Update README.md

README.md

@@ -1,3 +1,107 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: runwayml/stable-diffusion-v1-5
+tags:
+- text-to-image
+- diffusion-models
+- stable-diffusion
+- diffusers
+- image-generation
+- fast-sampling
+library_name: diffusers
+pipeline_tag: text-to-image
+---
+# Image Diffusion Preview with Consistency Solver (Google DeepMind)
+
+[paper](https://arxiv.org/abs/2512.13592)  [code](https://github.com/G-U-N/consolver)   [huggingface](https://huggingface.co/papers/2512.13592)  [model](https://huggingface.co/wangfuyun/consolver)
+
+# Quick Start
+
+```python
+Pythonimport torch
+from diffusers import StableDiffusionPipeline, DDIMScheduler
+from scheduler_ppo import PPOScheduler  # Provided in this repo
+from huggingface_hub import hf_hub_download
+
+# Download the trained factor_net checkpoint
+factor_net_path = hf_hub_download(
+    repo_id="wangfuyun/consolver",
+    filename="model.ckpt"
+)
+
+model_id = "runwayml/stable-diffusion-v1-5"
+prompt = "an astronaut riding a horse on the moon, highly detailed, 8k"
+num_inference_steps = 8
+guidance_scale = 3.0
+seed = 43
+height = width = 512
+
+def load_pipeline(scheduler_type="ddim"):
+    if scheduler_type == "ppo":
+        scheduler = PPOScheduler(
+            beta_end=0.012,
+            beta_schedule="scaled_linear",
+            beta_start=0.00085,
+            num_train_timesteps=1000,
+            steps_offset=1,
+            timestep_spacing="trailing",
+            order_dim=4,
+            scaler_dim=0,
+            use_conv=False,
+            factor_net_kwargs=dict(embedding_dim=64, hidden_dim=256, num_actions=11),
+        )
+    else:
+        scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler", timestep_spacing="trailing")
+
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id,
+        scheduler=scheduler,
+        safety_checker=None,
+        # torch_dtype=torch.float16,  # Uncomment for GPU memory savings
+    ).to("cuda")
+
+    if scheduler_type == "ppo" and factor_net_path:
+        weight = torch.load(factor_net_path, map_location="cpu")
+        pipe.scheduler.factor_net.load_state_dict(weight)
+        pipe.scheduler.factor_net.to("cuda")
+
+    return pipe
+
+generator = torch.Generator("cuda").manual_seed(seed)
+
+# DDIM baseline (8 steps)
+pipe_ddim = load_pipeline("ddim")
+image_ddim = pipe_ddim(prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale,
+                       generator=generator, height=height, width=width).images[0]
+image_ddim.save("ddim_result.jpg")
+
+# ConSolver (8 steps)
+pipe_consolver = load_pipeline("ppo")
+image_consolver = pipe_consolver(prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale,
+                                 generator=generator, height=height, width=width).images[0]
+image_consolver.save("consolver_result.jpg")
+```
+
+
+
+
+<div align="center">
+  <table>
+    <tr>
+      <td align="center">
+        <img src="https://github.com/user-attachments/assets/35f5f99a-ca5f-4919-82cf-04a67a2dbe13" alt="DDIM" width="80%" />
+      </td>
+      <td align="center">
+        <img src="https://github.com/user-attachments/assets/6428a663-b488-4ecc-b79c-4fcb431d5630" alt="Consistency Solver" width="80%" />
+      </td>
+    </tr>
+    <tr>
+      <td align="center">
+        <em>DDIM</em>
+      </td>
+      <td align="center">
+        <em>ConsistencySolver</em>
+      </td>
+    </tr>
+  </table>
+</div>