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ayushtues
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May 26, 2023
[WIP] Add Unet for consistency models
patrickvonplaten
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* Implement `CustomDiffusionAttnProcessor2_0` * Doc-strings and type annotations for `CustomDiffusionAttnProcessor2_0`. (#1) * Update attnprocessor.md * Update attention_processor.py * Interops for `CustomDiffusionAttnProcessor2_0`. * Formatted `attention_processor.py`. * Formatted doc-string in `attention_processor.py` * Conditional CustomDiffusion2_0 for training example. * Remove unnecessary reference impl in comments. * Fix `save_attn_procs`.
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Add glide modeling files
yoonseokjin
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* Implement `CustomDiffusionAttnProcessor2_0` * Doc-strings and type annotations for `CustomDiffusionAttnProcessor2_0`. (huggingface#1) * Update attnprocessor.md * Update attention_processor.py * Interops for `CustomDiffusionAttnProcessor2_0`. * Formatted `attention_processor.py`. * Formatted doc-string in `attention_processor.py` * Conditional CustomDiffusion2_0 for training example. * Remove unnecessary reference impl in comments. * Fix `save_attn_procs`.
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AmericanPresidentJimmyCarter
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* Implement `CustomDiffusionAttnProcessor2_0` * Doc-strings and type annotations for `CustomDiffusionAttnProcessor2_0`. (huggingface#1) * Update attnprocessor.md * Update attention_processor.py * Interops for `CustomDiffusionAttnProcessor2_0`. * Formatted `attention_processor.py`. * Formatted doc-string in `attention_processor.py` * Conditional CustomDiffusion2_0 for training example. * Remove unnecessary reference impl in comments. * Fix `save_attn_procs`.
sayakpaul
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add workflow to rebase with upstream main nightly.
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DN6
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shiv2110
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add model definition
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Enderfga
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Finding huggingface#1 — attention_kwargs plumbing: Both transformers now decorate forward() with @apply_lora_scale('attention_kwargs') (matches Wan); pipelines forward attention_kwargs to the transformer + encode_kv_cache, and the unused parameter is dropped from the inner _forward_train / _forward_cache / _forward_inference signatures. Pipeline docstrings updated to the standard wording. Finding huggingface#2 — naming: Rename far_cfg -> layout_cfg in the bidi transformer (the bidi path is not FAR; the FAR transformer keeps far_cfg, which is accurate there). Finding huggingface#3 — scheduler state machine: Add _step_index, _begin_index, step_index property, begin_index property, set_begin_index(), _init_step_index(). step() lazily initializes and advances the counter so downstream callbacks / composable schedulers can observe rollout progress. Sigma resolution remains a pure function of (timestep, r_timestep) — calling step() twice with identical args still returns identical prev_sample (idempotent). Finding huggingface#4 — redundant @torch.no_grad(): Drop the redundant decorators on bidi pipeline's encode_video and FAR pipeline's encode_kv_cache (callers are already in __call__'s no-grad scope). Finding huggingface#5 — dead code: Remove the unreachable temb.ndim == 2 else branch from the bidi transformer's output-norm path (condition_embedder.forward always returns a 3D temb). Finding huggingface#6 — private rename: forward_far_patchify[_inference] -> _forward_far_patchify[_inference] (only called internally by _forward_train / _forward_cache / _forward_inference). Finding huggingface#7 — pipeline comment numbering: Bidi + FAR pipelines renumber steps so the # 4. slot is no longer skipped. Finding huggingface#8 — mask-mod comment numbering: _build_causal_mask numbered comments now run 1) 2) 3) ... (was 1) 3) 4) ...). Tests: - New test_step_index_advances + test_set_begin_index_anchors_step_index in the scheduler test file exercise the new state machine. - All existing pipeline / transformer / scheduler tests still pass (85 passed, 85 skipped on CPU). Bit-exact: 8-step rollout vs the previous formulation, max abs diff = 0.0 (the new sigma-lookup is byte-identical to t/num_train_timesteps on this schedule).
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