Create README.md

README.md

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+A tiny random pipeline for testing purposes based on [THUDM/CogView4-6B](https://huggingface.co/THUDM/Cogview4-6B).
+
+```python
+from transformers import AutoTokenizer, GlmConfig, GlmModel
+from diffusers import CogView4Transformer2DModel, FlowMatchEulerDiscreteScheduler, AutoencoderKL, CogView4Pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat", trust_remote_code=True)
+
+config = GlmConfig(hidden_size=32, intermediate_size=8, num_hidden_layers=2, num_attention_heads=4, head_dim=8)
+text_encoder = GlmModel(config)
+
+transformer_kwargs = {
+    "patch_size": 2,
+    "in_channels": 4,
+    "num_layers": 2,
+    "attention_head_dim": 4,
+    "num_attention_heads": 4,
+    "out_channels": 4,
+    "text_embed_dim": 32,
+    "time_embed_dim": 8,
+    "condition_dim": 4,
+}
+transformer = CogView4Transformer2DModel(**transformer_kwargs)
+
+vae_kwargs = {
+    "block_out_channels": [32, 64],
+    "in_channels": 3,
+    "out_channels": 3,
+    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
+    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
+    "latent_channels": 4,
+    "sample_size": 128,
+}
+vae = AutoencoderKL(**vae_kwargs)
+
+scheduler = FlowMatchEulerDiscreteScheduler()
+
+pipe = CogView4Pipeline(tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler)
+pipe.save_pretrained("./dump-cogview4-dummy-pipe")
+
+```