Instructions to use YiYiXu/a2-diffusers-testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use YiYiXu/a2-diffusers-testing with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("YiYiXu/a2-diffusers-testing", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Write the real DPMSolverMultistep config
Browse filesscheduler_config.json said `UniPCMultistepScheduler` while model_index.json said `DPMSolverMultistepScheduler`, so it loaded as DPM carrying UniPC's field set. The pipeline now samples with `self.scheduler` instead of constructing its own, so the file has to be correct. The resulting scheduler is bit-identical -- same timesteps, same sigmas -- to the one the old `flow_solver='dpm'` branch built.
scheduler/scheduler_config.json
CHANGED
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@@ -1,13 +1,16 @@
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{
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-
"_class_name": "
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-
"_diffusers_version": "0.
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"beta_end": 0.02,
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"beta_schedule": "linear",
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"beta_start": 0.0001,
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"disable_corrector": [],
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"dynamic_thresholding_ratio": 0.995,
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"final_sigmas_type": "zero",
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"flow_shift": 5.0,
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"lower_order_final": true,
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"num_train_timesteps": 1000,
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"predict_x0": true,
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@@ -16,7 +19,7 @@
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"sample_max_value": 1.0,
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"solver_order": 2,
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"solver_p": null,
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-
"solver_type": "
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"steps_offset": 0,
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"thresholding": false,
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"time_shift_type": "exponential",
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@@ -26,5 +29,7 @@
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"use_dynamic_shifting": false,
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"use_exponential_sigmas": false,
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"use_flow_sigmas": true,
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-
"use_karras_sigmas": false
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}
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{
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+
"_class_name": "DPMSolverMultistepScheduler",
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"_diffusers_version": "0.40.0.dev0",
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"algorithm_type": "dpmsolver++",
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"beta_end": 0.02,
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"beta_schedule": "linear",
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"beta_start": 0.0001,
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"disable_corrector": [],
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"dynamic_thresholding_ratio": 0.995,
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+
"euler_at_final": false,
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"final_sigmas_type": "zero",
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"flow_shift": 5.0,
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+
"lambda_min_clipped": -Infinity,
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"lower_order_final": true,
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"num_train_timesteps": 1000,
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"predict_x0": true,
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"sample_max_value": 1.0,
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"solver_order": 2,
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"solver_p": null,
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+
"solver_type": "midpoint",
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"steps_offset": 0,
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"thresholding": false,
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"time_shift_type": "exponential",
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"use_dynamic_shifting": false,
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"use_exponential_sigmas": false,
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"use_flow_sigmas": true,
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+
"use_karras_sigmas": false,
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+
"use_lu_lambdas": false,
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+
"variance_type": null
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}
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