Diffusers weights

README.md

@@ -92,72 +92,6 @@ python inference.py --ckpt_dir 'PATH' --prompt "PROMPT" --height HEIGHT --width
 python inference.py --ckpt_dir 'PATH' --prompt "PROMPT" --input_image_path IMAGE_PATH --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED
 ```
 
-### Diffusers 🧨
-
-LTX Video is compatible with the [Diffusers Python library](https://huggingface.co/docs/diffusers/main/en/index). It supports both text-to-video and image-to-video generation.
-
-Make sure you install `diffusers` before trying out the examples below.
-
-```bash
-pip install -U git+https://github.com/huggingface/diffusers
-```
-
-Now, you can run the examples below:
-
-```py
-import torch
-from diffusers import LTXPipeline
-from diffusers.utils import export_to_video
-
-pipe = LTXPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-prompt = "A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage"
-negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
-
-video = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    width=704,
-    height=480,
-    num_frames=161,
-    num_inference_steps=50,
-).frames[0]
-export_to_video(video, "output.mp4", fps=24)
-```
-
-For image-to-video:
-
-```py
-import torch
-from diffusers import LTXImageToVideoPipeline
-from diffusers.utils import export_to_video, load_image
-
-pipe = LTXImageToVideoPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-image = load_image(
-    "https://huggingface.co/datasets/a-r-r-o-w/tiny-meme-dataset-captioned/resolve/main/images/8.png"
-)
-prompt = "A young girl stands calmly in the foreground, looking directly at the camera, as a house fire rages in the background. Flames engulf the structure, with smoke billowing into the air. Firefighters in protective gear rush to the scene, a fire truck labeled '38' visible behind them. The girl's neutral expression contrasts sharply with the chaos of the fire, creating a poignant and emotionally charged scene."
-negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
-
-video = pipe(
-    image=image,
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    width=704,
-    height=480,
-    num_frames=161,
-    num_inference_steps=50,
-).frames[0]
-export_to_video(video, "output.mp4", fps=24)
-```
-
-To learn more, check out the [official documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video). 
-
-Diffusers also supports directly loading from the original LTX checkpoints using the `from_single_file()` method. Check out [this section](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video#loading-single-files) to learn more.
-
 ## Limitations
 - This model is not intended or able to provide factual information.
 - As a statistical model this checkpoint might amplify existing societal biases.

ltx-video-2b-v0.9.safetensors

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model_index.json

@@ -1,24 +0,0 @@
-{
-  "_class_name": "LTXPipeline",
-  "_diffusers_version": "0.32.0.dev0",
-  "scheduler": [
-    "diffusers",
-    "FlowMatchEulerDiscreteScheduler"
-  ],
-  "text_encoder": [
-    "transformers",
-    "T5EncoderModel"
-  ],
-  "tokenizer": [
-    "transformers",
-    "T5Tokenizer"
-  ],
-  "transformer": [
-    "diffusers",
-    "LTXVideoTransformer3DModel"
-  ],
-  "vae": [
-    "diffusers",
-    "AutoencoderKLLTXVideo"
-  ]
-}

scheduler/scheduler_config.json

@@ -1,16 +1,8 @@
 {
-  "_class_name": "FlowMatchEulerDiscreteScheduler",
-  "_diffusers_version": "0.32.0.dev0",
-  "base_image_seq_len": 1024,
-  "base_shift": 0.95,
-  "invert_sigmas": false,
-  "max_image_seq_len": 4096,
-  "max_shift": 2.05,
+  "_class_name": "RectifiedFlowScheduler",
+  "_diffusers_version": "0.25.1",
   "num_train_timesteps": 1000,
-  "shift": 1.0,
-  "shift_terminal": 0.1,
-  "use_beta_sigmas": false,
-  "use_dynamic_shifting": true,
-  "use_exponential_sigmas": false,
-  "use_karras_sigmas": false
-}
+  "shifting": "SD3",
+  "base_resolution": null,
+  "target_shift_terminal": 0.1
+}

