yujiepan
/

FLUX.1-dev-tiny-random

@@ -2,197 +2,159 @@
 library_name: diffusers
 ---
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-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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 library_name: diffusers
 ---
+# yujiepan/stable-diffusion-3-tiny-random
+This pipeline is intended from debugging. It is adapted from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) with smaller size and randomly initialized parameters.
+## Usage
+```python
+import torch
+from diffusers import FluxPipeline
+pipe = FluxPipeline.from_pretrained("yujiepan/FLUX.1-dev-tiny-random", torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
+prompt = "A cat holding a sign that says hello world"
+image = pipe(
+    prompt,
+    height=1024,
+    width=1024,
+    guidance_scale=3.5,
+    num_inference_steps=50,
+    max_sequence_length=512,
+    generator=torch.Generator("cpu").manual_seed(0)
+).images[0]
+# image.save("flux-dev.png")
+```
+## Codes
+```python
+import importlib
+import torch
+import transformers
+import diffusers
+import rich
+def get_original_model_configs(
+    pipeline_cls: type[diffusers.FluxPipeline],
+    pipeline_id: str
+):
+    pipeline_config: dict[str, list[str]] = \
+        pipeline_cls.load_config(pipeline_id)
+    model_configs = {}
+    for subfolder, import_strings in pipeline_config.items():
+        if subfolder.startswith("_"):
+            continue
+        module = importlib.import_module(".".join(import_strings[:-1]))
+        cls = getattr(module, import_strings[-1])
+        if issubclass(cls, transformers.PreTrainedModel):
+            config_class: transformers.PretrainedConfig = cls.config_class
+            config = config_class.from_pretrained(
+                pipeline_id, subfolder=subfolder)
+            model_configs[subfolder] = config
+        elif issubclass(cls, diffusers.ModelMixin) and issubclass(cls, diffusers.ConfigMixin):
+            config = cls.load_config(pipeline_id, subfolder=subfolder)
+            model_configs[subfolder] = config
+        elif subfolder in ['scheduler', 'tokenizer', 'tokenizer_2', 'tokenizer_3']:
+            pass
+        else:
+            raise NotImplementedError(f"unknown {subfolder}: {import_strings}")
+    return model_configs
+def load_pipeline(pipeline_cls: type[diffusers.DiffusionPipeline], pipeline_id: str, model_configs: dict[str, dict]):
+    pipeline_config: dict[str, list[str]
+                          ] = pipeline_cls.load_config(pipeline_id)
+    components = {}
+    for subfolder, import_strings in pipeline_config.items():
+        if subfolder.startswith("_"):
+            continue
+        module = importlib.import_module(".".join(import_strings[:-1]))
+        cls = getattr(module, import_strings[-1])
+        print(f"Loading:", ".".join(import_strings))
+        if issubclass(cls, transformers.PreTrainedModel):
+            config = model_configs[subfolder]
+            component = cls(config)
+        elif issubclass(cls, transformers.PreTrainedTokenizerBase):
+            component = cls.from_pretrained(pipeline_id, subfolder=subfolder)
+        elif issubclass(cls, diffusers.ModelMixin) and issubclass(cls, diffusers.ConfigMixin):
+            config = model_configs[subfolder]
+            component = cls.from_config(config)
+        elif issubclass(cls, diffusers.SchedulerMixin) and issubclass(cls, diffusers.ConfigMixin):
+            component = cls.from_pretrained(pipeline_id, subfolder=subfolder)
+        else:
+            raise (f"unknown {subfolder}: {import_strings}")
+        components[subfolder] = component
+        if 'transformer' in component.__class__.__name__.lower():
+            print(component)
+    pipeline = pipeline_cls(**components)
+    return pipeline
+def get_pipeline():
+    torch.manual_seed(42)
+    pipeline_id = "black-forest-labs/FLUX.1-dev"
+    pipeline_cls = diffusers.FluxPipeline
+    model_configs = get_original_model_configs(pipeline_cls, pipeline_id)
+    HIDDEN_SIZE = 8
+    model_configs["text_encoder"].hidden_size = HIDDEN_SIZE
+    model_configs["text_encoder"].intermediate_size = HIDDEN_SIZE * 2
+    model_configs["text_encoder"].num_attention_heads = 2
+    model_configs["text_encoder"].num_hidden_layers = 2
+    model_configs["text_encoder"].projection_dim = HIDDEN_SIZE
+    model_configs["text_encoder_2"].d_model = HIDDEN_SIZE
+    model_configs["text_encoder_2"].d_ff = HIDDEN_SIZE * 2
+    model_configs["text_encoder_2"].d_kv = HIDDEN_SIZE // 2
+    model_configs["text_encoder_2"].num_heads = 2
+    model_configs["text_encoder_2"].num_layers = 2
+    model_configs["transformer"]["num_layers"] = 2
+    model_configs["transformer"]["num_single_layers"] = 4
+    model_configs["transformer"]["num_attention_heads"] = 2
+    model_configs["transformer"]["attention_head_dim"] = HIDDEN_SIZE
+    model_configs["transformer"]["pooled_projection_dim"] = HIDDEN_SIZE
+    model_configs["transformer"]["joint_attention_dim"] = HIDDEN_SIZE
+    model_configs["transformer"]["axes_dims_rope"] = (4, 2, 2)
+    # model_configs["transformer"]["caption_projection_dim"] = HIDDEN_SIZE
+    model_configs["vae"]["layers_per_block"] = 1
+    model_configs["vae"]["block_out_channels"] = [HIDDEN_SIZE] * 4
+    model_configs["vae"]["norm_num_groups"] = 2
+    model_configs["vae"]["latent_channels"] = 16
+    pipeline = load_pipeline(pipeline_cls, pipeline_id, model_configs)
+    return pipeline
+pipe = get_pipeline()
+pipe = pipe.to(torch.bfloat16)
+from pathlib import Path
+save_folder = '/tmp/yujiepan/FLUX.1-dev-tiny-random'
+Path(save_folder).mkdir(parents=True, exist_ok=True)
+pipe.save_pretrained(save_folder)
+pipe = diffusers.FluxPipeline.from_pretrained(save_folder, torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload()
+prompt = "A cat holding a sign that says hello world"
+image = pipe(
+    prompt,
+    height=1024,
+    width=1024,
+    guidance_scale=3.5,
+    num_inference_steps=50,
+    max_sequence_length=512,
+    generator=torch.Generator("cpu").manual_seed(0)
+).images[0]
+configs = get_original_model_configs(diffusers.FluxPipeline, save_folder)
+rich.print(configs)
+pipe.push_to_hub(save_folder.removeprefix('/tmp/'))
+```