docs: update README.md

README.md

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+---
+library_name: diffusers
+---
+
+
+
+Quick start:
+
+```python
+from diffusers.models import AutoencoderKL
+from diffusers import StableDiffusionPipeline
+from diffusers.schedulers.scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
+from PIL import Image
+import torch
+
+DPM_SOLVER_MULTI_STEP_SCHEDULER_CONFIG = {
+    "algorithm_type": "dpmsolver++",
+    "beta_end": 0.012,
+    "beta_schedule": "scaled_linear",
+    "beta_start": 0.00085,
+    "clip_sample": False,
+    "dynamic_thresholding_ratio": 0.995,
+    "euler_at_final": False,
+    "final_sigmas_type": "zero",
+    "lambda_min_clipped": float("-inf"),
+    "lower_order_final": True,
+    "num_train_timesteps": 1000,
+    "prediction_type": "epsilon",
+    "sample_max_value": 1.0,
+    "set_alpha_to_one": False,
+    "solver_order": 2,
+    "solver_type": "midpoint",
+    "steps_offset": 1,
+    "thresholding": False,
+    "timestep_spacing": "linspace",
+    "trained_betas": None,
+    "use_karras_sigmas": True,
+    "use_lu_lambdas": False,
+    "variance_type": None,
+}
+
+if __name__ == "__main__":
+    width = 512
+    height = int((width * 1.25 // 8) * 8)
+
+    vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
+    pipe = StableDiffusionPipeline.from_pretrained(
+        # "scenario-labs/Flat-2D_Animerge_v45-sharp",
+        "runwayml/stable-diffusion-v1-5",
+        use_safetensors=True,
+        safety_checker=None,
+        vae=vae
+    ).to("cuda")
+    pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+        DPM_SOLVER_MULTI_STEP_SCHEDULER_CONFIG,
+    )
+
+    prompt = "a cute robot digital illustration, full pose"
+    seed = 2544574284
+
+    images = []
+    scales = [-1, 0, 1, 1.5]
+
+    for scale in scales:
+        generator = torch.Generator(device="cpu").manual_seed(seed)
+        pipe.load_lora_weights("scenario-labs/more_details", weight_name="more_details.safetensors")
+        pipe.fuse_lora(lora_scale=scale)
+        image = pipe(
+            prompt,
+            generator=generator,
+            num_inference_steps=25,
+            num_samples=1,
+            width=width,
+            height=height
+        ).images[0]
+        pipe.unfuse_lora()
+        images.append(image)
+
+    # Combine images into a single row
+    combined_image = Image.new('RGB', (width * len(images), height))
+    x_offset = 0
+    for image in images:
+        combined_image.paste(image, (x_offset, 0))
+        x_offset += width
+
+    # Display the combined image
+    combined_image.save("demo.png")
+
+```