text_encoder/config.json

@@ -1,32 +0,0 @@
-{
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-  "architectures": [
-    "T5EncoderModel"
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-  "d_kv": 64,
-  "d_model": 4096,
-  "decoder_start_token_id": 0,
-  "dense_act_fn": "gelu_new",
-  "dropout_rate": 0.1,
-  "eos_token_id": 1,
-  "feed_forward_proj": "gated-gelu",
-  "initializer_factor": 1.0,
-  "is_encoder_decoder": true,
-  "is_gated_act": true,
-  "layer_norm_epsilon": 1e-06,
-  "model_type": "t5",
-  "num_decoder_layers": 24,
-  "num_heads": 64,
-  "num_layers": 24,
-  "output_past": true,
-  "pad_token_id": 0,
-  "relative_attention_max_distance": 128,
-  "relative_attention_num_buckets": 32,
-  "tie_word_embeddings": false,
-  "torch_dtype": "float32",
-  "transformers_version": "4.46.2",
-  "use_cache": true,
-  "vocab_size": 32128
-}

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tokenizer/added_tokens.json

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-{
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tokenizer/special_tokens_map.json

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-{
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tokenizer/spiece.model

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tokenizer/tokenizer_config.json

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unet/config.json

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+{
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+    "attention_bias": true,
+    "attention_head_dim": 64,
+    "attention_type": "default",
+    "caption_channels": 4096,
+    "cross_attention_dim": 2048,
+    "double_self_attention": false,
+    "dropout": 0.0,
+    "in_channels": 128,
+    "norm_elementwise_affine": false,
+    "norm_eps": 1e-06,
+    "norm_num_groups": 32,
+    "num_attention_heads": 32,
+    "num_embeds_ada_norm": 1000,
+    "num_layers": 28,
+    "num_vector_embeds": null,
+    "only_cross_attention": false,
+    "out_channels": 128,
+    "project_to_2d_pos": true,
+    "upcast_attention": false,
+    "use_linear_projection": false,
+    "qk_norm": "rms_norm",
+    "standardization_norm": "rms_norm",
+    "positional_embedding_type": "rope",
+    "positional_embedding_theta": 10000.0,
+    "positional_embedding_max_pos": [20, 2048, 2048],
+    "timestep_scale_multiplier": 1000
+}

ltx-video-2b-v0.9.1.safetensors → unet/unet_diffusion_pytorch_model.safetensors

@@ -1,3 +1,3 @@
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+size 7693623048

vae/config.json

@@ -1,32 +1 @@
-{
-  "_class_name": "AutoencoderKLLTXVideo",
-  "_diffusers_version": "0.32.0.dev0",
-  "block_out_channels": [
-    128,
-    256,
-    512,
-    512
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-  "decoder_causal": false,
-  "encoder_causal": true,
-  "in_channels": 3,
-  "latent_channels": 128,
-  "layers_per_block": [
-    4,
-    3,
-    3,
-    3,
-    4
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-  "out_channels": 3,
-  "patch_size": 4,
-  "patch_size_t": 1,
-  "resnet_norm_eps": 1e-06,
-  "scaling_factor": 1.0,
-  "spatio_temporal_scaling": [
-    true,
-    true,
-    true,
-    false
-  ]
-}
+{"_class_name": "CausalVideoAutoencoder", "dims": 3, "in_channels": 3, "out_channels": 3, "latent_channels": 128, "blocks": [["res_x", 4], ["compress_all", 1], ["res_x_y", 1], ["res_x", 3], ["compress_all", 1], ["res_x_y", 1], ["res_x", 3], ["compress_all", 1], ["res_x", 3], ["res_x", 4]], "scaling_factor": 1.0, "norm_layer": "pixel_norm", "patch_size": 4, "latent_log_var": "uniform", "use_quant_conv": false, "causal_decoder": false}

vae/diffusion_pytorch_model.safetensors

@@ -1,3 +0,0 @@
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text_encoder/model-00002-of-00004.safetensors → vae/vae_diffusion_pytorch_model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:f8a7a7e59e14c69d24e7c0e79b5c35f9d83497916c006e06e3f5f484eea676ba
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