat toy, further showing the sq41sh squish effect."
+
+image = load_image("https://huggingface.co/datasets/diffusers/cat_toy_example/resolve/main/1.jpeg")
+
+max_area = 480 * 832
+aspect_ratio = image.height / image.width
+mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
+height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
+width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
+image = image.resize((width, height))
+
+output = pipe(
+    image=image,
+    prompt=prompt,
+    height=height,
+    width=width,
+    num_frames=81,
+    guidance_scale=5.0,
+    num_inference_steps=28
+).frames[0]
+export_to_video(output, "output.mp4", fps=16)
+```
+
+---
+<div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
+  <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+    <h2 style="color: #24292e; margin-top: 0;">Recommended Settings</h2>
+    <ul style="margin-bottom: 0;">
+      <li><b>LoRA Strength:</b> 1.0</li>
+      <li><b>Embedded Guidance Scale:</b> 6.0</li>
+      <li><b>Flow Shift:</b> 5.0</li>
+    </ul>
+  </div>
+
+  <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+    <h2 style="color: #24292e; margin-top: 0;">Trigger Words</h2>
+    <p>The key trigger phrase is: <code style="background-color: #f0f0f0; padding: 3px 6px; border-radius: 4px;">sq41sh squish effect</code></p>
+  </div>
+
+  <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+    <h2 style="color: #24292e; margin-top: 0;">Prompt Template</h2>
+    <p>For best results, use this prompt structure:</p>
+    <div style="background-color: #f0f0f0; padding: 12px; border-radius: 6px; margin: 10px 0;">
+      <i>The video opens on a [object]. A knife, held by a hand, is coming into frame and hovering over the [object]. The knife then begins cutting into the [object] to c4k3 cakeify it. As the knife slices the [object] open, the inside of the [object] is revealed to be cake with chocolate layers. The knife cuts through and the contents of the [object] are revealed.</i>
+    </div>
+    <p>Simply replace <code style="background-color: #f0f0f0; padding: 3px 6px; border-radius: 4px;">[object]</code> with whatever you want to see cakeified!</p>
+  </div>
+
+  <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+    <h2 style="color: #24292e; margin-top: 0;">ComfyUI Workflow</h2>
+    <p>This LoRA works with a modified version of <a href="https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/main/example_workflows/wanvideo_480p_I2V_example_02.json" style="color: #0366d6; text-decoration: none;">Kijai's Wan Video Wrapper workflow</a>. The main modification is adding a Wan LoRA node connected to the base model.</p>
+    <img src="./workflow/workflow_screenshot.png" style="width: 100%; border-radius: 8px; margin: 15px 0; box-shadow: 0 4px 8px rgba(0,0,0,0.1);">
+    <p>See the Downloads section above for the modified workflow.</p>
+  </div>
+</div>
+
+<div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
+  <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+    <h2 style="color: #24292e; margin-top: 0;">Model Information</h2>
+    <p>The model weights are available in Safetensors format. See the Downloads section above.</p>
+  </div>
+
+  <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+    <h2 style="color: #24292e; margin-top: 0;">Training Details</h2>
+    <ul style="margin-bottom: 0;">
+      <li><b>Base Model:</b> Wan2.1 14B I2V 480p</li>
+      <li><b>Training Data:</b> 1.5 minutes of video (20 short clips of things being squished)</li>
+      <li><b>Epochs:</b> 18</li>
+    </ul>
+  </div>
+
+  <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+    <h2 style="color: #24292e; margin-top: 0;">Additional Information</h2>
+    <p>Training was done using <a href="https://github.com/tdrussell/diffusion-pipe" style="color: #0366d6; text-decoration: none;">Diffusion Pipe for Training</a></p>
+  </div>
+
+  <div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+    <h2 style="color: #24292e; margin-top: 0;">Acknowledgments</h2>
+    <p style="margin-bottom: 0;">Special thanks to Kijai for the ComfyUI Wan Video Wrapper and tdrussell for the training scripts!</p>
+  </div>
+</div>