Address PR comments: GLM-Image Port to FastVideo- Replaced diffusers FeedForward with FastVideo MLP in DiT.- Moved SDPAMetadata import to top of attention layer for cleaner code.- Removed unnecessary __call__ override from pipeline to use standard composition.- Consolidated CFG into a single batched pass with concatenated embeddings for performance.- Fixed attention mask compatibility issues with optimized FLASH_ATTN backend.- Robustly patched flash_attn unpad utility for variable version return values.

2026-07-07 15:00:48 -07:00
parent 02e1143f22
commit c54e0737d0
16 changed files with 1786 additions and 1 deletions
@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+GLM-Image inference example using FastVideo.
+
+This example demonstrates how to generate images using the GLM-Image model
+through FastVideo's pipeline infrastructure.
+
+Usage:
+    python examples/inference/basic/basic_glm_image.py
+"""
+
+import os
+
+# Set environment variables for single-GPU distributed setup
+os.environ.setdefault("MASTER_ADDR", "127.0.0.1")
+os.environ.setdefault("MASTER_PORT", "29501")
+os.environ.setdefault("RANK", "0")
+os.environ.setdefault("WORLD_SIZE", "1")
+
+from fastvideo.pipelines.basic.glm_image import GlmImagePipeline
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
+
+OUTPUT_PATH = "image_output"
+
+
+
+def main():
+    # Load the GLM-Image pipeline
+    pipe = GlmImagePipeline.from_pretrained(
+        "zai-org/GLM-Image",
+        num_gpus=1,
+        trust_remote_code=True,
+    )
+
+    # Create a batch with generation parameters
+    prompt = (
+        "A beautiful landscape photography with rolling hills, "
+        "a winding river, and a vibrant sunset in the background. "
+        "Warm golden light, photorealistic style."
+    )
+    
+    batch = ForwardBatch(
+        data_type="image",
+        prompt=prompt,
+        negative_prompt="",
+        height=1024,
+        width=1024,
+        num_inference_steps=50,
+        guidance_scale=7.5,
+        guidance_rescale=0.7,
+        do_classifier_free_guidance=True,
+        num_frames=1,
+        seed=42,
+    )
+    
+    # Generate the image
+    result = pipe.forward(batch, pipe.fastvideo_args)
+    
+    # Output is in result.output as a tensor [B, C, T, H, W]
+    print(f"Generated image tensor shape: {result.output.shape}")
+    
+    # Save the output image
+    import torch
+    from torchvision.utils import save_image
+    
+    os.makedirs(OUTPUT_PATH, exist_ok=True)
+    # result.output is [B, C, 1, H, W] for images
+    image_tensor = result.output.squeeze(2) # [B, C, H, W]
+    save_path = os.path.join(OUTPUT_PATH, "output.png")
+    save_image(image_tensor, save_path)
+    print(f"Image saved to {save_path}")
+
+
+if __name__ == "__main__":
+    main()