hf-internal-testing
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tiny-hidream-i1-pipe

Diffusers

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HiDreamImagePipeline

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linoyts HF Staff commited on Apr 14

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b340bef

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----
-library_name: diffusers
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+created using the following code based on https://github.com/huggingface/diffusers/blob/main/tests/pipelines/hidream/test_pipeline_hidream.py
+```python
+import numpy as np
+import torch
+from transformers import (
+    AutoTokenizer,
+    CLIPTextConfig,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    LlamaForCausalLM,
+    T5EncoderModel,
+)
+from diffusers import (
+    AutoencoderKL,
+    FlowMatchEulerDiscreteScheduler,
+    HiDreamImagePipeline,
+    HiDreamImageTransformer2DModel,
+)
+def get_dummy_components():
+        torch.manual_seed(0)
+        transformer = HiDreamImageTransformer2DModel(
+            patch_size=2,
+            in_channels=4,
+            out_channels=4,
+            num_layers=1,
+            num_single_layers=1,
+            attention_head_dim=8,
+            num_attention_heads=4,
+            caption_channels=[32, 16],
+            text_emb_dim=64,
+            num_routed_experts=4,
+            num_activated_experts=2,
+            axes_dims_rope=(4, 2, 2),
+            max_resolution=(32, 32),
+            llama_layers=(0, 1),
+        ).eval()
+        torch.manual_seed(0)
+        vae = AutoencoderKL(scaling_factor=0.3611, shift_factor=0.1159)
+        clip_text_encoder_config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+            hidden_act="gelu",
+            projection_dim=32,
+            max_position_embeddings=128,
+        )
+        torch.manual_seed(0)
+        text_encoder = CLIPTextModelWithProjection(clip_text_encoder_config)
+        torch.manual_seed(0)
+        text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)
+        torch.manual_seed(0)
+        text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        torch.manual_seed(0)
+        text_encoder_4 = LlamaForCausalLM.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM")
+        text_encoder_4.generation_config.pad_token_id = 1
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+        tokenizer_3 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
+        tokenizer_4 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM")
+        scheduler = FlowMatchEulerDiscreteScheduler()
+        components = {
+            "scheduler": scheduler,
+            "vae": vae,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "text_encoder_2": text_encoder_2,
+            "tokenizer_2": tokenizer_2,
+            "text_encoder_3": text_encoder_3,
+            "tokenizer_3": tokenizer_3,
+            "text_encoder_4": text_encoder_4,
+            "tokenizer_4": tokenizer_4,
+            "transformer": transformer,
+        }
+        return components
+if __name__ == "__main__":
+    components = get_dummy_components()
+    pipeline = HiDreamImagePipeline(**components)
+    pipeline.push_to_hub("hf-internal-testing/tiny-hidream-i1-pipe")
+```